GenomicRanges/DESCRIPTION0000644000175400017540000000524313032312066016027 0ustar00biocbuildbiocbuildPackage: GenomicRanges Title: Representation and manipulation of genomic intervals and variables defined along a genome Description: The ability to efficiently represent and manipulate genomic annotations and alignments is playing a central role when it comes to analyzing high-throughput sequencing data (a.k.a. NGS data). The GenomicRanges package defines general purpose containers for storing and manipulating genomic intervals and variables defined along a genome. More specialized containers for representing and manipulating short alignments against a reference genome, or a matrix-like summarization of an experiment, are defined in the GenomicAlignments and SummarizedExperiment packages respectively. Both packages build on top of the GenomicRanges infrastructure. Version: 1.26.2 Encoding: UTF-8 Author: P. Aboyoun, H. Pagès, and M. Lawrence Maintainer: Bioconductor Package Maintainer biocViews: Genetics, Infrastructure, Sequencing, Annotation, Coverage, GenomeAnnotation Depends: R (>= 2.10), methods, stats4, BiocGenerics (>= 0.17.5), S4Vectors (>= 0.9.47), IRanges (>= 2.7.8), GenomeInfoDb (>= 1.1.20) Imports: utils, stats, XVector LinkingTo: S4Vectors, IRanges Suggests: Biobase, AnnotationDbi (>= 1.21.1), annotate, Biostrings (>= 2.25.3), Rsamtools (>= 1.13.53), SummarizedExperiment (>= 0.1.5), Matrix, GenomicAlignments, rtracklayer, BSgenome, GenomicFeatures, Gviz, VariantAnnotation, AnnotationHub, DESeq2, DEXSeq, edgeR, KEGGgraph, BiocStyle, digest, RUnit, BSgenome.Hsapiens.UCSC.hg19, BSgenome.Scerevisiae.UCSC.sacCer2, KEGG.db, hgu95av2.db, org.Hs.eg.db, org.Mm.eg.db, org.Sc.sgd.db, pasilla, pasillaBamSubset, TxDb.Athaliana.BioMart.plantsmart22, TxDb.Dmelanogaster.UCSC.dm3.ensGene, TxDb.Hsapiens.UCSC.hg19.knownGene, BSgenome.Mmusculus.UCSC.mm10, TxDb.Mmusculus.UCSC.mm10.knownGene, RNAseqData.HNRNPC.bam.chr14, hgu95av2probe License: Artistic-2.0 Collate: utils.R phicoef.R transcript-utils.R constraint.R strand-utils.R range-squeezers.R GenomicRanges-class.R GenomicRanges-comparison.R GRanges-class.R GPos-class.R DelegatingGenomicRanges-class.R GNCList-class.R GenomicRangesList-class.R GRangesList-class.R makeGRangesFromDataFrame.R makeGRangesListFromDataFrame.R RangedData-methods.R findOverlaps-methods.R intra-range-methods.R inter-range-methods.R coverage-methods.R setops-methods.R nearest-methods.R absoluteRanges.R tileGenome.R tile-methods.R genomicvars.R zzz.R NeedsCompilation: yes Packaged: 2017-01-01 23:43:50 UTC; biocbuild GenomicRanges/NAMESPACE0000644000175400017540000000721013001230267015532 0ustar00biocbuildbiocbuilduseDynLib(GenomicRanges) import(methods) importFrom(utils, as.roman, .DollarNames) importFrom(stats, setNames) importFrom(stats4, summary) import(BiocGenerics) import(S4Vectors) import(IRanges) import(GenomeInfoDb) import(XVector) # only for the "Views" method for integer vectors, the # XIntegerViews class, and the "viewMins", "viewMaxs", and # "viewSums" methods for XIntegerViews objects ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ### Export S4 classes ### exportClasses( Constraint, ConstraintORNULL, GenomicRanges, GenomicRangesORmissing, GRanges, GPos, DelegatingGenomicRanges, GNCList, GenomicRangesORGRangesList, GRangesList, GenomicRangesList, SimpleGenomicRangesList ) ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ### Export S3 methods ### S3method(duplicated, GenomicRanges) S3method(sort, GenomicRanges) S3method(summary, GenomicRanges) ### We also export them thru the export() directive so that (a) they can be ### called directly, (b) tab-completion on the name of the generic shows them, ### and (c) methods() doesn't asterisk them. export( duplicated.GenomicRanges, sort.GenomicRanges, summary.GenomicRanges ) ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ### Export S4 methods for generics not defined in GenomicRanges ### exportMethods( length, names, "names<-", "[", "[<-", "[[", "[[<-", "$", "$<-", as.character, as.factor, as.data.frame, coerce, c, show, split, unlist, range, Ops, merge, ## Generics defined in the stats4 package: summary, ## Generics defined in the BiocGenerics package: duplicated, match, is.unsorted, order, sort, rank, union, intersect, setdiff, start, "start<-", end, "end<-", width, "width<-", strand, "strand<-", invertStrand, updateObject, ## Generics defined in S4Vectors: selfmatch, ## Generics defined in IRanges: elementMetadata, "elementMetadata<-", mcols, "mcols<-", values, "values<-", relistToClass, pcompare, ranges, "ranges<-", ngap, score, "score<-", findOverlaps, countOverlaps, shift, narrow, resize, flank, promoters, restrict, trim, reduce, gaps, disjoin, isDisjoint, disjointBins, coverage, punion, pintersect, psetdiff, pgap, precede, follow, nearest, distance, distanceToNearest, tile, slidingWindows, subset, ## Generics defined in GenomeInfoDb: seqinfo, "seqinfo<-", seqnames, "seqnames<-" ) ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ### Export non-generic functions ### export( phicoef, GRanges, .DollarNames.GenomicRanges, GPos, GNCList, GenomicRangesList, GRangesList, makeGRangesFromDataFrame, makeGRangesListFromDataFrame, makeGRangesListFromFeatureFragments, isSmallGenome, absoluteRanges, relativeRanges, tileGenome, bindAsGRanges, mcolAsRleList, binnedAverage ) ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ### Export S4 generics defined in GenomicRanges + export corresponding ### methods ### export( ## constraint.R: #constraint, "constraint<-", checkConstraint, ## range-squeezers.R: granges, grglist, rglist, ## GPos-class.R: pos ) ### Exactly the same list as above. exportMethods( #constraint, "constraint<-", checkConstraint, granges, grglist, rglist, pos ) ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ### Register S3 methods ### S3method(.DollarNames, GenomicRanges) GenomicRanges/NEWS0000644000175400017540000010401613001230267015014 0ustar00biocbuildbiocbuildCHANGES IN VERSION 1.26.0 ------------------------- NEW FEATURES o Add 'with.revmap' argument to "reduce" method for GRangesList objects. o Add 'with.revmap' argument to various "disjoin" methods. o makeGRangesFromDataFrame() now tries to turn the "start" and "end" columns of the input data frame into numeric vectors if they are not already. o Add makeGRangesListFromDataFrame() function. o Add "summary" method for GenomicRanges objects. o Add 'use.names' argument to the granges(), grglist(), and rglist() generics and methods, as well as to a bunch of "ranges" methods (for GRanges, GPos, GNCList, GRangesList, and DelegatingGenomicRanges). Default is TRUE to preserve existing behavior. o Add 'use.mcols' arguments to the "ranges" methods for GPos objects. SIGNIFICANT USER-LEVEL CHANGES DEPRECATED AND DEFUNCT BUG FIXES o Fix bug in distanceToNearest() related to ranges starting at zero. o Fix GRanges(Seqinfo()). CHANGES IN VERSION 1.24.0 ------------------------- NEW FEATURES o Add the GPos class, a container for storing a set of "genomic positions" (i.e. genomic ranges of width 1). Even though a GRanges object can be used for that, using a GPos object can be much more memory-efficient, especially when the object contains long runs of adjacent positions. o Add a bunch of "invertStrand" methods to support strand inversion of any "stranded" object (i.e. any object with a strand() getter and setter). E.g. invertStrand() works on GRanges, GRangesList, GAlignments, GAlignmentPairs, GAlignmentsList, and RangedSummarizedExperiment objects. o Add "is.unsorted" method for GenomicRanges objects (contributed by Pete Hickey). o base::rank() gained a new 'ties.method="last"' option and base::order() a new argument ('method') in R 3.3. Thus so do the "rank" and "order" methods for GenomicRanges objects. o Add "selfmatch" method for GenomicRanges objects. o Add "union" method for GRangesList objects. SIGNIFICANT USER-LEVEL CHANGES o Remove old SummarizedExperiment class from the GenomicRanges package (this class is now defined in the SummarizedExperiment package). o Move the following generic functions from the GenomicRanges package to the SummarizedExperiment package: - SummarizedExperiment - exptData, "exptData<-" - rowRanges, "rowRanges<-" - colData, "colData<-" - assayNames, "assayNames<-" - assays, "assays<-" - assay, "assay<-" o Rename "pintersect" and "psetdiff" methods for GRangesList objects -> "intersect" and "setdiff" without changing their behavior (they still do mendoapply(intersect, x, y) and mendoapply(setdiff, x, y), respectively). The old names were misnomers (see svn commit message for commit 113793 for more information). o Remove the ellipsis (...) from all the setops methods, except from: - "punion" method for signature GRanges#GRangesList; - "pintersect" and "psetdiff" methods for signature GRangesList#GRangesList; - "pgap" method for GRanges objects. o Use DESeq2 instead of DESeq in the vignettes (better late than never). DEPRECATED AND DEFUNCT o Remove GIntervalTree class and methods (were defunct in BioC 3.2). o Remove mapCoords() and pmapCoords() (were defunct in BioC 3.2). CHANGES IN VERSION 1.22.0 ------------------------- NEW FEATURES o Support coercions back and forth between a GRanges object and a character vector (or factor) with elements in the format 'chr1:2501-2800' or 'chr1:2501-2800:+'. o Add facilities for manipulating "genomic variables": bindAsGRanges(), mcolAsRleList(), and binnedAverage(). See ?genomicvars for more information. o Add "narrow" method for GRangesList objects. o Enhancement to the GRanges() constructor. If the 'ranges' argument is not supplied then the constructor proceeds in 2 steps: 1. An initial GRanges object is created with 'as(seqnames, "GRanges")'. 2. Then this GRanges object is updated according to whatever other arguments were supplied to the call to GRanges(). Because of this enhancement, GRanges(x) is now equivalent to 'as(x, "GRanges")' e.g. GRanges() can be called directly on a character vector representing ranges, or on a data.frame, or on any object for which coercion to GRanges is supported. o Add 'ignore.strand' argument to "range" and "reduce" methods for GRangesList objects. o Add coercion from SummarizedExperiment to RangedSummarizedExperiment (also available via updateObject()). See 1st item in DEPRECATED AND DEFUNCT section below for more information about this. o GNCList objects are now subsettable. o "coverage" methods now accept 'shift' and 'weight' supplied as an Rle. SIGNIFICANT USER-LEVEL CHANGES o Modify behavior of "*" strand in precede() / follow() to mimic 'ignore.strand=TRUE'. o Revisit "pintersect" methods for GRanges#GRanges, GRangesList#GRanges, and GRanges#GRangesList: - Sanitize their semantic. - Add 'drop.nohit.ranges' argument (FALSE by default). - If 'drop.nohit.ranges' is FALSE, the returned object now has a "hit" metadata column added to it to indicate the elements in 'x' that intersect with the corresponding element in 'y'. o binnedAverage() now treats 'numvar' as if it was set to zero on genomic positions where it's not set (typically happens when 'numvar' doesn't span the entire chromosomes because it's missing the trailing zeros). o GRanges() constructor no more mangles the names of the supplied metadata columns (e.g. if the column is "_tx_id"). o makeGRangesFromDataFrame() now accepts "." in strand column (treated as "*"). o GNCList() constructor now propagates the metadata columns. o Remove "seqnames" method for RangesList objects. DEPRECATED AND DEFUNCT o The SummarizedExperiment class defined in GenomicRanges is deprecated and replaced by 2 new classes defined in the new SummarizedExperiment package: SummarizedExperiment0 and RangedSummarizedExperiment. In BioC 2.3, the SummarizedExperiment class will be removed from the GenomicRanges package and the SummarizedExperiment0 class will be renamed SummarizedExperiment. To facilitate this transition, a coercion method was added to coerce from old SummarizedExperiment to new RangedSummarizedExperiment (this coercion is performed when calling updateObject() on an old SummarizedExperiment object). o makeSummarizedExperimentFromExpressionSet() and related stuff was moved to the new SummarizedExperiment package. o After being deprecated in BioC 3.1, the rowData accessor is now defunct (replaced with the rowRanges accessor). o After being deprecated in BioC 3.1, GIntervalTree objects and the "intervaltree" algorithm in findOverlaps() are now defunct. o After being deprecated in BioC 3.1, mapCoords() and pmapCoords() are now defunct. BUG FIXES o 2 tweaks to subsetting *by* an GenomicRanges: - Improve speed when the object to subset is a SimpleList (e.g. SimpleRleList). - Fix issue when the GenomicRanges subscript is empty. CHANGES IN VERSION 1.20.0 ------------------------- NEW FEATURES o Add coercion methods to go back and forth between ExpressionSet and SummarizedExperiment. o Add 'assayNames', 'assayNames<-' for SummarizedExperiment o assays() supports arrays of up to 4 dimensions. o Add GNCList() for preprocessing a GenomicRanges object into a GNCList object that can be used for fast overlap seach with findOverlaps(). GNCList() is a replacement for GIntervalTree() that uses Nested Containment Lists instead of interval trees. Unlike GIntervalTree(), GNCList() supports preprocessing of a GenomicRanges object with ranges located on a circular sequence. For a one time use, it's not advised to explicitely preprocess the input. This is because findOverlaps() or countOverlaps() will take care of it and do a better job at it (that is, they preprocess only what's needed when it's needed and release memory as they go). o All "findOverlaps" methods now support 'select' equal "last" or "arbitrary" (in addition to "all" and "first"). o Add absoluteRanges() and relativeRanges() to transform back and forth between absolute and relative genomic ranges. SIGNIFICANT USER-LEVEL CHANGES o Renamed 'rowData' and 'rowData<-' -> 'rowRanges', 'rowRanges<-'. Old names still work but are deprecated. o Some improvements to makeGRangesFromDataFrame(): - Improve internal logic used for finding the GRanges columns in the input. - If 'seqinfo' is not supplied, the seqlevels are now ordered according to the output of GenomeInfoDb::rankSeqlevels(). - Now an attempt is made to turn 'df' into a data frame (with 'as.data.frame(df)') if it's not a data frame or a DataFrame object. o The GRanges() constructor now propagates the metadata cols that are on 'ranges' if no metadata cols are explicitly passed to the constructor. DEPRECATED AND DEFUNCT o Deprecated 'rowData' and 'rowData<-' in favor of 'rowRanges' and 'rowRanges<-'. o Deprecated mapCoords() and pmapCoords(). They're replaced by mapToTranscripts() and pmapToTranscripts() from the GenomicFeatures package and mapToAlignments() and pmapToAlignments() from the GenomicAlignments package. o Deprecated GIntervalTree objects. o Removed "map" and "splitAsListReturnedClass" methods (were defunct in GenomicRanges 1.18.0). o Removed makeSeqnameIds() (was defunct in GenomicRanges 1.18.0). o Removed 'with.mapping' argunment from "reduce" methods (was defunct in GenomicRanges 1.18.0). BUG FIXES o Fix 'findOverlaps(..., type="start")' on GRangesList objects which has been broken for years. o Fix self overlap search on a GRanges object when 'ignore.strand=TRUE' (i.e. 'findOverlaps(gr, ignore.strand=TRUE)'). CHANGES IN VERSION 1.18.0 ------------------------- NEW FEATURES o Add 'use.mcols' arg to "ranges" method for GRangesList objects. o "assays<-" methods may be invoked with 'withDimnames' arg. o Add mapCoords() generic and methods (replacing map()). o Add granges,GenomicRanges method. o Add strand<-,GRangesList,character method for global replacement (i.e., all strands become 'value'). o Add resize,GRangesList-method. o Add DelegatingGenomicRanges class and vignette on how to extend GenomicRanges. o Document subsetting a named list-like object by a GRanges subscript. SIGNIFICANT USER-LEVEL CHANGES o Modify "show" methods for GRanges and GRangesList objects so they print a 1-line summary of the seqinfo component. o Remove as.data.frame,GRangesList-method; use as.data.frame,List. o "trim" method for GenomicRanges only trims out-of-bound ranges on non-circular sequences whose length is not NA. This behavior is consistent with the GenomicRanges validity method. o Changes to flank(), resize() and start/end/width setters: - no longer trim the result ranges when called on a GRanges - warning is issued by GenomicRanges validity method when out-of-bound ranges are on non-circular sequences whose length is not NA Note this behavior is now consistent with that of shift(). o Speed up validation of GenomicRanges objects by 1.2x. o Speed up trim() on GenomicRanges objects by 1.2x. o Improve warning when GenomicRanges object contains out-of-bound ranges. o Work on vignette HOWTOs: - split 'How to read BAM files into R' into 3 HOWTOs - split 'How to prepare a table of read counts for RNA-Seq differential gene expression' into 3 HOWTOs - split 'How to extract DNA sequences of gene regions' into 2 HOWTOs - make individual HOWTOs subsections of single HOWTO section o Follow renaming of TranscriptDb class to TxDb. o Replace references to plantsmart21 with plantsmart22. DEPRECATED AND DEFUNCT o Defunct map() (skip deprecation). Replace with mapCoords(). BUG FIXES o [cr]bind,SummarizedExperiment methods respect derived classes. o assays(se, withDimnames=TRUE) <- value no longer tries to access a slot 'withDimnames'. o cbind and rbind,SummarizedExperiment-methods respect derived classes o "ranges" method for GRangesList objects should not propagate inner metadata columns by default. o GRanges() constructor now preserves the seqlevels in the order supplied by the user. o Ensure tileGenome() breakpoints do not extend past end of genome. o Fix "show" method for GenomicRanges objects when 'showHeadLines' global option is set to Inf. o [rc]bind,SummarizeExperiment-methods now compare all elements. o Remove "==" and "<=" methods for GenomicRanges objects (not needed). CHANGES IN VERSION 1.16.0 ------------------------- NEW FEATURES o Add "subset" method for SummarizedExperiment objects. o Allow DataFrame in SummarizedExperiment assays. o Add 'use.mcols' arg (FALSE by default) to the granges(), grglist(), and rglist() generics (a.k.a. the range-squeezer generics). o Add coercion method from GRangesList to RangesList. o Add score() setter for GRangesList objects. o findOverlaps(..., type="within") now works on circular chromosomes. o Add 'ignore.strand' arg to "sort" method for GRanges objects. o Support subsetting of a named list-like object *by* a GenomicRanges subscript. o Support sort(granges, by = ~ score), i.e., a formula-based interface for sorting by the mcols. SIGNIFICANT USER-LEVEL CHANGES o Move many functionalities to the new GenomicAlignments package: - The GAlignments, GAlignmentPairs, and GAlignmentsList classes. - The qnarrow() generic and methods. - The "narrow" and "pintersect" methods for GAlignments and GAlignmentsList objects. - The low-level CIGAR utilities. - The "findOverlaps" methods for GAlignment* objects. - The summarizeOverlaps() generic and methods, and the "Counting reads with summarizeOverlaps" vignette. - findCompatibleOverlaps() and countCompatibleOverlaps(). - The findSpliceOverlaps() generic and methods. - The "overlap encodings" stuff i.e. the "encodeOverlaps" method for GRangesList objects, flipQuery(), selectEncodingWithCompatibleStrand(), isCompatibleWithSplicing(), isCompatibleWithSkippedExons(), extractSteppedExonRanks(), extractSpannedExonRanks(), extractSkippedExonRanks(), and extractQueryStartInTranscript(), and the "OverlapEncodings" vignette. o Rename 'with.mapping' arg -> 'with.revmap' in "reduce" methods. The old arg name is still working but deprecated. o Move makeSeqnameIds() function to the new GenomeInfoDb package and rename it rankSeqlevels(). The old name is still working but deprecated. o The "strand" methods now perform stricter checking and are guaranteed to always return a factor (or factor-Rle) with the "standard strand levels" and no NAs. Or to fail. BUG FIXES o Tweaks and fixes to various "strand" methods: - Methods for character vectors and factors do not accept NAs anymore (they raise an error). - Methods for integer and logical vectors map NAs to * (instead of NA). - Method for Rle objects now also works on character-, factor-, and integer-Rle objects (in addition to logical-Rle objects). CHANGES IN VERSION 1.14.0 ------------------------- NEW FEATURES o Add coercion from GenomicRangesList to RangedDataList. o Add "c" method for GAlignmentPairs objects. o Add coercion from GAlignmentPairs to GAlignmentsList. o Add 'inter.feature' and 'fragment' arguments to summarizeOverlaps(). o Add seqselect,GAlignments-method. o Add CIGAR utilities: explodeCigarOps(), explodeCigarOpLengths() cigarRangesAlongReferenceSpace(), cigarRangesAlongQuerySpace() cigarRangesAlongPairwiseSpace(), extractAlignmentRangesOnReference() cigarWidthAlongReferenceSpace(), cigarWidthAlongQuerySpace() cigarWidthAlongPairwiseSpace(). o Add seqlevels0() and restoreSeqlevels(). o Add seqlevelsInUse() getter for GRanges, GRangesList, GAlignments GAlignmentPairs, GAlignmentsList and SummarizedExperiment objects. o Add update,GAlignments method. o Add GIntervalTree class. o Add coercion from GAlignmentPairs to GAlignments. o Add sortSeqlevels(). o Add tileGenome(). o Add makeGRangesFromDataFrame() and coercion from data.frame or DataFrame to GRanges. SIGNIFICANT USER-LEVEL CHANGES o Renaming (with aliases from old to new names): - classes GappedAlignments -> GAlignments GappedAlignmentPairs -> GAlignmentPairs - functions GappedAlignments() -> GAlignments() GappedAlignmentPairs() -> GAlignmentPairs() readGappedAlignments() -> readGAlignments() readGappedAlignmentPairs() -> readGAlignmentPairs() o Remove 'asProperPairs' argument to readGAlignmentsList(). o Modify "show" method for Seqinfo object to honor showHeadLines and showTailLines global options. o 50x speedup or more when merging 2 Seqinfo objects, 1 very small and 1 very big. o Add dependency on new XVector package. o Enhanced examples for renaming seqlevels in seqlevels-utils.Rd. o More efficient reference class constructor for 'assays' slot of SummarizedExperiment objects. o 'colData' slot of SummarizedExperiment produced from call to summarizedOverlaps() now holds the class type and length of 'reads'. o 4x speedup to cigarToRleList(). o Relax SummarizedExperiment class validity. o Renaming (with aliases from old to new names): cigarToWidth() -> cigarWidthOnReferenceSpace(), and cigarToQWidth() -> cigarWidthOnQuerySpace(). o Improvements to summarizeOverlaps(): - mode 'Union': 1.5x to 2x speedup - mode 'IntersectionNotEmpty': 2x to 8x speedup + memory footprint reduced by ~ half o Change default 'use.names' to FALSE for readGAlignmentsList(). o Implement 'type="equal"' for findOverlaps,SummarizedExperiment methods. o Modify summarizeOverlaps() examples to use 'asMates=TRUE' instead of 'obeyQname=TRUE'. o Remove unneeded "window" method for GenomicRanges objects. o Speed up seqinfo() getter and setter on SummarizedExperiment objects and derivatives (e.g. VCF) by using direct access to 'rowData' slot. o coverage,GenomicRanges method now uses .Ranges.coverage() when using the defaults for 'shift' and 'width'. o Remove restriction that metadata column names must be different on a GRangesList and the unlisted GRanges. o GenomicRangesUseCases vignette has been redone and renamed to GenomicRangesHOWTOs DEPRECATED AND DEFUNCT o Defunct all "match" and "%in%" methods in the package except for those with the GenomicRanges,GenomicRanges signature. o Deprecate GappedAlignment*: - GappedAlignments and GappedAlignmentPairs classes - GappedAlignments() and GappedAlignmentPairs() constructors - readGappedAlignments() and readGappedAlignmentPairs() functions o Deprecate cigar util functions: cigarToWidth(), cigarToQWidth(), cigarToIRanges() splitCigar(), cigarToIRanges(), cigarToIRangesListByAlignment() cigarToIRangesListByRName(), cigarToWidth(), cigarToQWidth() cigarToCigarTable(), summarizeCigarTable() o Deprecate seqselect(). BUG FIXES o Fix bug in c,GAlignments for case when objects were unnamed. o Fix bug in flank,GenomicRanges (when 'ignore.strand=TRUE' 'start' was being set to TRUE). o Fix bug in behavior of summarizeOverlaps() count mode 'IntersectionNotEmpty' when 'inter.features=FALSE'. Shared regions are now removed before counting. o Fix bug in cigarToIRangesListByAlignment() when 'flag' is supplied and indicates some reads are unmapped. o Fix bug in summarizeOverlaps(..., mode='IntersectionNotEmpty') when 'features' has '-' and '+' elements and 'ignore.strand=TRUE'. o match,GenomicRanges,GenomicRanges method now handles properly objects with seqlevels not in the same order. CHANGES IN VERSION 1.12.0 ------------------------- NEW FEATURES o Implement "seqnameStyle" replacement method for Seqinfo object. 'seqnameStyle(x) <- style' works on any object with a "seqinfo" replacement method. o Add trim,GenomicRanges-method to trim out of bound ranges. o Add promoters,GenomicRanges and promoters,GRangesList methods. o Add "overlapsAny" methods as a replacement for the deprecated "%in%" methods. o Add 'ignore.strand' argument to match,GenomicRanges-method. o Add 'with.mapping' argument to "reduce" method for GenomicRanges objects. o Add "unname" method to remove dimnames from SummarizedExperiment. o Add "cbind" and "rbind" methods for SummarizedExperiment. o Add "seqselect", "seqselect<-" and "split" methods for SummarizedExperiment. o Add GAlignmentsList class. o Add readGAlignmentsList generic and methods. SIGNIFICANT USER-LEVEL CHANGES o resize,GenomicRanges method no longer checks that 'fix' is length-compatible with 'x' when 'x' is length zero. This allows for resize(x, w, fix = "end") without worrying about 'x' being zero-length. o Change the behavior of "distance". Previously adjacent ranges had a distance of 1 and overlapping had a distance of 0. Now both adjacent AND overlapping have a distance of 0. o shift,GenomicRanges-method no longer trims out of bound ranges. o "distanceToNearest" no longer drops ranges that have no hit but returns 'NA' for 'subjectHits' and 'distance'. o "genome" is no longer an invalid metadata colname for GenomicRanges objects. o 4x-8x speedup for doing coverage() on a GRanges or GRangesList with many seqlevels. o Remove ">=", "<", and ">" methods for GenomicRanges objects. o Speedup "seqinfo" setters for GenomicRanges and GappedAlignments by avoiding validation when not necessary. o readGappedAlignments can now pass a BamFile to readBamGappedAlignments. o Remove unneeded "unique" and "sort" methods for GenomicRanges objects. o Change behavior of "match" and "%in%" on GenomicRanges objects to use equality instead of overlap for comparing elements between GenomicRanges objects 'x' and 'table'. o match,GenomicRanges-method gets the same 'method' argumnet as the "duplicated" method for these objects. o Remove unneeded "countOverlaps" methods. o "classNameForDisplay" shortens the name of data type when displayed. o Add global options 'showHeadLines' and 'showTailLines' to control the number of head/tails lines displayed in show,GRanges and show,GappedAlignments methods. o "distanceToNearest" now returns a Hits object instead of DataFrame. DEPRECATED AND DEFUNCT o Remove defunct countGenomicOverlaps(), grg(), and globalToQuery() o Defunct previously deprecated '.ignoreElementMetadata' argmuent of c,GenomicRanges-method. o Deprecate all "match" and "%in%" methods in the package except for those with the GenomicRanges,GenomicRanges signature. o Deprecate "resolveHits" methods. BUG FIXES o Several bug fixes to "nearest". o Output of "findSpliceOverlaps" now displays 'NA' for ranges with no hits. CHANGES IN VERSION 1.10.0 ------------------------- NEW FEATURES o SummarizedExperiment gains direct GRanges / GRangesList interface to rowData. o Add "distanceToNearest" method for GenomicRanges objects. o SummarizedExperiment class can now be subset by row when there are no 'columns', and by column when there are no 'rows'. o Add 'drop.D.ranges' argument to coverage,GappedAlignments and coverage,GappedAlignmentPairs methods. o findOverlaps() now supports 'select="last"' and 'select="arbitrary"' (in addition to 'select="all"' and 'select="first"') on GenomicRanges objects. o summarizeOverlaps(..., mode="IntersectionStrict") now handles circular chromosomes. A warning is issued and circular chromosomes in 'reads' are omitted from counting. o Add disjoin,GRangesList method. o Add findSpliceOverlaps() for identifyng ranges (reads) that are compatible with a specific transcript isoform (the non-compatible ranges are analyzed for the presence of novel splice events). o Add ngap,GappedAlignmentPairs method. o Add introns() generic with methods for GappedAlignments and GappedAlignmentPairs objects. o No more arbitrary max of 3 gaps per read in isCompatibleWithSplicing() and isCompatibleWithSkippedExons(). o Add findCompatibleOverlaps() and countCompatibleOverlaps(). o Passing '...' down through as.data.frame(GRanges, ...) so user can tweak stringsAsFactors default for metadata columns. o Add extractSteppedExonRanks(), extractSpannedExonRanks() and extractQueryStartInTranscript() utilities (work with single- and paired-end reads). o Add 'flip.query.if.wrong.strand' arg (FALSE by default) to "encodeOverlaps" method for GRangesList objects. o Add makeSeqnameIds() low-level utility. SIGNIFICANT USER-LEVEL CHANGES o SummarizedExperiment rowData and assays operations have significant performance improvements. o mcols() is now the preferred way (over elementMetadata() or values()) to access the metadata columns of a GenomicRanges, GRangesList, GappedAlignments, GappedAlignmentPairs, SummarizedExperiment object, or any Vector object. elementMetadata() and values() might go away at some point in the (not so close) future. o Add "$" and "$<-" methods for GenomicRanges *only*. Provided as a convenience and as the result of strong popular demand. Note that those methods are not consistent with the other "$" and "$<-" methods in the IRanges/GenomicRanges infrastructure, and might confuse some users by making them believe that a GenomicRanges object can be manipulated as a data.frame-like object. It is therefore recommended to use them only interactively, and their use in scripts or packages is discouraged. For the latter, use 'mcols(x)$name' instead of 'x$name'. o No more warning when doing as(x, "GRanges") on a RangedData object with no "strand" column. o Refactor "[" method for GenomicRanges objects. The new implementation always preserves the names of the selected elements instead of trying to return a GenomicRanges object with unique names. This new behavior is consistent with subsetting of ordinary vectors and other Vector objects defined in IRanges/GenomicRanges. Also modify "seqselect" method for GenomicRanges objects so it also preserves the names of the selected elements (and thus remains consistent with new behavior of "[" method for GenomicRanges objects). o No more names on the integer vector returned by "ngap" method for GappedAlignments objects. o Many improvements to the "Overlap encodings" vignette. o Remove 'param' argument from summarizeOverlaps() generic. DEPRECATED AND DEFUNCT o Defunct previously deprecated grg() function. o Defunct previously deprecated countGenomicOverlaps() generic and methods. BUG FIXES o Fix several issues with "precede", "follow", "nearest", and "distance" methods for GenomicRanges objects. o Fix bug in summarizeOverlaps(..., ignore.strand=TRUE). o 6x speedup (and a 6x memory footprint reduction) or more when using encodeOverlaps() on big GRangesList objects. o Fix bug in renameSeqlevels() wrt order of rename vector. o Fix bug in selectEncodingWithCompatibleStrand(). CHANGES IN VERSION 1.8.0 ------------------------ NEW FEATURES o Add GappedAlignmentPairs class (with accessors first(), last(), left(), right(), seqnames(), strand(), isProperPair()), and readGappedAlignmentPairs() for dealing with paired-end reads. Most of the GappedAlignments functionalities (e.g. coercion to GRangesList, "findOverlaps" and related methods, "coverage", etc...) work on a GappedAlignmentPairs object. o Add encodeOverlaps,GRangesList,GRangesList,missing and related utilities flipQuery(), selectEncodingWithCompatibleStrand(), isCompatibleWithSplicing(), isCompatibleWithSkippedExons() and extractSkippedExonRanks(). o Add 'order.as.in.query' arg to grglist() and rglist(). o SummarizedExperiment gains direct access to colData columns with $, $<-, [[, and [[<- methods o Add map,GenomicRanges,GRangesList and map,GenomicRanges,GappedAlignments methods. These allow mapping from genome space to transcript space, and genome space to read space, respectively. o Add seqinfo methods (and friends) for RangedData, RangesList, and other IRanges data structures. These use metadata(x)$seqinfo. o Add disjointBins,GenomicRanges. o Add score,GRangesList and score,GenomicRanges (gets the score column like for RangedData). o Add RangedDataList -> GenomicRangesList coercion. o Add RleViewsList -> GRanges coercion. o Add pintersect,GRangesList,GRangesList o Add stack,GenomicRangesList o ignore.strand argument now more uniformly supported on set operations. o Add Ops,GenomicRanges (from rtracklayer). o Add strand,Rle (only logical-Rle is supported). o Add compare,GenomicRanges o Add 'drop.empty.ranges' arg (FALSE by default) to low-level cigar utilities cigarToIRanges(), cigarToIRangesListByAlignment(), and cigarToIRangesListByRName(). o Add 'reduce.ranges' arg to cigarToIRangesListByAlignment(). SIGNIFICANT USER-LEVEL CHANGES o grglist,GappedAlignments now carries over metadata columns. o Names are no longer forced to be unique when unlisting a GRangesList with use.names=TRUE. o seqnames() is now preferred over rname() on a GappedAlignments object. o cigarToIRangesListByAlignment() now returns a CompressedIRangesList instead of CompressedNormalIRangesList. o Low-level CIGAR utilities now ignore CIGAR operation P (instead of trowing an error). o The 'weight' arg in "coverage" method for GenomicRanges objects now can also be a single string naming a column in elementMetadata(x). o Ranges outside the sequences bounds of the underlying sequences are now accepted (with a warning) in GenomicRanges/GRangesList/GappedAlignments objects. o When called with 'ignore.strand=TRUE', the "range" and "disjoin" methods for GenomicRanges objects now behave like if they set the strand of the input to "*" before they do any computation. o When called with 'ignore.strand=TRUE', "reduce" method for GenomicRanges objects, and "union", "intersect" and "setdiff" methods for GRanges objects now set the strand of their arguments to "*" prior to any computation. o No more mangling of the names when combining GRanges objects ("c" method for GRanges objects was trying to return unique names). o Remove isCircularWithKnownLength() generic and methods (nobody knows, uses, or needs this). BUG FIXES o flank,GRangesList no longer forces 'use.names' to TRUE and 'both' to FALSE. o range,GenomicRanges was broken when object had no ranges o Fix integer overflow issue that can occur in cigarQNarrow() or cigarQNarrow() when the cigar vector is very long. CHANGES IN VERSION 1.6.0 ------------------------ NEW FEATURES o seqlevels() and seqinfo() setters have a new arg ('force', default is FALSE) to force dropping sequence levels currently in use. o Seqinfo objects now have a genome column that can be accessed with genome() getter/setter. o "pgap" method for c(x="GRanges", y="GRanges"). o Add comparison (==, <=, duplicated, unique, etc...) and ordering (order, sort, rank) methods for GenomicRanges objects. o Add "flank" method for GRangesList objects. o Add "isDisjoint" and "restrict" methods for GRanges and GRangesList objects. o Add GRangesList constructor makeGRangesListFromFeatureFragments(). o Add "names" and "names<-" methods for GappedAlignments objects. o Add 'ignore.strand' arg to a number of methods: - findOverlaps,GRangesList,RangesList - findOverlaps,GappedAlignments,ANY - findOverlaps,ANY,GappedAlignments o 'shift' and 'weight' arguments of "coverage" method for GenomicRanges objects now can be numeric vectors in addition to lists. o Add "c" method for GappedAlignments objects. SIGNIFICANT USER-VISIBLE CHANGES o readGappedAlignments() supports 2 new arguments: (1) 'use.names' (default is FALSE) for using the query template names (QNAME field in a SAM/BAM file) to set the names of the returned object, and (2) 'param' (default is NULL, otherwise a ScanBamParam object) for controlling what fields and which records are imported. readGappedAlignments() doesn't support the 'which' arg anymore. o The names of a GRanges/GRangesList/GappedAlignments object are not required to be unique anymore. o By default, the rownames are not set anymore on the DataFrame returned by elementMetadata() on a GRanges/GRangesList/GappedAlignments object. o 'width' arg of "coverage" method for GenomicRanges objects now must be NULL or numeric vector (instead of NULL or list). DEPRECATED AND DEFUNCT o Deprecate countGenomicOverlaps() in favor of summarizeOverlaps(). o Deprecate grg() in favor of granges(). BUG FIXES o Fix bug in "pintersect" methods operating on GappedAlignments objects. GenomicRanges/R/0000755000175400017540000000000013001761644014524 5ustar00biocbuildbiocbuildGenomicRanges/R/DelegatingGenomicRanges-class.R0000644000175400017540000000201013001230267022440 0ustar00biocbuildbiocbuild### ========================================================================= ### DelegatingGenomicRanges objects ### ------------------------------------------------------------------------- ### ### Virtual class that delegates GenomicRanges data access to a ### GenomicRanges delegate. ### setClass("DelegatingGenomicRanges", representation(delegate="GenomicRanges"), contains=c("GenomicRanges", "VIRTUAL")) ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ### Slot getters and setters. ### setMethod("seqnames", "DelegatingGenomicRanges", function(x) seqnames(x@delegate)) setMethod("ranges", "DelegatingGenomicRanges", function(x, ...) ranges(x@delegate, ...)) setMethod("strand", "DelegatingGenomicRanges", function(x) strand(x@delegate)) setMethod("seqinfo", "DelegatingGenomicRanges", function(x) seqinfo(x@delegate)) setMethod("update", "DelegatingGenomicRanges", function (object, ...) { object@delegate <- update(object@delegate, ...) object }) GenomicRanges/R/GNCList-class.R0000644000175400017540000001240613001230267017210 0ustar00biocbuildbiocbuild### ========================================================================= ### GNCList objects ### ------------------------------------------------------------------------- ### ### GNCList is a container for storing a preprocessed GenomicRanges object ### that can be used for fast findOverlaps(). ### setClass("GNCList", contains="GenomicRanges", representation( nclists="list", granges="GRanges" ) ) .get_circle_length <- function(x) { circle_length <- seqlengths(x) circle_length[!(isCircular(x) %in% TRUE)] <- NA_integer_ circle_length } ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ### Accessors ### setMethod("granges", "GNCList", function(x, use.names=TRUE, use.mcols=FALSE) { if (!isTRUEorFALSE(use.names)) stop("'use.names' must be TRUE or FALSE") if (!isTRUEorFALSE(use.mcols)) stop("'use.mcols' must be TRUE or FALSE") ans <- x@granges if (!use.names) names(ans) <- NULL if (use.mcols) mcols(ans) <- mcols(x) ans } ) setMethod("length", "GNCList", function(x) length(granges(x))) setMethod("names", "GNCList", function(x) names(granges(x))) setMethod("seqnames", "GNCList", function(x) seqnames(granges(x))) setMethod("start", "GNCList", function(x, ...) start(granges(x))) setMethod("end", "GNCList", function(x, ...) end(granges(x))) setMethod("width", "GNCList", function(x) width(granges(x))) setMethod("ranges", "GNCList", function(x, use.names=TRUE, use.mcols=FALSE) ranges(granges(x, use.names=use.names, use.mcols=use.mcols), use.names=TRUE, use.mcols=use.mcols) ) setMethod("strand", "GNCList", function(x) strand(granges(x))) setMethod("seqinfo", "GNCList", function(x) seqinfo(granges(x))) setAs("GNCList", "GRanges", function(from) granges(from, use.mcols=TRUE)) ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ### Constructor ### .extract_groups_from_GenomicRanges <- function(x) splitAsList(seq_along(x) - 1L, seqnames(x)) GNCList <- function(x) { if (!is(x, "GenomicRanges")) stop("'x' must be a GenomicRanges object") if (!is(x, "GRanges")) x <- as(x, "GRanges") ans_mcols <- mcols(x) mcols(x) <- NULL x_groups <- .extract_groups_from_GenomicRanges(x) x_ranges <- IRanges:::.shift_ranges_in_groups_to_first_circle(ranges(x), x_groups, .get_circle_length(x)) ranges(x) <- x_ranges x_nclists <- IRanges:::.nclists(x_ranges, x_groups) new2("GNCList", nclists=x_nclists, granges=x, elementMetadata=ans_mcols, check=FALSE) } setAs("GenomicRanges", "GNCList", function(from) GNCList(from)) ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ### Subsetting ### setMethod("extractROWS", "GNCList", function(x, i) as(callGeneric(as(x, "GRanges"), i), class(x)) ) ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ### findOverlaps_GNCList() ### ### NOT exported. findOverlaps_GNCList <- function(query, subject, maxgap=0L, minoverlap=1L, type=c("any", "start", "end", "within", "extend", "equal"), select=c("all", "first", "last", "arbitrary", "count"), ignore.strand=FALSE) { if (!(is(query, "GenomicRanges") && is(subject, "GenomicRanges"))) stop("'query' and 'subject' must be GenomicRanges objects") type <- match.arg(type) select <- match.arg(select) if (!isTRUEorFALSE(ignore.strand)) stop("'ignore.strand' must be TRUE or FALSE") si <- merge(seqinfo(query), seqinfo(subject)) q_seqlevels <- seqlevels(query) s_seqlevels <- seqlevels(subject) common_seqlevels <- intersect(q_seqlevels, s_seqlevels) NG <- length(common_seqlevels) q_group_idx <- match(common_seqlevels, q_seqlevels) # of length NG s_group_idx <- match(common_seqlevels, s_seqlevels) # of length NG ## Extract 'q_groups' and 's_groups' (both of length NG). q_groups <- .extract_groups_from_GenomicRanges(query)[q_group_idx] s_groups <- .extract_groups_from_GenomicRanges(subject)[s_group_idx] ## Extract 'nclists' and 'nclist_is_q' (both of length NG). if (is(subject, "GNCList")) { nclists <- subject@nclists[s_group_idx] nclist_is_q <- rep.int(FALSE, NG) } else if (is(query, "GNCList")) { nclists <- query@nclists[q_group_idx] nclist_is_q <- rep.int(TRUE, NG) } else { ## We'll do "on-the-fly preprocessing". nclists <- vector(mode="list", length=NG) nclist_is_q <- rep.int(NA, NG) } ## Extract 'circle_length' (of length NG). circle_length <- .get_circle_length(si)[q_group_idx] ## Extract 'q_space' and 's_space'. if (ignore.strand) { q_space <- s_space <- NULL } else { q_space <- as.integer(strand(query)) - 3L s_space <- as.integer(strand(subject)) - 3L } ## GO! IRanges:::NCList_find_overlaps_in_groups( ranges(query), q_space, q_groups, ranges(subject), s_space, s_groups, nclists, nclist_is_q, maxgap, minoverlap, type, select, circle_length) } GenomicRanges/R/GPos-class.R0000644000175400017540000003660613001230267016625 0ustar00biocbuildbiocbuild### ========================================================================= ### GPos objects ### ------------------------------------------------------------------------- ### ### The GPos class is a container for storing a set of genomic *positions*, ### that is, genomic ranges of width 1. Even though a GRanges object can ### be used for that, using a GPos object can be much more memory-efficient, ### especially when the object contains long runs of adjacent positions. ### setClass("GPos", contains="GenomicRanges", representation( pos_runs="GRanges", elementMetadata="DataFrame" ) ) ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ### Getters ### setMethod("length", "GPos", function(x) sum(width(x@pos_runs))) setMethod("names", "GPos", function(x) NULL) setReplaceMethod("names", "GPos", function(x, value) { if (!is.null(value)) stop(class(x), " objects don't accept names") x } ) setMethod("seqnames", "GPos", function(x) rep.int(seqnames(x@pos_runs), width(x@pos_runs)) ) setGeneric("pos", function(x) standardGeneric("pos")) setMethod("pos", "GPos", function(x) as.integer(ranges(x@pos_runs))) setMethod("start", "GPos", function(x) pos(x)) setMethod("end", "GPos", function(x) pos(x)) setMethod("width", "GPos", function(x) rep.int(1L, length(x))) setMethod("ranges", "GPos", function(x, use.names=TRUE, use.mcols=FALSE) { if (!isTRUEorFALSE(use.mcols)) stop("'use.mcols' must be TRUE or FALSE") ans <- IRanges(pos(x), width=1L) if (use.mcols) mcols(ans) <- mcols(x) ans } ) setMethod("strand", "GPos", function(x) rep.int(strand(x@pos_runs), width(x@pos_runs)) ) setMethod("seqinfo", "GPos", function(x) seqinfo(x@pos_runs)) ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ### Collapse runs of "stitchable ranges" ### ### In a Ranges object 'x', 2 ranges x[i] and x[i+1] are "stitchable" if ### start(x[i+1]) == end(x[i])+1. For example, in the following object: ### 1: .....xxxx............. ### 2: ...xx................. ### 3: .........xxx.......... ### 4: ............xxxxxx.... ### 5: ..................x... ### x[3] and x[4] are stitchable, and x[4] and x[5] are stitchable. So ### x[3], x[4], and x[5] form a run of "stitchable ranges" that will collapse ### into the following single range after stitching: ### .........xxxxxxxxxx... ### Note that x[1] and x[3] are not stitchable because they are not ### consecutive vector elements (but they would if we removed x[2]). ### ### If 'x' contains genomic ranges (i.e. is a GenomicRanges object), 2 ranges ### are "stitchable" if, in addition to the above, they are also on the same ### chromosome and strand. ### .stitch_GenomicRanges() below takes any GenomicRanges derivative and ### returns a GRanges object (so is NOT an endomorphism). ### Note that this transformation preserves 'sum(width(x))'. ### Also note that this is an "inter range transformation". However unlike ### range(), reduce(), gaps(), or disjoin(), its result depends on the order ### of the elements in the input vector. It's also idempotent like range(), ### reduce(), and disjoin() (gaps() is not). ### TODO: Define and export stitch() generic and method for Ranges objects ### in the IRanges package (in inter-range-methods.R). Then make ### .stitch_GenomicRanges() the "stitch" method for GenomicRanges objects and ### support the 'ignore.strand' argument. ### Maybe it would also make sense to have an isStitched() generic like we ### have isDisjoint() to provide a quick and easy way to check the state of ### the object before applying the transformation to it. In theory each ### idempotent inter range transformation could have a "state checker" so ### maybe add isReduced() too (range() probably doesn't need one). .stitch_GenomicRanges <- function(x, drop.empty.ranges=FALSE) { if (length(x) == 0L) return(granges(x)) # returning GRanges() would loose the seqinfo x_seqnames <- seqnames(x) x_strand <- strand(x) x_start <- start(x) x_end <- end(x) new_run <- x_seqnames[-1L] != x_seqnames[-length(x)] | x_strand[-1L] != x_strand[-length(x)] | Rle(x_start[-1L] != x_end[-length(x)] + 1L) new_run_idx <- which(new_run) start_idx <- c(1L, new_run_idx + 1L) end_idx <- c(new_run_idx, length(x)) ans_ranges <- IRanges(x_start[start_idx], x_end[end_idx]) if (drop.empty.ranges) { keep_idx <- which(width(ans_ranges) != 0L) ans_ranges <- ans_ranges[keep_idx] start_idx <- start_idx[keep_idx] } ans_seqnames <- x_seqnames[start_idx] ans_strand <- x_strand[start_idx] ans_mcols <- S4Vectors:::make_zero_col_DataFrame(length(start_idx)) ans_seqinfo <- seqinfo(x) ## To be as fast as possible, we don't use internal low-level constructor ## new_GRanges() and we don't check the new object. new2("GRanges", seqnames=ans_seqnames, ranges=ans_ranges, strand=ans_strand, elementMetadata=ans_mcols, seqinfo=ans_seqinfo, check=FALSE) } ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ### Validity ### ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ### Setters ### ### Supporting the seqinfo() setter makes the following work out-of-the-box: ### - The family of seqinfo-related setters: seqlevels(), seqlevelsStyle(), ### seqlengths(), isCircular(), and genome(). ### - pcompare() and all the binary comparison operators (==, <=, !=, ### >=, <, >). setReplaceMethod("seqinfo", "GPos", function(x, new2old=NULL, force=FALSE, value) { new_pos_runs <- callGeneric(x@pos_runs, new2old=new2old, force=force, value) x@pos_runs <- .stitch_GenomicRanges(new_pos_runs) x } ) ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ### Constructor ### ### Note that if 'pos_runs' is a GPos instance with no metadata or metadata ### columns, then 'identical(GPos(pos_runs), pos_runs)' is TRUE. GPos <- function(pos_runs=GRanges()) { if (!is(pos_runs, "GenomicRanges")) pos_runs <- as(pos_runs, "GenomicRanges", strict=FALSE) suppressWarnings(ans_len <- sum(width(pos_runs))) if (is.na(ans_len)) stop("too many genomic positions in 'pos_runs'") ans_mcols <- S4Vectors:::make_zero_col_DataFrame(ans_len) ans_pos_runs <- .stitch_GenomicRanges(pos_runs, drop.empty.ranges=TRUE) new2("GPos", pos_runs=ans_pos_runs, elementMetadata=ans_mcols, metadata=pos_runs@metadata, check=FALSE) } ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ### Coercion ### .from_GenomicRanges_to_GPos <- function(from) { if (!all(width(from) == 1L)) stop(wmsg("all the ranges in the ", class(from), " object to ", "coerce to GPos must have a width of 1")) if (!is.null(names(from))) warning(wmsg("because a GPos object cannot hold them, the names ", "on the ", class(from), " object couldn't be ", "propagated during its coercion to GPos")) ans <- GPos(from) mcols(ans) <- mcols(from) ans } setAs("GenomicRanges", "GPos", .from_GenomicRanges_to_GPos) ### The "as.data.frame" method for GenomicRanges objects works on a GPos ### object but returns a data.frame with identical "start" and "end" columns, ### and a "width" column filled with 1. We overwrite it to return a data.frame ### with a "pos" column instead of the "start" and "end" columns, and no ### "width" column. ### TODO: Turn this into an S3/S4 combo for as.data.frame.GPos setMethod("as.data.frame", "GPos", function(x, row.names=NULL, optional=FALSE, ...) { mcols_df <- as.data.frame(mcols(x), ...) data.frame(seqnames=as.factor(seqnames(x)), pos=pos(x), strand=as.factor(strand(x)), mcols_df, stringsAsFactors=FALSE) } ) ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ### Subsetting ### setMethod("extractROWS", "GPos", function(x, i) { i <- normalizeSingleBracketSubscript(i, x, as.NSBS=TRUE) ## TODO: Maybe make this the coercion method from NSBS to Ranges. if (is(i, "RangesNSBS")) { ir <- i@subscript ir <- ir[width(ir) != 0L] } else { ir <- as(as.integer(i), "IRanges") } map <- S4Vectors:::map_ranges_to_runs(width(x@pos_runs), start(ir), width(ir)) ## Because 'ir' has no zero-width ranges, 'spanned_nrun' cannot ## contain zeroes and so 'Ltrim' and 'Rtrim' cannot contain garbbage. offset_nrun <- map[[1L]] spanned_nrun <- map[[2L]] Ltrim <- map[[3L]] Rtrim <- map[[4L]] run_idx <- S4Vectors:::fancy_mseq(spanned_nrun, offset_nrun) new_pos_runs <- x@pos_runs[run_idx] if (length(run_idx) != 0L) { Rtrim_idx <- cumsum(spanned_nrun) Ltrim_idx <- c(1L, Rtrim_idx[-length(Rtrim_idx)] + 1L) trimmed_start <- start(new_pos_runs)[Ltrim_idx] + Ltrim trimmed_end <- end(new_pos_runs)[Rtrim_idx] - Rtrim start(new_pos_runs)[Ltrim_idx] <- trimmed_start end(new_pos_runs)[Rtrim_idx] <- trimmed_end suppressWarnings(new_len <- sum(width(new_pos_runs))) if (is.na(new_len)) stop("subscript is too big") } x@pos_runs <- .stitch_GenomicRanges(new_pos_runs) mcols(x) <- extractROWS(mcols(x), i) x } ) ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ### Show ### .make_naked_matrix_from_GPos <- function(x) { x_len <- length(x) x_mcols <- mcols(x) x_nmc <- if (is.null(x_mcols)) 0L else ncol(x_mcols) ans <- cbind(seqnames=as.character(seqnames(x)), pos=as.character(pos(x)), strand=as.character(strand(x))) if (x_nmc > 0L) { tmp <- do.call(data.frame, c(lapply(x_mcols, showAsCell), list(check.names=FALSE))) ans <- cbind(ans, `|`=rep.int("|", x_len), as.matrix(tmp)) } ans } show_GPos <- function(x, margin="", print.classinfo=FALSE, print.seqinfo=FALSE) { x_class <- class(x) x_len <- length(x) x_mcols <- mcols(x) x_nmc <- if (is.null(x_mcols)) 0L else ncol(x_mcols) cat(x_class, " object with ", x_len, " ", ifelse(x_len == 1L, "position", "positions"), " and ", x_nmc, " metadata ", ifelse(x_nmc == 1L, "column", "columns"), ":\n", sep="") ## S4Vectors:::makePrettyMatrixForCompactPrinting() assumes that head() ## and tail() work on 'xx'. xx <- as(x, "GPos") out <- S4Vectors:::makePrettyMatrixForCompactPrinting(xx, .make_naked_matrix_from_GPos) if (print.classinfo) { .COL2CLASS <- c( seqnames="Rle", pos="integer", strand="Rle" ) classinfo <- S4Vectors:::makeClassinfoRowForCompactPrinting(x, .COL2CLASS) ## A sanity check, but this should never happen! stopifnot(identical(colnames(classinfo), colnames(out))) out <- rbind(classinfo, out) } if (nrow(out) != 0L) rownames(out) <- paste0(margin, rownames(out)) ## We set 'max' to 'length(out)' to avoid the getOption("max.print") ## limit that would typically be reached when 'showHeadLines' global ## option is set to Inf. print(out, quote=FALSE, right=TRUE, max=length(out)) if (print.seqinfo) { cat(margin, "-------\n", sep="") cat(margin, "seqinfo: ", summary(seqinfo(x)), "\n", sep="") } } setMethod("show", "GPos", function(object) show_GPos(object, margin=" ", print.classinfo=TRUE, print.seqinfo=TRUE) ) ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ### Combining ### ### Note that supporting "[" and "c" makes "[<-" work out-of-the-box! ### ### 'Class' must be "GPos" or the name of a concrete GPos subclass. ### 'objects' must be a list of GPos objects. ### Returns an instance of class 'Class'. combine_GPos_objects <- function(Class, objects, use.names=TRUE, ignore.mcols=FALSE) { if (!isSingleString(Class)) stop("'Class' must be a single character string") if (!extends(Class, "GPos")) stop("'Class' must be the name of a class that extends GPos") if (!is.list(objects)) stop("'objects' must be a list") if (!isTRUEorFALSE(use.names)) stop("'use.names' must be TRUE or FALSE") ### TODO: Support 'use.names=TRUE'. if (use.names) stop("'use.names=TRUE' is not supported yet") if (!isTRUEorFALSE(ignore.mcols)) stop("'ignore.mcols' must be TRUE or FALSE") if (length(objects) != 0L) { ## TODO: Implement (in C) fast 'elementIsNull(objects)' in S4Vectors ## that does 'sapply(objects, is.null, USE.NAMES=FALSE)', and use it ## here. null_idx <- which(sapply(objects, is.null, USE.NAMES=FALSE)) if (length(null_idx) != 0L) objects <- objects[-null_idx] } if (length(objects) == 0L) return(new(Class)) ## TODO: Implement (in C) fast 'elementIs(objects, class)' in S4Vectors ## that does 'sapply(objects, is, class, USE.NAMES=FALSE)', and use it ## here. 'elementIs(objects, "NULL")' should work and be equivalent to ## 'elementIsNull(objects)'. if (!all(sapply(objects, is, Class, USE.NAMES=FALSE))) stop("the objects to combine must be ", Class, " objects (or NULLs)") objects_names <- names(objects) names(objects) <- NULL # so lapply(objects, ...) below returns an # unnamed list ## Combine "pos_runs" slots. pos_runs_slots <- lapply(objects, function(x) x@pos_runs) ## TODO: Use combine_GRanges_objects() here when it's available. ans_pos_runs <- .stitch_GenomicRanges(do.call(c, pos_runs_slots)) suppressWarnings(ans_len <- sum(width(ans_pos_runs))) if (is.na(ans_len)) stop("too many genomic positions to combine") ## Combine "mcols" slots. We don't need to use fancy ## S4Vectors:::rbind_mcols() for this because the "mcols" slot of a ## GPos object is guaranteed to be a DataFrame. if (ignore.mcols) { ans_mcols <- S4Vectors:::make_zero_col_DataFrame(ans_len) } else { mcols_slots <- lapply(objects, function(x) x@elementMetadata) ## Will fail if not all the GPos objects in 'objects' have ## exactly the same metadata cols. ans_mcols <- do.call(rbind, mcols_slots) } ## Make 'ans' and return it. new2(Class, pos_runs=ans_pos_runs, elementMetadata=ans_mcols, check=FALSE) } setMethod("c", "GPos", function (x, ..., ignore.mcols=FALSE, recursive=FALSE) { if (!identical(recursive, FALSE)) stop("\"c\" method for GPos objects ", "does not support the 'recursive' argument") if (missing(x)) { objects <- list(...) x <- objects[[1L]] } else { objects <- list(x, ...) } combine_GPos_objects(class(x), objects, use.names=FALSE, ignore.mcols=ignore.mcols) } ) GenomicRanges/R/GRanges-class.R0000644000175400017540000003736713001230267017310 0ustar00biocbuildbiocbuild### ========================================================================= ### GRanges objects ### ------------------------------------------------------------------------- ### setClass("GRanges", contains="GenomicRanges", representation( seqnames="Rle", ranges="IRanges", strand="Rle", elementMetadata="DataFrame", seqinfo="Seqinfo" ), prototype( seqnames=Rle(factor()), strand=Rle(strand()) ) ) ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ### "update" method ### ### Having update() redirect to BiocGenerics:::replaceSlots() on GRanges ### objects makes all the methods for GenomicRanges objects defined in ### R/GenomicRanges-class.R work on GRanges objects. setMethod("update", "GRanges", function(object, ...) BiocGenerics:::replaceSlots(object, ...) ) ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ### Validity ### .valid.GRanges.ranges <- function(x) { if (!is.null(x@ranges@elementMetadata)) return("slot 'ranges' cannot have metadata columns") NULL } .valid.GRanges.mcols <- function(x) { x_mcols <- x@elementMetadata if (!is.null(rownames(x_mcols))) return("'mcols(x)' cannot have row names") NULL } .valid.GRanges <- function(x) { c(.valid.GRanges.ranges(x), .valid.GRanges.mcols(x)) } setValidity2("GRanges", .valid.GRanges) ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ### Constructor ### .set_strand_mcols_seqinfo <- function(x, strand=NULL, mcols=NULL, seqlengths=NULL, seqinfo=NULL) { if (is.null(strand)) { x_strand <- strand(x) } else { x_strand <- strand } if (length(mcols) == 0L) { x_mcols <- mcols(x) } else { x_mcols <- mcols } if (is.null(seqlengths)) { x_seqlengths <- seqlengths(x) } else { x_seqlengths <- seqlengths } if (is.null(seqinfo)) { x_seqinfo <- seqinfo(x) } else { x_seqinfo <- seqinfo } new_GRanges(class(x), seqnames(x), ranges(x), x_strand, x_mcols, x_seqlengths, x_seqinfo) } ### Internal low-level constructor. Shared with other GRanges-like objects. new_GRanges <- function(Class, seqnames=NULL, ranges=NULL, strand=NULL, mcols=NULL, seqlengths=NULL, seqinfo=NULL) { if (is.null(ranges)) { if (!is.null(seqnames)) { x <- as(seqnames, Class) return(.set_strand_mcols_seqinfo(x, strand, mcols, seqlengths, seqinfo)) } ranges <- IRanges() } else { ranges <- as(ranges, "IRanges") } if (is.null(seqnames)) { seqnames <- Rle() } else { if (!is(seqnames, "Rle")) seqnames <- Rle(seqnames) if (!is.factor(runValue(seqnames))) runValue(seqnames) <- factor(runValue(seqnames), levels=unique(runValue(seqnames))) } if (is.null(strand)) { strand <- Rle(strand("*"), length(seqnames)) } else { if (!is(strand, "Rle")) strand <- Rle(strand) if (!is.factor(runValue(strand)) || !identical(levels(runValue(strand)), levels(strand()))) runValue(strand) <- strand(runValue(strand)) if (S4Vectors:::anyMissing(runValue(strand))) { warning("missing values in strand converted to \"*\"") runValue(strand)[is.na(runValue(strand))] <- "*" } } lx <- max(length(seqnames), length(ranges), length(strand)) if (lx > 1) { if (length(seqnames) == 1) seqnames <- rep(seqnames, lx) if (length(ranges) == 1) ranges <- rep(ranges, lx) if (length(strand) == 1) strand <- rep(strand, lx) } if (is.null(seqlengths)) seqlengths <- setNames(rep(NA_integer_, length(levels(seqnames))), levels(seqnames)) if (is.null(seqinfo)) seqinfo <- Seqinfo(names(seqlengths), seqlengths) ## in case we have seqlengths for unrepresented sequences runValue(seqnames) <- factor(runValue(seqnames), levels=seqnames(seqinfo)) ranges_mcols <- mcols(ranges) if (!is.null(ranges_mcols)) mcols(ranges) <- NULL ## Normalize 'mcols'. if (length(mcols) == 0L) { mcols <- ranges_mcols if (is.null(mcols)) mcols <- DataFrame() } else if (!is(mcols, "DataFrame")) { stop("'mcols' must be a DataFrame object") } if (nrow(mcols) == 0L && ncol(mcols) == 0L) { mcols <- S4Vectors:::make_zero_col_DataFrame(length(ranges)) } else if (!is.null(rownames(mcols))) { if (is.null(names(ranges))) names(ranges) <- rownames(mcols) rownames(mcols) <- NULL } new(Class, seqnames=seqnames, ranges=ranges, strand=strand, elementMetadata=mcols, seqinfo=seqinfo) } GRanges <- function(seqnames=NULL, ranges=NULL, strand=NULL, ..., seqlengths=NULL, seqinfo=NULL) { mcols <- DataFrame(..., check.names=FALSE) new_GRanges("GRanges", seqnames=seqnames, ranges=ranges, strand=strand, mcols=mcols, seqlengths=seqlengths, seqinfo=seqinfo) } setMethod("updateObject", "GRanges", function(object, ..., verbose=FALSE) { if (verbose) message("updateObject(object = 'GRanges')") if (is(try(object@seqinfo, silent=TRUE), "try-error")) { object <- new(class(object), seqnames = object@seqnames, ranges = object@ranges, strand = object@strand, elementMetadata = object@elementMetadata, metadata = object@metadata, seqinfo = Seqinfo(seqnames = names(object@seqlengths), seqlengths = object@seqlengths)) return(object) } if (is(try(validObject(object@seqinfo, complete=TRUE), silent=TRUE), "try-error")) { object@seqinfo <- updateObject(object@seqinfo) return(object) } object } ) ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ### Coercion ### setMethod("granges", "GenomicRanges", function(x, use.names=TRUE, use.mcols=FALSE) { if (!isTRUEorFALSE(use.mcols)) stop("'use.mcols' must be TRUE or FALSE") ans <- GRanges(seqnames(x), ranges(x, use.names=use.names), strand(x), seqinfo=seqinfo(x)) if (use.mcols) mcols(ans) <- cbind(extraColumnSlotsAsDF(x), mcols(x)) ans } ) setAs("GenomicRanges", "GRanges", function(from) granges(from, use.mcols=TRUE) ) .from_character_to_GRanges <- function(from) { stopifnot(is.character(from)) if (anyNA(from)) stop(wmsg("converting a character vector to a GRanges object ", "does not support NAs")) error_msg <- wmsg( "The character vector to convert to a GRanges object must contain ", "strings of the form \"chr1:2501-2800\" or \"chr1:2501-2800:+\" ", "(\"..\" being also supported as a separator between the start and ", "end positions). Strand can be \"+\", \"-\", \"*\", or missing." ) split0 <- CharacterList(strsplit(from, ":", fixed=TRUE)) split0_eltNROWS <- elementNROWS(split0) if (S4Vectors:::anyMissingOrOutside(split0_eltNROWS, 2L, 3L)) stop(error_msg) ans_strand <- as.character(ptail(split0, n=-2L)) ans_strand[is.na(ans_strand)] <- "*" split1 <- phead(split0, n=2L) ans_seqnames <- as.character(phead(split1, n=1L)) ranges <- as.character(ptail(split1, n=-1L)) ## We want to split on the first occurence of "-" that is preceeded by ## a digit (ignoring and removing the spaces in between if any). ranges <- sub("([[:digit:]])[[:space:]]*-", "\\1..", ranges) split2 <- CharacterList(strsplit(ranges, "..", fixed=TRUE)) split2_eltNROWS <- elementNROWS(split2) if (!all(split2_eltNROWS == 2L)) stop(error_msg) ans_start <- as.integer(phead(split2, n=1L)) ans_end <- as.integer(ptail(split2, n=1L)) ans_ranges <- IRanges(ans_start, ans_end, names=names(from)) GRanges(ans_seqnames, ans_ranges, ans_strand) } setAs("character", "GRanges", .from_character_to_GRanges) .from_factor_to_GRanges <- function(from) { from <- setNames(as.character(from), names(from)) .from_character_to_GRanges(from) } setAs("factor", "GRanges", .from_factor_to_GRanges) ### Does NOT propagate the ranges names and metadata columns i.e. always ### returns an unnamed GRanges object with no metadata columns. setAs("RangesList", "GRanges", function(from) { if (!length(from)) return(GRanges()) from <- as(from, "CompressedIRangesList") ranges <- unlist(from, use.names=FALSE) ranges <- IRanges(start=start(ranges), width=width(ranges)) ## From now, ranges is guaranteed to be an IRanges *instance*. if (is.null(space(from))) { stop("Cannot create GRanges when 'space(from)' is NULL") } gr <- GRanges(seqnames = space(from), ranges = ranges, strand = Rle("*", length(ranges))) seqinfo(gr) <- seqinfo(from) metadata(gr) <- metadata(from) gr }) setAs("RangedData", "GRanges", function(from) { ans_ranges <- unlist(ranges(from), use.names=FALSE) ans_mcols <- unlist(values(from), use.names=FALSE) rownames(ans_mcols) <- NULL whichStrand <- match("strand", colnames(ans_mcols)) if (is.na(whichStrand)) { ans_strand <- Rle(strand("*"), length(ans_ranges)) } else { ans_strand <- Rle(strand(from)) ans_mcols <- ans_mcols[-whichStrand] } ans <- GRanges(seqnames=space(from), ranges=ans_ranges, strand=ans_strand, ans_mcols, seqinfo=seqinfo(from)) metadata(ans) <- metadata(from) ans } ) .from_Seqinfo_to_GRanges <- function(from) { if (anyNA(seqlengths(from))) stop(wmsg("cannot create a GRanges object ", "from a Seqinfo object with NA seqlengths")) GRanges(seqnames(from), IRanges(rep.int(1L, length(from)), width=seqlengths(from), names=seqnames(from)), seqinfo=from) } setAs("Seqinfo", "GRanges", .from_Seqinfo_to_GRanges) setAs("Seqinfo", "RangesList", function(from) as(as(from, "GRanges"), "RangesList") ) setAs("ANY", "GenomicRanges", function(from) as(from, "GRanges")) ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ### Slot getters and setters ### setMethod("seqnames", "GRanges", function(x) x@seqnames) setMethod("ranges", "GRanges", function(x, use.names=TRUE, use.mcols=FALSE) { if (!isTRUEorFALSE(use.names)) stop("'use.names' must be TRUE or FALSE") if (!isTRUEorFALSE(use.mcols)) stop("'use.mcols' must be TRUE or FALSE") ans <- x@ranges if (!use.names) names(ans) <- NULL if (use.mcols) mcols(ans) <- mcols(x) ans } ) setMethod("strand", "GRanges", function(x) x@strand) setMethod("seqinfo", "GRanges", function(x) x@seqinfo) ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ### Subsetting ### setMethod("extractROWS", "GRanges", function(x, i) { if (missing(i) || !is(i, "Ranges")) i <- normalizeSingleBracketSubscript(i, x) ans_seqnames <- extractROWS(seqnames(x), i) ans_ranges <- extractROWS(ranges(x), i) ans_strand <- extractROWS(strand(x), i) ans_mcols <- extractROWS(mcols(x), i) ans_ecs <- lapply(extraColumnSlots(x), extractROWS, i) BiocGenerics:::replaceSlots(x, seqnames=ans_seqnames, ranges=ans_ranges, strand=ans_strand, elementMetadata=ans_mcols, .slotList=ans_ecs, check=FALSE) } ) setMethod("replaceROWS", "GRanges", function(x, i, value) { if (missing(i) || !is(i, "Ranges")) i <- normalizeSingleBracketSubscript(i, x) seqinfo(x) <- merge(seqinfo(x), seqinfo(value)) ans_seqnames <- replaceROWS(seqnames(x), i, seqnames(value)) ans_ranges <- replaceROWS(ranges(x), i, ranges(value)) ans_strand <- replaceROWS(strand(x), i, strand(value)) ans_mcols <- replaceROWS(mcols(x), i, mcols(value)) ans_ecs_names <- extraColumnSlotNames(x) ans_necs <- length(ans_ecs_names) if (ans_necs == 0L) { ans_ecs <- NULL } else { value_ecs_names <- extraColumnSlotNames(value) if (!identical(value_ecs_names[seq_len(ans_necs)], ans_ecs_names)) stop("'value' can have more extra column slots but not less") ans_ecs <- extraColumnSlotsAsDF(x) value_ecs <- extraColumnSlotsAsDF(value) ans_ecs <- replaceROWS(ans_ecs, i, value_ecs[seq_len(ans_necs)]) } BiocGenerics:::replaceSlots(x, seqnames=ans_seqnames, ranges=ans_ranges, strand=ans_strand, elementMetadata=ans_mcols, .slotList=as.list(ans_ecs)) } ) ### TODO: Refactor to use replaceROWS(). This will make the code much simpler ### and avoid a lot of duplication with the above "replaceROWS" method. setReplaceMethod("[", "GRanges", function(x, i, j, ..., value) { if (!is(value, "GenomicRanges")) stop("replacement value must be a GenomicRanges object") seqinfo(x) <- merge(seqinfo(x), seqinfo(value)) seqnames <- seqnames(x) ranges <- ranges(x) strand <- strand(x) ans_mcols <- mcols(x, FALSE) value_ecs <- extraColumnSlotsAsDF(value) x_ecs <- extraColumnSlotsAsDF(x) new_ecs <- value_ecs[!names(value_ecs) %in% names(x_ecs)] ecs_to_replace <- intersect(names(value_ecs), names(x_ecs)) if (missing(i)) { seqnames[] <- seqnames(value) ranges[] <- ranges(value) strand[] <- strand(value) if (missing(j)) ans_mcols[ , ] <- mcols(value, FALSE) else ans_mcols[ , j] <- mcols(value, FALSE) if (length(new_ecs) > 0L) ans_mcols[names(new_ecs)] <- new_ecs x_ecs[ecs_to_replace] <- value_ecs[ecs_to_replace] } else { i <- extractROWS(setNames(seq_along(x), names(x)), i) seqnames[i] <- seqnames(value) ranges[i] <- ranges(value) strand[i] <- strand(value) if (missing(j)) ans_mcols[i, ] <- mcols(value, FALSE) else ans_mcols[i, j] <- mcols(value, FALSE) if (length(new_ecs) > 0L) ans_mcols[i, names(new_ecs)] <- DataFrame(new_ecs) if (length(ecs_to_replace) > 0L) { x_ecs[i, ecs_to_replace] <- value_ecs[ecs_to_replace] } } BiocGenerics:::replaceSlots(x, seqnames=seqnames, ranges=ranges, strand=strand, elementMetadata=ans_mcols, .slotList=as.list(x_ecs)) } ) GenomicRanges/R/GRangesList-class.R0000644000175400017540000005236413001230267020136 0ustar00biocbuildbiocbuild### ========================================================================= ### GRangesList objects ### ------------------------------------------------------------------------- ### setClass("GRangesList", contains=c("CompressedList", "GenomicRangesList"), representation( unlistData="GRanges", elementMetadata="DataFrame" ), prototype( elementType="GRanges" ) ) setClassUnion("GenomicRangesORGRangesList", c("GenomicRanges", "GRangesList")) ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ### Validity. ### .valid.GRangesList.mcols <- function(x) { msg <- NULL x_mcols <- x@elementMetadata if (nrow(x_mcols) != length(x)) msg <- "'mcols(x)' has an incorrect number of rows" if (any(c("seqnames", "ranges", "strand", "start", "end", "width", "element") %in% colnames(x_mcols))) msg <- c(msg, paste("'mcols(x)' cannot have columns named \"seqnames\", ", "\"ranges\", \"strand\", \"start\", \"end\", \"width\", ", "or \"element\"")) if (!is.null(rownames(x_mcols))) msg <- c(msg, "'mcols(x)' cannot have row names") msg } .valid.GRangesList <- function(x) { c(.valid.GRangesList.mcols(x)) } setValidity2("GRangesList", .valid.GRangesList) ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ### Constructors. ### GRangesList <- function(...) { listData <- list(...) if (length(listData) == 1L && is.list(listData[[1L]])) listData <- listData[[1L]] if (length(listData) == 0L) { unlistData <- GRanges() } else { if (!all(sapply(listData, is, "GRanges"))) stop("all elements in '...' must be GRanges objects") unlistData <- suppressWarnings(do.call("c", unname(listData))) } relist(unlistData, PartitioningByEnd(listData)) } ### Typically, the field values will come from a file that needs to be loaded ### into a data.frame first. makeGRangesListFromFeatureFragments <- function(seqnames=Rle(factor()), fragmentStarts=list(), fragmentEnds=list(), fragmentWidths=list(), strand=character(0), sep=",") { fragmentStarts <- normargListOfIntegers(fragmentStarts, sep, "fragmentStarts") nfrag_per_feature <- elementNROWS(fragmentStarts) start <- unlist(fragmentStarts, recursive=FALSE, use.names=FALSE) fragmentEnds <- normargListOfIntegers(fragmentEnds, sep, "fragmentEnds") nend_per_elt <- elementNROWS(fragmentEnds) if (length(nend_per_elt) != 0L) { if (length(nfrag_per_feature) == 0L) nfrag_per_feature <- nend_per_elt else if (!identical(nend_per_elt, nfrag_per_feature)) stop("'fragmentStarts' and 'fragmentEnds' have ", "incompatible \"shapes\"") } end <- unlist(fragmentEnds, recursive=FALSE, use.names=FALSE) fragmentWidths <- normargListOfIntegers(fragmentWidths, sep, "fragmentWidths") nwidth_per_elt <- elementNROWS(fragmentWidths) if (length(nwidth_per_elt) != 0L) { if (length(nfrag_per_feature) == 0L) nfrag_per_feature <- nwidth_per_elt else if (!identical(nwidth_per_elt, nfrag_per_feature)) stop("\"shape\" of 'fragmentWidths' is incompatible ", "with \"shape\" of 'fragmentStarts' or 'fragmentEnds'") } width <- unlist(fragmentWidths, recursive=FALSE, use.names=FALSE) ranges <- IRanges(start=start, end=end, width=width) nfrag <- sum(nfrag_per_feature) if (nfrag != length(ranges)) stop("GenomicRanges internal error in makeGRangesListFromFields(): ", "nfrag != length(ranges). This should never happen. ", "Please report.") if (nfrag == 0L) { ## Cannot blindly subset by FALSE because it doesn't work on a ## zero-length Rle. if (length(seqnames) != 0L) seqnames <- seqnames[FALSE] if (length(strand) != 0L) strand <- strand[FALSE] } else { if (length(seqnames) != length(nfrag_per_feature) || length(strand) != length(nfrag_per_feature)) stop("length of 'seqnames' and/or 'strand' is incompatible ", "with fragmentStarts/Ends/Widths") seqnames <- rep.int(seqnames, nfrag_per_feature) strand <- rep.int(strand, nfrag_per_feature) } unlistData <- GRanges(seqnames=seqnames, ranges=ranges, strand=strand) partitioning <- PartitioningByEnd(cumsum(nfrag_per_feature), names=NULL) relist(unlistData, partitioning) } setMethod("updateObject", "GRangesList", function(object, ..., verbose=FALSE) { if (verbose) message("updateObject(object = 'GRangesList')") if (is(try(validObject(object@unlistData, complete=TRUE), silent=TRUE), "try-error")) { object@unlistData <- updateObject(object@unlistData) return(object) } object } ) ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ### Accessors. ### setMethod("seqnames", "GRangesList", function(x) { unlisted_x <- unlist(x, use.names=FALSE) relist(seqnames(unlisted_x), x) } ) ### NOT exported but used in GenomicAlignments package. replaceSeqnamesList <- function(x, value) { if (!is(value, "AtomicList") || !identical(elementNROWS(x), elementNROWS(value))) stop("replacement 'value' is not an AtomicList with the same ", "elementNROWS as 'x'") value <- unlist(value, use.names = FALSE) if (!is(value, "Rle")) value <- Rle(factor(value)) else if (!is.factor(runValue(value))) runValue(value) <- factor(runValue(value)) seqnames(x@unlistData) <- value x } setReplaceMethod("seqnames", "GRangesList", replaceSeqnamesList) setMethod("ranges", "GRangesList", function(x, use.names=TRUE, use.mcols=FALSE) { if (!isTRUEorFALSE(use.names)) stop("'use.names' must be TRUE or FALSE") if (!isTRUEorFALSE(use.mcols)) stop("'use.mcols' must be TRUE or FALSE") unlisted_x <- unlist(x, use.names=FALSE) unlisted_ans <- unlisted_x@ranges if (use.mcols) mcols(unlisted_ans) <- mcols(unlisted_x) ans <- relist(unlisted_ans, x) if (!use.names) names(ans) <- NULL if (use.mcols) mcols(ans) <- mcols(x) ans } ) setReplaceMethod("ranges", "GRangesList", function(x, value) { if (!is(value, "RangesList") || !identical(elementNROWS(x), elementNROWS(value))) stop("replacement 'value' is not a RangesList with the same ", "elementNROWS as 'x'") ranges(x@unlistData) <- as(unlist(value, use.names = FALSE), "IRanges") x } ) ### Same as for CompressedIRangesList. setMethod("start", "GRangesList", function(x, ...) { unlisted_x <- unlist(x, use.names=FALSE) relist(start(unlisted_x), x) } ) setReplaceMethod("start", "GRangesList", function(x, ..., value) { if (!is(value, "IntegerList") || !identical(elementNROWS(x), elementNROWS(value))) stop("replacement 'value' is not an IntegerList with the same ", "elementNROWS as 'x'") value <- unlist(value, use.names = FALSE) start(x@unlistData, ...) <- value x } ) ### Same as for CompressedIRangesList. setMethod("end", "GRangesList", function(x, ...) { unlisted_x <- unlist(x, use.names=FALSE) relist(end(unlisted_x), x) } ) setReplaceMethod("end", "GRangesList", function(x, ..., value) { if (!is(value, "IntegerList") || !identical(elementNROWS(x), elementNROWS(value))) stop("replacement 'value' is not an IntegerList with the same ", "elementNROWS as 'x'") value <- unlist(value, use.names = FALSE) end(x@unlistData, ...) <- value x } ) ### Same as for CompressedIRangesList. setMethod("width", "GRangesList", function(x) { unlisted_x <- unlist(x, use.names=FALSE) relist(width(unlisted_x), x) } ) setReplaceMethod("width", "GRangesList", function(x, ..., value) { if (!is(value, "IntegerList") || !identical(elementNROWS(x), elementNROWS(value))) stop("replacement 'value' is not an IntegerList with the same ", "elementNROWS as 'x'") value <- unlist(value, use.names = FALSE) width(x@unlistData, ...) <- value x } ) setMethod("strand", "GRangesList", function(x) { unlisted_x <- unlist(x, use.names=FALSE) relist(strand(unlisted_x), x) } ) ### NOT exported but used in GenomicAlignments package. replaceStrandList <- function(x, value) { if (!is(value, "AtomicList") || !identical(elementNROWS(x), elementNROWS(value))) stop("replacement 'value' is not an AtomicList with the same ", "elementNROWS as 'x'") value <- unlist(value, use.names = FALSE) if (!is(value, "Rle")) value <- Rle(strand(value)) else if (!is.factor(runValue(value)) || !identical(levels(runValue(value)), levels(strand()))) runValue(value) <- strand(runValue(value)) strand(x@unlistData) <- value x } setReplaceMethod("strand", c("GRangesList", "ANY"), replaceStrandList) setReplaceMethod("strand", c("GRangesList", "character"), function(x, ..., value) { if (length(value) > 1L) stop("length(value) must be 1") strand(x@unlistData) <- value x } ) ### NOT exported but used in GenomicAlignments package. getElementMetadataList <- function(x, use.names=FALSE, level = c("between", "within"), ...) { if (!isTRUEorFALSE(use.names)) stop("'use.names' must be TRUE or FALSE") level <- match.arg(level) if (level == "between") { ans <- x@elementMetadata if (use.names) rownames(ans) <- names(x) return(ans) } unlisted_x <- unlist(x, use.names=FALSE) unlisted_ans <- unlisted_x@elementMetadata if (use.names) rownames(unlisted_ans) <- names(unlisted_x) relist(unlisted_ans, x) } setMethod("elementMetadata", "GRangesList", getElementMetadataList) ### NOT exported but used in GenomicAlignments package. replaceElementMetadataList <- function(x, level = c("between", "within"), ..., value) { level <- match.arg(level) if (level == "between") { if (is.null(value)) value <- S4Vectors:::make_zero_col_DataFrame(length(x)) else if (!is(value, "DataFrame")) value <- DataFrame(value) if (!is.null(rownames(value))) rownames(value) <- NULL n <- length(x) k <- nrow(value) if (k != n) { if ((k == 0) || (k > n) || (n %% k != 0)) stop(k, " rows in value to replace ", n, "rows") value <- value[rep(seq_len(k), length.out = n), , drop=FALSE] } x@elementMetadata <- value } else { if (is.null(value)) { value <- S4Vectors:::make_zero_col_DataFrame(length(x@unlistData)) } else { if (!is(value, "SplitDataFrameList") || !identical(elementNROWS(x), elementNROWS(value))) { stop("replacement 'value' is not a SplitDataFrameList with ", "the same elementNROWS as 'x'") } value <- unlist(value, use.names = FALSE) } elementMetadata(x@unlistData) <- value } x } setReplaceMethod("elementMetadata", "GRangesList", replaceElementMetadataList) setMethod("seqinfo", "GRangesList", function(x) seqinfo(x@unlistData)) ### NOT exported but used in GenomicAlignments package. replaceSeqinfoList <- function(x, new2old=NULL, force=FALSE, value) { if (!is(value, "Seqinfo")) stop("the supplied 'seqinfo' must be a Seqinfo object") dangling_seqlevels <- GenomeInfoDb:::getDanglingSeqlevels(x, new2old=new2old, force=force, seqlevels(value)) if (length(dangling_seqlevels) != 0L) { dropme <- which(seqnames(x@unlistData) %in% dangling_seqlevels) x <- x[-unique(togroup(PartitioningByWidth(x), j=dropme))] } seqinfo(x@unlistData, new2old=new2old) <- value x } setReplaceMethod("seqinfo", "GRangesList", replaceSeqinfoList) setMethod("score", "GRangesList", function(x) { mcols(x)$score }) setReplaceMethod("score", "GRangesList", function(x, value) { mcols(x)$score <- value x }) ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ### Coercion. ### setAs("GRangesList", "CompressedIRangesList", function(from) ranges(from, use.mcols=TRUE) ) setAs("GRangesList", "IRangesList", function(from) ranges(from, use.mcols=TRUE) ) setAs("GRangesList", "RangesList", function(from) ranges(from, use.mcols=TRUE) ) setAs("GRanges", "GRangesList", function(from) as(from, "List")) setAs("RangedDataList", "GRangesList", function(from) GRangesList(lapply(from, as, "GRanges"))) ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ### Subsetting. ### .sBracketSubsetGRList <- function(x, i, j, ..., drop) { if (!missing(i)) { x <- callNextMethod(x = x, i = i) } if (!missing(j)) { if (!is.character(j)) stop("'j' must be a character vector") withinLevel <- (j %in% colnames(x@unlistData@elementMetadata)) if (any(withinLevel) && !all(withinLevel)) stop("'j' cannot mix between and within metadata column names") if (any(withinLevel)) { mcols(x, level="within") <- mcols(x, level="within")[, j, drop=FALSE] } else { mcols(x) <- mcols(x)[, j, drop=FALSE] } } x } setMethod("[", "GRangesList", .sBracketSubsetGRList) .sBracketReplaceGRList <- function(x, i, j, ..., value) { if (!is(value, class(x)[1])) stop(paste0("replacement value must be a ", class(x)[1], " object")) if (!missing(i)) i <- extractROWS(setNames(seq_along(x), names(x)), i) if (!missing(j)) { if (!is.character(j)) stop("'j' must be a character vector") withinLevel <- (j %in% colnames(x@unlistData@elementMetadata)) if (any(withinLevel) && !all(withinLevel)) stop("'j' cannot mix between and within metadata column names") if (missing(i)) { if (any(withinLevel)) { mcols(x, level="within")[, j] <- mcols(x, level="within") } else { mcols(x)[, j] <- mcols(x) } } else { if (any(withinLevel)) { mcols(x, level="within")[i, j] <- mcols(x, level="within") } else { mcols(x)[i, j] <- mcols(x) } } } callNextMethod(x = x, i = i, value = value) } setReplaceMethod("[", "GRangesList", .sBracketReplaceGRList) .dBracketReplaceGRList <- function(x, i, j, ..., value) { nameValue <- if (is.character(i)) i else "" i <- S4Vectors:::normargSubset2_iOnly(x, i, j, ..., .conditionPrefix=paste0("[[<-,", class(x)[1], "-method: ")) len <- length(x) if (i > len) { value <- list(value) if (nzchar(nameValue)) names(value) <- nameValue x <- c(x, do.call(getFunction(class(x)), value)) } else { x <- callNextMethod(x, i, ..., value=value) } validObject(x) x } setReplaceMethod("[[", "GRangesList", .dBracketReplaceGRList) ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ### Going from GRanges to GRangesList with extractList() and family. ### setMethod("relistToClass", "GRanges", function(x) "GRangesList") ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ### show method. ### ### NOT exported but used in GenomicAlignments package. showList <- function(object, showFunction, print.classinfo) { k <- length(object) cumsumN <- cumsum(elementNROWS(object)) N <- tail(cumsumN, 1) cat(class(object), " object of length ", k, ":\n", sep = "") if (k == 0L) { cat("<0 elements>\n\n") } else if ((k == 1L) || ((k <= 3L) && (N <= 20L))) { nms <- names(object) defnms <- paste0("[[", seq_len(k), "]]") if (is.null(nms)) { nms <- defnms } else { empty <- nchar(nms) == 0L nms[empty] <- defnms[empty] nms[!empty] <- paste0("$", nms[!empty]) } for (i in seq_len(k)) { cat(nms[i], "\n") showFunction(object[[i]], margin=" ", print.classinfo=print.classinfo) if (print.classinfo) print.classinfo <- FALSE cat("\n") } } else { sketch <- function(x) c(head(x, 3), "...", tail(x, 3)) if (k >= 3 && cumsumN[3L] <= 20) showK <- 3 else if (k >= 2 && cumsumN[2L] <= 20) showK <- 2 else showK <- 1 diffK <- k - showK nms <- names(object)[seq_len(showK)] defnms <- paste0("[[", seq_len(showK), "]]") if (is.null(nms)) { nms <- defnms } else { empty <- nchar(nms) == 0L nms[empty] <- defnms[empty] nms[!empty] <- paste0("$", nms[!empty]) } for (i in seq_len(showK)) { cat(nms[i], "\n") showFunction(object[[i]], margin=" ", print.classinfo=print.classinfo) if (print.classinfo) print.classinfo <- FALSE cat("\n") } if (diffK > 0) { cat("...\n<", k - showK, ifelse(diffK == 1, " more element>\n", " more elements>\n"), sep="") } } cat("-------\n") cat("seqinfo: ", summary(seqinfo(object)), "\n", sep="") } setMethod("show", "GRangesList", function(object) showList(object, show_GenomicRanges, TRUE) ) ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ### Deconstruction/reconstruction of a GRangesList into/from a GRanges ### object. ### ### For internal use only (not exported). ### ### Unlist GRangesList object 'x' into a GRanges object but the differences ### with the "unlist" method for GRangesList objects are: ### - The sequence names of the returned GRanges object are modified by ### embedding the "grouping by top-level element" information in them. ### - The seqinfo is modified accordingly. deconstructGRLintoGR <- function(x, expand.levels=FALSE) { ans <- x@unlistData f1 <- rep.int(seq_len(length(x)), elementNROWS(x)) f2 <- as.integer(seqnames(ans)) f12 <- paste(f1, f2, sep="|") ## Compute 'ans_seqinfo'. if (expand.levels) { x_nlev <- length(seqlevels(x)) i1 <- rep(seq_len(length(x)), each=x_nlev) i2 <- rep.int(seq_len(x_nlev), length(x)) } else { oo <- orderIntegerPairs(f1, f2) of1 <- f1[oo] of2 <- f2[oo] ## TODO: Support 'method="presorted"' in duplicatedIntegerPairs() for ## when the 2 input vectors are already sorted. notdups <- !duplicatedIntegerPairs(of1, of2) i1 <- of1[notdups] i2 <- of2[notdups] } x_seqinfo <- seqinfo(x) ans_seqlevels <- paste(i1, i2, sep="|") ans_seqlengths <- unname(seqlengths(x_seqinfo))[i2] ans_isCircular <- unname(isCircular(x_seqinfo))[i2] ans_seqinfo <- Seqinfo(ans_seqlevels, ans_seqlengths, ans_isCircular) ## The 2 following modifications must be seen as a single atomic ## operation since doing the 1st without doing the 2nd would leave 'ans' ## in a broken state. ans@seqnames <- Rle(factor(f12, ans_seqlevels)) ans@seqinfo <- ans_seqinfo ans } ### The "inverse" transform of deconstructGRLintoGR(). ### More precisely, reconstructGRLfromGR() transforms GRanges object 'gr' ### with sequence names in the "f1|f2" format (as produced by ### deconstructGRLintoGR() above) back into a GRangesList object with the ### same length & names & metadata columns & seqinfo as 'x'. ### The fundamental property of this deconstruction/reconstruction mechanism ### is that, for any GRangesList object 'x': ### ### reconstructGRLfromGR(deconstructGRLintoGR(x), x) is identical to x ### reconstructGRLfromGR <- function(gr, x) { snames <- strsplit(as.character(seqnames(gr)), "|", fixed=TRUE) m12 <- matrix(as.integer(unlist(snames)), ncol=2, byrow=TRUE) ## Restore the real sequence names. f2 <- m12[ , 2L] x_seqlevels <- seqlevels(x) ## The 2 following modifications must be seen as a single atomic ## operation since doing the 1st without doing the 2nd would leave 'ans' ## in a broken state. gr@seqnames <- Rle(factor(x_seqlevels[f2], x_seqlevels)) gr@seqinfo <- seqinfo(x) ## Split. f1 <- m12[ , 1L] ans <- split(gr, factor(f1, levels=seq_len(length(x)))) names(ans) <- names(x) metadata(ans) <- metadata(x) mcols(ans) <- mcols(x) ans } GenomicRanges/R/GenomicRanges-class.R0000644000175400017540000006305713001230267020476 0ustar00biocbuildbiocbuild### ========================================================================= ### The GenomicRanges interface ### ------------------------------------------------------------------------- ### ### TODO: The 'constraint' slot could be moved to the Vector class (or to the ### Annotated class) so any Vector object could be constrained. setClass("GenomicRanges", contains="Vector", representation( "VIRTUAL"#, #No more constraint slot for now... #constraint="ConstraintORNULL" ) ) setClassUnion("GenomicRangesORmissing", c("GenomicRanges", "missing")) ### The code in this file will work out-of-the-box on 'x' as long as ### seqnames(x), ranges(x), strand(x), seqlengths(x), seqinfo(), ### and update(x) are defined. ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ### Getters. ### setMethod("length", "GenomicRanges", function(x) length(seqnames(x))) setMethod("names", "GenomicRanges", function(x) names(ranges(x))) #setMethod("constraint", "GenomicRanges", function(x) x@constraint) ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ### Extra column slots (implemented by subclasses) ### extraColumnSlots <- function(x) { sapply(extraColumnSlotNames(x), slot, object = x, simplify = FALSE) } extraColumnSlotsAsDF <- function(x) { ## low-level fast path; otherwise, would need to wrap some things with I() new("DataFrame", listData = extraColumnSlots(x), nrows = length(x)) } setGeneric("extraColumnSlotNames", function(x) standardGeneric("extraColumnSlotNames")) setMethod("extraColumnSlotNames", "ANY", function(x) character()) ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ### 2 low-level helper functions to deal with out-of-bound ranges. ### ### Returns index of out-of-bound ranges located on non-circular sequences ### whose length is not NA. Works on a GenomicRanges or GAlignments object. get_out_of_bound_index <- function(x) { if (length(x) == 0L) return(integer(0)) x_seqnames_id <- as.integer(seqnames(x)) x_seqlengths <- unname(seqlengths(x)) seqlevel_is_circ <- unname(isCircular(x)) %in% TRUE seqlength_is_na <- is.na(x_seqlengths) seqlevel_has_bounds <- !(seqlevel_is_circ | seqlength_is_na) which(seqlevel_has_bounds[x_seqnames_id] & (start(x) < 1L | end(x) > x_seqlengths[x_seqnames_id])) } ### Also works on a GenomicRanges or GAlignments object. Note that GAlignments ### objects are not trimmable so use 'suggest.trim=FALSE' on them. make_out_of_bound_warning_msg <- function(x, idx, suggest.trim) { where <- seqlevels(x)[unique(as.integer(seqnames(x))[idx])] if (length(where) == 1L) { on_what <- paste0("sequence ", where) } else if (length(where) == 2L) { on_what <- paste0("sequences ", where[1L], " and ", where[2L]) } else { seqlevels_in1string <- paste0(head(where, n=-1L), collapse=", ") on_what <- paste0("sequences ", seqlevels_in1string, ", and ", tail(where, n=1L)) } msg <- c(class(x), " object contains ", length(idx), " out-of-bound ", "range", if (length(idx) >= 2L) "s" else "", " located on ", on_what, ". ", "Note that only ranges located on a non-circular ", "sequence whose length is not NA can be considered ", "out-of-bound (use seqlengths() and isCircular() to ", "get the lengths and circularity flags of the underlying ", "sequences).") if (suggest.trim) msg <- c(msg, " You can use trim() to trim these ranges. ", "See ?`trim,GenomicRanges-method` for more information.") msg } ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ### Validity. ### ### TODO: Should we enforce ranges(x) to be an IRanges *instance* (i.e. ### class(ranges(x) == "IRanges")) instead of just an IRanges *object* (i.e. ### is(ranges(x), "IRanges"))? Right now I can create a GRanges object where ### the ranges are a Views object on a very long DNAString subject with ### something like 'GRanges("chr1", Views(subject, start=1:2, end=5))'. ### Sounds cool but there are also some potential complications with this... .valid.GenomicRanges.length <- function(x) { x_len <- length(x) checkCoreGetterReturnedLength <- function(getter) { if (NROW(get(getter)(x)) != x_len) paste0("NROW(", getter, "(x)) != length(x)") } pbs1 <- unlist(lapply(c("seqnames", "ranges", "strand", "mcols"), checkCoreGetterReturnedLength)) checkExtraColumnLength <- function(slotname) { if (NROW(slot(x, slotname)) != x_len) paste0("NROW(x@", slotname, ") != length(x)") } pbs2 <- unlist(lapply(extraColumnSlotNames(x), checkExtraColumnLength)) c(pbs1, pbs2) } ### Used in GenomicAlignments. .valid.GenomicRanges.seqnames <- function(x) { x_seqnames <- seqnames(x) if (!is(x_seqnames, "Rle") || !is.factor(runValue(x_seqnames))) return("'seqnames(x)' must be a 'factor' Rle") if (!is.null(names(x_seqnames))) return("'seqnames(x)' must be a 'factor' Rle with no names") if (S4Vectors:::anyMissing(runValue(x_seqnames))) return("'seqnames(x)' contains missing values") NULL } .valid.GenomicRanges.ranges <- function(x) { if (class(ranges(x)) != "IRanges") return("'ranges(x)' must be an IRanges instance") NULL } .valid.GenomicRanges.strand <- function(x) { if (!is.factor(runValue(strand(x))) || !identical(levels(runValue(strand(x))), levels(strand()))) { msg <- c("'strand' should be a 'factor' Rle with levels c(", paste0('"', levels(strand()), '"', collapse=", "), ")") return(paste(msg, collapse="")) } if (S4Vectors:::anyMissing(runValue(strand(x)))) return("'strand' contains missing values") NULL } ### NOTE: This list is also included in the man page for GRanges objects. ### Keep the 2 lists in sync! ### We don't put "genome" in that list in order to facilitate import of GFF3 ### files as GRanges objects (see ?import.gff3 in rtracklayer). INVALID.GR.COLNAMES <- c("seqnames", "ranges", "strand", "seqlevels", "seqlengths", "isCircular", #"genome", "start", "end", "width", "element") .valid.GenomicRanges.mcols <- function(x) { if (any(INVALID.GR.COLNAMES %in% colnames(mcols(x)))) { msg <- c("names of metadata columns cannot be one of ", paste0("\"", INVALID.GR.COLNAMES, "\"", collapse=", ")) return(paste(msg, collapse=" ")) } NULL } ### Also used by the validity method for GAlignments objects. valid.GenomicRanges.seqinfo <- function(x, suggest.trim=FALSE) { x_seqinfo <- seqinfo(x) if (!identical(seqlevels(x_seqinfo), levels(seqnames(x)))) { msg <- c("'seqlevels(seqinfo(x))' and 'levels(seqnames(x))'", "are not identical") return(paste(msg, collapse=" ")) } idx <- get_out_of_bound_index(x) if (length(idx) != 0L) { msg <- make_out_of_bound_warning_msg(x, idx, suggest.trim) warning(wmsg(msg)) } NULL } ## For convenience, validate the extra column slots that are virtual ## classes. Since they are not directly constructed, any validity ## checks specific to the virtual class have probably not been called. .valid.GenomicRanges.ecs <- function(x) { virtuals <- Filter(isVirtualClass, getSlots(class(x))[extraColumnSlotNames(x)]) unlist(lapply(names(virtuals), function(nm) validObject(slot(x, nm)))) } .valid.GenomicRanges <- function(x) { c(.valid.GenomicRanges.length(x), .valid.GenomicRanges.seqnames(x), .valid.GenomicRanges.ranges(x), .valid.GenomicRanges.strand(x), .valid.GenomicRanges.mcols(x), valid.GenomicRanges.seqinfo(x, suggest.trim=TRUE), .valid.GenomicRanges.ecs(x)) #checkConstraint(x, constraint(x))) } setValidity2("GenomicRanges", .valid.GenomicRanges) ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ### Coercion. ### ### Propagate the names. setMethod("as.character", "GenomicRanges", function(x, ignore.strand=FALSE) { if (!isTRUEorFALSE(ignore.strand)) stop(wmsg("'ignore.strand' must be TRUE or FALSE")) if (length(x) == 0L) return(setNames(character(0), names(x))) ans <- paste0(seqnames(x), ":", start(x), "-", end(x)) names(ans) <- names(x) if (ignore.strand) return(ans) x_strand <- strand(x) if (all(x_strand == "*")) return(ans) setNames(paste0(ans, ":", x_strand), names(x)) } ) ### The as.factor() generic doesn't have the ... argument so this method ### cannot support the 'ignore.strand' argument. setMethod("as.factor", "GenomicRanges", function(x) factor(as.character(x), levels=as.character(sort(unique(x)))) ) setAs("GenomicRanges", "Grouping", function(from) { to <- as(grouping(seqnames(from), strand(from), start(from), end(from)), "ManyToOneGrouping") mcols(to)$granges <- granges(from)[end(PartitioningByEnd(to))] to }) setMethod("as.data.frame", "GenomicRanges", function(x, row.names=NULL, optional=FALSE, ...) { ranges <- ranges(x) if (missing(row.names)) row.names <- names(x) if (!is.null(names(x))) names(x) <- NULL mcols_df <- as.data.frame(mcols(x), ...) if (length(extraColumnSlotNames(x)) > 0L) mcols_df <- cbind(as.data.frame(extraColumnSlotsAsDF(x), ...), mcols_df) data.frame(seqnames=as.factor(seqnames(x)), start=start(x), end=end(x), width=width(x), strand=as.factor(strand(x)), mcols_df, row.names=row.names, stringsAsFactors=FALSE) } ) setAs("GenomicRanges", "RangesList", function(from) { strand_mcols <- DataFrame(strand=strand(from), mcols(from)) ecs <- extraColumnSlotsAsDF(from) if (length(ecs)) strand_mcols <- cbind(strand_mcols, ecs) rngs <- ranges(from) mcols(rngs) <- strand_mcols rl <- split(rngs, seqnames(from)) mcols(rl) <- DataFrame(seqlengths=seqlengths(from), isCircular=isCircular(from), genome=genome(from)) metadata(rl) <- metadata(from) metadata(rl)$seqinfo <- seqinfo(from) rl } ) setAs("GenomicRanges", "RangedData", function(from) { mcols <- mcols(from) ecs <- extraColumnSlotsAsDF(from) if (length(ecs)) mcols <- cbind(mcols, ecs) rd <- RangedData(ranges(from), strand=strand(from), mcols, space=seqnames(from)) mcols(ranges(rd)) <- DataFrame(seqlengths=seqlengths(from), isCircular=isCircular(from), genome=genome(from)) metadata(ranges(rd)) <- metadata(from) metadata(ranges(rd))$seqinfo <- seqinfo(from) rd } ) ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ### Setters. ### setReplaceMethod("names", "GenomicRanges", function(x, value) { names(ranges(x)) <- value x } ) ### Used in GenomicAlignments. .normalize_seqnames_replacement_value <- function(value, x) { if (!is(value, "Rle")) value <- Rle(value) if (!is.factor(runValue(value))) runValue(value) <- factor(runValue(value)) if (!identical(levels(value), seqlevels(x))) stop("levels of supplied 'seqnames' must be ", "identical to 'seqlevels(x)'") S4Vectors:::V_recycle(value, x, x_what="value", skeleton_what="x") } setReplaceMethod("seqnames", "GenomicRanges", function(x, value) { value <- .normalize_seqnames_replacement_value(value, x) update(x, seqnames=value) } ) setReplaceMethod("ranges", "GenomicRanges", function(x, value) { if (class(value) != "IRanges") value <- as(value, "IRanges") mcols(value) <- NULL value <- S4Vectors:::V_recycle(value, x, x_what="value", skeleton_what="x") update(x, ranges=value) } ) normargGenomicRangesStrand <- function(strand, n) { if (!is(strand, "Rle")) strand <- Rle(strand) if (!is.factor(runValue(strand)) || !identical(levels(runValue(strand)), levels(strand()))) runValue(strand) <- strand(runValue(strand)) k <- length(strand) if (k != n) { if (k != 1L && (k == 0L || k > n || n %% k != 0L)) stop("supplied 'strand' has ", k, " elements (", n, " expected)") strand <- rep(strand, length.out=n) } strand } setReplaceMethod("strand", "GenomicRanges", function(x, value) { value <- normargGenomicRangesStrand(value, length(x)) x <- update(x, strand=value, check=FALSE) msg <- .valid.GenomicRanges.strand(x) if (!is.null(msg)) stop(msg) x } ) setReplaceMethod("seqinfo", "GenomicRanges", function(x, new2old=NULL, force=FALSE, value) { if (!is(value, "Seqinfo")) stop("the supplied 'seqinfo' must be a Seqinfo object") dangling_seqlevels <- GenomeInfoDb:::getDanglingSeqlevels(x, new2old=new2old, force=force, seqlevels(value)) if (length(dangling_seqlevels) != 0L) x <- x[!(seqnames(x) %in% dangling_seqlevels)] old_seqinfo <- seqinfo(x) new_seqnames <- GenomeInfoDb:::makeNewSeqnames(x, new2old=new2old, seqlevels(value)) x <- update(x, seqnames=new_seqnames, seqinfo=value, check=FALSE) geom_has_changed <- GenomeInfoDb:::sequenceGeometryHasChanged( seqinfo(x), old_seqinfo, new2old=new2old) if (any(geom_has_changed, na.rm=TRUE)) { msg <- valid.GenomicRanges.seqinfo(x, suggest.trim=TRUE) if (!is.null(msg)) stop(msg) } x } ) setMethod("score", "GenomicRanges", function(x) mcols(x)$score) setReplaceMethod("score", "GenomicRanges", function(x, value) { mcols(x)$score <- value x }) #setReplaceMethod("constraint", "GenomicRanges", # function(x, value) # { # if (isSingleString(value)) # value <- new(value) # if (!is(value, "ConstraintORNULL")) # stop("the supplied 'constraint' must be a ", # "Constraint object, a single string, or NULL") # x@constraint <- value # validObject(x) # x # } #) ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ### Updating and cloning. ### ### An object is either 'update'd in place (usually with a replacement ### method) or 'clone'd (copied), with specified slots/fields overridden. ### For an object with a pure S4 slot representation, these both map to ### initialize. Reference classes will want to override 'update'. Other ### external representations need further customization. setGeneric("clone", function(x, ...) standardGeneric("clone")) # not exported setMethod("clone", "ANY", # not exported function(x, ...) { if (nargs() > 1L) update(x, ...) else x } ) ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ### Ranges methods. ### setMethod("start", "GenomicRanges", function(x, ...) start(ranges(x))) setMethod("end", "GenomicRanges", function(x, ...) end(ranges(x))) setMethod("width", "GenomicRanges", function(x) width(ranges(x))) setReplaceMethod("start", "GenomicRanges", function(x, ..., value) { new_ranges <- `start<-`(ranges(x), ..., value=value) update(x, ranges=new_ranges, ...) } ) setReplaceMethod("end", "GenomicRanges", function(x, ..., value) { new_ranges <- `end<-`(ranges(x), ..., value=value) update(x, ranges=new_ranges, ...) } ) setReplaceMethod("width", "GenomicRanges", function(x, ..., value) { new_ranges <- `width<-`(ranges(x), ..., value=value) update(x, ranges=new_ranges, ...) } ) ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ### Subsetting. ### ### Needed only because we want to support x[i, j] subsetting. setMethod("[", "GenomicRanges", function(x, i, j, ..., drop) { if (length(list(...)) > 0L) stop("invalid subsetting") if (!missing(i)) x <- extractROWS(x, i) if (missing(j)) return(x) new_mcols <- mcols(x)[ , j, drop=FALSE] clone(x, elementMetadata=new_mcols, check=FALSE) } ) ### Subset a named list-like object *by* a GenomicRanges subscript. ### The returned object 'ans' is as follow: ### (a) 'ans' is parallel to 'gr'. ### (b) 'names(ans)' is identical to 'as.character(seqnames(gr))'. ### (c) 'elementNROWS(ans)' is the same as 'width(gr)'. ### (d) 'class(ans)' is 'relistToClass(x[[1]])' e.g. CompressedRleList if ### 'x' is an RleList object, or DNAStringSet is 'x' is a DNAStringSet ### object. .subset_by_GenomicRanges <- function(x, gr) { if (!(is.list(x) || is(x, "List"))) stop(wmsg("'x' must be a list-like object when subsetting ", "by a GenomicRanges subscript")) x_names <- names(x) if (is.null(x_names)) stop(wmsg("'x' must have names when subsetting ", "by a GenomicRanges subscript")) if (anyDuplicated(x_names)) stop(wmsg("'x' must have unique names when subsetting ", "by a GenomicRanges subscript")) irl <- split(ranges(gr), seqnames(gr), drop=TRUE) seqlevels_in_use <- names(irl) seqlevels2names <- match(seqlevels_in_use, x_names) if (any(is.na(seqlevels2names))) stop(wmsg("when subsetting by a GenomicRanges subscript, the names ", "of the object to subset must contain the seqnames of the ", "subscript")) ## Handle empty case. if (length(gr) == 0L) { if (length(x) != 0L) { x1 <- x[[1L]] } else if (is(x, "CompressedList")) { x1 <- unlist(x, use.names=FALSE) } else { x1 <- new(elementType(x)) } unlisted_ans <- x1[0] ans_partitioning <- PartitioningByEnd(names=character(0)) return(relist(unlisted_ans, ans_partitioning)) } tmp <- lapply(seq_along(seqlevels_in_use), function(i) { seqlevel <- seqlevels_in_use[i] name <- x_names[seqlevels2names[i]] extractList(x[[name]], irl[[seqlevel]]) }) ## Unsplit 'tmp'. ans <- do.call(c, tmp) ans_len <- length(gr) idx <- unlist(split(seq_len(ans_len), seqnames(gr), drop=TRUE)) revidx <- integer(ans_len) revidx[idx] <- seq_len(ans_len) names(ans) <- names(idx) ans <- ans[revidx] ans } setMethod("[", c("List", "GenomicRanges"), function(x, i, j, ..., drop=TRUE) { if (!missing(j) || length(list(...)) > 0L) stop("invalid subsetting") .subset_by_GenomicRanges(x, i) } ) setMethod("[", c("list", "GenomicRanges"), function(x, i, j, ..., drop=TRUE) { if (!missing(j) || length(list(...)) > 0L) stop("invalid subsetting") .subset_by_GenomicRanges(x, i) } ) ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ### $ and $<- methods ### ### Provided as a convenience, for GenomicRanges *only*, and as the result ### of strong popular demand. ### Note that those methods are not consistent with the other $ and $<- ### methods in the IRanges/GenomicRanges infrastructure, and might confuse ### some users by making them believe that a GenomicRanges object can be ### manipulated as a data.frame-like object. ### Therefore we recommend using them only interactively, and we discourage ### their use in scripts or packages. For the latter, use 'mcols(x)$name' ### instead of 'x$name'. ### .DollarNames.GenomicRanges <- function(x, pattern) grep(pattern, names(mcols(x)), value=TRUE) setMethod("$", "GenomicRanges", function(x, name) mcols(x)[[name]] ) setReplaceMethod("$", "GenomicRanges", function(x, name, value) {mcols(x)[[name]] <- value; x} ) ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ### Combining. ### ### Not exported. 'x' *must* be an unnamed list of length >= 1 (not checked). .unlist_list_of_GenomicRanges <- function(x, ignore.mcols=FALSE) { if (!isTRUEorFALSE(ignore.mcols)) stop("'ignore.mcols' must be TRUE or FALSE") ans_class <- class(x[[1L]]) ans_seqinfo <- do.call(merge, lapply(x, seqinfo)) ans_seqnames <- do.call(c, lapply(x, seqnames)) ans_ranges <- do.call(c, lapply(x, ranges)) ans_strand <- do.call(c, lapply(x, strand)) if (ignore.mcols) { ans_mcols <- S4Vectors:::make_zero_col_DataFrame(length(ans_ranges)) } else { ans_mcols <- do.call(rbind, lapply(x, mcols, FALSE)) } if (length(extraColumnSlotNames(x[[1L]])) > 0L) { ans_ecs <- do.call(rbind, lapply(x, extraColumnSlotsAsDF)) do.call(new, c(list(ans_class, seqnames=ans_seqnames, ranges=ans_ranges, strand=ans_strand, elementMetadata=ans_mcols, seqinfo=ans_seqinfo), as.list(ans_ecs))) } else { new(ans_class, seqnames=ans_seqnames, ranges=ans_ranges, strand=ans_strand, elementMetadata=ans_mcols, seqinfo=ans_seqinfo) } } setMethod("c", "GenomicRanges", function(x, ..., ignore.mcols=FALSE, recursive=FALSE) { if (!identical(recursive, FALSE)) stop("'recursive' argument not supported") if (missing(x)) args <- unname(list(...)) else args <- unname(list(x, ...)) .unlist_list_of_GenomicRanges(args, ignore.mcols=ignore.mcols) } ) ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ### Displaying ### .GenomicRanges_summary <- function(object) { object_class <- class(object) object_len <- length(object) object_mcols <- mcols(object) object_nmc <- if (is.null(object_mcols)) 0L else ncol(object_mcols) paste0(object_class, " object with ", object_len, " ", ifelse(object_len == 1L, "range", "ranges"), " and ", object_nmc, " metadata ", ifelse(object_nmc == 1L, "column", "columns")) } ### S3/S4 combo for summary.GenomicRanges summary.GenomicRanges <- function(object, ...) .GenomicRanges_summary(object, ...) setMethod("summary", "GenomicRanges", summary.GenomicRanges) .make_naked_matrix_from_GenomicRanges <- function(x) { x_len <- length(x) x_mcols <- mcols(x) x_nmc <- if (is.null(x_mcols)) 0L else ncol(x_mcols) ans <- cbind(seqnames=as.character(seqnames(x)), ranges=showAsCell(ranges(x)), strand=as.character(strand(x))) extraColumnNames <- extraColumnSlotNames(x) if (length(extraColumnNames) > 0L) { ans <- do.call(cbind, c(list(ans), lapply(extraColumnSlots(x), showAsCell))) } if (x_nmc > 0L) { tmp <- do.call(data.frame, c(lapply(x_mcols, showAsCell), list(check.names=FALSE))) ans <- cbind(ans, `|`=rep.int("|", x_len), as.matrix(tmp)) } ans } ### If 'x' is a GRanges object, 'coerce.internally.to.GRanges' has no effect. ### If it's a GenomicRanges object that is not a GRanges object, then ### show_GenomicRanges() will coerce it to a GRanges object unless ### 'coerce.internally.to.GRanges' is set to FALSE. Use this if coercing 'x' ### to GRanges is not supported or is too expensive but only if 'x' supports ### head() and tail(). show_GenomicRanges <- function(x, margin="", print.classinfo=FALSE, print.seqinfo=FALSE, coerce.internally.to.GRanges=TRUE) { cat(.GenomicRanges_summary(x), ":\n", sep="") ## S4Vectors:::makePrettyMatrixForCompactPrinting() assumes that head() ## and tail() work on 'xx'. if (coerce.internally.to.GRanges) { xx <- as(x, "GRanges", strict=FALSE) } else { xx <- x } out <- S4Vectors:::makePrettyMatrixForCompactPrinting(xx, .make_naked_matrix_from_GenomicRanges) if (print.classinfo) { .COL2CLASS <- c( seqnames="Rle", ranges="IRanges", strand="Rle" ) extraColumnNames <- extraColumnSlotNames(x) .COL2CLASS <- c(.COL2CLASS, getSlots(class(x))[extraColumnNames]) classinfo <- S4Vectors:::makeClassinfoRowForCompactPrinting(x, .COL2CLASS) ## A sanity check, but this should never happen! stopifnot(identical(colnames(classinfo), colnames(out))) out <- rbind(classinfo, out) } if (nrow(out) != 0L) rownames(out) <- paste0(margin, rownames(out)) ## We set 'max' to 'length(out)' to avoid the getOption("max.print") ## limit that would typically be reached when 'showHeadLines' global ## option is set to Inf. print(out, quote=FALSE, right=TRUE, max=length(out)) if (print.seqinfo) { cat(margin, "-------\n", sep="") cat(margin, "seqinfo: ", summary(seqinfo(x)), "\n", sep="") } } setMethod("show", "GenomicRanges", function(object) show_GenomicRanges(object, margin=" ", print.classinfo=TRUE, print.seqinfo=TRUE) ) setMethod("showAsCell", "GenomicRanges", function(object) as.character(object)) GenomicRanges/R/GenomicRanges-comparison.R0000644000175400017540000003104613001230267021534 0ustar00biocbuildbiocbuild### ========================================================================= ### Comparing and ordering genomic ranges ### ------------------------------------------------------------------------- ### ### I. UNIQUE AND DUPLICATED ELEMENTS WITHIN A GenomicRanges OBJECT ### --------------------------------------------------------------- ### Two elements of a GenomicRanges object (i.e. two genomic ranges) are ### considered equal iff they are on the same underlying sequence and strand, ### and have the same start and width. duplicated() and unique() on a ### GenomicRanges object are conforming to this. ### ### II. ORDERING THE ELEMENTS OF A GenomicRanges OBJECT ### --------------------------------------------------- ### The "natural order" for the elements of a GenomicRanges object is to order ### them (a) first by sequence level, (b) then by strand, (c) then by start, ### (d) and finally by width. ### This way, the space of genomic ranges is totally ordered. ### Note that the "reduce" method for GenomicRanges uses this "natural order" ### implicitly. Also, note that, because we already do (c) and (d) for regular ### ranges, genomic ranges that belong to the same underlying sequence ### and strand are ordered like regular ranges. ### order(), sort(), and rank() on a GenomicRanges object are using this ### "natural order". ### ### III. ELEMENT-WISE (AKA "PARALLEL") COMPARISON OF 2 GenomicRanges OBJECTS ### ------------------------------------------------------------------------ ### We want the "==", "!=", "<=", ">=", "<" and ">" operators between 2 ### GenomicRanges objects to be compatible with the "natural order" defined ### previously. Defining those operators when the 2 objects have *identical* ### seqlevels() is straighforward but we can in fact extend this comparison ### to the following situation: ### (A) 'e1' and 'e2' have compatible sets of underlying sequences, that is, ### 'seqinfo(e1)' and 'seqinfo(e2)' can be merged. ### (B) 'seqlevels(e1)' and 'seqlevels(e2)' are in the same order. Note that ### (A) guarantees that the seqlevels of one is a subset of the seqlevels ### of the other. (B) is saying that this subset should be a subsequence. ### Pre-comparison step: if (A) and (B) are satisfied, then the 2 seqinfo() are ### merged and the seqlevels() of the result is assigned back to each object ### to compare. This is a way to have 2 objects with identical seqlevels() ### before the comparison can actually be performed and meaningful. ### The reason (B) is required for the pre-comparison step is because we want ### this step to preserve the original order of the seqlevels() in *both* ### objects. Without this precaution, the expected anti-symetric property of ### some operators would not be satisfied e.g. 'any(e1 < e2 & e2 < e1)' could ### be TRUE. ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ### pcompare() ### ### Doing 'pcompare(x, y)' on 2 vector-like objects 'x' and 'y' of length 1 ### must return an integer less than, equal to, or greater than zero if the ### single element in 'x' is considered to be respectively less than, equal ### to, or greater than the single element in 'y'. ### ### On 2 GenomicRanges objects, it returns one of the 13 predefined codes ### (>= -6 and <= 6) used by the method for Ranges objects when 'x[i]' and ### 'y[i]' are on the same space (i.e. on the same underlying sequence and ### strand). Otherwise, it returns a code that is < -6 if 'x[i] < y[i]', ### and > 6 if 'x[i] > y[i]'. ### See '?pcompare' (in IRanges) for the 13 predefined codes. .pcompare_GenomicRanges <- function(x, y) { ## Pre-comparison step (see above for details). ## merge() will fail if 'x' and 'y' don't have compatible underlying ## sequences. seqinfo <- merge(seqinfo(x), seqinfo(y)) seqlevels <- seqlevels(seqinfo) if (any(diff(match(seqlevels(y), seqlevels)) < 0L)) stop("the 2 objects to compare have seqlevels in incompatible orders") ## This should only insert new seqlevels in the existing ones i.e. it ## should NEVER drop or reorder existing levels seqlevels(x) <- seqlevels(y) <- seqlevels a <- pcompare(ranges(x), ranges(y)) b <- as.integer(strand(x)) - as.integer(strand(y)) c <- as.integer(seqnames(x)) - as.integer(seqnames(y)) ## Note that sign() always returns a numeric vector, even on an integer ## vector. a + 13L * as.integer(sign(b) + 3L * sign(c)) } ### The "pcompare" method. setMethod("pcompare", c("GenomicRanges", "GenomicRanges"), function(x, y) .pcompare_GenomicRanges(x, y) ) ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ### duplicated() ### ### unique() will work out-of-the-box on a GenomicRanges object thanks to the ### method for Vector objects. ### .duplicated.GenomicRanges <- function(x, incomparables=FALSE, fromLast=FALSE, method=c("auto", "quick", "hash")) { if (!identical(incomparables, FALSE)) stop("\"duplicated\" method for GenomicRanges objects ", "only accepts 'incomparables=FALSE'") duplicatedIntegerQuads(as.factor(seqnames(x)), as.factor(strand(x)), start(x), width(x), fromLast=fromLast, method=method) } ### S3/S4 combo for duplicated.GenomicRanges duplicated.GenomicRanges <- function(x, incomparables=FALSE, ...) .duplicated.GenomicRanges(x, incomparables=incomparables, ...) setMethod("duplicated", "GenomicRanges", .duplicated.GenomicRanges) ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ### match() ### ### %in% will work out-of-the-box on GenomicRanges objects thanks to the ### method for Vector objects. ### setMethod("match", c("GenomicRanges", "GenomicRanges"), function(x, table, nomatch=NA_integer_, incomparables=NULL, method=c("auto", "quick", "hash"), ignore.strand=FALSE) { if (!isSingleNumberOrNA(nomatch)) stop("'nomatch' must be a single number or NA") if (!is.integer(nomatch)) nomatch <- as.integer(nomatch) if (!is.null(incomparables)) stop("\"match\" method for GenomicRanges objects ", "only accepts 'incomparables=NULL'") if (!isTRUEorFALSE(ignore.strand)) stop("'ignore.strand' must be TRUE or FALSE") ## Calling merge() is the way to check that 'x' and 'table' are based ## on the same reference genome. merge(seqinfo(x), seqinfo(table)) x_seqnames <- relevelSeqnamesForMatch(x, table) if (ignore.strand) { x_strand <- integer(length(x)) table_strand <- integer(length(table)) } else { x_strand <- as.factor(strand(x)) table_strand <- as.factor(strand(table)) } ## Equivalent to (but faster than): ## findOverlaps(x, table, type="equal", select="first") ## except when 'x' and 'table' both contain empty ranges. matchIntegerQuads(x_seqnames, x_strand, start(x), width(x), as.factor(seqnames(table)), table_strand, start(table), width(table), nomatch=nomatch, method=method) } ) relevelSeqnamesForMatch <- function(x, table) { x_seqnames <- as.factor(seqnames(x)) if (!hasHead(seqlevels(x), seqlevels(table)) && !hasHead(seqlevels(table), seqlevels(x))) { x_seqnames <- factor(x_seqnames, union(seqlevels(table), seqlevels(x))) } x_seqnames } ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ### selfmatch() ### setMethod("selfmatch", "GenomicRanges", function(x, method=c("auto", "quick", "hash"), ignore.strand=FALSE) { if (!isTRUEorFALSE(ignore.strand)) stop("'ignore.strand' must be TRUE or FALSE") x_seqnames <- as.factor(seqnames(x)) if (ignore.strand) { x_strand <- integer(length(x)) } else { x_strand <- as.factor(strand(x)) } selfmatchIntegerQuads(x_seqnames, x_strand, start(x), width(x), method=method) } ) ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ### order() and related methods. ### ### The "order" and "rank" methods for GenomicRanges objects are consistent ### with the order implied by pcompare(). ### is.unsorted() is a quick/cheap way of checking whether a GenomicRanges ### object is already sorted, e.g., called prior to a costly sort. ### sort() will work out-of-the-box on a GenomicRanges object thanks to the ### method for Vector objects. ### .GenomicRanges_as_IntegerQuads <- function(x, ignore.strand=FALSE) { if (!isTRUEorFALSE(ignore.strand)) stop("'ignore.strand' must be TRUE of FALSE") a <- S4Vectors:::decodeRle(seqnames(x)) if (ignore.strand) { b <- integer(length(x)) } else { b <- S4Vectors:::decodeRle(strand(x)) } c <- start(x) d <- width(x) list(a, b, c, d) } setMethod("is.unsorted", "GenomicRanges", function(x, na.rm=FALSE, strictly=FALSE, ignore.strand=FALSE) { if (!identical(na.rm, FALSE)) warning("\"is.unsorted\" method for GenomicRanges objects ", "ignores the 'na.rm' argument") if (!isTRUEorFALSE(strictly)) stop("'strictly' must be TRUE of FALSE") ## It seems that creating the integer quads below is faster when ## 'x' is already sorted (TODO: Investigate why). Therefore, and ## somewhat counterintuitively, is.unsorted() can be faster when 'x' ## is already sorted (which, in theory, is the worst-case scenario ## because S4Vectors:::sortedIntegerQuads() will then need to take a ## full walk on 'x') than when it is unsorted (in which case ## S4Vectors:::sortedIntegerQuads() might stop walking on 'x' after ## checking its first 2 elements only -- the best-case scenario). quads <- .GenomicRanges_as_IntegerQuads(x, ignore.strand) !S4Vectors:::sortedIntegerQuads(quads[[1L]], quads[[2L]], quads[[3L]], quads[[4L]], strictly=strictly) } ) .order_GenomicRanges <- function(x, decreasing=FALSE, ignore.strand=FALSE) { if (!isTRUEorFALSE(decreasing)) stop("'decreasing' must be TRUE or FALSE") quads <- .GenomicRanges_as_IntegerQuads(x, ignore.strand) orderIntegerQuads(quads[[1L]], quads[[2L]], quads[[3L]], quads[[4L]], decreasing=decreasing) } ### TODO: Support the 'ignore.strand' argument (the signature of the order() ### generic doesn't make this as straightforward as it could be). ### 'na.last' is pointless (GenomicRanges objects don't contain NAs) so is ### ignored. ### 'method' is also ignored at the moment. setMethod("order", "GenomicRanges", function(..., na.last=TRUE, decreasing=FALSE, method=c("shell", "radix")) { ## Turn off this warning for now since it triggers spurious warnings ## when calling sort() on a GRangesList object. The root of the ## problem is inconsistent defaults for 'na.last' between order() and ## sort(), as reported here: ## https://stat.ethz.ch/pipermail/r-devel/2015-November/072012.html #if (!identical(na.last, TRUE)) # warning("\"order\" method for GenomicRanges objects ", # "ignores the 'na.last' argument") if (!isTRUEorFALSE(decreasing)) stop("'decreasing' must be TRUE or FALSE") ## All arguments in '...' are guaranteed to be GenomicRanges objects. args <- list(...) if (length(args) == 1L) return(.order_GenomicRanges(args[[1L]], decreasing)) order_args <- c(unlist(lapply(args, .GenomicRanges_as_IntegerQuads), recursive=FALSE, use.names=FALSE), list(na.last=na.last, decreasing=decreasing)) do.call(order, order_args) } ) ### S3/S4 combo for sort.GenomicRanges .sort.GenomicRanges <- function(x, decreasing=FALSE, ignore.strand=FALSE, by) { if (missing(by)) { oo <- .order_GenomicRanges(x, decreasing, ignore.strand) } else { if (!identical(ignore.strand, FALSE)) warning("'ignore.strand' ignored when 'by' is specified") oo <- S4Vectors:::orderBy(by, x, decreasing=decreasing) } extractROWS(x, oo) } sort.GenomicRanges <- function(x, decreasing=FALSE, ...) .sort.GenomicRanges(x, decreasing=decreasing, ...) setMethod("sort", "GenomicRanges", .sort.GenomicRanges) GenomicRanges/R/GenomicRangesList-class.R0000644000175400017540000000266213001230267021325 0ustar00biocbuildbiocbuild### ========================================================================= ### GenomicRangesList objects ### ------------------------------------------------------------------------- ### ### A List of GenomicRanges objects. Subclasses not necessarily have the same ### "compound" semantics as GRangesList. ### setClass("GenomicRangesList", prototype = prototype(elementType = "GenomicRanges"), contains = "List") setClass("SimpleGenomicRangesList", contains = c("GenomicRangesList", "SimpleList")) ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ### Constructor. ### GenomicRangesList <- function(...) { args <- list(...) if (length(args) == 1 && is.list(args[[1]])) args <- args[[1]] S4Vectors:::new_SimpleList_from_list("SimpleGenomicRangesList", args) } ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ### Coercion. ### setAs("RangedDataList", "GenomicRangesList", function(from) GenomicRangesList(lapply(from, as, "GRanges"))) setAs("GenomicRangesList", "RangedDataList", function(from) RangedDataList(lapply(from, as, "RangedData"))) ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ### Utilities. ### setMethod("stack", "GenomicRangesList", function(x, indName = "sample") { x_flat <- unlist(x, use.names = FALSE) mcols(x_flat) <- cbind(IRanges:::.stack.ind(x, indName), mcols(x_flat)) x_flat }) GenomicRanges/R/RangedData-methods.R0000644000175400017540000000237013001230267020274 0ustar00biocbuildbiocbuild### ========================================================================= ### RangedData/RangesList implementation of the GenomicRanges API ### ------------------------------------------------------------------------- ### ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ### seqinfo ### setMethod("seqinfo", "List", function(x) { si <- metadata(x)$seqinfo if (is.null(si)) { sn <- names(x) if (is.null(sn)) sn <- as.character(seq_len(length(x))) si <- Seqinfo(unique(sn)) } si }) setMethod("seqinfo", "RangesList", function(x) { si <- callNextMethod() if (!is.null(universe(x))) genome(si) <- universe(x) si }) ### FIXME: needs sanity checks setReplaceMethod("seqinfo", "List", function(x, value) { metadata(x)$seqinfo <- value x }) setMethod("seqinfo", "RangedData", function(x) seqinfo(ranges(x))) setReplaceMethod("seqinfo", "RangedData", function(x, value) { seqinfo(ranges(x)) <- value x }) ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ### seqnames ### setMethod("seqnames", "RangedData", function(x) space(x)) GenomicRanges/R/absoluteRanges.R0000644000175400017540000001210713001230267017616 0ustar00biocbuildbiocbuild### ========================================================================= ### absoluteRanges() & related ### ------------------------------------------------------------------------- ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ### isSmallGenome() ### ### TODO: Maybe this could be re-used in tileGenome(). normarg_seqlengths <- function(seqlengths) { if (!is.numeric(seqlengths)) stop(wmsg("'seqlengths' must be a non-empty numeric vector")) if (length(seqlengths) == 0L) return(setNames(integer(0), character(0))) seqlengths_names <- names(seqlengths) if (is.null(seqlengths_names)) stop(wmsg("'seqlengths' must be named")) if (any(seqlengths_names %in% c(NA_character_, ""))) stop(wmsg("'seqlengths' has names that are NA or the empty string")) if (any(duplicated(seqlengths_names))) stop(wmsg("'seqlengths' has duplicated names")) if (!is.integer(seqlengths)) seqlengths <- setNames(as.integer(seqlengths), seqlengths_names) if (any(seqlengths < 0L, na.rm=TRUE)) stop(wmsg("'seqlengths' contains negative values")) seqlengths } ### 'seqlengths' can be an integer or numeric vector, or any object from which ### the sequence lengths can be extracted with seqlengths(). ### Returns TRUE if the total length of the underlying sequences is <= ### '.Machine$integer.max' (e.g. Fly genome), FALSE if not (e.g. Human genome), ### or NA if it cannot be computed (because some sequence lengths are NA). isSmallGenome <- function(seqlengths) { if (is.numeric(seqlengths)) { seqlengths <- normarg_seqlengths(seqlengths) } else { seqlengths <- seqlengths(seqlengths) } if (any(is.na(seqlengths))) return(NA) ## sum() will emit a "integer overflow" warning and return NA if the sum ## is > .Machine$integer.max total_length <- suppressWarnings(sum(seqlengths)) !is.na(total_length) } ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ### absoluteRanges() ### ### Transform the genomic ranges in 'x' into "absolute" ranges i.e. into ### ranges counted from the beginning of the virtual sequence obtained by ### concatenating all the sequences in the genome (in the order reported by ### 'seqlevels(x)'). Ignore the strand. ### ONLY WORK ON A SMALL GENOME! (see isSmallGenome() above) absoluteRanges <- function(x) { if (!is(x, "GenomicRanges")) stop(wmsg("'x' must be a GenomicRanges object")) x_seqlengths <- seqlengths(x) if (!isTRUE(isSmallGenome(x_seqlengths))) stop(wmsg("the total length of the underlying sequences is too big ", "or couldn't be computed (because some lengths are NA)")) x_seqids <- as.integer(seqnames(x)) idx <- which(start(x) < 1L | end(x) > x_seqlengths[x_seqids]) if (length(idx) != 0L) stop(wmsg("Some ranges in 'x' are not within the bounds of ", "the sequence that they belong to. Cannot convert ", "them into absolute ranges.")) offsets <- c(0L, cumsum(unname(x_seqlengths)[-length(x_seqlengths)])) x_ranges <- ranges(x) shift(x_ranges, shift=offsets[x_seqids]) } ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ### relativeRanges() ### ### The reverse of absoluteRanges(). ### ONLY WORK ON A SMALL GENOME! (see isSmallGenome() above) relativeRanges <- function(x, seqlengths) { if (!is(x, "Ranges")) stop(wmsg("'x' must be a Ranges object")) if (is.numeric(seqlengths)) { ans_seqlengths <- normarg_seqlengths(seqlengths) ans_seqinfo <- Seqinfo(seqnames=names(ans_seqlengths), seqlengths=ans_seqlengths) } else { if (is(seqlengths, "Seqinfo")) { ans_seqinfo <- seqlengths } else { ans_seqinfo <- seqinfo(seqlengths) } ans_seqlengths <- seqlengths(ans_seqinfo) } if (!isTRUE(isSmallGenome(ans_seqlengths))) stop(wmsg("the total length of the sequences specified ", "thru 'seqlengths' is too big or couldn't be ", "computed (because some lengths are NA)")) offsets <- c(0L, cumsum(unname(ans_seqlengths))) ## Map each range in 'x' to a sequence in the genome. ticks <- offsets + 1L start2seqid <- findInterval(start(x), ticks) end2seqid <- findInterval(end(x), ticks) if (!identical(start2seqid, end2seqid)) stop(wmsg("Some ranges in 'x' cannot be mapped to a sequence in the ", "genome because they cross sequence boundaries. ", "Cannot convert them into relative ranges.")) if (any(start2seqid < 1L) || any(start2seqid > length(ans_seqlengths))) stop(wmsg("Some ranges in 'x' cannot be mapped to a sequence in the ", "genome because they are outside the boundaries of the ", "genome. Cannot convert them into relative ranges.")) ans_ranges <- shift(x, shift=-offsets[start2seqid]) ans_seqnames <- names(ans_seqlengths)[start2seqid] GRanges(ans_seqnames, ans_ranges, seqinfo=ans_seqinfo) } GenomicRanges/R/constraint.R0000644000175400017540000001212113001230267017020 0ustar00biocbuildbiocbuild### ========================================================================= ### Enforcing constraints thru Constraint objects ### ------------------------------------------------------------------------- ### ### Attaching a Constraint object to an object of class A (the "constrained" ### object) is meant to be a convenient/reusable/extensible way to enforce ### a particular set of constraints on particular instances of A. It's an ### alternative to the more traditional approach that consists in creating ### subclasses of A and implementing specific validity methods for each of ### them. However, using constraints offers the following advantages over the ### traditional approach: ### (a) The traditional approach often tends to lead to a proliferation ### of subclasses of A. ### (b) Constraints can easily be re-used across different classes without ### the need to create any new class. ### (c) Constraints can easily be combined. ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ### Constraint objects. ### ### Virtual class with no slots. setClass("Constraint", representation("VIRTUAL")) ### Like the Constraint virtual class itself, concrete constraint subclasses ### cannot have slots. A Constraint object doesn't need to contain anything ### anyway so by enforcing this we make sure that "combining" constraints ### (i.e. creating a Constraint subclass that extends more than one concrete ### Constraint subclass) always work smoothly (no slot clashes). setValidity2("Constraint", function(x) { if (length(slotNames(x)) != 0L) return("Constraint objects cannot have slots") NULL } ) ### Constrained objects can store a Constraint object (or NULL if no ### constraints) in their 'constraint' slot. setClassUnion("ConstraintORNULL", c("Constraint", "NULL")) ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ### constraint() accessor. ### ### Get or set the Constraint object stored in the constrained object. ### Classes that want to support constraints need to implement methods for ### those 2 generics. setGeneric("constraint", function(x) standardGeneric("constraint")) setGeneric("constraint<-", signature="x", function(x, value) standardGeneric("constraint<-") ) ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ### The checkConstraint() generic. ### ### Returns a matrix with 1 signature per row. Less specific signatures are ### guaranteed to be *before* (i.e. lower row index) the more specific ones. .nextCheckConstraintSignatures <- function(xClass, constraintClass) { ## Loosely inspired by validObject(). xClassDef <- getClassDef(xClass) xAncestors <- sapply(xClassDef@contains, slot, "superClass") xAncestors <- c("ANY", rev(xAncestors), xClass) constraintClassDef <- getClassDef(constraintClass) constraintAncestors <- sapply(constraintClassDef@contains, slot, "superClass") constraintAncestors <- c("ANY", rev(constraintAncestors), constraintClass) signatures <- NULL for (class1 in xAncestors) { for (class2 in constraintAncestors) { methodDef <- selectMethod("checkConstraint", c(class1, class2), optional=TRUE, doCache=TRUE) if (!is.null(methodDef)) signatures <- rbind(signatures, methodDef@defined) } } unique(signatures) } ### The checkConstraint() generic function implements its own dispatch ### algorithm. Like validity methods, "checkConstraint" methods must return ### NULL or a character vector describing the problems found. suppressWarnings( setGeneric("checkConstraint", signature=c("x", "constraint"), function(x, constraint, verbose=FALSE) { errors <- NULL signatures <- .nextCheckConstraintSignatures(class(x), class(constraint)) if (is.null(signatures)) return(errors) ## We check from less specific to more specific constraints. for (i in seq_len(nrow(signatures))) { sig <- signatures[i, ] sigString <- paste(names(sig), paste0('"', sig, '"'), sep="=", collapse=", ") if (verbose) message("Calling \"checkConstraint\" method for\n", " ", sigString) method <- getMethod("checkConstraint", sig) errors <- method(x, constraint) if (length(errors) != 0L) { errors <- paste0("from \"checkConstraint\" method for c(", sigString, "): ", errors) ## If a constraint is not satisfied, we don't check the ## remaining constraints (so when implementing a constraint ## a developer can assume that the less specific constraints ## are satisfied). break } } errors } ) ) GenomicRanges/R/coverage-methods.R0000644000175400017540000000617513001230267020104 0ustar00biocbuildbiocbuild### ========================================================================= ### "coverage" methods ### ------------------------------------------------------------------------- ### ### TODO: Merge with Biostrings:::.V_recycle() and put in IRanges. .recycle <- function(x, skeleton_len, x.label, skeleton.label) { x_len <- length(x) if (x_len == skeleton_len) return(x) if (x_len < skeleton_len) { if (x_len == 0L) stop("cannot recycle zero-length '", x.label, "' ", "to the length of '", skeleton.label, "'") } else { if (x_len >= 2L) stop("'", x.label, "' is longer than '", skeleton.label, "'") } if (skeleton_len %% x_len != 0L) warning("'", x.label, "' length is not a divisor ", "of '", skeleton.label, "' length") rep(x, length.out=skeleton_len) } ### Returns a list-like object. .normarg_shift_or_weight <- function(arg, arg.label, x) { if (is.list(arg) || is(arg, "List")) { if (!identical(names(arg), seqlevels(x))) stop("when '", arg.label, "' is a list-like object, it must ", "have 1 list element per seqlevel in 'x', and its names ", "must be exactly 'seqlevels(x)'") return(arg) } if (isSingleString(arg)) { x_mcols <- mcols(x) if (!is(x_mcols, "DataTable") || sum(colnames(x_mcols) == arg) != 1L) stop("'mcols(x)' has 0 or more than 1 \"", arg, "\" columns") arg <- x_mcols[ , arg] } if (!(is.numeric(arg) || is(arg, "Rle") && is.numeric(runValue(arg)))) stop("'", arg.label, "' must be a numeric vector, a single string, ", "or a list-like object") split(.recycle(arg, length(x), arg.label, "x"), seqnames(x)) } setMethod("coverage", "GenomicRanges", function(x, shift=0L, width=NULL, weight=1L, method=c("auto", "sort", "hash")) { ## Normalize 'shift'. shift <- .normarg_shift_or_weight(shift, "shift", x) ## Just handle the default 'width' here. Non default will be checked ## in IRanges:::.CompressedIRangesList.coverage(). if (is.null(width)) width <- seqlengths(x) ## Normalize 'weight'. weight <- .normarg_shift_or_weight(weight, "weight", x) x_ranges_list <- split(ranges(x), seqnames(x)) circle.length <- seqlengths(x) circle.length[!(isCircular(x) %in% TRUE)] <- NA_integer_ IRanges:::.CompressedIRangesList.coverage(x_ranges_list, shift=shift, width=width, weight=weight, circle.length=circle.length, method=method, x_names.label="'seqlevels(x)'") } ) setMethod("coverage", "GRangesList", function(x, shift=0L, width=NULL, weight=1L, method=c("auto", "sort", "hash")) { coverage(x@unlistData, shift=shift, width=width, weight=weight, method=method) } ) GenomicRanges/R/findOverlaps-methods.R0000644000175400017540000002231513001230267020737 0ustar00biocbuildbiocbuild### ========================================================================= ### findOverlaps methods ### ------------------------------------------------------------------------- ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ### "findOverlaps" methods for GenomicRanges objects ### findOverlaps_GenomicRanges <- function(query, subject, maxgap=0L, minoverlap=1L, type=c("any", "start", "end", "within", "equal"), select=c("all", "first", "last", "arbitrary"), ignore.strand=FALSE) { type <- match.arg(type) select <- match.arg(select) findOverlaps_GNCList(query, subject, maxgap=maxgap, minoverlap=minoverlap, type=type, select=select, ignore.strand=ignore.strand) } setMethod("findOverlaps", c("GenomicRanges", "GenomicRanges"), findOverlaps_GenomicRanges ) ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ### "findOverlaps" methods for GRangesList objects ### .overlap_score <- function(hits, query, subject) { q_ranges <- ranges(query)[queryHits(hits)] s_ranges <- ranges(subject)[subjectHits(hits)] 1L + pmin.int(end(q_ranges), end(s_ranges)) - pmax.int(start(q_ranges), start(s_ranges)) } .aggregated_sum <- function(x, f1, f2) { sm <- selfmatchIntegerPairs(f1, f2) S4Vectors:::tabulate2(sm, length(sm), weight=x)[sm] } setMethod("findOverlaps", c("GRangesList", "GRangesList"), function(query, subject, maxgap=0L, minoverlap=1L, type=c("any", "start", "end", "within"), select=c("all", "first", "last", "arbitrary"), ignore.strand=FALSE) { if (!isSingleNumber(maxgap) || maxgap < 0) stop("'maxgap' must be a non-negative integer") type <- match.arg(type) select <- match.arg(select) unlisted_query <- unlist(query, use.names=FALSE) query_groups <- togroup(PartitioningByWidth(query)) unlisted_subject <- unlist(subject, use.names=FALSE) subject_groups <- togroup(PartitioningByWidth(subject)) if (type == "start") { keep <- which(S4Vectors:::diffWithInitialZero(subject_groups) != 0L) unlisted_subject <- unlisted_subject[keep] subject_groups <- subject_groups[keep] } else if (type == "end") { keep <- end(subject@partitioning)[elementNROWS(subject) > 0L] unlisted_subject <- unlisted_subject[keep] subject_groups <- subject_groups[keep] } ans00 <- findOverlaps(unlisted_query, unlisted_subject, maxgap=maxgap, type=type, select="all", ignore.strand=ignore.strand) if (minoverlap > 1L) { score <- .overlap_score(ans00, unlisted_query, unlisted_subject) score <- .aggregated_sum(score, query_groups[queryHits(ans00)], subject_groups[subjectHits(ans00)]) mcols(ans00) <- DataFrame(score=score) } if (type == "within") { ans01 <- remapHits(ans00, Rnodes.remapping=subject_groups, new.nRnode=length(subject)) ans11 <- remapHits(ans01, Lnodes.remapping=query_groups, new.nLnode=length(query), with.counts=TRUE) keep_idx <- which(mcols(ans11)[ , "counts"] == elementNROWS(query)[queryHits(ans11)]) mcols(ans11) <- NULL ans <- ans11[keep_idx] } else { ans <- remapHits(ans00, Lnodes.remapping=query_groups, new.nLnode=length(query), Rnodes.remapping=subject_groups, new.nRnode=length(subject)) } if (minoverlap > 1L) { keep_idx <- which(mcols(ans)[ , "score"] >= minoverlap) mcols(ans) <- NULL ans <- ans[keep_idx] } selectHits(ans, select=select) } ) setMethod("findOverlaps", c("GRangesList", "GenomicRanges"), function(query, subject, maxgap=0L, minoverlap=1L, type=c("any", "start", "end", "within"), select=c("all", "first", "last", "arbitrary"), ignore.strand=FALSE) { if (!isSingleNumber(maxgap) || maxgap < 0) stop("'maxgap' must be a non-negative integer") type <- match.arg(type) select <- match.arg(select) unlisted_query <- unlist(query, use.names=FALSE) query_groups <- togroup(PartitioningByWidth(query)) ans00 <- findOverlaps(unlisted_query, subject, maxgap=maxgap, type=type, select="all", ignore.strand=ignore.strand) if (minoverlap > 1L) { score <- .overlap_score(ans00, unlisted_query, subject) score <- .aggregated_sum(score, query_groups[queryHits(ans00)], subjectHits(ans00)) mcols(ans00) <- DataFrame(score=score) } if (type == "within") { ans10 <- remapHits(ans00, Lnodes.remapping=query_groups, new.nLnode=length(query), with.counts=TRUE) keep_idx <- which(mcols(ans10)[ , "counts"] == elementNROWS(query)[queryHits(ans10)]) mcols(ans10) <- NULL ans <- ans10[keep_idx] } else { ans <- remapHits(ans00, Lnodes.remapping=query_groups, new.nLnode=length(query)) } if (minoverlap > 1L) { keep_idx <- which(mcols(ans)[ , "score"] >= minoverlap) mcols(ans) <- NULL ans <- ans[keep_idx] } selectHits(ans, select=select) } ) setMethod("findOverlaps", c("GenomicRanges", "GRangesList"), function(query, subject, maxgap=0L, minoverlap=1L, type=c("any", "start", "end", "within"), select=c("all", "first", "last", "arbitrary"), ignore.strand=FALSE) { if (!isSingleNumber(maxgap) || maxgap < 0) stop("'maxgap' must be a non-negative integer") type <- match.arg(type) select <- match.arg(select) unlisted_subject <- unlist(subject, use.names=FALSE) subject_groups <- togroup(PartitioningByWidth(subject)) if (type == "start") { keep <- which(S4Vectors:::diffWithInitialZero(subject_groups) != 0L) unlisted_subject <- unlisted_subject[keep] subject_groups <- subject_groups[keep] } else if (type == "end") { keep <- end(subject@partitioning)[elementNROWS(subject) > 0L] unlisted_subject <- unlisted_subject[keep] subject_groups <- subject_groups[keep] } ans00 <- findOverlaps(query, unlisted_subject, maxgap=maxgap, type=type, select="all", ignore.strand=ignore.strand) if(minoverlap > 1L) { score <- .overlap_score(ans00, query, unlisted_subject) score <- .aggregated_sum(score, queryHits(ans00), subject_groups[subjectHits(ans00)]) mcols(ans00) <- DataFrame(score=score) } ans <- remapHits(ans00, Rnodes.remapping=subject_groups, new.nRnode=length(subject)) if (minoverlap > 1L) { keep_idx <- which(mcols(ans)[ , "score"] >= minoverlap) mcols(ans) <- NULL ans <- ans[keep_idx] } selectHits(ans, select=select) } ) ### Needed by chipseq package. setMethod("findOverlaps", c("RangedData", "GenomicRanges"), function(query, subject, maxgap=0L, minoverlap=1L, type=c("any", "start", "end", "within"), select=c("all", "first", "last", "arbitrary"), ignore.strand=FALSE) { ## Calls "findOverlaps" method for c("GenomicRanges", "GenomicRanges") ## defined above. findOverlaps(as(query, "GRanges"), subject, maxgap=maxgap, minoverlap=minoverlap, type=match.arg(type), select=match.arg(select), ignore.strand=ignore.strand) } ) ### ========================================================================= ### findOverlaps-based methods ### ------------------------------------------------------------------------- countOverlaps_GenomicRanges <- function(query, subject, maxgap=0L, minoverlap=1L, type=c("any", "start", "end", "within", "equal"), ignore.strand=FALSE) { type <- match.arg(type) ans <- findOverlaps_GNCList(query, subject, maxgap=maxgap, minoverlap=minoverlap, type=type, select="count", ignore.strand=ignore.strand) names(ans) <- names(query) ans } setMethod("countOverlaps", c("GenomicRanges", "GenomicRanges"), countOverlaps_GenomicRanges ) GenomicRanges/R/genomicvars.R0000644000175400017540000001321713001230267017160 0ustar00biocbuildbiocbuild### ========================================================================= ### Manipulate genomic variables i.e. data/variable defined along a genome ### ------------------------------------------------------------------------- ### ### The concept of genomic variables could be formalized via a dedicated ### container. This container could be a simple extension of GRanges with no ### additional slots and the following constraints: ### - The ranges are unstranded (i.e. strand is set to * for all ranges). ### - The ranges are disjoint and ordered. ### Then the metadata columns are the genomic variables. ### For now, we just use a GRanges object. We make sure it's disjoint and we ### ignore its strand. We don't mind if it's not ordered and make sure that ### the code that operates on it works properly even if it's not ordered. ### setAs("RleList", "GRanges", function(from) { score <- unlist(runValue(from), use.names=FALSE) lens <- runLength(from) ir <- unlist(RangesList(lapply(lens, successiveIRanges)), use.names=FALSE) nrun <- lengths(lens) gr <- GRanges(rep(names(from), nrun), ir, score=score) seqlengths(gr) <- lengths(from) gr }) setAs("RleViewsList", "GRanges", function(from) { as(as(from, "RangedData"), "GRanges") }) ### Represent a collection of named RleList objects as a GRanges with 1 ### metadata column per RleList object. bindAsGRanges <- function(...) { args <- list(...) if (length(args) == 0L) stop("nothing to bind") ## TODO: Implement (in C) fast 'elementIs(objects, class)' in S4Vectors ## that does 'sapply(objects, is, class, USE.NAMES=FALSE)', and use it ## here. if (!all(sapply(args, is, "RleList", USE.NAMES=FALSE))) stop("the objects to bind must be RleList objects") ans_seqlevels <- names(args[[1L]]) if (is.null(ans_seqlevels)) stop("the RleList objects to combine must have names") if (any(ans_seqlevels %in% c(NA_character_, "")) || anyDuplicated(ans_seqlevels)) stop(wmsg("the names on the RleList objects cannot contain NAs, ", "empty strings, or duplicates")) if (!all(sapply(args[-1L], function(arg) identical(names(arg), ans_seqlevels)))) stop(wmsg("the RleList objects to combine must have the same length ", "and the same names in the same order")) DFL <- cbind(...) # named CompressedSplitDataFrameList unlisted_DFL <- unlist(DFL, use.names=FALSE) # DataFrame DFL_partitioning <- PartitioningByEnd(DFL) ## Prepare 'ans_seqnames'. ans_seqnames <- Rle(factor(ans_seqlevels, levels=ans_seqlevels), width(DFL_partitioning)) ## Prepare 'ans_ranges'. ans_width <- unlisted_DFL[ , "runLength"] width_list <- relist(ans_width, DFL) ans_end <- unlist(lapply(width_list, cumsum), use.names=FALSE) ans_ranges <- IRanges(end=ans_end, width=ans_width) ## Prepare 'ans_seqlengths'. ans_seqlengths <- setNames(ans_end[end(DFL_partitioning)], names(DFL)) ## First column is "runLength". Get rid of it. ans_mcols <- unlisted_DFL[-1L] ans <- new_GRanges("GRanges", ans_seqnames, ans_ranges, mcols=ans_mcols, seqlengths=ans_seqlengths) ## Keep only ranges for which at least one variable is not NA. keep_idx <- which(rowSums(!is.na(mcols(ans))) != 0L) ans[keep_idx] } ### Return a named RleList with 1 list element per seqlevel in 'x'. ### Works on any metadata column that can be put in Rle form (i.e. any atomic ### vector or factor). mcolAsRleList <- function(x, varname) { if (!is(x, "GenomicRanges")) stop("'x' must be a GRanges object") var <- mcols(x)[ , varname] ## If 'var' is numeric, then we can use coverage(). #if (is.numeric(var)) # return(coverage(x, weight=var)) ## Otherwise 'x' must be disjoint and we compute the RleList in a loop. ## This would also work on a numeric metadata column but would be slower ## than using coverage(), especially if 'x' has many seqlevels. if (!isDisjoint(x, ignore.strand=TRUE)) stop(wmsg("cannot turn non-numeric metadata column into a ", "named RleList object when 'x' is not disjoint ", "(ignoring the strand)")) rg_per_chrom <- split(ranges(x), seqnames(x)) var_per_chrom <- split(var, seqnames(x)) rle_list <- mapply( function(seqlen, ir, v) { if (is.na(seqlen)) seqlen <- max(end(ir)) rle <- Rle(v[NA_integer_], seqlen) rle[ir] <- Rle(v, width(ir)) rle }, seqlengths(x), rg_per_chrom, var_per_chrom, SIMPLIFY=FALSE ) as(rle_list, "SimpleRleList") } binnedAverage <- function(bins, numvar, varname) { if (!is(bins, "GRanges")) stop("'x' must be a GRanges object") if (!is(numvar, "RleList")) stop("'numvar' must be an RleList object") if (!identical(seqlevels(bins), names(numvar))) stop("'seqlevels(bin)' and 'names(numvar)' must be identical") ## A version of viewMeans() that pads "out of limits" views with zeros. viewMeans2 <- function(v) { means <- viewMeans(v) w0 <- width(v) w1 <- width(trim(v)) means <- means * w1 / w0 means[w0 != 0L & w1 == 0L] <- 0 means } bins_per_chrom <- split(ranges(bins), seqnames(bins)) means_list <- lapply(names(numvar), function(seqname) { v <- Views(numvar[[seqname]], bins_per_chrom[[seqname]]) viewMeans2(v) }) new_mcol <- unsplit(means_list, as.factor(seqnames(bins))) mcols(bins)[[varname]] <- new_mcol bins } GenomicRanges/R/inter-range-methods.R0000644000175400017540000002610213001761644020524 0ustar00biocbuildbiocbuild### ========================================================================= ### Inter-range methods ### ------------------------------------------------------------------------- ### ### 'revmap_unlisted' and 'revmap_partitioning': IntegerList and Partitioning ### objects representing the "revmap object", which is *conceptually* an ### IntegerListList object (of the same length as 'revmap_partitioning'). ### *Conceptually* because, well, we don't actually have such container... ### 'old2new': IntegerList of the same length as the "revmap object". .translate_revmap <- function(revmap_unlisted, revmap_partitioning, old2new) { ## 'times' has the length of the "revmap object". times <- sum(relist(width(PartitioningByEnd(revmap_unlisted)), revmap_partitioning)) ## 'offset' has the length of 'revmap_unlisted@unlistData'. offset <- rep.int(start(PartitioningByEnd(old2new)) - 1L, times) revmap_flat <- revmap_unlisted@unlistData revmap_unlisted@unlistData <- old2new@unlistData[revmap_flat + offset] revmap_unlisted } ### 'rgl2' and 'rgl' must be List objects (typically RangesList or GRangesList) ### of the same length, both with a "revmap" inner metadata column. .fix_inner_revmap_mcol <- function(rgl2, rgl) { unlisted_rgl2 <- unlist(rgl2, use.names=FALSE) unlisted_revmap2 <- mcols(unlisted_rgl2)$revmap revmap <- relist(mcols(unlist(rgl, use.names=FALSE))$revmap, rgl) mcols(unlisted_rgl2)$revmap <- .translate_revmap(unlisted_revmap2, rgl2, revmap) rgl2 <- relist(unlisted_rgl2, rgl2) } ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ### Deconstruction/reconstruction of a GenomicRanges object into/from an ### IRangesList object. ### ### For internal use only (not exported). ### .make_spaceid <- function(x, ignore.strand=FALSE, drop=FALSE) { spaceid <- seqnames(x) runValue(spaceid) <- 3L * as.integer(runValue(spaceid)) if (!ignore.strand) { strandid <- strand(x) runValue(strandid) <- as.integer(runValue(strandid)) - 3L spaceid <- spaceid + strandid } if (!drop) { levels <- as.character(seq_len(3L * length(seqlevels(x)))) runValue(spaceid) <- structure(runValue(spaceid), levels=levels, class="factor") } spaceid } ### Work on any GenomicRanges derivative and return a CompressedIRangesList ### instance. deconstructGRintoRGL <- function(x, with.revmap=FALSE, ignore.strand=FALSE, drop=FALSE) { if (!isTRUEorFALSE(with.revmap)) stop("'with.revmap' must be TRUE or FALSE") if (!isTRUEorFALSE(ignore.strand)) stop("'ignore.strand' must be TRUE or FALSE") x_ranges <- unname(ranges(x)) if (with.revmap) mcols(x_ranges) <- DataFrame(revmap=seq_along(x_ranges)) x_spaceid <- .make_spaceid(x, ignore.strand=ignore.strand, drop=drop) split(x_ranges, x_spaceid) } ### Return a GRanges instance. reconstructGRfromRGL <- function(rgl, x) { ## Prepare 'ans_ranges'. ans_ranges <- unlist(rgl, use.names=FALSE) ## Prepare 'ans_seqnames' and 'ans_strand'. rgl_eltNROWS <- elementNROWS(rgl) spaceid <- as.integer(names(rgl)) - 1L ans_seqnames <- Rle(structure(spaceid %/% 3L + 1L, levels=seqlevels(x), class="factor"), rgl_eltNROWS) ans_strand <- Rle(structure(spaceid %% 3L + 1L, levels=levels(strand()), class="factor"), rgl_eltNROWS) ## Prepare 'ans_mcols'. ans_mcols <- mcols(ans_ranges) if (is.null(ans_mcols)) { ans_mcols <- S4Vectors:::make_zero_col_DataFrame(length(ans_ranges)) } else { mcols(ans_ranges) <- NULL } ## Prepare 'ans_seqinfo'. ans_seqinfo <- seqinfo(x) ## To be as fast as possible, we don't use internal low-level constructor ## new_GRanges() and we don't check the new object. new2("GRanges", seqnames=ans_seqnames, ranges=ans_ranges, strand=ans_strand, elementMetadata=ans_mcols, seqinfo=ans_seqinfo, check=FALSE) } ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ### range() ### ### Always return a GRanges *instance* whatever GenomicRanges derivative the ### input is, so does NOT act like an endomorphism in general. setMethod("range", "GenomicRanges", function(x, ..., ignore.strand=FALSE, na.rm=FALSE) { if (!identical(na.rm, FALSE)) warning("'na.rm' argument is ignored") args <- unname(list(x, ...)) args <- lapply(args, granges) gr <- do.call(c, args) rgl <- deconstructGRintoRGL(gr, ignore.strand=ignore.strand, drop=TRUE) rgl2 <- callGeneric(rgl) reconstructGRfromRGL(rgl2, gr) } ) ### Overwrite above method with optimized method for GPos objects. ### Like the above method, return a GRanges instance. setMethod("range", "GPos", function(x, ..., ignore.strand=FALSE, na.rm=FALSE) callGeneric(x@pos_runs, ..., ignore.strand=ignore.strand, na.rm=na.rm) ) setMethod("range", "GRangesList", function(x, ..., ignore.strand=FALSE, na.rm=FALSE) { gr <- deconstructGRLintoGR(x) ## "range" method for GRanges objects is fast. gr2 <- callGeneric(gr, ..., ignore.strand=ignore.strand, na.rm=na.rm) reconstructGRLfromGR(gr2, x) } ) ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ### reduce() ### ### Always return a GRanges *instance* whatever GenomicRanges derivative the ### input is, so does NOT act like an endomorphism in general. setMethod("reduce", "GenomicRanges", function(x, drop.empty.ranges=FALSE, min.gapwidth=1L, with.revmap=FALSE, with.inframe.attrib=FALSE, ignore.strand=FALSE) { if (!identical(with.inframe.attrib, FALSE)) stop("'with.inframe.attrib' argument not supported ", "when reducing a GenomicRanges object") rgl <- deconstructGRintoRGL(x, with.revmap=with.revmap, ignore.strand=ignore.strand, drop=TRUE) rgl2 <- callGeneric(rgl, drop.empty.ranges=drop.empty.ranges, min.gapwidth=min.gapwidth, with.revmap=with.revmap) if (with.revmap) rgl2 <- .fix_inner_revmap_mcol(rgl2, rgl) reconstructGRfromRGL(rgl2, x) } ) setMethod("reduce", "GRangesList", function(x, drop.empty.ranges=FALSE, min.gapwidth=1L, with.revmap=FALSE, with.inframe.attrib=FALSE, ignore.strand=FALSE) { if (!identical(with.inframe.attrib, FALSE)) stop("'with.inframe.attrib' argument is not supported ", "when reducing a GRangesList object") gr <- deconstructGRLintoGR(x) gr2 <- callGeneric(gr, drop.empty.ranges=drop.empty.ranges, min.gapwidth=min.gapwidth, with.revmap=with.revmap, ignore.strand=ignore.strand) ans <- reconstructGRLfromGR(gr2, x) ## Localize 'revmap'. if (with.revmap) { unlisted_ans <- unlist(ans, use.names=FALSE) mcols(unlisted_ans)$revmap <- IRanges:::global2local_revmap(mcols(unlisted_ans)$revmap, ans, x) ans <- relist(unlisted_ans, ans) } ans } ) setMethod("reduce", "GenomicRangesList", function(x, drop.empty.ranges = FALSE, ...) { endoapply(x, reduce, drop.empty.ranges=drop.empty.ranges, ...) }) ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ### gaps() ### ### Always return a GRanges *instance* whatever GenomicRanges derivative the ### input is, so does NOT act like an endomorphism in general. setMethod("gaps", "GenomicRanges", function(x, start=1L, end=seqlengths(x)) { seqlevels <- seqlevels(x) if (!is.null(names(start))) start <- start[seqlevels] if (!is.null(names(end))) end <- end[seqlevels] start <- S4Vectors:::recycleVector(start, length(seqlevels)) start <- rep(start, each=3L) end <- S4Vectors:::recycleVector(end, length(seqlevels)) end <- rep(end, each=3L) rgl <- deconstructGRintoRGL(x) rgl2 <- callGeneric(rgl, start=start, end=end) reconstructGRfromRGL(rgl2, x) } ) ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ### disjoin() ### ### Always return a GRanges *instance* whatever GenomicRanges derivative the ### input is, so does NOT act like an endomorphism in general. setMethod("disjoin", "GenomicRanges", function(x, with.revmap=FALSE, ignore.strand=FALSE) { rgl <- deconstructGRintoRGL(x, with.revmap=with.revmap, ignore.strand=ignore.strand, drop=TRUE) rgl2 <- callGeneric(rgl, with.revmap=with.revmap) if (with.revmap) rgl2 <- .fix_inner_revmap_mcol(rgl2, rgl) reconstructGRfromRGL(rgl2, x) } ) setMethod("disjoin", "GRangesList", function(x, with.revmap=FALSE, ignore.strand=FALSE) { gr <- deconstructGRLintoGR(x) gr2 <- callGeneric(gr, with.revmap=with.revmap, ignore.strand=ignore.strand) ans <- reconstructGRLfromGR(gr2, x) ## Localize 'revmap'. if (with.revmap) { unlisted_ans <- unlist(ans, use.names=FALSE) mcols(unlisted_ans)$revmap <- IRanges:::global2local_revmap(mcols(unlisted_ans)$revmap, ans, x) ans <- relist(unlisted_ans, ans) } ans } ) ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ### isDisjoint() ### setMethod("isDisjoint", "GenomicRanges", function(x, ignore.strand=FALSE) { rgl <- deconstructGRintoRGL(x, ignore.strand=ignore.strand, drop=TRUE) all(callGeneric(rgl)) } ) ### Overwrite above method with optimized method for GPos objects. setMethod("isDisjoint", "GPos", function(x, ignore.strand=FALSE) callGeneric(x@pos_runs, ignore.strand) ) setMethod("isDisjoint", "GRangesList", function(x, ignore.strand=FALSE) { gr <- deconstructGRLintoGR(x, expand.levels=TRUE) rgl <- deconstructGRintoRGL(gr, ignore.strand=ignore.strand) ans <- callGeneric(rgl) ans <- colSums(matrix(!ans, ncol=length(x))) == 0L names(ans) <- names(x) ans } ) ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ### disjointBins() ### setMethod("disjointBins", "GenomicRanges", function(x, ignore.strand=FALSE) { x_spaceid <- .make_spaceid(x, ignore.strand=ignore.strand, drop=TRUE) rgl <- split(unname(ranges(x)), x_spaceid) ans <- callGeneric(rgl) unsplit(ans, x_spaceid) } ) GenomicRanges/R/intra-range-methods.R0000644000175400017540000002465613001230267020524 0ustar00biocbuildbiocbuild### ========================================================================= ### Intra-range methods ### ------------------------------------------------------------------------- ### ### The methods implemented in this file should behave consistently with ### those defined in IRanges intra-range-methods.R ### ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ### shift() ### setMethod("shift", "GenomicRanges", function(x, shift=0L, use.names=TRUE) { new_ranges <- shift(ranges(x), shift=shift, use.names=use.names) ranges(x) <- new_ranges x } ) ### Overwrite above method with optimized method for GPos objects. ### A GPos object cannot hold names so the 'use.names' arg has no effect. setMethod("shift", "GPos", function(x, shift=0L, use.names=TRUE) { x@pos_runs <- callGeneric(x@pos_runs, shift=shift) x } ) setMethod("shift", "GRangesList", function(x, shift=0L, use.names=TRUE) { ## Unlist 'x'. unlisted_x <- unlist(x, use.names=FALSE) ## Recycle and unlist 'shift'. if (!is(shift, "List")) shift <- as(shift, "List") shift <- S4Vectors:::VH_recycle(shift, x, "shift", "x") unlisted_shift <- unlist(shift, use.names=FALSE) ## Compute unlisted 'ans'. unlisted_ans <- shift(unlisted_x, shift=unlisted_shift, use.names=use.names) ## Relist and return. relist(unlisted_ans, x) } ) ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ### narrow() ### setMethod("narrow", "GenomicRanges", function(x, start=NA, end=NA, width=NA, use.names=TRUE) { new_ranges <- narrow(ranges(x), start=start, end=end, width=width, use.names=use.names) ranges(x) <- new_ranges x } ) setMethod("narrow", "GRangesList", function(x, start=NA, end=NA, width=NA, use.names=TRUE) { gr <- narrow(x@unlistData, start=start, end=end, width=width, use.names=use.names) relist(gr, x@partitioning) } ) ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ### resize() ### setMethod("resize", "GenomicRanges", function(x, width, fix="start", use.names=TRUE, ignore.strand=FALSE) { if (!missing(fix) && length(x) > 0L && (length(fix) > length(x) || length(x) %% length(fix) > 0L)) stop("'x' is not a multiple of 'fix' length") if (!isTRUEorFALSE(ignore.strand)) stop("'ignore.strand' must be TRUE or FALSE") if (ignore.strand) { fix <- Rle(rep.int(fix, length(x))) } else { revFix <- c(start="end", end="start", center="center") if (length(x) == 0L) fix <- character() else fix <- ifelse(strand(x) == "-", revFix[fix], fix) } new_ranges <- resize(ranges(x), width=width, fix=fix, use.names=use.names) ranges(x) <- new_ranges x } ) setMethod("resize", "GRangesList", function(x, width, fix="start", use.names=TRUE, ignore.strand=FALSE) { ## Unlist 'x'. unlisted_x <- unlist(x, use.names=FALSE) ## Recycle and unlist 'width'. if (!is(width, "List")) width <- as(width, "List") width <- S4Vectors:::VH_recycle(width, x, "width", "x") unlisted_width <- unlist(width, use.names=FALSE) ## Compute unlisted 'ans'. unlisted_ans <- resize(unlisted_x, unlisted_width, fix=fix, use.names=use.names, ignore.strand=ignore.strand) ## Relist and return. relist(unlisted_ans, x) } ) ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ### flank() ### setMethod("flank", "GenomicRanges", function(x, width, start=TRUE, both=FALSE, use.names=TRUE, ignore.strand=FALSE) { if (!isTRUEorFALSE(ignore.strand)) stop("'ignore.strand' must be TRUE or FALSE") if (ignore.strand) start <- rep.int(start, length(x)) else start <- as.vector(start == (strand(x) != "-")) new_ranges <- flank(ranges(x), width=width, start=start, both=both, use.names=use.names) ranges(x) <- new_ranges x } ) setMethod("flank", "GRangesList", function(x, width, start=TRUE, both=FALSE, use.names=TRUE, ignore.strand=FALSE) { ## Unlist 'x'. unlisted_x <- unlist(x, use.names=FALSE) ## Recycle and unlist 'width'. if (!is(width, "List")) width <- as(width, "List") width <- S4Vectors:::VH_recycle(width, x, "width", "x") unlisted_width <- unlist(width, use.names=FALSE) ## Compute unlisted 'ans'. unlisted_ans <- flank(unlisted_x, unlisted_width, start=start, both=both, use.names=use.names, ignore.strand=ignore.strand) ## Relist and return. relist(unlisted_ans, x) } ) ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ### promoters() ### setMethod("promoters", "GenomicRanges", function(x, upstream=2000, downstream=200, ...) { if (!isSingleNumber(upstream)) stop("'upstream' must be a single integer") if (!is.integer(upstream)) upstream <- as.numeric(upstream) if (!isSingleNumber(downstream)) stop("'downstream' must be a single integer") if (!is.integer(downstream)) downstream <- as.numeric(downstream) if (upstream < 0 | downstream < 0) stop("'upstream' and 'downstream' must be integers >= 0") if (any(strand(x) == "*")) warning("'*' ranges were treated as '+'") on_plus <- which(strand(x) == "+" | strand(x) == "*") on_plus_TSS <- start(x)[on_plus] start(x)[on_plus] <- on_plus_TSS - upstream end(x)[on_plus] <- on_plus_TSS + downstream - 1L on_minus <- which(strand(x) == "-") on_minus_TSS <- end(x)[on_minus] end(x)[on_minus] <- on_minus_TSS + upstream start(x)[on_minus] <- on_minus_TSS - downstream + 1L x } ) setMethod("promoters", "GRangesList", function(x, upstream=2000, downstream=200, ...) { x@unlistData <- promoters(x@unlistData, upstream=upstream, downstream=downstream) x } ) ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ### reflect() ### ### TODO: Add "reflect" method for GenomicRanges objects. ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ### restrict() ### .checkParms <- function(x, parm) { if (!all(is.na(parm))) { if (!all(names(parm) %in% levels(seqnames(x)))) stop("start should be a named numeric vector ", "corresponding to seqnames") } temp <- structure(rep(NA_integer_, length(levels(seqnames(x)))), names=levels(seqnames(x))) temp[names(parm)] <- parm temp } .restrictRngs <- function(x, start, end, keep.all.ranges, use.names) { tmp <- names(x) names(x) <- seq_len(length(x)) rng <- ranges(x) res <- restrict(ranges(x), start, end, keep.all.ranges, use.names=TRUE) x <- x[as.numeric(names(res))] ranges(x) <- res if (!use.names) names(x) <- NULL else names(x) <- tmp[as.numeric(names(res))] x } setMethod("restrict", "GenomicRanges", function(x, start=NA, end=NA, keep.all.ranges=FALSE, use.names=TRUE) { if (is.null(names(start)) && is.null(names(end))) return(.restrictRngs(x, start, end,keep.all.ranges, use.names)) nms <- names(mcols(x)) mcols(x) <- cbind(mcols(x), DataFrame(posIndx=seq_len(length(x)))) splt <- split(x, seqnames(x)) start <- .checkParms(x, start) end <- .checkParms(x, end) res <- lapply(names(splt), function(i) { .restrictRngs(splt[[i]], start=start[i], end=end[i], keep.all.ranges, use.names) }) names(res) <- names(splt) ord <- unlist(GRangesList(res), use.names=FALSE) df <- data.frame(orig=mcols(ord)[["posIndx"]], final=seq_len(length(ord))) indx <- order(df[["orig"]], df[["final"]]) ord <- ord[indx, ] mcols(ord) <- subset(mcols(ord), select=nms) ord } ) setMethod("restrict", "GRangesList", function(x, start = NA, end = NA, keep.all.ranges = FALSE, use.names = TRUE) { endoapply(x, restrict, start=start, end=end,keep.all.ranges=keep.all.ranges , use.names=use.names ) }) ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ### trim() ### setMethod("trim", "GenomicRanges", function(x, use.names=TRUE) { ## We trim only out-of-bound ranges located on non-circular sequences ## whose length is not NA. ## See get_out_of_bound_index() in GenomicRanges-class.R idx <- get_out_of_bound_index(x) if (length(idx) == 0L) return(x) new_ranges <- ranges(x) seqnames_id <- as.integer(seqnames(x))[idx] new_end <- unname(seqlengths(x))[seqnames_id] new_ranges[idx] <- restrict(new_ranges[idx], start=1L, end=new_end, keep.all.ranges=TRUE) ranges(x) <- new_ranges x } ) ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ### Zooming (symmetrically scales the width). ### setMethod("Ops", c("GenomicRanges", "numeric"), function(e1, e2) { if (S4Vectors:::anyMissing(e2)) stop("NA not allowed as zoom factor") e2 <- recycleNumericArg(e2, "e2", length(e1)) if (.Generic == "*") { e2 <- ifelse(e2 < 0, abs(1/e2), e2) resize(e1, width(e1) / e2, fix="center") } else { if (.Generic == "-") { e2 <- -e2 .Generic <- "+" } if (.Generic == "+") { if (any(-e2*2 > width(e1))) stop("adjustment would result in ranges ", "with negative widths") resize(e1, width(e1) + e2*2, fix="center") } } } ) GenomicRanges/R/makeGRangesFromDataFrame.R0000644000175400017540000002360013001230267021415 0ustar00biocbuildbiocbuild### ========================================================================= ### makeGRangesFromDataFrame() ### ------------------------------------------------------------------------- ### Must return NULL or a Seqinfo object. .normarg_seqinfo <- function(seqinfo) { if (is.null(seqinfo) || is(seqinfo, "Seqinfo")) return(seqinfo) if (is.character(seqinfo)) return(Seqinfo(seqinfo)) if (is.numeric(seqinfo)) { seqlevels <- names(seqinfo) if (is.null(seqlevels)) stop("when a numeric vector, 'seqinfo' must have names") return(Seqinfo(seqlevels, seqlengths=seqinfo)) } stop("'seqinfo' must be NULL, or a Seqinfo object, or a character vector ", "of seqlevels, or a named numeric vector of sequence lengths") } .normarg_field <- function(field, what) { if (!is.character(field) || any(is.na(field))) stop("'", what, ".field' must be a character vector with no NAs") tolower(field) } .collect_prefixes <- function(df_colnames, field) { df_colnames_nc <- nchar(df_colnames) prefixes <- lapply(field, function(suf) { pref_nc <- df_colnames_nc - nchar(suf) idx <- which(substr(df_colnames, pref_nc + 1L, df_colnames_nc) == suf) substr(df_colnames[idx], 1L, pref_nc[idx]) }) unique(unlist(prefixes)) } .find_start_end_cols <- function(df_colnames, start.field, end.field) { idx1 <- which(df_colnames %in% start.field) idx2 <- which(df_colnames %in% end.field) if (length(idx1) == 1L && length(idx2) == 1L) return(list(c(start=idx1, end=idx2), "")) if (length(idx1) == 0L && length(idx2) == 0L) { prefixes1 <- .collect_prefixes(df_colnames, start.field) prefixes2 <- .collect_prefixes(df_colnames, end.field) if (length(prefixes1) == 1L && length(prefixes2) == 1L && prefixes1 == prefixes2) { prefix <- prefixes1 idx1 <- which(df_colnames %in% paste0(prefix, start.field)) idx2 <- which(df_colnames %in% paste0(prefix, end.field)) if (length(idx1) == 1L && length(idx2) == 1L) return(list(c(start=idx1, end=idx2), prefix)) } } stop("cannnot determine start/end columns") } .find_width_col <- function(df_colnames, width.field, prefix) { idx <- which(df_colnames %in% paste0(prefix, width.field)) if (length(idx) == 0L) idx <- which(df_colnames %in% width.field) if (length(idx) == 0L) return(NA_integer_) if (length(idx) >= 2L) { warning("cannnot determine width column unambiguously") return(idx[[1L]]) } idx } .find_seqnames_col <- function(df_colnames, seqnames.field, prefix) { idx <- which(df_colnames %in% paste0(prefix, seqnames.field)) if (length(idx) == 0L) idx <- which(df_colnames %in% seqnames.field) if (length(idx) == 0L) stop("cannnot find seqnames column") if (length(idx) >= 2L) stop("cannnot determine seqnames column unambiguously") idx } .find_strand_col <- function(df_colnames, strand.field, prefix) { idx <- which(df_colnames %in% paste0(prefix, strand.field)) if (length(idx) == 0L) idx <- which(df_colnames %in% strand.field) if (length(idx) == 0L) return(NA_integer_) if (length(idx) >= 2L) stop("Cannnot determine strand column unambiguously. ", "(You can use\n 'ignore.strand=FALSE' to ignore ", "strand information.)") idx } ### Returns a named integer vector of length 5. Names are: seqnames, start, ### end, width, and strand. The values must be valid column numbers, except ### for the width and strand elements that can also be NAs. .find_GRanges_cols <- function(df_colnames, seqnames.field=c("seqnames", "seqname", "chromosome", "chrom", "chr", "chromosome_name", "seqid"), start.field="start", end.field=c("end", "stop"), strand.field="strand", ignore.strand=FALSE) { ## Automatic detection of seqnames/start/end/strand columns is case ## insensitive. df_colnames0 <- tolower(df_colnames) seqnames.field0 <- .normarg_field(seqnames.field, "seqnames") start.field0 <- .normarg_field(start.field, "start") end.field0 <- .normarg_field(end.field, "end") start_end_cols <- .find_start_end_cols(df_colnames0, start.field0, end.field0) prefix <- start_end_cols[[2L]] ## Name of "width" field is not under user control for now (until we need ## need that). width_col <- .find_width_col(df_colnames0, "width", prefix) seqnames_col <- .find_seqnames_col(df_colnames0, seqnames.field0, prefix) if (ignore.strand) { strand_col <- NA_integer_ } else { strand.field0 <- .normarg_field(strand.field, "strand") strand_col <- .find_strand_col(df_colnames0, strand.field0, prefix) } c(seqnames=seqnames_col, start_end_cols[[1L]], width=width_col, strand=strand_col) } .get_data_frame_col_as_numeric <- function(df, col) { ans <- df[[col]] if (is(ans, "Rle")) ans <- S4Vectors:::decodeRle(ans) if (!is.numeric(ans)) { if (is.factor(ans)) ans <- as.character(ans) ans <- suppressWarnings(as.numeric(ans)) if (anyNA(ans)) stop(wmsg("some values in the ", "\"", names(df)[[col]], "\" ", "column cannot be turned into numeric values")) } ans } ### 'df' must be a data.frame or DataFrame object. makeGRangesFromDataFrame <- function(df, keep.extra.columns=FALSE, ignore.strand=FALSE, seqinfo=NULL, seqnames.field=c("seqnames", "seqname", "chromosome", "chrom", "chr", "chromosome_name", "seqid"), start.field="start", end.field=c("end", "stop"), strand.field="strand", starts.in.df.are.0based=FALSE) { ## Check args. if (is.character(df)) # for people that provide the path to a file stop("'df' must be a data.frame or DataFrame object") if (!(is.data.frame(df) || is(df, "DataFrame"))) df <- as.data.frame(df) if (!isTRUEorFALSE(keep.extra.columns)) stop("'keep.extra.columns' must be TRUE or FALSE") if (!isTRUEorFALSE(ignore.strand)) stop("'ignore.strand' must be TRUE or FALSE") ans_seqinfo <- .normarg_seqinfo(seqinfo) if (!isTRUEorFALSE(starts.in.df.are.0based)) stop("'starts.in.df.are.0based' must be TRUE or FALSE") granges_cols <- .find_GRanges_cols(names(df), seqnames.field=seqnames.field, start.field=start.field, end.field=end.field, strand.field=strand.field, ignore.strand=ignore.strand) ## Prepare 'ans_seqnames'. ans_seqnames <- df[[granges_cols[["seqnames"]]]] ## Prepare 'ans_ranges'. ans_start <- .get_data_frame_col_as_numeric(df, granges_cols[["start"]]) ans_end <- .get_data_frame_col_as_numeric(df, granges_cols[["end"]]) if (starts.in.df.are.0based) ans_start <- ans_start + 1L ans_names <- rownames(df) if (identical(ans_names, as.character(seq_len(nrow(df))))) ans_names <- NULL ans_ranges <- IRanges(ans_start, ans_end, names=ans_names) ## Prepare 'ans_strand'. if (is.na(granges_cols[["strand"]]) || ignore.strand) { ans_strand <- "*" } else { ans_strand <- as.character(df[[granges_cols[["strand"]]]]) ans_strand[ans_strand %in% "."] <- "*" } ## Prepare 'ans_mcols'. if (keep.extra.columns) { drop_idx <- c(granges_cols[["seqnames"]], granges_cols[["start"]], granges_cols[["end"]]) if (!is.na(granges_cols[["width"]])) drop_idx <- c(drop_idx, granges_cols[["width"]]) if (!is.na(granges_cols[["strand"]])) drop_idx <- c(drop_idx, granges_cols[["strand"]]) ans_mcols <- df[-drop_idx] } else { ans_mcols <- NULL } ## Prepare 'ans_seqinfo'. if (is.null(ans_seqinfo)) { ## Only if 'ans_seqnames' is a factor-Rle, we preserve the seqlevels ## in the order they are in 'levels(ans_seqnames)'. Otherwise, we ## order them according to rankSeqlevels(). seqlevels <- levels(ans_seqnames) if (is.null(seqlevels)) { seqlevels <- unique(ans_seqnames) if (!is.character(seqlevels)) seqlevels <- as.character(seqlevels) } if (!(is(ans_seqnames, "Rle") && is.factor(runValue(ans_seqnames)))) seqlevels[rankSeqlevels(seqlevels)] <- seqlevels ans_seqinfo <- Seqinfo(seqlevels) } ## Make and return the GRanges object. GRanges(ans_seqnames, ans_ranges, strand=ans_strand, ans_mcols, seqinfo=ans_seqinfo) } setAs("data.frame", "GRanges", function(from) makeGRangesFromDataFrame(from, keep.extra.columns=TRUE) ) setAs("DataFrame", "GRanges", function(from) makeGRangesFromDataFrame(from, keep.extra.columns=TRUE) ) GenomicRanges/R/makeGRangesListFromDataFrame.R0000644000175400017540000000276613001230267022263 0ustar00biocbuildbiocbuild### ========================================================================= ### makeGRangesListFromDataFrame() ### ------------------------------------------------------------------------- ### 'df' must be a data.frame or DataFrame object. makeGRangesListFromDataFrame <- function(df, split.field = NULL, names.field = NULL, ...) { splitIdx <- namesIdx <- integer() if (!is.null(split.field)) { if (!isSingleString(split.field)) stop("'split.field' must be a single string") splitIdx <- which(names(df) %in% split.field) if (!length(splitIdx)) stop("'split.field' is not in 'names(df)'") if (length(splitIdx) > 1L) stop("'split.field' matched more than one 'names(df)'") splitField <- df[[split.field]] } else splitField <- seq_len(nrow(df)) if (!is.null(names.field)) { if (!isSingleString(names.field)) stop("'names.field' must be a single string") namesIdx <- which(names(df) %in% names.field) if (!length(namesIdx)) stop("'names.field' is not found in 'names(df)'") if (length(namesIdx) > 1L) stop("'names.field' matched more than one 'names(df)'") namesField <- df[[names.field]] } else namesField <- NULL if (length(c(splitIdx, namesIdx))) df <- df[, -c(splitIdx, namesIdx)] gr <- makeGRangesFromDataFrame(df, ...) names(gr) <- namesField S4Vectors::split(gr, splitField) } GenomicRanges/R/nearest-methods.R0000644000175400017540000003055313001230267017747 0ustar00biocbuildbiocbuild### ========================================================================= ### nearest (and related) methods ### ------------------------------------------------------------------------- ### ### Dependencies : ### ### distanceToNearest ### | | ### nearest distance ### | | ### precede follow .orderNumeric <- function(x) # unstable order sort.list(x, na.last=NA, method="quick") .GenomicRanges_findNearest0 <- function (query, subject, sentinel, leftOf=TRUE) ## query 'leftOf' subject { zeroInSubject <- subject %in% 0L if (any(zeroInSubject)) subject <- unique(c(subject, sentinel)) else subject <- c(subject, sentinel) ord <- .orderNumeric(subject) subject <- subject[ord] rle <- Rle(subject) subject <- runValue(rle) ## zero in query i <- findInterval(query - !leftOf, subject) + leftOf zeroInQuery <- i %in% 0L if (any(zeroInQuery)) { if (leftOf) i[zeroInQuery] <- 2L else i[zeroInQuery] <- 1L } ## zero in subject if (any(zeroInSubject)) i[subject[i] %in% sentinel & subject[i] != 0L] <- NA_integer_ else i[subject[i] %in% sentinel] <- NA_integer_ IRanges:::vectorToHits(i, rle, ord) } .findNearest_distance <- function(hit, query, subject, leftOf=TRUE) ## query 'leftOf' of subject { if (leftOf) start(subject)[subjectHits(hit)] - end(query)[queryHits(hit)] else start(query)[queryHits(hit)] - end(subject)[subjectHits(hit)] } .Hits <- function(queryHits, subjectHits, queryLength=as.integer(max(c(0, queryHits))), subjectLength=as.integer(max(c(0, subjectHits)))) { o <- orderIntegerPairs(queryHits, subjectHits) Hits(queryHits[o], subjectHits[o], queryLength, subjectLength, sort.by.query=TRUE) } .findPrecedeFollow_pmin <- function(hit0, dist0, hit1, dist1) ## hit0, hit1 are (possibly multiple) hits on same query; repeated ## hits are equidistant within hit0 or hit1, but not necessarily ## between { stopifnot(queryLength(hit0) == queryLength(hit1)) stopifnot(subjectLength(hit0) == subjectLength(hit1)) stopifnot(length(hit0) == length(dist0) || length(hit1) == length(dist1)) n <- queryLength(hit0) d0 <- d1 <- integer() d0[n] <- d1[n] <- NA_integer_ i0 <- queryHits(hit0) i1 <- queryHits(hit1) d0[i0] <- dist0 d1[i1] <- dist1 dMin <- pmin.int(d0, d1, na.rm=TRUE) i0 <- dist0 == dMin[i0] i1 <- dist1 == dMin[i1] .Hits(c(queryHits(hit0)[i0], queryHits(hit1)[i1]), c(subjectHits(hit0)[i0], subjectHits(hit1)[i1]), queryLength(hit0), subjectLength(hit0)) } .GenomicRanges_findPrecedeFollow <- function(query, subject, select, ignore.strand, where=c("precede", "follow")) { if (!length(query) || !length(subject)) return(Hits(nLnode=length(query), nRnode=length(subject), sort.by.query=TRUE)) leftOf <- "precede" == match.arg(where) if (ignore.strand) strand(query) <- strand(subject) <- "+" if (leftOf) { plusfun <- function(xstart, xend, ystart, yend, sentinel) .GenomicRanges_findNearest0(xend, ystart, sentinel, leftOf) minusfun <- function(xstart, xend, ystart, yend, sentinel) .GenomicRanges_findNearest0(xstart, yend, sentinel, !leftOf) } else { plusfun <- function(xstart, xend, ystart, yend, sentinel) .GenomicRanges_findNearest0(xstart, yend, sentinel, leftOf) minusfun <- function(xstart, xend, ystart, yend, sentinel) .GenomicRanges_findNearest0(xend, ystart, sentinel, !leftOf) } ## sentinels marking seqlevels ends endq <- start(query) + width(query) # end(query) incorrect for 0-width ends <- start(subject) + width(subject) maxend <- max(max(endq), max(ends)) + 1 lvls <- union(seqlevels(query), seqlevels(subject)) offset <- setNames((seq_along(lvls) - 1) * maxend, lvls) stopifnot(typeof(offset) == "double") # avoid integer overflow sentinel <- c(0, seq_along(lvls) * maxend) ## offset for sentinels queryOff <- unname(offset[as.character(seqnames(query))]) queryStart <- start(query) + queryOff queryEnd <- end(query) + queryOff # true end + offset qid <- seq_along(query) subjectOff <- unname(offset[as.character(seqnames(subject))]) subjectStart <- start(subject) + subjectOff subjectEnd <- end(subject) + subjectOff # true end + offset spid <- which(strand(subject) != "-") smid <- which(strand(subject) != "+") ## '+' query idx <- which(strand(query) == "+") phit <- plusfun(queryStart[idx], queryEnd[idx], subjectStart[spid], subjectEnd[spid], sentinel) phit <- .Hits(qid[idx][queryHits(phit)], spid[subjectHits(phit)]) ## '-' query idx <- which(strand(query) == "-") mhit <- minusfun(queryStart[idx], queryEnd[idx], subjectStart[smid], subjectEnd[smid], sentinel) mhit <- .Hits(qid[idx][queryHits(mhit)], smid[subjectHits(mhit)]) ### '*' query equivalent to ignore.strand = TRUE idx <- which(strand(query) == "*") bhit <- local({ qid <- qid[idx] phit <- plusfun(queryStart[idx], queryEnd[idx], subjectStart, subjectEnd, sentinel) .Hits(qid[queryHits(phit)], subjectHits(phit), length(query), length(subject)) }) ## clean up qryHits <- c(queryHits(phit), queryHits(mhit), queryHits(bhit)) subjHits <- c(subjectHits(phit), subjectHits(mhit), subjectHits(bhit)) if ("arbitrary" == select) { hits <- integer() hits[length(query)] <- NA_integer_ idx <- !duplicated(qryHits) ## ties hits[qryHits[idx]] <- subjHits[idx] } else { hits <- .Hits(qryHits, subjHits, length(query), length(subject)) } hits } ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ### precede() and follow() ### setMethod("precede", c("GenomicRanges", "GenomicRanges"), function(x, subject, select=c("arbitrary", "all"), ignore.strand=FALSE) { select <- match.arg(select) .GenomicRanges_findPrecedeFollow(x, subject, select, ignore.strand, "precede") } ) setMethod("precede", c("GenomicRanges", "missing"), function(x, subject, select=c("arbitrary", "all"), ignore.strand=FALSE) { select <- match.arg(select) .GenomicRanges_findPrecedeFollow(x, subject, select, ignore.strand, "precede") } ) setMethod("follow", c("GenomicRanges", "GenomicRanges"), function(x, subject, select=c("arbitrary", "all"), ignore.strand=FALSE) { select <- match.arg(select) .GenomicRanges_findPrecedeFollow(x, subject, select, ignore.strand, "follow") } ) setMethod("follow", c("GenomicRanges", "missing"), function(x, subject, select=c("arbitrary", "all"), ignore.strand=FALSE) { select <- match.arg(select) .GenomicRanges_findPrecedeFollow(x, subject, select, ignore.strand, "follow") } ) ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ### nearest() ### .filterHits <- function(hits, i, map) { m <- as.matrix(hits[as(hits, "IRanges")[i]]) m[, 1L] <- map[m[, 1L]] m } .nearest <- function(x, subject, select, ignore.strand, drop.self=FALSE) { ## overlapping ranges if (drop.self) { ol <- findOverlaps(x, select=select, ignore.strand=ignore.strand, drop.self=TRUE) } else { ol <- findOverlaps(x, subject, select=select, ignore.strand=ignore.strand) } if (select == "all") { olv <- selectHits(ol, select="first") } else { olv <- ol } ## non-overlapping ranges if (length(x <- x[is.na(olv)]) != 0) { p <- precede(x, subject, select, ignore.strand) f <- follow(x, subject, select, ignore.strand) ## terminate if no results if (!length(p) && !length(f)) { if (is(olv, "Hits") && !length(olv) || all(is.na(olv))) { if (select == "all") return(Hits(nLnode=length(x), nRnode=length(subject), sort.by.query=TRUE)) else if (select == "arbitrary") return (rep(NA, length(x))) } } if (select == "all") { p0 <- p p <- selectHits(p, select="first") f0 <- f f <- selectHits(f, select="first") } ## choose nearest or not missing pdist <- .nearestDistance(x, subject, p) fdist <- .nearestDistance(x, subject, f) pnearest <- ifelse(pdist == fdist, p < f, pdist < fdist) isNA <- is.na(pnearest) pnearest[isNA] <- is.na(f[isNA]) if (select == "all") { map <- which(is.na(olv)) pnearest[pdist == fdist] <- TRUE m <- rbind(as.matrix(ol), .filterHits(p0, pnearest, map), .filterHits(f0, !pnearest, map)) m <- m[orderIntegerPairs(m[, 1L], m[, 2L]),, drop=FALSE] ol@from <- unname(m[, 1L]) ol@to <- unname(m[, 2L]) } else { olv[is.na(olv)] <- ifelse(pnearest, p, f) ol <- olv } } ol } .nearestDistance <- function(x, subject, index) { if (length(index)) { maxStart <- pmax.int(start(x), start(subject)[index]) minEnd <- pmin.int(end(x), end(subject)[index]) pmax.int(maxStart - minEnd - 1L, 0L) } else NA } setMethod("nearest", c("GenomicRanges", "GenomicRanges"), function(x, subject, select=c("arbitrary", "all"), ignore.strand=FALSE) { select <- match.arg(select) .nearest(x, subject, select=select, ignore.strand=ignore.strand) } ) setMethod("nearest", c("GenomicRanges", "missing"), function(x, subject, select=c("arbitrary", "all"), ignore.strand=FALSE) { select <- match.arg(select) .nearest(x, x, select=select, ignore.strand=ignore.strand, drop.self=TRUE) } ) ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ### distance() ### setMethod("distance", c("GenomicRanges", "GenomicRanges"), function(x, y, ignore.strand=FALSE, ...) { if (!isTRUEorFALSE(ignore.strand)) stop("'ignore.strand' must be TRUE or FALSE") d <- distance(ranges(x), ranges(y)) mismtch <- as.character(seqnames(x)) != as.character(seqnames(y)) if (any(mismtch)) d[mismtch] <- NA if (!ignore.strand) { idx <- as.numeric(strand(x)) + as.numeric(strand(y)) if (any(idx == 3)) d[idx == 3] <- NA } d } ) ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ### distanceToNearest() ### setMethod("distanceToNearest", c("GenomicRanges", "GenomicRanges"), function(x, subject, ignore.strand=FALSE, ...) { x_nearest <- nearest(x, subject, ignore.strand=ignore.strand, ...) .distanceToNearest(x_nearest, x, subject, ignore.strand=ignore.strand) } ) setMethod("distanceToNearest", c("GenomicRanges", "missing"), function(x, subject, ignore.strand=FALSE, ...) { x_nearest <- nearest(x, ignore.strand=ignore.strand, ...) .distanceToNearest(x_nearest, x, x, ignore.strand=ignore.strand) } ) .distanceToNearest <- function(x_nearest, x, subject, ignore.strand) { ## 'x_nearest' is Hits when select = all if (is(x_nearest, "Hits")) { queryHits <- queryHits(x_nearest) subjectHits <- subjectHits(x_nearest) } else { ## 'x_nearest' is Integer vector when select = arbitrary queryHits <- seq_along(x)[!is.na(x_nearest)] subjectHits <- x_nearest[!is.na(x_nearest)] } if (!length(subjectHits) || all(is.na(subjectHits))) { Hits(nLnode=length(x), nRnode=length(subject), distance=integer(0), sort.by.query=TRUE) } else { distance <- distance(x[queryHits], subject[subjectHits], ignore.strand=ignore.strand) Hits(queryHits, subjectHits, length(x), length(subject), distance, sort.by.query=TRUE) } } GenomicRanges/R/phicoef.R0000644000175400017540000000153113001230267016254 0ustar00biocbuildbiocbuild### Based on http://en.wikipedia.org/wiki/Phi_coefficient phicoef <- function(x, y=NULL) { if (is.null(y)) { if (!is.integer(x) || length(x) != 4L) stop("when 'y' is not supplied, 'x' must be ", "a 2x2 integer matrix or an integer vector of length 4") a <- x[1L] c <- x[2L] b <- x[3L] d <- x[4L] } else { if (!is.logical(x) || !is.logical(y) || length(x) != length(y)) stop("when 'y' is supplied, 'x' and 'y' must be ", "2 logical vectors of the same length") a <- sum(x & y) b <- sum(x & !y) c <- sum(!x & y) d <- sum(!x & !y) } a <- as.double(a) b <- as.double(b) c <- as.double(c) d <- as.double(d) div <- sqrt((a + b) * (c + d) * (a + c) * (b + d)) (a * d - b * c) / div } GenomicRanges/R/range-squeezers.R0000644000175400017540000000323513001230267017762 0ustar00biocbuildbiocbuild### ========================================================================= ### Generic functions for squeezing the ranges out of a range-based object ### ------------------------------------------------------------------------- ### Extract the ranges as a GRanges object. setGeneric("granges", signature="x", function(x, use.names=TRUE, use.mcols=FALSE, ...) standardGeneric("granges") ) ### Extract the ranges as a GRangesList object. setGeneric("grglist", signature="x", function(x, use.names=TRUE, use.mcols=FALSE, ...) standardGeneric("grglist") ) ### Extract the ranges as a RangesList object. ### TODO: This one should probably be in IRanges together with ranges(), which ### is another range-squeezer. ### TODO: For consistency the ranges() generic should also get the 'use.mcols' ### arg with default to FALSE. setGeneric("rglist", signature="x", function(x, use.names=TRUE, use.mcols=FALSE, ...) standardGeneric("rglist") ) ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ### Pairs methods ### setMethod("grglist", "Pairs", function(x, use.names=TRUE, use.mcols=FALSE) { stopifnot(isTRUEorFALSE(use.mcols)) grl <- zipup(granges(first(x)), granges(second(x))) if (!use.mcols) { mcols(grl) <- NULL } grl }) setMethod("rglist", "Pairs", function(x, use.names=TRUE, use.mcols=FALSE) { stopifnot(isTRUEorFALSE(use.mcols)) rl <- zipup(ranges(first(x)), ranges(second(x))) if (!use.mcols) { mcols(rl) <- NULL } rl }) GenomicRanges/R/setops-methods.R0000644000175400017540000004552213001230267017625 0ustar00biocbuildbiocbuild### ========================================================================= ### Set operations ### ------------------------------------------------------------------------- ### TODO: What's the impact of circularity on the set operations? ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ### 3 low-level helper functions. ### ### A fast implementation of 'mendoapply(FUN, x, y)' for GRangesList objects. ### Assume 'x' and 'y' are 2 GRangesList objects (not checked) of the same ### length (checked). .fast_binary_mendoapply <- function(FUN, x, y, ...) { FUN <- match.fun(FUN) if (length(x) != length(y)) stop("'x' and 'y' must have the same length") seqinfo(x) <- merge(seqinfo(x), seqinfo(y)) seqlevels(y) <- seqlevels(x) xgr <- deconstructGRLintoGR(x, expand.levels=TRUE) ygr <- deconstructGRLintoGR(y, expand.levels=TRUE) gr <- FUN(xgr, ygr, ...) reconstructGRLfromGR(gr, x) } ### Both return a named integer vector where the names are guaranteed to be ### 'seqlevels(x)'. ### .minStartPerGRangesSequence <- function(x) { cil <- splitAsList(start(x), seqnames(x)) # CompressedIntegerList object ## The 4 lines below are equivalent to: ## ans <- min(cil) ## ans[elementNROWS(v) == 0L] <- NA_integer_ ## but much faster! ## TODO: Replace with the above, but only when the "min" method for ## CompressedIntegerList objects (implemented in the IRanges package) ## is as fast as the "viewMins" method for XIntegerViews objects ## (implemented in C in the XVector package). Ideally, the 2 methods ## should share the same underlying code. v <- Views(cil@unlistData, cil@partitioning) # XIntegerViews object ans <- viewMins(v) ans[width(v) == 0L] <- NA_integer_ names(ans) <- names(v) ans } .maxEndPerGRangesSequence <- function(x) { cil <- splitAsList(end(x), seqnames(x)) # CompressedIntegerList object ## The 4 lines below are equivalent to: ## ans <- max(cil) ## ans[elementNROWS(v) == 0L] <- NA_integer_ ## but much faster! ## TODO: Replace with the above, but only when the "max" method for ## CompressedIntegerList objects (implemented in the IRanges package) ## is as fast as the "viewMaxs" method for XIntegerViews objects ## (implemented in C in the XVector package). Ideally, the 2 methods ## should share the same underlying code. v <- Views(cil@unlistData, cil@partitioning) # XIntegerViews object ans <- viewMaxs(v) ans[width(v) == 0L] <- NA_integer_ names(ans) <- names(v) ans } ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ### union(), intersect(), setdiff() ### ### Always return a GRanges *instance* whatever GenomicRanges derivatives are ### passed to it (e.g. GNCList or GPos), so does NOT act like an endomorphism ### in general. setMethod("union", c("GenomicRanges", "GenomicRanges"), function(x, y, ignore.strand=FALSE) { if (!isTRUEorFALSE(ignore.strand)) stop("'ignore.strand' must be TRUE or FALSE") x <- granges(x) y <- granges(y) if (ignore.strand) strand(x) <- strand(y) <- "*" reduce(c(x, y), drop.empty.ranges=TRUE) } ) .unsupported_union <- function(x, y) stop("union() between a ", class(x), " and a ", class(y), " object ", "is not supported") setMethod("union", c("GenomicRanges", "Vector"), .unsupported_union) setMethod("union", c("Vector", "GenomicRanges"), .unsupported_union) setMethod("union", c("GRangesList", "GRangesList"), function(x, y, ...) .fast_binary_mendoapply("union", x, y, ...) ) ### Always return a GRanges *instance* whatever GenomicRanges derivatives are ### passed to it (e.g. GNCList or GPos), so does NOT act like an endomorphism ### in general. setMethod("intersect", c("GenomicRanges", "GenomicRanges"), function(x, y, ignore.strand=FALSE) { if (!isTRUEorFALSE(ignore.strand)) stop("'ignore.strand' must be TRUE or FALSE") x <- granges(x) y <- granges(y) if (ignore.strand) strand(x) <- strand(y) <- "*" seqinfo(x) <- merge(seqinfo(x), seqinfo(y)) ## If merge() is going to issue a warning, we don't want to get ## it twice. seqinfo(y) <- suppressWarnings(merge(seqinfo(y), seqinfo(x))) seqlengths <- seqlengths(x) ## If the length of a sequence is unknown (NA), then we use ## the max end value found on that sequence in 'x' or 'y'. seqlengths[is.na(seqlengths)] <- .maxEndPerGRangesSequence(c(x, y))[is.na(seqlengths)] setdiff(x, gaps(y, end=seqlengths)) } ) .unsupported_intersect <- function(x, y) stop("intersect() between a ", class(x), " and a ", class(y), " object ", "is not supported") setMethod("intersect", c("GenomicRanges", "Vector"), .unsupported_intersect) setMethod("intersect", c("Vector", "GenomicRanges"), .unsupported_intersect) setMethod("intersect", c("GRangesList", "GRangesList"), function(x, y, ...) .fast_binary_mendoapply("intersect", x, y, ...) ) ### Always return a GRanges *instance* whatever GenomicRanges derivatives are ### passed to it (e.g. GNCList or GPos), so does NOT act like an endomorphism ### in general. setMethod("setdiff", c("GenomicRanges", "GenomicRanges"), function(x, y, ignore.strand=FALSE) { if (!isTRUEorFALSE(ignore.strand)) stop("'ignore.strand' must be TRUE or FALSE") x <- granges(x) y <- granges(y) if (ignore.strand) strand(x) <- strand(y) <- "*" seqinfo(x) <- merge(seqinfo(x), seqinfo(y)) ## If merge() is going to issue a warning, we don't want to get ## it twice. seqinfo(y) <- suppressWarnings(merge(seqinfo(y), seqinfo(x))) seqlengths <- seqlengths(x) ## If the length of a sequence is unknown (NA), then we use ## the max end value found on that sequence in 'x' or 'y'. seqlengths[is.na(seqlengths)] <- .maxEndPerGRangesSequence(c(x, y))[is.na(seqlengths)] gaps(union(gaps(x, end=seqlengths), y), end=seqlengths) } ) .unsupported_setdiff <- function(x, y) stop("setdiff() between a ", class(x), " and a ", class(y), " object ", "is not supported") setMethod("setdiff", c("GenomicRanges", "Vector"), .unsupported_setdiff) setMethod("setdiff", c("Vector", "GenomicRanges"), .unsupported_setdiff) setMethod("setdiff", c("GRangesList", "GRangesList"), function(x, y, ...) .fast_binary_mendoapply("setdiff", x, y, ...) ) ### ========================================================================= ### Parallel set operations ### ------------------------------------------------------------------------- ## check for equality without requiring identical level sets ## instead, one level set must be subset of the other, like merge,Seqinfo compatibleSeqnames <- function(x, y) { if (length(x) != length(y)) stop("'x' and 'y' must be of equal length") if (!is(x, "Rle") || !is(y, "Rle")) stop("'x' and 'y' must be Rle objects") xLevels <- levels(x) yLevels <- levels(y) if (length(xLevels) > length(yLevels)) diffLevels <- setdiff(yLevels, xLevels) else diffLevels <- setdiff(xLevels, yLevels) if (length(diffLevels)) stop("Level set of 'x' must be subset of that of 'y', or vice versa") runValue(x) <- as.character(runValue(x)) runValue(y) <- as.character(runValue(y)) x == y } allCompatibleSeqnamesAndStrand <- function(x, y) { all(compatibleSeqnames(seqnames(x), seqnames(y)) & compatibleStrand(strand(x), strand(y))) } ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ### punion() ### setMethod("punion", c("GRanges", "GRanges"), function(x, y, fill.gap=FALSE, ignore.strand=FALSE) { if (length(x) != length(y)) stop("'x' and 'y' must have the same length") if (!isTRUEorFALSE(ignore.strand)) stop("'ignore.strand' must be TRUE or FALSE") if (ignore.strand) strand(y) <- strand(x) mcols(x) <- NULL seqinfo(x) <- merge(seqinfo(x), seqinfo(y)) if (!allCompatibleSeqnamesAndStrand(x, y)) stop("'x' and 'y' elements must have compatible 'seqnames' ", "and 'strand' values") ranges(x) <- punion(ranges(x), ranges(y), fill.gap=fill.gap) x } ) ### FIXME: This is currently not doing a "punion" at all. It just appends ### the ranges in 'y' to their corresponding element in 'x'. ### 2 proposals for a more punion-like semantic: ### (a) for (i in seq_len(length(x))) ### x[[i]] <- punion(x[[i]], y[rep.int(i, length(x[[i]]))]) ### (b) for (i in seq_len(length(x))) ### x[[i]] <- union(x[[i]], y[i]) ### Note that behaviour (b) could also be considered a valid candidate for ### a union,GRangesList,GRanges method (which we don't have at the moment). setMethod("punion", c("GRangesList", "GRanges"), function(x, y, fill.gap=FALSE) { n <- length(x) if (n != length(y)) stop("'x' and 'y' must have the same length") mcols(x@unlistData) <- NULL mcols(y) <- NULL ans <- split(c(x@unlistData, y), c(Rle(seq_len(n), elementNROWS(x)), Rle(seq_len(n)))) names(ans) <- names(x) ans } ) setMethod("punion", c("GRanges", "GRangesList"), function(x, y, ...) callGeneric(y, x, ...) ) ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ### pintersect() ### ### 'x' and 'y' must have the same length. An 'y' of length 1 is accepted and ### is recycled to the length of 'x'. ### Return a GRanges object. If 'drop.nohit.ranges' is FALSE (the default) the ### returned object is parallel to 'x' with the original ranges modified ### (seqnames, names, and metadata are untouched), so this can be seen as an ### "intra-range transformation". The original metadata columns are propagated ### and a new "hit" column added to them to indicate elements in 'x' that ### intersect with the corresponding element in 'y'. For these elements the ### range in the returned object is guaranteed to be a subrange of the ### original ranges. If 'ignore.strand' or 'strict.strand' is TRUE, then the ### returned GRanges has the same strand as 'x'. Otherwise, for elements in 'x' ### that are on the "*" strand and hit the corresponding element in 'y', it ### has the strand of 'y'. ### If 'drop.nohit.ranges' is TRUE everything is the same except that elements ### in 'x' that don't intersect with the corresponding element in 'y' are ### removed from the result (so the result is no more parallel to the input) ### and no "hit" metadata column is added to it. .pintersect_GRanges_GRanges <- function(x, y, drop.nohit.ranges=FALSE, ignore.strand=FALSE, strict.strand=FALSE) { stopifnot(is(x, "GRanges"), is(y, "GRanges")) if (length(y) != length(x)) { if (length(y) != 1L) stop(wmsg("'y' must have the length of 'x' or length 1")) y <- rep.int(y, length(x)) } if (!isTRUEorFALSE(drop.nohit.ranges)) stop(wmsg("'drop.nohit.ranges' must be TRUE or FALSE")) if (!isTRUEorFALSE(ignore.strand)) stop(wmsg("'ignore.strand' must be TRUE or FALSE")) if (!isTRUEorFALSE(strict.strand)) stop(wmsg("'strict.strand' must be TRUE or FALSE")) if (ignore.strand) { if (strict.strand) warning(wmsg("'strict.strand' is ignored ", "when 'ignore.strand' is TRUE")) strand(y) <- strand(x) } else if (!strict.strand) { x_strand <- strand(x) idx <- strand(x) == "*" & strand(y) != "*" strand(x)[idx] <- strand(y)[idx] idx <- strand(y) == "*" & strand(x) != "*" strand(y)[idx] <- strand(x)[idx] } x_seqlevels <- seqlevels(x) ## Check compatibility of underlying genomes. seqinfo(x) <- merge(seqinfo(x), seqinfo(y)) ## Align the seqlevels of 'y' to those of 'x' so that comparing the ## seqnames of the 2 objects thru as.integer(seqnames()) is meaningful). seqlevels(y) <- seqlevels(x) ## Restore original seqlevels on 'x' (this preserves their order). seqlevels(x) <- x_seqlevels new_start <- pmax.int(start(x), start(y)) new_end <- pmin.int(end(x), end(y)) is_hit <- as.integer(seqnames(x)) == as.integer(seqnames(y)) & strand(x) == strand(y) & new_end >= new_start - 1L if (drop.nohit.ranges) { x <- extractROWS(x, is_hit) new_start <- extractROWS(new_start, is_hit) new_end <- extractROWS(new_end, is_hit) new_names <- names(x) } else { ## For elements in 'x' and 'y' that don't hit each other, we return ## an artificial zero-width intersection that starts on 'start(x)'. nohit_idx <- which(!is_hit) nohit_start <- start(x)[nohit_idx] new_start[nohit_idx] <- nohit_start new_end[nohit_idx] <- nohit_start - 1L new_names <- names(x) mcols(x)$hit <- as.logical(is_hit) if (!(ignore.strand || strict.strand)) strand(x)[nohit_idx] <- x_strand[nohit_idx] } ranges(x) <- IRanges(new_start, new_end, names=new_names) x } setMethod("pintersect", c("GRanges", "GRanges"), function(x, y, drop.nohit.ranges=FALSE, ignore.strand=FALSE, strict.strand=FALSE) .pintersect_GRanges_GRanges(x, y, drop.nohit.ranges=drop.nohit.ranges, ignore.strand=ignore.strand, strict.strand=strict.strand) ) ### This is equivalent to 'mendoapply(pintersect, x, y)'. Therefore the ### returned GRangesList object has the same shape as 'x'. ### To get the 'mendoapply(intersect, x, y)' behavior, the user should use ### 'strict.strand=TRUE' and call 'reduce( , drop.empty.ranges=TRUE)' on ### the returned object. .pintersect_GRangesList_GRanges <- function(x, y, drop.nohit.ranges=FALSE, ignore.strand=FALSE, strict.strand=FALSE) { stopifnot(is(x, "GRangesList"), is(y, "GRanges")) if (length(y) != length(x)) { if (length(y) != 1L) stop(wmsg("'y' must have the length of 'x' or length 1")) y <- rep.int(y, length(x)) } if (!isTRUEorFALSE(drop.nohit.ranges)) stop(wmsg("'drop.nohit.ranges' must be TRUE or FALSE")) unlisted_x <- unlist(x, use.names=FALSE) y2 <- rep.int(y, elementNROWS(x)) ## 'unlisted_ans' parallel to 'x'. unlisted_ans <- .pintersect_GRanges_GRanges(unlisted_x, y2, drop.nohit.ranges=FALSE, ignore.strand=ignore.strand, strict.strand=strict.strand) if (drop.nohit.ranges) { is_hit <- relist(mcols(unlisted_ans)$hit, x) mcols(unlisted_ans)$hit <- NULL ans <- relist(unlisted_ans, x)[is_hit] } else { ans <- relist(unlisted_ans, x) } ans } setMethod("pintersect", c("GRangesList", "GRanges"), function(x, y, drop.nohit.ranges=FALSE, ignore.strand=FALSE, strict.strand=FALSE) .pintersect_GRangesList_GRanges(x, y, drop.nohit.ranges=drop.nohit.ranges, ignore.strand=ignore.strand, strict.strand=strict.strand) ) setMethod("pintersect", c("GRanges", "GRangesList"), function(x, y, drop.nohit.ranges=FALSE, ignore.strand=FALSE, strict.strand=FALSE) .pintersect_GRangesList_GRanges(y, x, drop.nohit.ranges=drop.nohit.ranges, ignore.strand=ignore.strand, strict.strand=strict.strand) ) ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ### psetdiff() ### setMethod("psetdiff", c("GRanges", "GRanges"), function(x, y, ignore.strand=FALSE) { if (length(x) != length(y)) stop("'x' and 'y' must have the same length") if (!isTRUEorFALSE(ignore.strand)) stop("'ignore.strand' must be TRUE or FALSE") if (ignore.strand) strand(y) <- strand(x) mcols(x) <- NULL seqinfo(x) <- merge(seqinfo(x), seqinfo(y)) ok <- compatibleSeqnames(seqnames(x), seqnames(y)) & compatibleStrand(strand(x), strand(y)) ## Update the ranges. ansRanges <- ranges(x) ansRanges <- replaceROWS(ansRanges, ok, callGeneric(extractROWS(ranges(x), ok), extractROWS(ranges(y), ok))) ranges(x) <- ansRanges ## Update the strand. ansStrand <- strand(x) resolveStrand <- as(ansStrand == "*", "IRanges") if (length(resolveStrand) > 0L) ansStrand[as.integer(resolveStrand)] <- extractROWS(strand(y), resolveStrand) strand(x) <- ansStrand x } ) ### TODO: Review the semantic of this method (see previous TODO's for ### "punion" and "pintersect" methods for GRanges,GRangesList). setMethod("psetdiff", c("GRanges", "GRangesList"), function(x, y, ignore.strand=FALSE) { ansSeqinfo <- merge(seqinfo(x), seqinfo(y)) if (length(x) != length(y)) stop("'x' and 'y' must have the same length") ok <- compatibleSeqnames(rep(seqnames(x), elementNROWS(y)), seqnames(y@unlistData)) if (!ignore.strand) ok <- ok & compatibleStrand(rep(strand(x), elementNROWS(y)), strand(y@unlistData)) if (!all(ok)) { ok <- new2("CompressedLogicalList", unlistData=as.vector(ok), partitioning=y@partitioning) y <- y[ok] } ansRanges <- gaps(ranges(y), start=start(x), end=end(x)) ansSeqnames <- rep(seqnames(x), elementNROWS(ansRanges)) ansStrand <- rep(strand(x), elementNROWS(ansRanges)) ansGRanges <- GRanges(ansSeqnames, unlist(ansRanges, use.names=FALSE), ansStrand) seqinfo(ansGRanges) <- ansSeqinfo relist(ansGRanges, PartitioningByEnd(ansRanges)) } ) setMethod("pgap", c("GRanges", "GRanges"), function(x, y, ignore.strand=FALSE, ...) { if (length(x) != length(y)) stop("'x' and 'y' must have the same length") if (!isTRUEorFALSE(ignore.strand)) stop("'ignore.strand' must be TRUE or FALSE") if (ignore.strand) strand(y) <- strand(x) mcols(x) <- NULL seqinfo(x) <- merge(seqinfo(x), seqinfo(y)) if (!allCompatibleSeqnamesAndStrand(x, y)) stop("'x' and 'y' elements must have compatible 'seqnames' ", "and 'strand' values") ranges(x) <- callGeneric(ranges(x), ranges(y), ...) x } ) GenomicRanges/R/strand-utils.R0000644000175400017540000001277513001230267017304 0ustar00biocbuildbiocbuild### ========================================================================= ### Strand utilities ### ------------------------------------------------------------------------- ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ### Some "strand" and "strand<-" methods ### setMethod("strand", "missing", function(x) factor(levels=c("+","-","*"))) setMethod("strand", "NULL", function(x) strand()) setMethod("strand", "character", function(x) { lvls <- levels(strand()) if (!all(x %in% lvls)) stop("strand values must be in '", paste(lvls, collapse="' '"), "'") factor(x, levels=lvls) } ) setMethod("strand", "factor", function(x) { if (any(is.na(x))) stop("NA not a valid strand value, use \"*\" instead") lvls <- levels(strand()) x_levels <- levels(x) if (identical(x_levels, lvls)) return(x) invalid_levels <- setdiff(x_levels, lvls) if (length(invalid_levels) != 0L) stop("invalid strand levels in 'x': ", paste(invalid_levels, collapse=", ")) factor(x, levels=lvls) } ) setMethod("strand", "integer", function(x) { lvls <- c(1L, -1L, NA) if (!all(x %in% lvls)) stop("strand values must be in '", paste(lvls, collapse="' '"), "'") ans <- rep.int(strand("*"), length(x)) ans[x == 1L] <- "+" ans[x == -1L] <- "-" ans } ) setMethod("strand", "logical", function(x) { ans <- rep.int(strand("*"), length(x)) ans[!x] <- "+" ans[ x] <- "-" ans } ) setMethod("strand", "Rle", function(x) { x_runValue <- runValue(x) if (!(is.character(x_runValue) || is.factor(x_runValue) || is.integer(x_runValue) || is.logical(x_runValue))) stop("\"strand\" method for Rle objects only works on a ", "character-, factor-, integer-, or logical-Rle object") runValue(x) <- strand(x_runValue) x } ) setMethod("strand", "RleList", function(x) relist(strand(unlist(x, use.names=FALSE)), x) ) setMethod("strand", "DataTable", function(x) { ans <- x[["strand"]] if (is.null(ans)) { ans <- rep.int(strand("*"), nrow(x)) } else { ans <- strand(ans) } ans } ) setReplaceMethod("strand", "DataTable", function(x, value) { x$strand <- normargGenomicRangesStrand(value, nrow(x)) x }) ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ### Some "invertStrand" methods ### .invert_strand_factor <- function(x) { x_codes <- as.integer(x) switch_idx <- which(x_codes <= 2L) x[switch_idx] <- structure(3L - x_codes[switch_idx], levels=levels(x), class=class(x)) x } ### One method for each of the "strand" methods defined above that return a ### factor (except for the method for "missing"). setMethods("invertStrand", list("NULL", "character", "factor", "integer", "logical"), function(x) .invert_strand_factor(strand(x)) ) setMethod("invertStrand", "Rle", function(x) { ans <- strand(x) runValue(ans) <- invertStrand(runValue(ans)) ans } ) setMethod("invertStrand", "RleList", function(x) relist(invertStrand(unlist(x, use.names=FALSE)), x) ) ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ### compatibleStrand() generic and methods ### setGeneric("compatibleStrand", signature=c("x","y"), # not exported function(x, y) standardGeneric("compatibleStrand") ) setMethod("compatibleStrand", c("factor", "factor"), # not exported function(x, y) { lvls <- levels(strand()) if (length(x) != length(y)) stop("'x' and 'y' must be of equal length") if (!identical(levels(x), lvls) || !identical(levels(y), lvls)) stop("strand values must be in '", paste(lvls, collapse="' '"), "'") levels(x) <- c("1", "-1", "0") x <- as.integer(as.character(x)) levels(y) <- c("1", "-1", "0") y <- as.integer(as.character(y)) ans <- x * y != -1L if (S4Vectors:::anyMissing(ans)) { fix <- which(is.na(ans)) ans[fix] <- (x[fix] == 0L) | (y[fix] == 0L) if (S4Vectors:::anyMissing(ans)) ans[is.na(ans)] <- FALSE } ans } ) setMethod("compatibleStrand", c("Rle", "Rle"), # not exported function(x, y) { lvls <- levels(strand()) if (length(x) != length(y)) stop("'x' and 'y' must be of equal length") if (!identical(levels(runValue(x)), lvls) || !identical(levels(runValue(y)), lvls)) stop("strand values must be in '", paste(lvls, collapse="' '"), "'") levels(x) <- c("1", "-1", "0") runValue(x) <- as.integer(as.character(runValue(x))) levels(y) <- c("1", "-1", "0") runValue(y) <- as.integer(as.character(runValue(y))) ans <- x * y != -1L if (S4Vectors:::anyMissing(runValue(ans))) { fix <- which(is.na(runValue(ans))) runValue(ans)[fix] <- (runValue(x) == 0L)[fix] | (runValue(y)[fix] == 0L) if (S4Vectors:::anyMissing(runValue(ans))) runValue(ans)[is.na(runValue(ans))] <- FALSE } ans } ) GenomicRanges/R/tile-methods.R0000644000175400017540000000131313001230267017233 0ustar00biocbuildbiocbuild### ========================================================================= ### "tile" and "slidingWindows" methods ### ------------------------------------------------------------------------- ### setMethod("tile", "GenomicRanges", function(x, n, width) { sn <- seqnames(x) strand <- strand(x) x <- ranges(x) tiles <- callGeneric() gr <- GRanges(rep(sn, elementNROWS(tiles)), unlist(tiles), rep(strand, elementNROWS(tiles))) relist(gr, tiles) }) setMethod("slidingWindows", "GenomicRanges", function(x, width, step = 1L) { windows <- slidingWindows(ranges(x), width, step) gr <- rep(granges(x), lengths(windows)) ranges(gr) <- unlist(windows) relist(gr, windows) }) GenomicRanges/R/tileGenome.R0000644000175400017540000001456513001230267016742 0ustar00biocbuildbiocbuild### ========================================================================= ### tileGenome() ### ------------------------------------------------------------------------- .make_breakpoints_for_cut_last_tile_in_chrom <- function(seqlengths, tilewidth) { tile_relative_breakpoints <- lapply(seqlengths, function(seqlength) { nfulltile <- seqlength %/% tilewidth if (nfulltile == 0L) return(seqlength) relative_breakpoints <- ceiling(tilewidth * seq_len(nfulltile)) if (relative_breakpoints[[nfulltile]] >= seqlength) return(relative_breakpoints) c(relative_breakpoints, seqlength) }) chrom_breakpoints <- cumsum(as.numeric(seqlengths)) chrom_offsets <- c(0, head(chrom_breakpoints, n=-1L)) ntile_per_chrom <- elementNROWS(tile_relative_breakpoints) unlist(tile_relative_breakpoints, use.names=FALSE) + rep.int(chrom_offsets, ntile_per_chrom) } ### 'old_breakpoints' and 'new_breakpoints' must be non-negative non-decreasing ### numeric or integer vectors of length >= 1 with no NAs. In addition, ### 'old_breakpoints' must be named. None of this is checked (we trust the ### caller). ### Returns a NumericList (or IntegerList) object with one list element per ### unique new breakpoint. .superimpose_breakpoints <- function(old_breakpoints, new_breakpoints) { ## Set names on 'new_breakpoints'. new_breakpoints <- pmin.int(new_breakpoints, tail(old_breakpoints, 1)) new2old <- 1L + findInterval(new_breakpoints - 1L, old_breakpoints) names(new_breakpoints) <- names(old_breakpoints)[new2old] ## Compute 'all_breakpoints'. all_breakpoints <- c(old_breakpoints, new_breakpoints) unique_idx <- !duplicated(all_breakpoints) all_breakpoints <- all_breakpoints[unique_idx] ## Compute 'all2new' mapping. old2new <- 1L + findInterval(old_breakpoints - 1L, new_breakpoints) all2new <- c(old2new, seq_along(new_breakpoints)) all2new <- all2new[unique_idx] ## Compute and return final result. unname(splitAsList(all_breakpoints, all2new)) } .get_relative_ends <- function(absolute_ends, chrom_breakpoints) { chrom_offsets <- c(0, head(chrom_breakpoints, n=-1L)) names(chrom_offsets) <- names(chrom_breakpoints) absolute_ends <- unlist(absolute_ends, use.names=FALSE) absolute_ends - chrom_offsets[names(absolute_ends)] } .get_relative_starts <- function(relative_ends, seqnames) { relative_starts <- rep.int(1L, length(relative_ends)) run_lens <- runLength(seqnames) run_starts <- c(1L, cumsum(head(run_lens, n=-1L)) + 1L) idx <- S4Vectors:::fancy_mseq(run_lens - 1L, offset=run_starts) relative_starts[idx] <- relative_ends[idx - 1L] + 1L relative_starts } ### 'seqlengths' must be a non-negative numeric or integer vector of length ### >= 1 with no NAs and with unique names. ### Only one of 'ntile' and 'tilewidth' can be specified. tileGenome <- function(seqlengths, ntile, tilewidth, cut.last.tile.in.chrom=FALSE) { if (!isTRUEorFALSE(cut.last.tile.in.chrom)) stop("'cut.last.tile.in.chrom' must be TRUE or FALSE") if (is(seqlengths, "Seqinfo")) { gr_seqinfo <- seqlengths seqlengths <- seqlengths(seqlengths) } else { gr_seqinfo <- NULL } ## Check 'seqlengths'. if (!is.numeric(seqlengths) || length(seqlengths) == 0L) stop("'seqlengths' must be a non-empty numeric vector") seqlengths_names <- names(seqlengths) if (is.null(seqlengths_names)) stop("'seqlengths' must be named") if (any(seqlengths_names %in% c(NA_character_, ""))) stop("'seqlengths' has names that are NA or the empty string") if (any(duplicated(seqlengths_names))) stop("'seqlengths' has duplicated names") if (!is.integer(seqlengths)) seqlengths <- setNames(as.integer(seqlengths), seqlengths_names) if (S4Vectors:::anyMissingOrOutside(seqlengths, lower=0L)) stop("'seqlengths' contains NAs or negative values") chrom_breakpoints <- setNames(cumsum(as.numeric(seqlengths)), seqlengths_names) genome_size <- chrom_breakpoints[[length(chrom_breakpoints)]] if (!missing(ntile)) { if (!missing(tilewidth)) stop("only one of 'ntile' and 'tilewidth' can be specified") if (cut.last.tile.in.chrom) stop("'cut.last.tile.in.chrom' must be FALSE ", "when 'ntile' is supplied") ## Check 'ntile'. if (!isSingleNumber(ntile)) stop("'ntile' must be a single number") if (ntile < 1 || ntile > genome_size) stop("'ntile' must be >= 1 and <= genome size") if (!is.integer(ntile)) { if (ntile > .Machine$integer.max) stop("'ntile' must be <= .Machine$integer.max") ntile <- as.integer(ntile) } } else { if (missing(tilewidth)) stop("one of 'ntile' and 'tilewidth' must be specified") ## Check 'tilewidth'. if (!isSingleNumber(tilewidth)) stop("'tilewidth' must be a single number") if (tilewidth < 1 || tilewidth > genome_size) stop("'tilewidth' must be >= 1 and <= genome size") if (cut.last.tile.in.chrom) { tile_breakpoints <- .make_breakpoints_for_cut_last_tile_in_chrom(seqlengths, tilewidth) } else { ntile <- ceiling(genome_size / tilewidth) if (ntile > .Machine$integer.max) stop("this 'tilewidth' is too small") ntile <- as.integer(ntile) } } if (!cut.last.tile.in.chrom) { tilewidth <- genome_size / ntile tile_breakpoints <- ceiling(tilewidth * seq_len(ntile)) } absolute_ends <- .superimpose_breakpoints(chrom_breakpoints, tile_breakpoints) gr_end <- .get_relative_ends(absolute_ends, chrom_breakpoints) gr_seqnames <- Rle(factor(names(gr_end), levels=names(seqlengths))) gr_start <- .get_relative_starts(gr_end, gr_seqnames) if (is.null(gr_seqinfo)) gr_seqinfo <- Seqinfo(seqnames=names(seqlengths), seqlengths=seqlengths) gr <- GRanges(gr_seqnames, IRanges(gr_start, gr_end), seqinfo=gr_seqinfo) if (cut.last.tile.in.chrom) return(gr) relist(gr, absolute_ends) } GenomicRanges/R/transcript-utils.R0000644000175400017540000000342413001230267020171 0ustar00biocbuildbiocbuild### ------------------------------------------------------------------------- ### Some low-level (non exported) utility functions to operate on transcripts ### represented as groups of exon ranges. ### ### These functions are implemented in C. This file only contains R wrappers ### for the .Call2 entry points. Those wrappers are not doing any argument ### checking and therefore are considered "unsafe". They are in turn called ### by "safe" and user-friendly higher level wrappers defined in ### GenomicFeatures. The reason why the "unsafe" wrappers are here and not in ### the GenomicFeatures package was to keep GenomicFeatures free of native ### code. ### ### For all the functions below: ### o 'exonStarts', 'exonEnds' are assumed to be lists of integer vectors. ### The two lists are assumed to have the "same shape" i.e. ### elementNROWS() returns identical vectors on them; ### o 'strand' is assumed to be a character vector with allowed values ### "+" and "-" only; ### o 'decreasing.rank.on.minus.strand' is assumed to be TRUE or FALSE. ### o 'error.if.out.of.bounds' is assumed to be TRUE or FALSE. unsafe.transcriptWidths <- function(exonStarts, exonEnds) { .Call2("transcript_widths", exonStarts, exonEnds, PACKAGE="GenomicRanges") } ### 'tlocs' is assumed to be a list of integer vectors of the same length (but ### not necessarily the "same shape") as 'exonStarts' and 'exonEnds'. unsafe.transcriptLocs2refLocs <- function(tlocs, exonStarts, exonEnds, strand, decreasing.rank.on.minus.strand, error.if.out.of.bounds) { .Call2("tlocs2rlocs", tlocs, exonStarts, exonEnds, strand, decreasing.rank.on.minus.strand, error.if.out.of.bounds, PACKAGE="GenomicRanges") } GenomicRanges/R/utils.R0000644000175400017540000000156613001230267016007 0ustar00biocbuildbiocbuild### ========================================================================= ### Some low-level (non exported) utility functions. ### ------------------------------------------------------------------------- hasHead <- function(x, h) { identical(head(x, length(h)), h) } ### TODO: Use this in GenomicFeatures::transcriptLocs2refLocs() and remove ### GenomicFeatures:::.normargExonStartsOrEnds(). ### Used in GenomicAlignments package. normargListOfIntegers <- function(arg, sep, argname) { if (is.list(arg)) return(arg) if (is(arg, "IntegerList")) return(as.list(arg)) if (is.character(arg)) return(strsplitAsListOfIntegerVectors(arg, sep=sep)) stop("'", argname, "' must be a list of integer vectors, ", "an IntegerList object,\n or a character vector where ", "each element is a comma-separated list of\n integers") } GenomicRanges/R/zzz.R0000644000175400017540000000023013001230267015467 0ustar00biocbuildbiocbuild.onUnload <- function(libpath) { library.dynam.unload("GenomicRanges", libpath) } .test <- function() BiocGenerics:::testPackage("GenomicRanges") GenomicRanges/build/0000755000175400017540000000000013032312064015412 5ustar00biocbuildbiocbuildGenomicRanges/build/vignette.rds0000644000175400017540000000126213032312064017752 0ustar00biocbuildbiocbuildTO0-6M ihMmݯ\S.CIcӤCRw}GqS.RY/uTe]*d>eAw ,@鞲xՙ}Lːq6">dJp?,FZEoIӄ?5yL3B3Dqb tBzJS-8N ػ&#sjy=3ۤwcҦ>{LGdil?nsXcxa2 5 /,`XI0/m3qr,YLog%RK&uUT1 kt0_>Iv!ʂfH!Su=pxRp-׏޻ȤCc}qTB8]IljS 76R%#l4usMk c9FA}=ӟhxhGPHH%Xv$ Pd:׷60YQ)DCTR)aFYpT7%KhqFA.c8%{bʒRcdaO`f6e[.eTDsOC QQn'}\FXmX7r'\}*ĵdk?uuuw^aPzN>RGenomicRanges/inst/0000755000175400017540000000000013032312063015267 5ustar00biocbuildbiocbuildGenomicRanges/inst/CITATION0000644000175400017540000000165413001230267016433 0ustar00biocbuildbiocbuildcitEntry(entry="article", title = "Software for Computing and Annotating Genomic Ranges", author = personList( as.person("Michael Lawrence" ), as.person("Wolfgang Huber" ), as.person("Herv\\'e Pag\\`es" ), as.person("Patrick Aboyoun" ), as.person("Marc Carlson" ), as.person("Robert Gentleman" ), as.person("Martin Morgan" ), as.person("Vincent Carey" )), year = 2013, journal = "{PLoS} Computational Biology", volume = "9", issue = "8", doi = "10.1371/journal.pcbi.1003118", url = "http://www.ploscompbiol.org/article/info%3Adoi%2F10.1371%2Fjournal.pcbi.1003118", textVersion = "Lawrence M, Huber W, Pag\\`es H, Aboyoun P, Carlson M, et al. (2013) Software for Computing and Annotating Genomic Ranges. PLoS Comput Biol 9(8): e1003118. doi:10.1371/journal.pcbi.1003118" ) GenomicRanges/inst/doc/0000755000175400017540000000000013032312064016035 5ustar00biocbuildbiocbuildGenomicRanges/inst/doc/ExtendingGenomicRanges.R0000644000175400017540000000234613032312064022554 0ustar00biocbuildbiocbuild### R code from vignette source 'ExtendingGenomicRanges.Rnw' ### Encoding: UTF-8 ################################################### ### code chunk number 1: style ################################################### BiocStyle::latex(use.unsrturl=FALSE) ################################################### ### code chunk number 2: options ################################################### options(width=72) options(showHeadLines=3) options(showTailLines=3) ################################################### ### code chunk number 3: granges-ranges ################################################### library(GenomicRanges) selectMethod(ranges, "GRanges") ################################################### ### code chunk number 4: delegating-granges-ranges ################################################### selectMethod(ranges, "DelegatingGenomicRanges") ################################################### ### code chunk number 5: gnclist-granges ################################################### getSlots("GNCList")["granges"] ################################################### ### code chunk number 6: vranges ################################################### GenomicRanges:::extraColumnSlotNames(VariantAnnotation:::VRanges()) GenomicRanges/inst/doc/ExtendingGenomicRanges.Rnw0000644000175400017540000001113713032312063023116 0ustar00biocbuildbiocbuild% \VignetteIndexEntry{Extending GenomicRanges} % \VignetteDepends{GenomicRanges, VariantAnnotation} % \VignetteKeywords{ranges} % \VignettePackage{GenomicRanges} \documentclass{article} \usepackage[authoryear,round]{natbib} <>= BiocStyle::latex(use.unsrturl=FALSE) @ \title{Extending \Biocpkg{GenomicRanges}} \author{Michael Lawrence, Bioconductor Team} \date{Edited: Oct 2014; Compiled: \today} \begin{document} \maketitle \tableofcontents <>= options(width=72) options(showHeadLines=3) options(showTailLines=3) @ %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \section{Introduction} The goal of \Biocpkg{GenomicRanges} is to provide general containers for genomic data. The central class, at least from the user perspective, is \Rclass{GRanges}, which formalizes the notion of ranges, while allowing for arbitrary ``metadata columns'' to be attached to it. These columns offer the same flexibility as the venerable \Rclass{data.frame} and permit users to adapt \Rclass{GRanges} to a wide variety of \textit{adhoc} use-cases. The more we encounter a particular problem, the better we understand it. We eventually develop a systematic approach for solving the most frequently encountered problems, and every systematic approach deserves a systematic implementation. For example, we might want to formally store genetic variants, with information on alleles and read depths. The metadata columns, which were so useful during prototyping, are inappropriate for extending the formal semantics of our data structure: for the sake of data integrity, we need to ensure that the columns are always present and that they meet certain constraints. We might also find that our prototype does not scale well to the increased data volume that often occurs when we advance past the prototype stage. \Rclass{GRanges} is meant mostly for prototyping and stores its data in memory as simple R data structures. We may require something more specialized when the data are large; for example, we might store the data as a Tabix-indexed file, or in a database. The \Biocpkg{GenomicRanges} package does not directly solve either of these problems, because there are no general solutions. However, it is adaptible to specialized use cases. \section{The \Rclass{GenomicRanges} abstraction} Unbeknownst to many, most of the \Rclass{GRanges} implementation is provided by methods on the \Rclass{GenomicRanges} class, the virtual parent class of \Rclass{GRanges}. \Rclass{GenomicRanges} methods provide everything except for the actual data storage and retrieval, which \Rclass{GRanges} implements directly using slots. For example, the ranges are retrieved like this: <>= library(GenomicRanges) selectMethod(ranges, "GRanges") @ An alternative implementation is \Rclass{DelegatingGenomicRanges}, which stores all of its data in a delegate \Rclass{GenomicRanges} object: <>= selectMethod(ranges, "DelegatingGenomicRanges") @ This abstraction enables us to pursue more efficient implementations for particular tasks. One example is \Rclass{GNCList}, which is indexed for fast range queries, we expose here: <>= getSlots("GNCList")["granges"] @ The \Biocpkg{MutableRanges} package in svn provides other, untested examples. \section{Formalizing \texttt{mcols}: Extra column slots} An orthogonal problem to data storage is adding semantics by the formalization of metadata columns, and we solve it using the ``extra column slot'' mechanism. Whenever \Rclass{GenomicRanges} needs to operate on its metadata columns, it also delegates to the internal \Rfunction{extraColumnSlotNames} generic, methods of which should return a character vector, naming the slots in the \Rclass{GenomicRanges} subclass that correspond to columns (i.e., they have one value per range). It extracts the slot values and manipulates them as it would a metadata column -- except they are now formal slots, with formal types. An example is the \Rclass{VRanges} class in \Biocpkg{VariantAnnotation}. It stores information on the variants by adding these column slots: <>= GenomicRanges:::extraColumnSlotNames(VariantAnnotation:::VRanges()) @ Mostly for historical reasons, \Rclass{VRanges} extends \Rclass{GRanges}. However, since the data storage mechanism and the set of extra column slots are orthogonal, it is probably best practice to take a composition approach by extending \Rclass{DelegatingGenomicRanges}. \end{document} GenomicRanges/inst/doc/ExtendingGenomicRanges.pdf0000644000175400017540000031106513032312064023125 0ustar00biocbuildbiocbuild%PDF-1.5 % 23 0 obj << /Length 2771 /Filter /FlateDecode >> stream xZIoW4| q3 s3IIy[-MRv<6Z^zJe^፭2]F.ό.suVMnMγd>s,wW*W&SyTU޿ILnm љo?qfyq6@fZMQ7\&ZedAg $ӭצ<Ȟu7<\t{=5dIa6( yum&]#Km@ʉIp:k'JDO<! yIHGWߦ".dYj[宬Ap~B1ln3e|{J>TfEw6[_}]eszg5M=u:/UFCC̓YMޔD=#NZQiSPtld~@#H雑q橿A0W- fv/e1ymk2W4|y}b}s =rۮ o yh+oD'3Hu6oQ%X4Ym3>xєyʱs}ih`cOlߙxl=yض$|x&oKQ$x""&tJ[1R5W<:cj@FK}vÈiG-\68rG ea=e-Lzfc 8 pc Vrywo+mI5ưUt[&:P\\g P&Y+~ӈgNpI|&y{Jjp?=oisV>P?x> ߼ZBݒGVix$)}me7q:&wYЕc0G]1nC7МCAHJl07-[ Z=e) _+g@ ͉|a٦F [j7ƥ/b6z)Su$Ʋp \ĥi*Ԁ[NOn|U y~'9'*bCreH$'ɯx-LbG/]鶨)*%A!xvDoPԎHm -P6|X0Mz1u ig! Td,}Dǐa7 H )J S&3H+J Cs}<,ZsO R =H*>Ra?j? 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'GRanges_and_GRangesList_slides.Rnw' ### Encoding: UTF-8 ################################################### ### code chunk number 1: setup ################################################### options(width=84) plotRanges <- function(x, xlim = x, main = deparse(substitute(x)), col = "black", sep = 0.5, ...) { height <- 1 if (is(xlim, "Ranges")) xlim <- c(min(start(xlim)), max(end(xlim))) bins <- disjointBins(IRanges(start(x), end(x) + 1)) plot.new() par(mai=c(0.5, 0.2, 1.2, 0.2)) plot.window(xlim, c(0, max(bins)*(height + sep))) ybottom <- bins * (sep + height) - height rect(start(x)-0.5, ybottom, end(x)+0.5, ybottom + height, col = col, ...) title(main, cex.main=2.8, font.main=1) axis(1) } ################################################### ### code chunk number 2: GRanges_constructor ################################################### library(GenomicRanges) gr1 <- GRanges(seqnames=Rle(c("ch1", "chMT"), c(2, 4)), ranges=IRanges(16:21, 20), strand=rep(c("+", "-", "*"), 2)) gr1 ################################################### ### code chunk number 3: GRanges_accessors1 ################################################### length(gr1) seqnames(gr1) ranges(gr1) ################################################### ### code chunk number 4: GRanges_accessors2 ################################################### start(gr1) end(gr1) width(gr1) strand(gr1) strand(gr1) <- c("-", "-", "+") strand(gr1) ################################################### ### code chunk number 5: GRanges_accessors3 ################################################### names(gr1) <- LETTERS[1:6] gr1 names(gr1) ################################################### ### code chunk number 6: GRanges_accessors4 ################################################### mcols(gr1) <- DataFrame(score=11:16, GC=seq(1, 0, length=6)) gr1 mcols(gr1) ################################################### ### code chunk number 7: GRanges_accessors5 ################################################### seqinfo(gr1) seqlevels(gr1) seqlengths(gr1) seqlengths(gr1) <- c(50000, 800) seqlengths(gr1) ################################################### ### code chunk number 8: GRanges_Vector_ops1 ################################################### gr1[c("F", "A")] gr1[strand(gr1) == "+"] ################################################### ### code chunk number 9: GRanges_Vector_ops2 ################################################### gr1 <- gr1[-5] gr1 ################################################### ### code chunk number 10: GRanges_Vector_ops3 ################################################### gr2 <- GRanges(seqnames="ch2", ranges=IRanges(start=c(2:1,2), width=6), score=15:13, GC=seq(0, 0.4, length=3)) gr12 <- c(gr1, gr2) gr12 ################################################### ### code chunk number 11: GRanges_Vector_ops4 ################################################### gr12[length(gr12)] == gr12 duplicated(gr12) unique(gr12) ################################################### ### code chunk number 12: GRanges_sort ################################################### sort(gr12) ################################################### ### code chunk number 13: GRanges_split ################################################### split(gr12, seqnames(gr12)) ################################################### ### code chunk number 14: ranges-ir0-plot ################################################### library(IRanges) ir0 <- IRanges(start=c(7, 9, 12, 14, 22:24), end=c(15, 11, 12, 18, 26, 27, 28)) png("ranges-ir0-plot.png", width=800, height=170) plotRanges(ir0, xlim=c(5, 35), main="ir0", col="blue") dev.off() ################################################### ### code chunk number 15: ranges-shift-ir0-plot ################################################### png("ranges-shift-ir0-plot.png", width=800, height=170) plotRanges(shift(ir0, 5), xlim=c(5, 35), main="shift(ir0, 5)", col="blue") dev.off() ################################################### ### code chunk number 16: ranges-reduce-ir0-plot ################################################### png("ranges-reduce-ir0-plot.png", width=800, height=170) plotRanges(reduce(ir0), xlim=c(5, 35), main="reduce(ir0)", col="blue") dev.off() ################################################### ### code chunk number 17: ranges-disjoin-ir0-plot ################################################### png("ranges-disjoin-ir0-plot.png", width=800, height=170) plotRanges(disjoin(ir0), xlim=c(5, 35), main="disjoin(ir0)", col="blue") dev.off() ################################################### ### code chunk number 18: GRanges_Ranges_ops1 ################################################### gr2 shift(gr2, 50) ################################################### ### code chunk number 19: GRanges_Ranges_ops2 ################################################### gr1 resize(gr1, 12) ################################################### ### code chunk number 20: GRanges_Ranges_ops3 ################################################### gr1 flank(gr1, 3) ################################################### ### code chunk number 21: GRanges_range ################################################### gr3 <- shift(gr1, c(35000, rep(0, 3), 100)) width(gr3)[c(3,5)] <- 117 gr3 range(gr3) ################################################### ### code chunk number 22: GRanges_reduce ################################################### gr3 reduce(gr3) ################################################### ### code chunk number 23: GRanges_gaps ################################################### gr3 gaps(gr3) ################################################### ### code chunk number 24: GRanges_disjoin ################################################### gr3 disjoin(gr3) ################################################### ### code chunk number 25: GRanges_coverage1 ################################################### cvg12 <- coverage(gr12) cvg12 ################################################### ### code chunk number 26: GRanges_coverage2 ################################################### mean(cvg12) max(cvg12) ################################################### ### code chunk number 27: slice_coverage ################################################### sl12 <- slice(cvg12, lower=1) sl12 elementNROWS(sl12) sl12$chMT mean(sl12$chMT) max(sl12$chMT) ################################################### ### code chunk number 28: findOverlaps1 ################################################### library(pasillaBamSubset) untreated1_chr4() library(GenomicAlignments) reads <- readGAlignments(untreated1_chr4()) ################################################### ### code chunk number 29: findOverlaps2 ################################################### reads <- as(reads, "GRanges") reads[1:4] ################################################### ### code chunk number 30: findOverlaps3 ################################################### library(TxDb.Dmelanogaster.UCSC.dm3.ensGene) txdb <- TxDb.Dmelanogaster.UCSC.dm3.ensGene dm3_genes <- genes(txdb) ################################################### ### code chunk number 31: findOverlaps4 ################################################### hits <- findOverlaps(reads, dm3_genes) head(hits) ################################################### ### code chunk number 32: GRangesList_constructor ################################################### grl <- GRangesList(gr3, gr2) grl ################################################### ### code chunk number 33: GRangesList_accessors1 ################################################### length(grl) ################################################### ### code chunk number 34: GRangesList_accessors2 ################################################### seqnames(grl) ################################################### ### code chunk number 35: GRangesList_accessors3 ################################################### strand(grl) ################################################### ### code chunk number 36: GRangesList_accessors4 ################################################### ranges(grl) ################################################### ### code chunk number 37: GRangesList_accessors5 ################################################### start(grl) end(grl) width(grl) ################################################### ### code chunk number 38: GRangesList_accessors6 ################################################### names(grl) <- c("TX1", "TX2") grl ################################################### ### code chunk number 39: GRangesList_accessors7 ################################################### mcols(grl)$geneid <- c("GENE1", "GENE2") mcols(grl) grl ################################################### ### code chunk number 40: GRangesList_accessors8 ################################################### seqinfo(grl) ################################################### ### code chunk number 41: GRangesList_Vector_ops1 ################################################### grl[c("TX2", "TX1")] ################################################### ### code chunk number 42: GRangesList_Vector_ops2 ################################################### c(grl, GRangesList(gr3)) ################################################### ### code chunk number 43: GRangesList_List_ops1 ################################################### grl[[2]] elementNROWS(grl) unlisted <- unlist(grl, use.names=FALSE) # same as c(grl[[1]], grl[[2]]) unlisted ################################################### ### code chunk number 44: GRangesList_List_ops2 ################################################### grl100 <- relist(shift(unlisted, 100), grl) grl100 ################################################### ### code chunk number 45: GRangesList_List_ops3 ################################################### grl100b <- endoapply(grl, shift, 100) grl100b mcols(grl100) mcols(grl100b) ################################################### ### code chunk number 46: GRangesList_Ranges_ops1 ################################################### grl ################################################### ### code chunk number 47: GRangesList_Ranges_ops2 ################################################### shift(grl, 100) ################################################### ### code chunk number 48: GRangesList_Ranges_ops3 ################################################### grl ################################################### ### code chunk number 49: GRangesList_Ranges_ops4 ################################################### flank(grl, 10) ################################################### ### code chunk number 50: GRangesList_Ranges_ops5 ################################################### grl ################################################### ### code chunk number 51: GRangesList_Ranges_ops6 ################################################### range(grl) ################################################### ### code chunk number 52: GRangesList_Ranges_ops7 ################################################### grl ################################################### ### code chunk number 53: GRangesList_Ranges_ops8 ################################################### reduce(grl) ################################################### ### code chunk number 54: GRangesList_Ranges_ops9 ################################################### grl2 <- grl grl2[[1]] <- grl2[[1]][3]; grl2[[2]] <- grl2[[2]][1] grl3 <- unname(grl2) grl3[[1]] <- narrow(unname(grl3[[1]]), start=5, end=-5) ################################################### ### code chunk number 55: GRangesList_Ranges_ops10 ################################################### grl2 grl3 ################################################### ### code chunk number 56: GRangesList_Ranges_ops11 ################################################### setdiff(grl2, grl3) GenomicRanges/inst/doc/GRanges_and_GRangesList_slides.Rnw0000644000175400017540000006607313032312063024515 0ustar00biocbuildbiocbuild%\VignetteIndexEntry{A quick introduction to GRanges and GRangesList objects (slides)} %\VignetteDepends{IRanges, GenomicRanges, pasillaBamSubset, GenomicAlignments, TxDb.Dmelanogaster.UCSC.dm3.ensGene} \SweaveOpts{keep.source=TRUE, eps=FALSE, width=9, height=3} \documentclass[8pt]{beamer} \usepackage{slides} \renewcommand\Rclass[1]{{\texttt{#1}\index{#1 (class)}}} % A Beamer Quickstart: % http://www.math.umbc.edu/~rouben/beamer/ \newcommand\DefaultBackground{\setbeamertemplate{background canvas}[vertical shading][bottom=black!10,top=black!10]\setbeamertemplate{background canvas}[vertical shading][bottom=blue!40,top=blue!15]} \DefaultBackground \setbeamercolor{block body example}{bg=white} \definecolor{YESgreen}{RGB}{0, 160, 80} \newcommand\YES{\textcolor{YESgreen}{\textbf{YES}}} \newcommand\PartiallySupported{\textcolor{YESgreen}{\textbf{partially supported}}} \definecolor{NOgray}{RGB}{144, 144, 144} \newcommand\NO{\textcolor{NOgray}{\textbf{NO}}} %\AtBeginSection[] %{ % \setbeamertemplate{background canvas}[vertical shading][bottom=black!47,top=black!47] % \begin{frame}{} % \tableofcontents[currentsection,currentsubsection] % \end{frame} % \DefaultBackground %} %\AtBeginSubsection[] %{ % \setbeamertemplate{background canvas}[vertical shading][bottom=black!47,top=black!47] % \begin{frame}{} % \tableofcontents[currentsection,currentsubsection] % \end{frame} % \DefaultBackground %} \title{A quick introduction to GRanges and GRangesList objects} \author{Herv\'e Pag\`es\\ \href{mailto:hpages@fredhutch.org}{hpages@fredhutch.org}\\ \---\\ Michael Lawrence\\ \href{mailto:lawrence.michael@gene.com}{lawrence.michael@gene.com}} %\institute[FHCRC]{Fred Hutchinson Cancer Research Center\\ % Seattle, WA} \date{July 2015} \begin{document} <>= options(width=84) plotRanges <- function(x, xlim = x, main = deparse(substitute(x)), col = "black", sep = 0.5, ...) { height <- 1 if (is(xlim, "Ranges")) xlim <- c(min(start(xlim)), max(end(xlim))) bins <- disjointBins(IRanges(start(x), end(x) + 1)) plot.new() par(mai=c(0.5, 0.2, 1.2, 0.2)) plot.window(xlim, c(0, max(bins)*(height + sep))) ybottom <- bins * (sep + height) - height rect(start(x)-0.5, ybottom, end(x)+0.5, ybottom + height, col = col, ...) title(main, cex.main=2.8, font.main=1) axis(1) } @ \maketitle \frame{\tableofcontents} %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \section{\Rclass{GRanges} objects} %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \begin{frame}[fragile] \frametitle{The \Rclass{GRanges} class is a container for...} ... storing a set of {\em genomic ranges} (a.k.a. {\em genomic regions} or {\em genomic intervals}). \begin{block}{} \begin{itemize} \item Each genomic range is described by a chromosome name, a {\em start}, an {\em end}, and a strand. \item {\em start} and {\em end} are both {\bf 1-based} positions relative to the 5' end of the plus strand of the chromosome, even when the range is on the minus strand. \item {\em start} and {\em end} are both considered to be included in the interval (except when the range is empty). \item The {\em width} of the range is the number of genomic positions included in it. So {\em width} = {\em end} - {\em start} + 1. \item {\em end} is always >= {\em start}, except for empty ranges (a.k.a. zero-width ranges) where {\em end} = {\em start} - 1. \end{itemize} Note that the {\em start} is always the leftmost position and the {\em end} the rightmost, even when the range is on the minus strand. Gotcha: A TSS is at the {\em end} of the range associated with a transcript located on the minus strand. \end{block} \end{frame} %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \subsection{The \Rcode{GRanges()} constructor} \begin{frame}[fragile] \frametitle{The \Rcode{GRanges()} constructor} \begin{exampleblock}{} {\small <>= library(GenomicRanges) gr1 <- GRanges(seqnames=Rle(c("ch1", "chMT"), c(2, 4)), ranges=IRanges(16:21, 20), strand=rep(c("+", "-", "*"), 2)) gr1 @ } \end{exampleblock} \end{frame} %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \subsection{\Rclass{GRanges} accessors} \begin{frame}[fragile] \frametitle{\Rclass{GRanges} accessors: \Rcode{length()}, \Rcode{seqnames()}, \Rcode{ranges()}} \begin{exampleblock}{} {\small <>= length(gr1) seqnames(gr1) ranges(gr1) @ } \end{exampleblock} \end{frame} \begin{frame}[fragile] \frametitle{\Rclass{GRanges} accessors: \Rcode{start()}, \Rcode{end()}, \Rcode{width()}, \Rcode{strand()}} \begin{exampleblock}{} {\small <>= start(gr1) end(gr1) width(gr1) strand(gr1) strand(gr1) <- c("-", "-", "+") strand(gr1) @ } \end{exampleblock} \end{frame} \begin{frame}[fragile] \frametitle{\Rclass{GRanges} accessors: \Rcode{names()}} \begin{exampleblock}{} {\small <>= names(gr1) <- LETTERS[1:6] gr1 names(gr1) @ } \end{exampleblock} \end{frame} \begin{frame}[fragile] \frametitle{\Rclass{GRanges} accessors: \Rcode{mcols()}} Like with most \Bioconductor{} vector-like objects, {\em metadata columns} can be added to a \Rclass{GRanges} object: \begin{exampleblock}{} {\small <>= mcols(gr1) <- DataFrame(score=11:16, GC=seq(1, 0, length=6)) gr1 mcols(gr1) @ } \end{exampleblock} \end{frame} \begin{frame}[fragile] \frametitle{\Rclass{GRanges} accessors: \Rcode{seqinfo()}, \Rcode{seqlevels()}, \Rcode{seqlengths()}} \begin{exampleblock}{} <>= seqinfo(gr1) seqlevels(gr1) seqlengths(gr1) seqlengths(gr1) <- c(50000, 800) seqlengths(gr1) @ \end{exampleblock} \end{frame} %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \subsection{Vector operations on \Rclass{GRanges} objects} \setbeamertemplate{background canvas}[vertical shading][bottom=green!20,top=green!20] \begin{frame}[fragile] \frametitle{Vector operations on \Rclass{GRanges} objects} \begin{block}{} What we call {\em vector operations} are operations that work on any ordinary vector: \begin{itemize} \item \Rcode{length()}, \Rcode{names()} \item Single-bracket subsetting: \Rcode{[} \item Combining: \Rcode{c()} \item \Rcode{split()}, \Rcode{relist()} \item Comparing: \Rcode{==}, \Rcode{!=}, \Rcode{match()}, \Rcode{\%in\%}, \Rcode{duplicated()}, \Rcode{unique()} \item Ordering: \Rcode{<=}, \Rcode{>=}, \Rcode{<}, \Rcode{>}, \Rcode{order()}, \Rcode{sort()}, \Rcode{rank()} \end{itemize} \Rclass{GRanges} objects support all these {\em vector operations} $==>$ They're considered {\em vector-like} objects. \end{block} \end{frame} \DefaultBackground \begin{frame}[fragile] \frametitle{Vector operations on \Rclass{GRanges} objects: Single-bracket subsetting} \begin{exampleblock}{} {\small <>= gr1[c("F", "A")] gr1[strand(gr1) == "+"] @ } \end{exampleblock} \end{frame} \begin{frame}[fragile] \frametitle{Vector operations on \Rclass{GRanges} objects: Single-bracket subsetting} \begin{exampleblock}{} {\small <>= gr1 <- gr1[-5] gr1 @ } \end{exampleblock} \end{frame} \begin{frame}[fragile] \frametitle{Vector operations on \Rclass{GRanges} objects: Combining} \begin{exampleblock}{} {\small <>= gr2 <- GRanges(seqnames="ch2", ranges=IRanges(start=c(2:1,2), width=6), score=15:13, GC=seq(0, 0.4, length=3)) gr12 <- c(gr1, gr2) gr12 @ } \end{exampleblock} \end{frame} \begin{frame}[fragile] \frametitle{Vector operations on \Rclass{GRanges} objects: Comparing} \begin{exampleblock}{} {\small <>= gr12[length(gr12)] == gr12 duplicated(gr12) unique(gr12) @ } \end{exampleblock} \end{frame} \begin{frame}[fragile] \frametitle{Vector operations on \Rclass{GRanges} objects: Ordering} \begin{exampleblock}{} {\small <>= sort(gr12) @ } \end{exampleblock} \end{frame} \begin{frame}[fragile] \frametitle{Splitting a \Rclass{GRanges} object} \begin{exampleblock}{} {\small <>= split(gr12, seqnames(gr12)) @ } \end{exampleblock} \end{frame} \setbeamertemplate{background canvas}[vertical shading][bottom=orange!40,top=orange!20] \begin{frame}[fragile] \frametitle{Exercise 1} \setbeamertemplate{enumerate items}{\alph{enumi}.} \begin{enumerate} \item Load the \Rpackage{GenomicRanges} package. \item Open the man page for the \Rclass{GRanges} class and run the examples in it. \item Extract from \Rclass{GRanges} object \Rcode{gr} the elements (i.e. ranges) with a score between 4 and 8. \item Split \Rcode{gr} by strand. \end{enumerate} \end{frame} \DefaultBackground %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \subsection{Range-based operations on \Rclass{GRanges} objects} \setbeamertemplate{background canvas}[vertical shading][bottom=green!20,top=green!20] \begin{frame}[fragile] \frametitle{An overview of {\em range-based} operations} \begin{columns}[t] \begin{column}{0.44\textwidth} \begin{block}{} {\bf Intra range transformations} \Rcode{shift()}, \Rcode{narrow()}, \Rcode{resize()}, \Rcode{flank()} \end{block} \begin{block}{} {\bf Inter range transformations} \Rcode{range()}, \Rcode{reduce()}, \Rcode{gaps()}, \Rcode{disjoin()} \end{block} \begin{block}{} {\bf Range-based set operations} \Rcode{union()}, \Rcode{intersect()}, \Rcode{setdiff()}, \Rcode{punion()}, \Rcode{pintersect()}, \Rcode{psetdiff()}, \Rcode{pgap()} \end{block} \end{column} \begin{column}{0.44\textwidth} \begin{block}{} {\bf Coverage and slicing} \Rcode{coverage()}, \Rcode{slice()} \end{block} \begin{block}{} {\bf Finding/counting overlapping ranges} \Rcode{findOverlaps()}, \Rcode{countOverlaps()} \end{block} \begin{block}{} {\bf Finding the nearest range neighbor} \Rcode{nearest()}, \Rcode{precede()}, \Rcode{follow()} \end{block} \begin{block}{} and more... \end{block} \end{column} \end{columns} \end{frame} \begin{frame}[fragile] \frametitle{Examples of some common {\em range-based} operations} \begin{exampleblock}{} <>= library(IRanges) ir0 <- IRanges(start=c(7, 9, 12, 14, 22:24), end=c(15, 11, 12, 18, 26, 27, 28)) png("ranges-ir0-plot.png", width=800, height=170) plotRanges(ir0, xlim=c(5, 35), main="ir0", col="blue") dev.off() @ <>= png("ranges-shift-ir0-plot.png", width=800, height=170) plotRanges(shift(ir0, 5), xlim=c(5, 35), main="shift(ir0, 5)", col="blue") dev.off() @ <>= png("ranges-reduce-ir0-plot.png", width=800, height=170) plotRanges(reduce(ir0), xlim=c(5, 35), main="reduce(ir0)", col="blue") dev.off() @ <>= png("ranges-disjoin-ir0-plot.png", width=800, height=170) plotRanges(disjoin(ir0), xlim=c(5, 35), main="disjoin(ir0)", col="blue") dev.off() @ \begin{figure} \centering \includegraphics[width=0.8\textwidth,height=!]{ranges-ir0-plot}\\ \includegraphics[width=0.8\textwidth,height=!]{ranges-shift-ir0-plot}\\ \includegraphics[width=0.8\textwidth,height=!]{ranges-reduce-ir0-plot}\\ \includegraphics[width=0.8\textwidth,height=!]{ranges-disjoin-ir0-plot} %\caption{Range-based operations} \end{figure} \end{exampleblock} \end{frame} \DefaultBackground \begin{frame}[fragile] \frametitle{Range-based operations on \Rclass{GRanges} objects} \begin{exampleblock}{} {\small <>= gr2 shift(gr2, 50) @ } \end{exampleblock} \end{frame} \begin{frame}[fragile] \frametitle{Range-based operations on \Rclass{GRanges} objects (continued)} \begin{exampleblock}{} {\small <>= gr1 resize(gr1, 12) @ } \end{exampleblock} \end{frame} \begin{frame}[fragile] \frametitle{Range-based operations on \Rclass{GRanges} objects (continued)} \begin{exampleblock}{} {\small <>= gr1 flank(gr1, 3) @ } \end{exampleblock} \end{frame} \begin{frame}[fragile] \frametitle{Range-based operations on \Rclass{GRanges} objects (continued)} \begin{exampleblock}{} {\small <>= gr3 <- shift(gr1, c(35000, rep(0, 3), 100)) width(gr3)[c(3,5)] <- 117 gr3 range(gr3) @ } \end{exampleblock} \end{frame} \begin{frame}[fragile] \frametitle{Range-based operations on \Rclass{GRanges} objects (continued)} \begin{exampleblock}{} {\small <>= gr3 reduce(gr3) @ } \end{exampleblock} \end{frame} \begin{frame}[fragile] \frametitle{Range-based operations on \Rclass{GRanges} objects (continued)} \begin{exampleblock}{} {\scriptsize <>= gr3 gaps(gr3) @ } \end{exampleblock} \end{frame} \begin{frame}[fragile] \frametitle{Range-based operations on \Rclass{GRanges} objects (continued)} \begin{exampleblock}{} {\scriptsize <>= gr3 disjoin(gr3) @ } \end{exampleblock} \end{frame} \setbeamertemplate{background canvas}[vertical shading][bottom=orange!40,top=orange!20] \begin{frame}[fragile] \frametitle{Exercise 2} \setbeamertemplate{enumerate items}{\alph{enumi}.} Using \Rclass{GRanges} object \Rcode{gr} created at Exercise 1: \begin{enumerate} \item Shift the ranges in \Rcode{gr} by 1000 positions to the right. \item What method is called when doing \Rcode{shift()} on a \Rclass{GRanges} object? Find the man page for this method. \end{enumerate} \end{frame} \DefaultBackground \begin{frame}[fragile] \frametitle{Coverage} \begin{exampleblock}{} {\small <>= cvg12 <- coverage(gr12) cvg12 @ } \end{exampleblock} \end{frame} \begin{frame}[fragile] \frametitle{Coverage (continued)} \begin{exampleblock}{} {\small <>= mean(cvg12) max(cvg12) @ } \end{exampleblock} \end{frame} \begin{frame}[fragile] \frametitle{Slicing the coverage} \begin{exampleblock}{} {\small <>= sl12 <- slice(cvg12, lower=1) sl12 elementNROWS(sl12) sl12$chMT mean(sl12$chMT) max(sl12$chMT) @ } \end{exampleblock} \end{frame} \begin{frame}[fragile] \frametitle{findOverlaps()} \begin{block}{} Load aligned reads from a BAM file: \end{block} \begin{exampleblock}{} {\small <>= library(pasillaBamSubset) untreated1_chr4() library(GenomicAlignments) reads <- readGAlignments(untreated1_chr4()) @ } \end{exampleblock} \begin{block}{} and store them in a \Rclass{GRanges} object: \end{block} \begin{exampleblock}{} {\small <>= reads <- as(reads, "GRanges") reads[1:4] @ } \end{exampleblock} \end{frame} \begin{frame}[fragile] \frametitle{findOverlaps() (continued)} \begin{block}{} Load the gene ranges from a {\em TxDb} package: \end{block} \begin{exampleblock}{} {\small <>= library(TxDb.Dmelanogaster.UCSC.dm3.ensGene) txdb <- TxDb.Dmelanogaster.UCSC.dm3.ensGene dm3_genes <- genes(txdb) @ } \end{exampleblock} \begin{block}{} and find the overlaps between the reads and the genes: \end{block} \begin{exampleblock}{} {\small <>= hits <- findOverlaps(reads, dm3_genes) head(hits) @ } \end{exampleblock} \end{frame} \setbeamertemplate{background canvas}[vertical shading][bottom=orange!40,top=orange!20] \begin{frame}[fragile] \frametitle{Exercise 3} \setbeamertemplate{enumerate items}{\alph{enumi}.} \begin{enumerate} \item Recreate \Rclass{GRanges} objects \Rcode{reads} and \Rcode{dm3\_genes} from previous slides. \item What method is called when calling \Rcode{findOverlaps()} on them? Open the man page for this method. \item Find the overlaps between the 2 objects but this time the strand should be ignored. \end{enumerate} \end{frame} \DefaultBackground \setbeamertemplate{background canvas}[vertical shading][bottom=orange!40,top=orange!20] \begin{frame}[fragile] \frametitle{Exercise 4} \setbeamertemplate{enumerate items}{\alph{enumi}.} In this exercise we want to get the exon sequences for the dm3 genome. \begin{enumerate} \item Extract the exon ranges from \Rcode{txdb}. \item Load the \Rpackage{BSgenome.Dmelanogaster.UCSC.dm3} package. \item Use \Rcode{getSeq()} to extract the exon sequences from the \Rcode{BSgenome} object in \Rpackage{BSgenome.Dmelanogaster.UCSC.dm3}. \end{enumerate} \end{frame} \DefaultBackground %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \section{\Rclass{GRangesList} objects} %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \begin{frame}[fragile] \frametitle{The \Rclass{GRangesList} class is a container for...} storing a list of {\em compatible} \Rclass{GRanges} objects. \begin{block}{} {\em compatible} means: \begin{itemize} \item they are relative to the same genome, \item AND they have the same metadata columns (accessible with the \Rcode{mcols()} accessor). \end{itemize} \end{block} \end{frame} %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \subsection{The \Rcode{GRangesList()} constructor} \begin{frame}[fragile] \frametitle{The \Rcode{GRangesList()} constructor} \begin{exampleblock}{} {\small <>= grl <- GRangesList(gr3, gr2) grl @ } \end{exampleblock} \end{frame} %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \subsection{\Rclass{GRangesList} accessors} \begin{frame}[fragile] \frametitle{\Rclass{GRangesList} accessors} \begin{exampleblock}{} <>= length(grl) @ \end{exampleblock} \begin{columns}[t] \begin{column}{0.44\textwidth} \begin{exampleblock}{} {\small <>= seqnames(grl) @ } \end{exampleblock} \end{column} \begin{column}{0.44\textwidth} \begin{exampleblock}{} {\small <>= strand(grl) @ } \end{exampleblock} \end{column} \end{columns} \end{frame} \begin{frame}[fragile] \frametitle{\Rclass{GRangesList} accessors (continued)} \begin{columns}[t] \begin{column}{0.44\textwidth} \begin{exampleblock}{} {\small <>= ranges(grl) @ } \end{exampleblock} \end{column} \begin{column}{0.44\textwidth} \begin{exampleblock}{} {\small <>= start(grl) end(grl) width(grl) @ } \end{exampleblock} \end{column} \end{columns} \end{frame} \begin{frame}[fragile] \frametitle{\Rclass{GRangesList} accessors (continued)} \begin{exampleblock}{} {\small <>= names(grl) <- c("TX1", "TX2") grl @ } \end{exampleblock} \end{frame} \begin{frame}[fragile] \frametitle{\Rclass{GRangesList} accessors (continued)} \begin{exampleblock}{} {\scriptsize <>= mcols(grl)$geneid <- c("GENE1", "GENE2") mcols(grl) grl @ } \end{exampleblock} \end{frame} \begin{frame}[fragile] \frametitle{\Rclass{GRangesList} accessors (continued)} \begin{exampleblock}{} <>= seqinfo(grl) @ \end{exampleblock} \end{frame} %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \subsection{Vector operations on \Rclass{GRangesList} objects} \setbeamertemplate{background canvas}[vertical shading][bottom=green!20,top=green!20] \begin{frame}[fragile] \frametitle{Vector operations on \Rclass{GRangesList} objects} \begin{block}{} Only the following {\em vector operations} are supported on \Rclass{GRangesList} objects: \begin{itemize} \item \Rcode{length()}, \Rcode{names()} \item Single-bracket subsetting: \Rcode{[} \item Combining: \Rcode{c()} \end{itemize} \end{block} \end{frame} \DefaultBackground \begin{frame}[fragile] \frametitle{Vector operations on \Rclass{GRangesList} objects} \begin{exampleblock}{} {\small <>= grl[c("TX2", "TX1")] @ } \end{exampleblock} \end{frame} \begin{frame}[fragile] \frametitle{Vector operations on \Rclass{GRangesList} objects (continued)} \begin{exampleblock}{} {\scriptsize <>= c(grl, GRangesList(gr3)) @ } \end{exampleblock} \end{frame} %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \subsection{List operations on \Rclass{GRangesList} objects} \setbeamertemplate{background canvas}[vertical shading][bottom=green!20,top=green!20] \begin{frame}[fragile] \frametitle{List operations on \Rclass{GRangesList} objects} \begin{block}{} What we call {\em list operations} are operations that work on an ordinary list: \begin{itemize} \item Double-bracket subsetting: \Rcode{[[} \item \Rcode{elementNROWS()}, \Rcode{unlist()} \item \Rcode{lapply()}, \Rcode{sapply()}, \Rcode{endoapply()} \item \Rcode{mendoapply()} (not covered in this presentation) \end{itemize} \Rclass{GRangesList} objects support all these {\em list operations} $==>$ They're considered {\em list-like} objects. \end{block} \end{frame} \DefaultBackground \begin{frame}[fragile] \frametitle{elementNROWS() and unlist()} \begin{exampleblock}{} {\scriptsize <>= grl[[2]] elementNROWS(grl) unlisted <- unlist(grl, use.names=FALSE) # same as c(grl[[1]], grl[[2]]) unlisted @ } \end{exampleblock} \end{frame} \begin{frame}[fragile] \frametitle{relist()} \begin{exampleblock}{} {\small <>= grl100 <- relist(shift(unlisted, 100), grl) grl100 @ } \end{exampleblock} \end{frame} \begin{frame}[fragile] \frametitle{endoapply()} \begin{exampleblock}{} {\scriptsize <>= grl100b <- endoapply(grl, shift, 100) grl100b mcols(grl100) mcols(grl100b) @ } \end{exampleblock} \end{frame} %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \subsection{Range-based operations on \Rclass{GRangesList} objects} \begin{frame}[fragile] \frametitle{Range-based operations on \Rclass{GRangesList} objects} \begin{columns}[t] \begin{column}{0.49\textwidth} \begin{exampleblock}{} {\scriptsize <>= grl @ } \end{exampleblock} \end{column} \begin{column}{0.49\textwidth} \begin{exampleblock}{} {\scriptsize <>= shift(grl, 100) @ } \end{exampleblock} \end{column} \end{columns} \begin{block}{} \Rcode{shift(grl, 100)} is equivalent to \Rcode{endoapply(grl, shift, 100)} \end{block} \end{frame} \begin{frame}[fragile] \frametitle{Range-based operations on \Rclass{GRangesList} objects (continued)} \begin{columns}[t] \begin{column}{0.49\textwidth} \begin{exampleblock}{} {\scriptsize <>= grl @ } \end{exampleblock} \end{column} \begin{column}{0.49\textwidth} \begin{exampleblock}{} {\scriptsize <>= flank(grl, 10) @ } \end{exampleblock} \end{column} \end{columns} \begin{block}{} \Rcode{flank(grl, 10)} is equivalent to \Rcode{endoapply(grl, flank, 10)} \end{block} \end{frame} \begin{frame}[fragile] \frametitle{Range-based operations on \Rclass{GRangesList} objects (continued)} \begin{columns}[t] \begin{column}{0.49\textwidth} \begin{exampleblock}{} {\scriptsize <>= grl @ } \end{exampleblock} \end{column} \begin{column}{0.49\textwidth} \begin{exampleblock}{} {\scriptsize <>= range(grl) @ } \end{exampleblock} \end{column} \end{columns} \begin{block}{} \Rcode{range(grl)} is equivalent to \Rcode{endoapply(grl, range)} \end{block} \end{frame} \begin{frame}[fragile] \frametitle{Range-based operations on \Rclass{GRangesList} objects (continued)} \begin{columns}[t] \begin{column}{0.49\textwidth} \begin{exampleblock}{} {\scriptsize <>= grl @ } \end{exampleblock} \end{column} \begin{column}{0.49\textwidth} \begin{exampleblock}{} {\scriptsize <>= reduce(grl) @ } \end{exampleblock} \end{column} \end{columns} \begin{block}{} \Rcode{reduce(grl)} is equivalent to \Rcode{endoapply(grl, reduce)} \end{block} \end{frame} \begin{frame}[fragile] \frametitle{Range-based operations on \Rclass{GRangesList} objects (continued)} <>= grl2 <- grl grl2[[1]] <- grl2[[1]][3]; grl2[[2]] <- grl2[[2]][1] grl3 <- unname(grl2) grl3[[1]] <- narrow(unname(grl3[[1]]), start=5, end=-5) @ \begin{columns}[t] \begin{column}{0.49\textwidth} \begin{exampleblock}{} {\scriptsize <>= grl2 grl3 @ } \end{exampleblock} \end{column} \begin{column}{0.49\textwidth} \begin{exampleblock}{} {\scriptsize <>= setdiff(grl2, grl3) @ } \end{exampleblock} \end{column} \end{columns} \begin{block}{} \Rcode{setdiff(grl2, grl)} is equivalent to \Rcode{mendoapply(setdiff, grl2, grl)} \end{block} \end{frame} %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \section{Other resources} %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \begin{frame}[fragile] \frametitle{Other resources} \begin{itemize} \item Great slides from Michael on ranges sequences and alignments: \url{http://bioconductor.org/help/course-materials/2014/CSAMA2014/2_Tuesday/lectures/Ranges_Sequences_and_Alignments-Lawrence.pdf} \item Vignettes in the \Rpackage{GenomicRanges} package (\Rcode{browseVignettes("GenomicRanges")}). \item \Rclass{GRanges} and \Rclass{GRangesList} man pages in the \Rpackage{GenomicRanges} package. \item Vignettes and \Rclass{GAlignments} man page in the \Rpackage{GenomicAlignments} package. \item \Bioconductor{} support site: \url{http://support.bioconductor.org/} \item The {\em genomic ranges} paper: Michael Lawrence, Wolfgang Huber, Herv\'e Pag\`es, Patrick Aboyoun, Marc Carlson, Robert Gentleman, Martin T. Morgan, Vincent J. Carey. Software for Computing and Annotating Genomic Ranges. {\em PLOS Computational Biology}, 4(3), 2013. \end{itemize} \end{frame} %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \end{document} GenomicRanges/inst/doc/GRanges_and_GRangesList_slides.pdf0000644000175400017540000075207413032312064024524 0ustar00biocbuildbiocbuild%PDF-1.5 % 10 0 obj << /Type /XObject /Subtype /Form /BBox [0 0 362.835 272.126] /FormType 1 /Matrix [1 0 0 1 0 0] /Resources 11 0 R /Length 15 /Filter /FlateDecode >> stream xP( endstream endobj 13 0 obj << /Type /XObject /Subtype /Form /BBox [0 0 362.835 272.126] /FormType 1 /Matrix [1 0 0 1 0 0] /Resources 14 0 R /Length 15 /Filter /FlateDecode >> stream xP( endstream endobj 88 0 obj << /Length 932 /Filter /FlateDecode >> stream xWn0+xf(mR)$=⤵ՠw)EAj8ܝ%؊ ?b" U3k{3-k/* 1" m/Lܖ$سQbق2r`+xJ!Am5S0%y%`ҵ.\Gse Q]' )OZȞc4;\['e%ڄUQ w1AKn I-DJj BX rmr#&Q[{ݐ$8Ht_g=ueiw>W E;+I=/yl[Z$ohr돤8 }v " FoO~&\y/~p[FD/lK 8B% R夝F)ԯ.O3!c*![-rsTeU"")Hq%h~w4QV)56Mq2^ö@ ^ÃJǺ6TwZHr )JW%(擼c‘+Id!8O#8JJV_ b O=a/ exuL琼,p ^]8%bbٺo^LgZf*s/ חl3UfKTvC:ކv1 r\ո^,k2"U}7k0~N\M?I5Rr$= 5iT(MMyj}O <{Gb0#VA =[3|]twќ1kҌv\F+ϥ WxXx}=݄1% endstream endobj 132 0 obj << /Length 1073 /Filter /FlateDecode >> stream xWKo7Whq-^ԍbZnѿo%Zq {kA?'ˁ)oqʂ9!p I$#Ts) 3NuL0VJ e]"yȪHQeW/ YT gWUO %rdjMRGmZKJAdb^ӹl@U4LUWenyΐIR&ҧxO.-$鉷QSuLO#DJ\rH`Lo=i 4_^KA kc.qo[v#z7Aƶ}Ct*pm޼_ʳfYѨx;aL@abu͒+_mNW@*b1p/ [KTߑ7O*2*?r4h=Y4w4&k&D2~;m%{!mkfUD_TV>YZ} Wrӑ5!@3r"!r0)qg.r5x⹷" %3;|8W#("TmLAFR]S j&> stream xXKoFWV 7$w @&As(ط-HwfߤhY&*";;;oKZ\xqFg p/~ݜ#)U1z=Ji[6uAm.FH]g#(cFeo MxeԔSk! d mq^*^ʊS Iw## / /1Xg~iQp0rYٴi;²(_d=ΦkK-ˈCcZS 0%t̠5 !I`8"0 Jq9oTԺQ@-[#C I&i >̚XY HHWзeޤ+DI i \5qn2v+sI69 mws$TP:Fk(Z$C%j*`\*]qJJV"7`MPq$t3Cx4q-d=Z={#QbOXD>Is,QPlhF 0Ls~G@3;=ὁ@8 㽉 snn! ;[ʾ2/>%m0Z\HrE3ưqAO h4jfCBk$@W$pEe żcr1eE;Ή!'(XN { 5\Wpp>b͵_MK!d3ISuDKϽ:Kz%.! 9 &LҕR5jd$LKYsЇMTvU-VZ!A᳘l.Lƾik-*kyހ= UM,dFF6H" WKkj؀ ͐և7[`&(؎Q35T4$! &\&lVx99 @El _f۠f D6$oQlT,(+ڹY8Laxt뜵sXhndf OڹyOL@5.!:Ol a;r}`"Vi0BU vĂ\gʋL󔙎w&a bq#M6AYf=lAȤ) Fi+- Y doftVut֍cìei-%;[tpm4swjHet7ޥlq5wa,Y'Ld 0@DC܂8έBIIzUmQ Y}pvsCcmxzP4yfm[wvr[僧Rꨰ+78{BRADzMH{cOsFAƯyZa9[8֘zBqvȕw֧K #za4H;gcg6 !zn?8?T;3G* XGZ"t\3LwY8Jᇳ4DCRl%+;#-''+SLB퇐6# Ӑ k*GSذ>eE>Id~YkEFB [CrB.;/4) R~Wݛ1[u\b6&ūK"oݱu\a|WrG5 O  ,{BX}yYs*Ehm!g;۽ZלmCks(j?xfVȦ*:CRiJsO,̧5q9 t&e endstream endobj 12 0 obj << /Type /ObjStm /N 100 /First 831 /Length 2054 /Filter /FlateDecode >> stream xZ]S}ׯCZ}SkupR]6ayYP4.iu}Νn6x-!`E(>ӟ2̤“8י87! 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ΑR6d֍S^󾀺ڃHc晢j_#Zrqki<RlV2&%?ozaq¼![]Oo+k>92˯-$$/Qp!𰘤9-ʶI1˜xTݟ: DJGG!Yx!p s")."AHJ NjbV&Sm&!yRfZd82H6[(Jˮ3N;ۢpLͽ䌪[,v(0[mxyHX a|yyZwۆn PiT),MɓNY&%{t28w. %7ۍ@AdU#>c)`Bg@;/K9| i) I.$Ic5;d7bxo O VDDQmyT@Edd< ;O79ځ6oP IbSE%um,Des=dP55k;κW;<O0E E1v} Nt,Gv Iw58)ѢwU1EMI_+䦆vtPhۘ3v(Xݘy"D8\ DZlxF1ֺPK?DlA{FKILl^EuEɻYdL)pɏ8@@+̪t~7' }yZ%b2:L,@MB%]yTK4'9~^E4jMZ.)ͪT0Z(5)YA)k}$lxQ02@JJ>]lpa2^uˇssi TGh([ʩku]gl ɣrv${cank}Wf9'|1>0&hw 7*Omovu_KH|S}{#c:FUa}աnWqc܎q 5ćW%5P0RmNQcM`I} -,hds&jo5ljGG>C] (ԮgR=ow+;7|*<5m0,D#O9(F~"'Գ*xUS%oOה=5c ^[pU޹yqC]&~g yh endstream endobj 325 0 obj << /Length 1234 /Filter /FlateDecode >> stream xWYo7~ׯ $Y#Ԁmyp7BG% bHJ\@.-k"rؒ?T D&mxaz8(JIq!%9's4K齊ߠ_Q?@3RDGR ʜpCj.Px%jaYN4JQ6u@R [e sr1D8e(λEU"$l?^82GHBZs|!벒Oo u%+*W?)/v&h`q1Ix(L>Y_.}d$Hp$1=3Isy.bgTF-`☒:Sk&iHdNdg py2H6{(ͤPZr.վDi[km)m@dC_.~߂g J͏c_yNIn”܆xˤON'%$nPuO#\E0iL/>6p#m5 җSRK \3Eְ#oƯ&||  ~vKW@#(Ҷy;YWnx GRۣ70`7"K>mPrXbvrJurDjq{?^Q RHvUN7=1p3ݰrCϫ~[ a7CpA=`YO2$ޏ(5Ux4O @'39}:i}W[h7]ΡmvE* 2Y 0GQݗu ׬6R>9Htq7CvnuCG-jߠwqq7+$IM~0`BJt]k܅/z<7_vEj;fcTFY€k/^R}:iV3DI Ә hC̽~Ϊd}p]pƀN'[E rLFZ%$#~LAs5q^ ړ*RW ,P ڌ_ ~WB﬽Za#i9Aeub endstream endobj 327 0 obj << /Type /XObject /Subtype /Form /BBox [0 0 362.835 272.126] /FormType 1 /Matrix [1 0 0 1 0 0] /Resources 328 0 R /Length 15 /Filter /FlateDecode >> stream xP( endstream endobj 351 0 obj << /Length 1457 /Filter /FlateDecode >> stream xWKo#7 WEm=b>t/EhcoƏ< KR#x2'l̈")~$EQ2wD}#-X|VӝݽfwKU].O:uBKI\47DWl7Q $ew Ep>BBK6)1;bl%"7]"XI C&t"E$hnᭁIi(%ڂ^gZCۺP}.,98Ix ޚf %~JGPG;ADH>~\I"steZ!h_e#0u2Q!8^CPO1KeIK yRBެ4x[  "N&iAGH動Y^k_IAYx ȜHZ27R8tW/:!"UH02k,X04U"QI=(;zxGA\xI{R0)w|)(}W DLOr;r\sc?J9KJGCIʒAX&%{t28)o yJ.8YGL@T*s6cy.2fxn6mBpti#u?MBJBKv ÔJÎGq5|ZB&Np *}awϔmsxkO?M]6eՂ׹_J8n,%@lP jĢE/:<3)ZP KN&pF$?g5N>! -M&p)W`qցuq2 ԒiNٵƓGFCj]ܨuH83$@(IEd7g#GHQDhKt/ەz(,|)_Hިg|KZ{b) endstream endobj 355 0 obj << /Type /XObject /Subtype /Form /BBox [0 0 362.835 272.126] /FormType 1 /Matrix [1 0 0 1 0 0] /Resources 356 0 R /Length 15 /Filter /FlateDecode >> stream xP( endstream endobj 357 0 obj << /Type /XObject /Subtype /Form /BBox [0 0 362.835 272.126] /FormType 1 /Matrix [1 0 0 1 0 0] /Resources 358 0 R /Length 15 /Filter /FlateDecode >> stream xP( endstream endobj 381 0 obj << /Length 1333 /Filter /FlateDecode >> stream xWYo7~ׯ B % @AYKRF^;3q0dk EJx&=ZrUN__ :ד@|A,-h䁥^1lM)pp~}); )뜐 \@Lp]!A*s)Ng.bѶPm&#yV^ # {:-VdJ13Crewbzw(˅ԚQmZsBψbi 7-U6T[(AT^ِ?e++lOg% oO)Oލ@&WjUCG| '6ͷRi57p ~=!J\))k ;=goWv,bj5øV` g𷘯qUgύ"+8Nsx.7 3дD=25kcLuvp;lD!_6у5Ph@r`~ʿ~ok0S)ý(.m-&p-u`Q;kX-8>pi8 4u6ƘpNJսsT@/iJ<2XnTJSv178xV >o&Ua OCR@.<%ee%SB1RXk](J:xiP R /A֏tOJDX[CXO&"jk`m1q3tt|M 10U]y6>`[ QS/`ʾH`Fyz-}w ٩9̞$LlMFx?^n2dl*v&~Xv9n%o7<}/0 endstream endobj 278 0 obj << /Type /ObjStm /N 100 /First 915 /Length 1741 /Filter /FlateDecode >> stream xZMoFW̱; vm>5|Pl6qH,r,hYeᒻo~W./ԓG@/rzۅM!fr8z&NxbK &9 -BΎRH)wu"J/umRQ. 2$̖rtbz[doSM A$ dBBd{(p1"j$|aSSs h&G\'(5^Zd\Y [{}> -'{`$X#D7Py%ZSиbdu^B^sQc2Y$5˄rݨbJY9QfD7V_P@ ΥP쪝9mKͣ|u-`ޚIVͳlE'A.IbMEGC55ݓٟ/gܚiwHLb HQyo6z݃},1 Xj4<ݠ.5YKljsr}wãlՒnTbr@6 RJ6,'-84uxmOGOjw+:mU;_]ahI̯˳;t~n/f{wԍ:vr6Iw0[bэ&/<8[֩e^mP&Y+A\o(^Qx;o/cWsYpAC1nQ2p/$-ϱdȤQ2X29(e܃E:/U$WvI djB$z$${I6uθ'-,Y,xg,F&0u"1sC3 ILe(߂@a=(C~Sdrq}"J\[)_6jڬ|U[֩U> stream xWKoFWBCf0$u=z |phVjJȥ?3%MI$m Tٙo͒qvj$`,^f1rt0i EL\l TR0Ui0+6ڣPogf++i@wJ1 jCSv -ҍAG__C5ǻ*Ɍ [jg',CV%gvɓn,JdpBu;I>|24Q~TYK1÷4KnzҐhg&}   6 LM䚽/K_L(˱tz\*q)m1].\lpUrv}+ [o?&>tLCXkzQnz4.•{#b6 A m) nۤ;&}MɁ4Na 0Ҹп-{r-t9"`qìѬ $i!\P@E_3 N:] 8dLXVI8*d\X p[WDĢ0rXf\}xX ~?b?FVGg,6N X_:˨`:*.!Ay(Zt6HtF#XOR{jq'[DoABJߡ$픲1/ v.'^o_ф[EtGK1D1E$"Ms!! ^DvA)'\+]הf>Sxh э_[B:s?/wOKHmy ]bkuw9z-Rl5S\#y1[#qPc[ڶ ,|tRgvHPB[lF endstream endobj 432 0 obj << /Length 1456 /Filter /FlateDecode >> stream xWKo7W=%@6AYˏbɆ/>I7Or9;erKYf[O_ un_;aX3Ұ)6IM RNyT]d[F` * a(v\'{sJ1 l Sc[PJ,e:غ8P;6d<^dPN9"*T &[9qȊN);+s4ǀ2Eu2:"ND(.uS,RlVo d!MJ ?>d yJx"=\t-ϰr8UN/z(,$0>C<ȜW.xې嶐FR^ѓ?yQeR'K'xqI[(dq7^ګ%jG\RӇU{_MU/5BIB nf8Ě揉ttj1}JfiJLq𷘯pWdr+uZe5E9GdVljh p136`~-_hqiz_OYmEoG<!p1qZ"`xYavh֊Zhn(ÏMc Xܣ0&|8L@d>i؝F*J az{E'7(7p8fyDHBTAa.lJusL(lQ/g*OV9>K~-4rYkf5PL JbI7`Λ :@Jvv^M)˲a!}y>ޚxTol8":`c@bHPv[P/nE Sÿ0>@XhƓZEu.ʡiapw A<\S*!4r } 01мTluաжmppxJE6Fy=Ž!Aޜ,v k:ٟ5NΧip81z~IhH(3P endstream endobj 458 0 obj << /Length 1442 /Filter /FlateDecode >> stream xWKSG+Jy?(L9\H Iփ%?>e2P؉V3=M3\Jwh]0h) tT󝓍z[1E,HWtg[:2Kas!* XYE4[[qq2A8LcB!9!;'h9)MdGz|YQ9gQs{JA+@2!LW^1%e& tggfY%9$Њj* ZIMDT<v8hV$H|x#b89PiDA_tA0vWҘx,zl -OqPsOuQp РL+?Y7ģ{t?1'GHHZS%v89\(`\"wj!Hy\'J$fBE,aT=Q5Ҹ^$Iv3AlRӄ7?&JU24)j<_g D%ʷ˼`hBs m7\$~xY,?:9$ѭF`0*ގ*b@l { ) iPCtK!hލkb- 6pK2$gşnq=`z>XJ!D12(.KP\j9ti;[Ic083xó ph,?8^wPJ)ta _a|EᆎQ͊}vIwUǩH%y7fO=:g g~K]"R2~nǾ| YO9T K~&#_.C1&ҧ"t~{fEdUp1¨3">gXc]UXr KAX˵o߽",aE~^cO²Q!-wS/o}r BԴ~ Y fq-Y(y7~m/a &>̩&-n ~m|ԏ#בy꡹\q<~> stream xWKo7Wj 0ФEa[YKIr kIIlmcV\gy\F ##5FFF׏^5y9:aG5IZh&ۤ}R:. 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d!J:} YX5= !cC]ਦLj#3~ p1; ֏TK-Ǔ\sV tV:"YRyl06[UPnxQ˳9.y%&v("OFݍ*o>X^T*jYJBTڧYFlا3o.ȽvBzxz6mП%-_ .֏إFWX&S=E>:9;LVVػტ'_,3 ºǰ\/hH+nϩt3]~@ڝ"~( , ΅(Eh԰儫xC;T}PM pՆڽQ R7; F vHtީ(,:Eg",1$ב1I0AV#}:!G$٭"I$ho.3j@F" k#{tOcGjzBCPӹ Tuv o^ܦ(0" nsǦyB]|w w6_=IEjnv X q~Mb/5̢E}A)8|K(_zq LK endstream endobj 1129 0 obj << /Type /ObjStm /N 100 /First 1014 /Length 1769 /Filter /FlateDecode >> stream xZKsFcrfYr9$@a`v>*xmް/zJ1Y+6\#8sV-x\\̂,b_@&J hd'#x`dOY2C2l}ò%D&&!DhžXe-ߣ%4q_@AN1:Y*9H 7ou𲄹D*( %Cj-`0#s.^y sdǦ!{x IWEȩKsI1FcD7# 8#XFDޘ(ˣ g54%%ڒEЖ:YŠ][d!mʎYVeA:@dme(K%8Jt&#e.e.kEOY}{z;֛fsj(0gq}ԖƬ~Ԝoi^ofom .63|mV+IW<ޜ7WU~m.k(U"9Qq6cbuUC0uhAJԯبrLTTp*x QBN";Ev)SdȮE~{]w%oOCi$ϋ6љB~ԋG5p"px|.+?›ZBJI,:ܢܳN֒.P\(]E6stNxXk 1Q;c7KRK|7/֐@:!TB$}S+]"/&۰:K?EOa-IZ+=KOHϘ'N'|RKBUnP,- 3Z*{A !c+!PjgؒPq%]">x>7Kg cc0! ;lV}UwH{=+ة廈.r8bw9<bwCo?o>$2 L+$r%(G QrA"E9*rTȑc>rKx }iߝ;xc&3|k<&~m?[\,WO}+]%:~ͫyr%\ƞ$Ua6nYMWص=R3U}}>'6 8sqy@{&#8{\=r8ٱp6 gp6 g:MvlCG͍Mmlg8|ٛar&mNOwO[ptWna~fL_3+)7;QWmLWi3q]\ɽu $N@HW!UH*t\WEFEFEFEFEFEFEFE1q];p] ץp] uםu';q݉um{0 endstream endobj 1258 0 obj << /Length 1526 /Filter /FlateDecode >> stream xWKoFW }o`h&hElK6rɏ̾Hь'j@ᒳSً>/G<| ɞ^8 hlsƤS\:n"ۈ|o!e#fQR.y6);3bds[(T:+%x 8`o+v8xN _5_yP2QD/h%R Lv{>p|hȘmYh4V_Tx+1SCsu"))%'vH+OwcC Ҭ']ozg oKe!g{=Yвfa7;VL|V7pOH1aHųp0x:IK"P;2Ȳn1&+ %fJBIHמ'/nR"nҲ"w (QBir*2o = e>[|xȲq J^ۆo )R &,!)^Y!e{ w68 ]Kg{Ji>EƸ!'Ыr '(hؾO@[%dC+BAlDkX {[6Z33aͬRVwZjc<ǿjK'ˇϕJQ-_ Ľ>[|~<ͯ]CA x/bnpi^eVI  T`Gii޷CHM(l!epNۮ^pxlIӁ# ˁhґ(&+>ƝK%~f{\{_x^bj-dEPIѧ@.yw??7H<XRog5aDhTlZ!9&[/SR)i"}Qws<; w܄^URMQIq-ȦPlqB \wm4n-)\_G•nl{ I*?PmkUm[2}@&\Ӡ6_Cb@0 !%?DqQDWv7i lF Ǡ؄AdK  :Kk~ב!yH؂"_DڟLjA{QE"Q> stream xP( endstream endobj 1284 0 obj << /Length 1342 /Filter /FlateDecode >> stream xWMo7WV~/ =iE rp{PVFlK7䒻v:F Tjw3yoC`'Lغ_$z1xn {>K˚?,:aۢ>+0<*6$ɘ\47"lz\B솑%\ JH8ԾҚ)IgY:/"7SBD=.#&>I}^t4kH>1xm1J],G¥ZTDP@ > +bkQ$(Uҫ sټyDLX^dA/\Hr2OҊ5/2nX1D&<ڂ |AU"bg<̻^ `q;I Z*'Vķ$#1y3 B9!mkA@Yq@*C9@^m$H'Dr6`h#%,)af;PjvZZ$Y.kRRs|]$@%6y^6}ek.UK%ghJ⫌*)/dE)9켅$+m%6P.%@|"cӟ4ԴFi+|yO`EI%B[a[ixʎzo./F gWM4 A`ڪCDAklNe i +pɾZ–itcTlkU,vGbGq )Dp ncMΈ}GRYaSE fͤʙ܏T\Y"[6^C%4Κ;Y\P'Hzaq@h>⶷ZawowdRH"ilB$4A;.Kiބ"M =@!>z}GEn$p-˜A!&NP,ӔƸ ÁzkgCST >I=K:>Q{#DE endstream endobj 1288 0 obj << /Type /XObject /Subtype /Form /BBox [0 0 362.835 272.126] /FormType 1 /Matrix [1 0 0 1 0 0] /Resources 1289 0 R /Length 15 /Filter /FlateDecode >> stream xP( endstream endobj 1290 0 obj << /Type /XObject /Subtype /Form /BBox [0 0 362.835 272.126] /FormType 1 /Matrix [1 0 0 1 0 0] /Resources 1291 0 R /Length 15 /Filter /FlateDecode >> stream xP( endstream endobj 1314 0 obj << /Length 1522 /Filter /FlateDecode >> stream xW[o6~ϯ eT^D,m+M t@Oq.C,v;IV7Af&yx7R3v`9 |۹4p?-5[!pJcom @JUi63F#f+iw8o\*($ 3v>Di$J7::]qH~휳ly8Ҙ gLʟvtz&>aa>]xYQ)eBZ=ũrxTZjNgd4DZ 9Xr"qg+}<oG@K>N]AC=@_{;l4-1Sp9]~%Ӟ }.8p?<(l̉9Np?qCҍE2-p%q@ % w)admjO,ΐI> i3DTVZdYd}Hfbqϔ]d>yG^QtŭənL:EX!e>[:$^ðx9~{ kCv|$R^h=y ˠON 'I!O1Fqd[D1|9FW=i*RptH_&HI$`3$5Zk69ejW39-n]iGX5R )mUqcuKJL}δ#Ld+U#E|T\kfe^p8(`2qSD`dz2D}֤(琅>=оY磥f\֋RKU8$ƉpXqVRxqQhoE"lMXN)灈i8a?SJi8jL/VI7|84Rè HbaZ"KQ_@DPUK/ge-k[`z(AdEwPp#}+rh",ϖ>ygyz8 ׸"֌l/|ȸ8p IX]|yCP4M y5cQ#Ws\s=R iGD4$]?1R1nFgmʿ%~0-xXI1j).g]z/z+CmKͷ8" _b&nJSG\_*~-S Y!Spc̸׮ E@/7cQ"*RO{.1b)MmFAS/qMR3f6.B|q[~ǵPEۍMn}ֻQ$nU+/`]r|{ $ˏW"K؏fv endstream endobj 1339 0 obj << /Length 1398 /Filter /FlateDecode >> stream xWIo7WЃf&܇ MR ڃm >cIvaɕd#~یGmcF$Ǎ`@Yo;Ҳ;z[ ҟflH:&etw. hqKj Sv !d;[V * Hl\-oRaŽ!9$I+9X: u#{"K{UxTm*PL:vr f`q.wAVJXV_}6@]ؼPT-S BPWm$!'qF)_7 [~٭$yn)rR!M QW$M gF{d V; -!"l9 .@'6U:{ vmBDU(:a_E1T?/)@!BV &ֶ0e T5ŽpmF~GѓX\BF>MPVTb7|xЊ/` yR bEoZٿϊ$E(b)+ǒTϽ$7VpI%IV"Q> stream xMs}L.3YRd!QTNXiDCcF5\ıʽs{pĵeȁbifD%GhD1kr&bUSL&dQYT0NTTGaJK)URk\2e)U(KXrtڮQN:"=,=\*| i347#ɭ靀eq_)ct(nk0] U2Te RU5JgyNbyj2v/iы_fVDULcZ Qej s R5m18)Tv)qQE؍Um듘U EW&.%L\UL> `R 曬3KAfM$IݢRNn;Y N*.ثyվ,eslV"{x)W''~lvw5w?:z{:Gw!X#_:?ڜnoכ''YtzГv~c:ǨE9_X/ay]Svwr <[nCa~h\͞NN?٩Tynt1V/(R:͘.{n{y6 )/{8/"F@Koov͸G齿WG۷9tӊ DqnhΨX,GĪdT3dDg7#fD3M9r4dɔ)'SNF_eoIK%ҟۚ:ƾ13qneŖPyzD++\KHq/^l\~);$M"I nN>-\db?S$oJAJ$90ER$EIu ktz;(pHg:P' 9i"ɝ>Gwtq@)@#G#n#%rN=$tvǙSAyr|J56>tavnu9;#cnqqE/c;ms ǧכ=6Ǥs`/c~X_]o^5Go?~?ysz}wdI#մͥ;NW&+ʵ)H8lB[f0[6FH)}elW1*F_Ŕ)S.\Lr1bՔŅ61φ\-͆l-φl-͆l-̶0l }IV#8aOc{ǟb'_OZ`0Pv_2[;BYW>> stream xWKoFW}p<4h ֡BK S%A~3 ENܤiTٝ73;K)΄/`SMN<{"YmL IGc\6,] |omLa({#Ũ`x'Jԅv^zW BM]#4) X{i-`{_xb4,rv|f1M}daYUBZ} cVE skJIb Q^#A-M%8 '9|aw [g`IiRґǪ*g[=,1*D\kIxK#9sJ'=;=IRhIa1qK^<}/L̉N}p:p6 #1bH^rt6'uIzGKNk8Yҙ xjJ]Y/'3|W$\ ne)(RNdWSsg[MsS#C|t>C%^+Ll!?ȵex`ϮtG#}7ÆRډV li='Ө?4?\RCa3?g`І_%eUCe5':4dz&&OYKUxA30ӜYG㬈ȿy2a6>U"m; 'x7FQL5%}!>B΁OU!H; Rnf6)o_f$b:!-|Q!1&L\_Y&Z&e]ee_Y9]492CWwxbd 2CmFQ9ktz5շLiS\JƮJe5t똸|XJfNEg!#P TYM`&z$M'4 \綒b:GTHGЈ4)k ;0&ެ6! 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\ \ \ \ \ \ \ ʍrH`h`h`h`h`h`h~3oɱhX:"""""""""b|ֈmmmmmmmmməPl$C%{"l4l4l4l4l4l4l4l4oX!Ad d ˈLCFBq2OY(d,L Y)d2&dl98yʍEʸlCv.ݲG>/rD ?=3QrF99/\rE5.7ܒrGޑld(>,B̿Ia(lͣGD/ACCCCCCCCCCC{ 2$&yaq/7~?Gߩ~~7g4f+d,HY*dJVY+cN6h(<9*elq&e]2!e}_A9$MɏrJN9+\rI.*ܐrKn]9Su^6F$elq&e]2!e}_Kxyރ)#p)rB&夜rF99/\rEܡ\rK_"_ܑ|ܓ@#y,OdJ3y./䥼ׂ@ir`v_Nt/\׿7˰pPrPrPrPrPrPrPrPrP:%eAAAAAA]9-/\r`Q % % % % % % % % % % % % % % % % %eexy_ȣ|RJJJJJJJJJJJJJJJJ̬w$KD__fIQRQQQQQQQQQQQQQQQQQQL({_z7'&DD""HD$"DD""HD|DD""HoI+Cx9\ E"aHX$, E"aHX$, EOK$F&}ByDI$I$@ H$I$@ H$[ٙ EB ?zH3`HX$, E"aHX$, E"aHoQgfW{Ma2Sfg2G= 0.2 * length(ids)]) ################################################### ### code chunk number 79: cseq_13 ################################################### tmp <- unlist(qseq_on_ref_id2, use.names=FALSE) qseq_on_ref2 <- relist(unlist(qseq_on_ref, use.names=FALSE)[tmp], qseq_on_ref_id2) ################################################### ### code chunk number 80: cseq_14 ################################################### split_factor <- rep.int(seqnames(gr), elementNROWS(qseq_on_ref2)) qseq_on_ref2 <- unlist(qseq_on_ref2, use.names=FALSE) qseq_on_ref2_by_chrom <- splitAsList(qseq_on_ref2, split_factor) qseq_pos_by_chrom <- splitAsList(start(gr), split_factor) cm_by_chrom <- lapply(names(qseq_pos_by_chrom), function(seqname) consensusMatrix(qseq_on_ref2_by_chrom[[seqname]], as.prob=TRUE, shift=qseq_pos_by_chrom[[seqname]]-1, width=seqlengths(gr)[[seqname]])) names(cm_by_chrom) <- names(qseq_pos_by_chrom) ################################################### ### code chunk number 81: cseq_15 ################################################### lapply(cm_by_chrom, dim) ################################################### ### code chunk number 82: cseq_16 ################################################### cs_by_chrom <- lapply(cm_by_chrom, function(cm) { ## need to "fix" 'cm' because consensusString() ## doesn't like consensus matrices with columns ## that contain only zeroes (e.g., chromosome ## positions with no coverage) idx <- colSums(cm) == 0L cm["+", idx] <- 1 DNAString(consensusString(cm, ambiguityMap="N")) }) ################################################### ### code chunk number 83: cseq_17 ################################################### cs_by_chrom ################################################### ### code chunk number 84: bin_1 ################################################### library(BSgenome.Scerevisiae.UCSC.sacCer2) set.seed(55) my_var <- RleList( lapply(seqlengths(Scerevisiae), function(seqlen) { tmp <- sample(50L, seqlen, replace=TRUE) %/% 50L Rle(cumsum(tmp - rev(tmp))) } ), compress=FALSE) my_var ################################################### ### code chunk number 85: bin_2 ################################################### bins <- tileGenome(seqinfo(Scerevisiae), tilewidth=100, cut.last.tile.in.chrom=TRUE) ################################################### ### code chunk number 86: bin_3 ################################################### binnedAverage(bins, my_var, "binned_var") ################################################### ### code chunk number 87: SessionInfo ################################################### sessionInfo() GenomicRanges/inst/doc/GenomicRangesHOWTOs.Rnw0000644000175400017540000011362413032312063022260 0ustar00biocbuildbiocbuild%\VignetteIndexEntry{GenomicRanges HOWTOs} %\VignetteDepends{GenomicRanges, Rsamtools, GenomicAlignments, pasillaBamSubset, TxDb.Dmelanogaster.UCSC.dm3.ensGene, TxDb.Athaliana.BioMart.plantsmart22, AnnotationHub, DESeq2, edgeR, TxDb.Hsapiens.UCSC.hg19.knownGene, GenomicFeatures, Biostrings, BSgenome.Hsapiens.UCSC.hg19, KEGG.db, KEGGgraph, BSgenome.Scerevisiae.UCSC.sacCer2} %\VignetteKeywords{sequence, sequencing, alignments} %\VignettePackage{GenomicRanges} \documentclass{article} \usepackage[authoryear,round]{natbib} <>= BiocStyle::latex(use.unsrturl=FALSE) @ \title{\Biocpkg{GenomicRanges} HOWTOs} \author{Bioconductor Team} \date{Edited: July 2014; Compiled: \today} \begin{document} \maketitle \tableofcontents <>= options(width=72) options(showHeadLines=3) options(showTailLines=3) .precomputed_results_path <- "precomputed_results" @ %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \section{Introduction} \subsection{Purpose of this document} This document is a collection of {\it HOWTOs}. Each {\it HOWTO} is a short section that demonstrates how to use the containers and operations implemented in the \Biocpkg{GenomicRanges} and related packages (\Biocpkg{IRanges}, \Biocpkg{Biostrings}, \Biocpkg{Rsamtools}, \Biocpkg{GenomicAlignments}, \Biocpkg{BSgenome}, and \Biocpkg{GenomicFeatures}) to perform a task typically found in the context of a high throughput sequence analysis. Unless stated otherwise, the {\it HOWTOs} are self contained, independent of each other, and can be studied and reproduced in any order. \subsection{Prerequisites and additional recommended reading} We assume the reader has some previous experience with \R{} and with basic manipulation of \Rcode{GRanges}, \Rcode{GRangesList}, \Rcode{Rle}, \Rcode{RleList}, and \Rcode{DataFrame} objects. See the ``An Introduction to Genomic Ranges Classes'' vignette located in the \Biocpkg{GenomicRanges} package (in the same folder as this document) for an introduction to these containers. Additional recommended readings after this document are the ``Software for Computing and Annotating Genomic Ranges'' paper[\citet{Lawrence2013ranges}] and the ``Counting reads with \Rfunction{summarizeOverlaps}'' vignette located in the \Biocpkg{GenomicAlignments} package. To display the list of vignettes available in the \Biocpkg{GenomicRanges} package, use \Rcode{browseVignettes("GenomicRanges")}. \subsection{Input data and terminology used across the HOWTOs} In order to avoid repetition, input data, concepts and terms used in more than one {\it HOWTO} are described here: \begin{itemize} \item {\bf The \Biocpkg{pasillaBamSubset} data package}: contains both a BAM file with single-end reads (untreated1\_chr4) and a BAM file with paired-end reads (untreated3\_chr4). Each file is a subset of chr4 from the "Pasilla" experiment. <>= library(pasillaBamSubset) untreated1_chr4() untreated3_chr4() @ See \Rcode{?pasillaBamSubset} for more information. <>= ?pasillaBamSubset @ \item {\bf Gene models and \Rclass{TxDb} objects}: A \textit{gene model} is essentially a set of annotations that describes the genomic locations of the known genes, transcripts, exons, and CDS, for a given organism. In \Bioconductor{} it is typically represented as a \Rclass{TxDb} object but also sometimes as a \Rclass{GRanges} or \Rclass{GRangesList} object. The \Biocpkg{GenomicFeatures} package contains tools for making and manipulating \Rclass{TxDb} objects. \end{itemize} %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \section{HOWTOs} %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \subsection{How to read single-end reads from a BAM file} As sample data we use the \Biocpkg{pasillaBamSubset} data package described in the introduction. <>= library(pasillaBamSubset) un1 <- untreated1_chr4() # single-end reads @ Several functions are available for reading BAM files into \R{}: \begin{verbatim} readGAlignments() readGAlignmentPairs() readGAlignmentsList() scanBam() \end{verbatim} \Rfunction{scanBam} is a low-level function that returns a list of lists and is not discussed further here. See \Rcode{?scanBam} in the \Biocpkg{Rsamtools} package for more information. Single-end reads can be loaded with the \Rfunction{readGAlignments} function from the \Biocpkg{GenomicAlignments} package. <>= library(GenomicAlignments) gal <- readGAlignments(un1) @ Data subsets can be specified by genomic position, field names, or flag criteria in the \Rcode{ScanBamParam}. Here we input records that overlap position 1 to 5000 on the negative strand with \Rcode{flag} and \Rcode{cigar} as metadata columns. <>= what <- c("flag", "cigar") which <- GRanges("chr4", IRanges(1, 5000)) flag <- scanBamFlag(isMinusStrand = TRUE) param <- ScanBamParam(which=which, what=what, flag=flag) neg <- readGAlignments(un1, param=param) neg @ Another approach to subsetting the data is to use \Rfunction{filterBam}. This function creates a new BAM file of records passing user-defined criteria. See \Rcode{?filterBam} in the \Biocpkg{Rsamtools} package for more information. %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \subsection{How to read paired-end reads from a BAM file} As sample data we use the \Biocpkg{pasillaBamSubset} data package described in the introduction. <>= library(pasillaBamSubset) un3 <- untreated3_chr4() # paired-end reads @ Paired-end reads can be loaded with the \Rfunction{readGAlignmentPairs} or \Rfunction{readGAlignmentsList} function from the \Biocpkg{GenomicAlignments} package. These functions use the same mate paring algorithm but output different objects. Let's start with \Rfunction{readGAlignmentPairs}: <>= un3 <- untreated3_chr4() gapairs <- readGAlignmentPairs(un3) @ The \Robject{GAlignmentPairs} class holds only pairs; reads with no mate or with ambiguous pairing are discarded. Each list element holds exactly 2 records (a mated pair). Records can be accessed as the \Rcode{first} and\Rcode{last} segments in a template or as \Rcode{left} and \Rcode{right} alignments. See \Rcode{?GAlignmentPairs} in the \Biocpkg{GenomicAlignments} package for more information. <>= gapairs @ For \Rcode{readGAlignmentsList}, mate pairing is performed when \Rcode{asMates} is set to \Rcode{TRUE} on the \Rcode{BamFile} object, otherwise records are treated as single-end. <>= galist <- readGAlignmentsList(BamFile(un3, asMates=TRUE)) @ \Robject{GAlignmentsList} is a more general `list-like' structure that holds mate pairs as well as non-mates (i.e., singletons, records with unmapped mates etc.) A \Rcode{mates\_status} metadata column (accessed with \Rfunction{mcols}) indicates which records were paired. <>= galist @ Non-mated reads are returned as groups by QNAME and contain any number of records. Here the non-mate groups range in size from 1 to 9. <>= non_mates <- galist[unlist(mcols(galist)$mate_status) == "unmated"] table(elementNROWS(non_mates)) @ %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \subsection{How to read and process a big BAM file by chunks in order to reduce memory usage} A large BAM file can be iterated through in chunks by setting a \Rcode{yieldSize} on the \Rclass{BamFile} object. As sample data we use the \Biocpkg{pasillaBamSubset} data package described in the introduction. <>= library(pasillaBamSubset) un1 <- untreated1_chr4() bf <- BamFile(un1, yieldSize=100000) @ Iteration through a BAM file requires that the file be opened, repeatedly queried inside a loop, then closed. Repeated calls to \Rfunction{readGAlignments} without opening the file first result in the same 100000 records returned each time. <>= open(bf) cvg <- NULL repeat { chunk <- readGAlignments(bf) if (length(chunk) == 0L) break chunk_cvg <- coverage(chunk) if (is.null(cvg)) { cvg <- chunk_cvg } else { cvg <- cvg + chunk_cvg } } close(bf) cvg @ %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \subsection{How to compute read coverage} The ``read coverage'' is the number of reads that cover a given genomic position. Computing the read coverage generally consists in computing the coverage at each position in the genome. This can be done with the \Rcode{coverage()} function. As sample data we use the \Biocpkg{pasillaBamSubset} data package described in the introduction. <>= library(pasillaBamSubset) un1 <- untreated1_chr4() # single-end reads library(GenomicAlignments) reads1 <- readGAlignments(un1) cvg1 <- coverage(reads1) cvg1 @ Coverage on chr4: <>= cvg1$chr4 @ Average and max coverage: <>= mean(cvg1$chr4) max(cvg1$chr4) @ Note that \Rcode{coverage()} is a generic function with methods for different types of objects. See \Rcode{?coverage} for more information. %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \subsection{How to find peaks in read coverage} ChIP-Seq analysis usually involves finding peaks in read coverage. This process is sometimes called ``peak calling'' or ``peak detection''. Here we're only showing a naive way to find peaks in the object returned by the \Rcode{coverage()} function. \Bioconductor{} packages \Biocpkg{BayesPeak}, \Biocpkg{bumphunter}, \Biocpkg{Starr}, \Biocpkg{CexoR}, \Biocpkg{exomePeak}, \Biocpkg{RIPSeeker}, and others, provide sophisticated peak calling tools for ChIP-Seq, RIP-Seq, and other kind of high throughput sequencing data. Let's assume \Rcode{cvg1} is the object returned by \Rcode{coverage()} (see previous {\it HOWTO} for how to compute it). We can use the \Rcode{slice()} function to find the genomic regions where the coverage is greater or equal to a given threshold. <>= chr4_peaks <- slice(cvg1$chr4, lower=500) chr4_peaks length(chr4_peaks) # nb of peaks @ The weight of a given peak can be defined as the number of aligned nucleotides that belong to the peak (a.k.a. the area under the peak in mathematics). It can be obtained with \Rcode{sum()}: <>= sum(chr4_peaks) @ %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \subsection{How to retrieve a gene model from the UCSC genome browser} See introduction for a quick description of what \textit{gene models} and \Rclass{TxDb} objects are. We can use the \Rcode{make\-Transcript\-Db\-From\-UCSC()} function from the \Biocpkg{GenomicFeatures} package to import a UCSC genome browser track as a \Rclass{TxDb} object. <>= library(GenomicFeatures) ### Internet connection required! Can take several minutes... txdb <- makeTxDbFromUCSC(genome="sacCer2", tablename="ensGene") @ See \Rcode{?makeTxDbFromUCSC} in the \Biocpkg{GenomicFeatures} package for more information. Note that some of the most frequently used gene models are available as TxDb packages. A TxDb package consists of a pre-made \Rclass{TxDb} object wrapped into an annotation data package. Go to \url{http://bioconductor.org/packages/release/BiocViews.html#\_\_\_TxDb} to browse the list of available TxDb packages. <>= library(TxDb.Hsapiens.UCSC.hg19.knownGene) txdb <- TxDb.Hsapiens.UCSC.hg19.knownGene txdb @ Extract the transcript coordinates from this gene model: <>= transcripts(txdb) @ %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \subsection{How to retrieve a gene model from Ensembl} See introduction for a quick description of what \textit{gene models} and \Rclass{TxDb} objects are. We can use the \Rcode{make\-Transcript\-Db\-From\-Biomart()} function from the \Biocpkg{GenomicFeatures} package to retrieve a gene model from the Ensembl Mart. <>= library(GenomicFeatures) ### Internet connection required! Can take several minutes... txdb <- makeTxDbFromBiomart(biomart="ensembl", dataset="hsapiens_gene_ensembl") @ See \Rcode{?makeTxDbFromBiomart} in the \Biocpkg{GenomicFeatures} package for more information. Note that some of the most frequently used gene models are available as TxDb packages. A TxDb package consists of a pre-made \Rclass{TxDb} object wrapped into an annotation data package. Go to \url{http://bioconductor.org/packages/release/BiocViews.html#\_\_\_TxDb} to browse the list of available TxDb packages. <>= library(TxDb.Athaliana.BioMart.plantsmart22) txdb <- TxDb.Athaliana.BioMart.plantsmart22 txdb @ Extract the exon coordinates from this gene model: <>= exons(txdb) @ %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \subsection{How to load a gene model from a GFF or GTF file} See introduction for a quick description of what \textit{gene models} and \Rclass{TxDb} objects are. We can use the \Rcode{make\-Transcript\-Db\-From\-GFF()} function from the \Biocpkg{GenomicFeatures} package to import a GFF or GTF file as a \Rclass{TxDb} object. <>= library(GenomicFeatures) gff_file <- system.file("extdata", "GFF3_files", "a.gff3", package="GenomicFeatures") txdb <- makeTxDbFromGFF(gff_file, format="gff3") txdb @ See \Rcode{?makeTxDbFromGFF} in the \Biocpkg{GenomicFeatures} package for more information. Extract the exon coordinates grouped by gene from this gene model: <>= exonsBy(txdb, by="gene") @ %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \subsection{How to retrieve a gene model from \Biocpkg{AnnotationHub}} When a gene model is not available as a \Rclass{GRanges} or \Rclass{GRangesList} object or as a \Bioconductor{} data package, it may be available on \Biocpkg{AnnotationHub}. In this {\it HOWTO}, will look for a gene model for Drosophila melanogaster on \Biocpkg{AnnotationHub}. Create a `hub' and then filter on Drosophila melanogaster: <>= library(AnnotationHub) ### Internet connection required! hub <- AnnotationHub() hub <- subset(hub, hub$species=='Drosophila melanogaster') @ There are 87 files that match Drosophila melanogaster. If you look at the metadata in hub, you can see that the 7th record representes a GRanges object from UCSC <>= length(hub) head(names(hub)) head(hub$title, n=10) ## then look at a specific slice of the hub object. hub[7] @ So you can retrieve that dm3 file as a \Rcode{GRanges} like this: <>= gr <- hub[[names(hub)[7]]] summary(gr) @ The metadata fields contain the details of file origin and content. <>= metadata(gr) @ Split the \Rclass{GRanges} object by gene name to get a \Rclass{GRangesList} object of transcript ranges grouped by gene. <>= txbygn <- split(gr, gr$name) @ You can now use \Rcode{txbygn} with the \Rcode{summarizeOverlaps} function to prepare a table of read counts for RNA-Seq differential gene expression. Note that before passing \Rcode{txbygn} to \Rcode{summarizeOverlaps}, you should confirm that the seqlevels (chromosome names) in it match those in the BAM file. See \Rcode{?renameSeqlevels}, \Rcode{?keepSeqlevels} and \Rcode{?seqlevels} for examples of renaming seqlevels. %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \subsection{How to annotate peaks in read coverage} [coming soon...] %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \subsection{How to prepare a table of read counts for RNA-Seq differential gene expression} Methods for RNA-Seq gene expression analysis generally require a table of counts that summarize the number of reads that overlap or `hit' a particular gene. In this {\it HOWTO} we count with the \Rcode{summarizeOverlaps} function from the \Biocpkg{GenomicAlignments} package and create a count table from the results. Other packages that provide read counting are \Biocpkg{Rsubread} and \Biocpkg{easyRNASeq}. The \Biocpkg{parathyroidSE} package vignette contains a workflow on counting and other common operations required for differential expression analysis. As sample data we use the \Biocpkg{pasillaBamSubset} data package described in the introduction. <>= library(pasillaBamSubset) reads <- c(untrt1=untreated1_chr4(), # single-end reads untrt3=untreated3_chr4()) # paired-end reads @ \Rcode{summarizeOverlaps} requires the name of a BAM file(s) and a {\textit gene model} to count against. See introduction for a quick description of what a \textit{gene models} is. The gene model must match the genome build the reads in the BAM file were aligned to. For the pasilla data this is dm3 Dmelanogaster which is available as a \Bioconductor{} package. Load the package and extract the exon ranges grouped by gene: <>= library(TxDb.Dmelanogaster.UCSC.dm3.ensGene) exbygene <- exonsBy(TxDb.Dmelanogaster.UCSC.dm3.ensGene, "gene") @ \Rcode{exbygene} is a \Rclass{GRangesList} object with one list element per gene in the gene model. \Rcode{summarizeOverlaps} automatically sets a \Rcode{yieldSize} on large BAM files and iterates over them in chunks. When reading paired-end data set the \Rcode{singleEnd} argument to FALSE. See ?\Rfunction{summarizeOverlaps} for details reguarding the count \Rcode{modes} and additional arguments. <>= library(GenomicAlignments) se <- summarizeOverlaps(exbygene, reads, mode="IntersectionNotEmpty") @ The return object is a \Rcode{SummarizedExperiment} with counts accessible with the \Rcode{assays} accessor: <>= class(se) head(table(assays(se)$counts)) @ The count vector is the same length as \Rcode{exbygene}: <>= identical(length(exbygene), length(assays(se)$counts)) @ A copy of \Rcode{exbygene} is stored in the \Rcode{se} object and accessible with \Rcode{rowRanges} accessor: <>= rowRanges(se) @ Two popular packages for RNA-Seq differential gene expression are \Biocpkg{DESeq2} and \Biocpkg{edgeR}. Tables of counts per gene are required for both and can be easily created with a vector of counts. Here we use the counts from our \Rclass{SummarizedExperiment} object: <>= colData(se)$trt <- factor(c("untrt", "untrt"), levels=c("trt", "untrt")) colData(se) library(DESeq2) deseq <- DESeqDataSet(se, design= ~ 1) library(edgeR) edger <- DGEList(assays(se)$counts, group=rownames(colData(se))) @ %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \subsection{How to summarize junctions from a BAM file containing RNA-Seq reads} As sample data we use the \Biocpkg{pasillaBamSubset} data package described in the introduction. <>= library(pasillaBamSubset) un1 <- untreated1_chr4() # single-end reads library(GenomicAlignments) reads1 <- readGAlignments(un1) reads1 @ For each alignment, the aligner generated a CIGAR string that describes its "geometry", that is, the locations of insertions, deletions and junctions in the alignment. See the SAM Spec available on the SAMtools website for the details (\url{http://samtools.sourceforge.net/}). The \Rcode{summarizeJunctions()} function from the \Biocpkg{GenomicAlignments} package can be used to summarize the junctions in \Rcode{reads1}. <>= junc_summary <- summarizeJunctions(reads1) junc_summary @ See \Rcode{?summarizeJunctions} in the \Biocpkg{GenomicAlignments} package for more information. %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \subsection{How to get the exon and intron sequences of a given gene} The exon and intron sequences of a gene are essentially the DNA sequences of the introns and exons of all known transcripts of the gene. The first task is to identify all transcripts associated with the gene of interest. Our sample gene is the human TRAK2 which is involved in regulation of endosome-to-lysosome trafficking of membrane cargo. The Entrez gene id is `66008'. <>= trak2 <- "66008" @ The \Biocpkg{TxDb.Hsapiens.UCSC.hg19.knownGene} data package contains the gene model corresponding to the UCSC `Known Genes' track. <>= library(TxDb.Hsapiens.UCSC.hg19.knownGene) txdb <- TxDb.Hsapiens.UCSC.hg19.knownGene @ The transcript ranges for all the genes in the gene model can be extracted with the \Rfunction{transcriptsBy} function from the \Biocpkg{GenomicFeatures} package. They will be returned in a named \Rclass{GRangesList} object containing all the transcripts grouped by gene. In order to keep only the transcripts of the TRAK2 gene we will subset the \Rclass{GRangesList} object using the \Rcode{[[} operator. <>= library(GenomicFeatures) trak2_txs <- transcriptsBy(txdb, by="gene")[[trak2]] trak2_txs @ \Rcode{trak2\_txs} is a \Rclass{GRanges} object with one range per transcript in the TRAK2 gene. The transcript names are stored in the \Rcode{tx\_name} metadata column. We will need them to subset the extracted intron and exon regions: <>= trak2_tx_names <- mcols(trak2_txs)$tx_name trak2_tx_names @ The exon and intron genomic ranges for all the transcripts in the gene model can be extracted with the \Rfunction{exonsBy} and \Rfunction{intronsByTranscript} functions, respectively. Both functions return a \Rclass{GRangesList} object. Then we keep only the exon and intron for the transcripts of the TRAK2 gene by subsetting each \Rclass{GRangesList} object by the TRAK2 transcript names. Extract the exon regions: <>= trak2_exbytx <- exonsBy(txdb, "tx", use.names=TRUE)[trak2_tx_names] elementNROWS(trak2_exbytx) @ ... and the intron regions: <>= trak2_inbytx <- intronsByTranscript(txdb, use.names=TRUE)[trak2_tx_names] elementNROWS(trak2_inbytx) @ Next we want the DNA sequences for these exons and introns. The \Rfunction{getSeq} function from the \Biocpkg{Biostrings} package can be used to query a \Biocpkg{BSgenome} object with a set of genomic ranges and retrieve the corresponding DNA sequences. <>= library(BSgenome.Hsapiens.UCSC.hg19) @ Extract the exon sequences: <>= trak2_ex_seqs <- getSeq(Hsapiens, trak2_exbytx) trak2_ex_seqs trak2_ex_seqs[["uc002uyb.4"]] trak2_ex_seqs[["uc002uyc.2"]] @ ... and the intron sequences: <>= trak2_in_seqs <- getSeq(Hsapiens, trak2_inbytx) trak2_in_seqs trak2_in_seqs[["uc002uyb.4"]] trak2_in_seqs[["uc002uyc.2"]] @ %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \subsection{How to get the CDS and UTR sequences of genes associated with colorectal cancer} In this {\it HOWTO} we extract the CDS and UTR sequences of genes involved in colorectal cancer. The workflow extends the ideas presented in the previous {\it HOWTO} and suggests an approach for identifying disease-related genes. \subsubsection{Build a gene list} We start with a list of gene or transcript ids. If you do not have pre-defined list one can be created with the \Biocpkg{KEGG.db} and \Biocpkg{KEGGgraph} packages. Updates to the data in the \Biocpkg{KEGG.db} package are no longer available, however, the resource is still useful for identifying pathway names and ids. Create a table of KEGG pathways and ids and search on the term `cancer'. <>= library(KEGG.db) pathways <- toTable(KEGGPATHNAME2ID) pathways[grepl("cancer", pathways$path_name, fixed=TRUE),] @ Use the "05210" id to query the KEGG web resource (accesses the currently maintained data). <>= library(KEGGgraph) dest <- tempfile() retrieveKGML("05200", "hsa", dest, "internal") @ The suffix of the KEGG id is the Entrez gene id. The \Rfunction{translateKEGGID2GeneID} simply removes the prefix leaving just the Entrez gene ids. <>= crids <- as.character(parseKGML2DataFrame(dest)[,1]) crgenes <- unique(translateKEGGID2GeneID(crids)) head(crgenes) @ \subsubsection{Identify genomic coordinates} The list of gene ids is used to extract genomic positions of the regions of interest. The Known Gene table from UCSC will be the annotation and is available as a \Bioconductor{} package. <>= library(TxDb.Hsapiens.UCSC.hg19.knownGene) txdb <- TxDb.Hsapiens.UCSC.hg19.knownGene @ If an annotation is not available as a \Bioconductor{} annotation package it may be available in \Biocpkg{AnnotationHub}. Additionally, there are functions in \Biocpkg{GenomicFeatures} which can retrieve data from UCSC and Ensembl to create a \Robject{TxDb}. See \Rcode{?makeTxDbFromUCSC} for more information. As in the previous {\it HOWTO} we need to identify the transcripts corresponding to each gene. The transcript id (or name) is used to isolate the UTR and coding regions of interest. This grouping of transcript by gene is also used to re-group the final sequence results. The \Rcode{transcriptsBy} function outputs both the gene and transcript identifiers which we use to create a map between the two. The \Rcode{map} is a \Robject{CharacterList} with gene ids as names and transcript ids as the list elements. <>= txbygene <- transcriptsBy(txdb, "gene")[crgenes] ## subset on colorectal genes map <- relist(unlist(txbygene, use.names=FALSE)$tx_id, txbygene) map @ Extract the UTR and coding regions. <>= cds <- cdsBy(txdb, "tx") threeUTR <- threeUTRsByTranscript(txdb) fiveUTR <- fiveUTRsByTranscript(txdb) @ Coding and UTR regions may not be present for all transcripts specified in \Rcode{map}. Consequently, the subset results will not be the same length. This length discrepancy must be taken into account when re-listing the final results by gene. <>= txid <- unlist(map, use.names=FALSE) cds <- cds[names(cds) %in% txid] threeUTR <- threeUTR[names(threeUTR) %in% txid] fiveUTR <- fiveUTR[names(fiveUTR) %in% txid] @ Note the different lengths of the subset regions. <>= length(txid) ## all possible transcripts length(cds) length(threeUTR) length(fiveUTR) @ These objects are \Robject{GRangesList}s with the transcript id as the outer list element. <>= cds @ \subsubsection{Extract sequences from BSgenome} The \Rcode{BSgenome} packages contain complete genome sequences for a given organism. Load the \Rcode{BSgenome} package for homo sapiens. <>= library(BSgenome.Hsapiens.UCSC.hg19) genome <- BSgenome.Hsapiens.UCSC.hg19 @ Use \Rfunction{extractTranscriptSeqs} to extract the UTR and coding regions from the \Rcode{BSgenome}. This function retrieves the sequences for an any \Robject{GRanges} or \Robject{GRangesList} (i.e., not just transcripts like the name implies). <>= threeUTR_seqs <- extractTranscriptSeqs(genome, threeUTR) fiveUTR_seqs <- extractTranscriptSeqs(genome, fiveUTR) cds_seqs <- extractTranscriptSeqs(genome, cds) @ The return values are \Robject{DNAStringSet} objects. <>= cds_seqs @ Our final step is to collect the coding and UTR regions (currently organzied by transcript) into groups by gene id. The \Rfunction{relist} function groups the sequences of a \Robject{DNAStringSet} object into a \Robject{DNAStringSetList} object, based on the specified \Rcode{skeleton} argument. The \Rcode{skeleton} must be a list-like object and only its shape (i.e. its element lengths) matters (its exact content is ignored). A simple form of \Rcode{skeleton} is to use a partitioning object that we make by specifying the size of each partition. The partitioning objects are different for each type of region because not all transcripts had a coding or 3' or 5' UTR region defined. <>= lst3 <- relist(threeUTR_seqs, PartitioningByWidth(sum(map %in% names(threeUTR)))) lst5 <- relist(fiveUTR_seqs, PartitioningByWidth(sum(map %in% names(fiveUTR)))) lstc <- relist(cds_seqs, PartitioningByWidth(sum(map %in% names(cds)))) @ There are 239 genes in \Rcode{map} each of which have 1 or more transcripts. The table of element lengths shows how many genes have each number of transcripts. For example, 47 genes have 1 transcript, 48 genes have 2 etc. <>= length(map) table(elementNROWS(map)) @ The lists of DNA sequences all have the same length as \Rcode{map} but one or more of the element lengths may be zero. This would indicate that data were not available for that gene. The tables below show that there was at least 1 coding region available for all genes (i.e., none of the element lengths are 0). However, both the 3' and 5' UTR results have element lengths of 0 which indicates no UTR data were available for that gene. <>= table(elementNROWS(lstc)) table(elementNROWS(lst3)) names(lst3)[elementNROWS(lst3) == 0L] ## genes with no 3' UTR data table(elementNROWS(lst5)) names(lst5)[elementNROWS(lst5) == 0L] ## genes with no 5' UTR data @ %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \subsection{How to create DNA consensus sequences for read group `families'} The motivation for this {\it HOWTO} comes from a study which explored the dynamics of point mutations. The mutations of interest exist with a range of frequencies in the control group (e.g., 0.1\% - 50\%). PCR and sequencing error rates make it difficult to identify low frequency events (e.g., < 20\%). When a library is prepared with Nextera, random fragments are generated followed by a few rounds of PCR. When the genome is large enough, reads aligning to the same start position are likely descendant from the same template fragment and should have identical sequences. The goal is to elimininate noise by grouping the reads by common start position and discarding those that do not exceed a certain threshold within each family. A new consensus sequence will be created for each read group family. \subsubsection{Sort reads into groups by start position} Load the BAM file into a GAlignments object. <>= library(Rsamtools) bamfile <- system.file("extdata", "ex1.bam", package="Rsamtools") param <- ScanBamParam(what=c("seq", "qual")) library(GenomicAlignments) gal <- readGAlignments(bamfile, use.names=TRUE, param=param) @ Use the \Rfunction{sequenceLayer} function to {\it lay} the query sequences and quality strings on the reference. <>= qseq <- setNames(mcols(gal)$seq, names(gal)) qual <- setNames(mcols(gal)$qual, names(gal)) qseq_on_ref <- sequenceLayer(qseq, cigar(gal), from="query", to="reference") qual_on_ref <- sequenceLayer(qual, cigar(gal), from="query", to="reference") @ Split by chromosome. <>= qseq_on_ref_by_chrom <- splitAsList(qseq_on_ref, seqnames(gal)) qual_on_ref_by_chrom <- splitAsList(qual_on_ref, seqnames(gal)) pos_by_chrom <- splitAsList(start(gal), seqnames(gal)) @ For each chromosome generate one GRanges object that contains unique alignment start positions and attach 3 metadata columns to it: the number of reads, the query sequences, and the quality strings. <>= gr_by_chrom <- lapply(seqlevels(gal), function(seqname) { qseq_on_ref2 <- qseq_on_ref_by_chrom[[seqname]] qual_on_ref2 <- qual_on_ref_by_chrom[[seqname]] pos2 <- pos_by_chrom[[seqname]] qseq_on_ref_per_pos <- split(qseq_on_ref2, pos2) qual_on_ref_per_pos <- split(qual_on_ref2, pos2) nread <- elementNROWS(qseq_on_ref_per_pos) gr_mcols <- DataFrame(nread=unname(nread), qseq_on_ref=unname(qseq_on_ref_per_pos), qual_on_ref=unname(qual_on_ref_per_pos)) gr <- GRanges(Rle(seqname, nrow(gr_mcols)), IRanges(as.integer(names(nread)), width=1)) mcols(gr) <- gr_mcols seqlevels(gr) <- seqlevels(gal) gr }) @ Combine all the GRanges objects obtained in (4) in 1 big GRanges object: <>= gr <- do.call(c, gr_by_chrom) seqinfo(gr) <- seqinfo(gal) @ `gr' is a GRanges object that contains unique alignment start positions: <>= gr[1:6] @ Look at qseq\_on\_ref and qual\_on\_ref. <>= qseq_on_ref qual_on_ref @ 2 reads align to start position 13. Let's have a close look at their sequences: <>= mcols(gr)$qseq_on_ref[[6]] @ and their qualities: <>= mcols(gr)$qual_on_ref[[6]] @ Note that the sequence and quality strings are those projected to the reference so the first letter in those strings are on top of start position 13, the 2nd letter on top of position 14, etc... \subsubsection{Remove low frequency reads} For each start position, remove reads with and under-represented sequence (e.g. threshold = 20\% for the data used here which is low coverage). A unique number is assigned to each unique sequence. This will make future calculations easier and a little bit faster. <>= qseq_on_ref <- mcols(gr)$qseq_on_ref tmp <- unlist(qseq_on_ref, use.names=FALSE) qseq_on_ref_id <- relist(match(tmp, tmp), qseq_on_ref) @ Quick look at `qseq\_on\_ref\_id': It's an IntegerList object with the same length and "shape" as `qseq\_on\_ref'. <>= qseq_on_ref_id @ Remove the under represented ids from each list element of `qseq\_on\_ref\_id': <>= qseq_on_ref_id2 <- endoapply(qseq_on_ref_id, function(ids) ids[countMatches(ids, ids) >= 0.2 * length(ids)]) @ Remove corresponding sequences from `qseq\_on\_ref': <>= tmp <- unlist(qseq_on_ref_id2, use.names=FALSE) qseq_on_ref2 <- relist(unlist(qseq_on_ref, use.names=FALSE)[tmp], qseq_on_ref_id2) @ \subsubsection{Create a consensus sequence for each read group family} Compute 1 consensus matrix per chromosome: <>= split_factor <- rep.int(seqnames(gr), elementNROWS(qseq_on_ref2)) qseq_on_ref2 <- unlist(qseq_on_ref2, use.names=FALSE) qseq_on_ref2_by_chrom <- splitAsList(qseq_on_ref2, split_factor) qseq_pos_by_chrom <- splitAsList(start(gr), split_factor) cm_by_chrom <- lapply(names(qseq_pos_by_chrom), function(seqname) consensusMatrix(qseq_on_ref2_by_chrom[[seqname]], as.prob=TRUE, shift=qseq_pos_by_chrom[[seqname]]-1, width=seqlengths(gr)[[seqname]])) names(cm_by_chrom) <- names(qseq_pos_by_chrom) @ 'cm\_by\_chrom' is a list of consensus matrices. Each matrix has 17 rows (1 per letter in the DNA alphabet) and 1 column per chromosome position. <>= lapply(cm_by_chrom, dim) @ Compute the consensus string from each consensus matrix. We'll put "+" in the strings wherever there is no coverage for that position, and "N" where there is coverage but no consensus. <>= cs_by_chrom <- lapply(cm_by_chrom, function(cm) { ## need to "fix" 'cm' because consensusString() ## doesn't like consensus matrices with columns ## that contain only zeroes (e.g., chromosome ## positions with no coverage) idx <- colSums(cm) == 0L cm["+", idx] <- 1 DNAString(consensusString(cm, ambiguityMap="N")) }) @ The new consensus strings. <>= cs_by_chrom @ %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \subsection{How to compute binned averages along a genome} In some applications (e.g. visualization), there is the need to compute the average of a variable defined along a genome (a.k.a. genomic variable) for a set of predefined fixed-width regions (sometimes called "bins"). The genomic variable is typically represented as a named \Robject{RleList} object with one list element per chromosome. One such example is coverage. Here we create an artificial genomic variable: <>= library(BSgenome.Scerevisiae.UCSC.sacCer2) set.seed(55) my_var <- RleList( lapply(seqlengths(Scerevisiae), function(seqlen) { tmp <- sample(50L, seqlen, replace=TRUE) %/% 50L Rle(cumsum(tmp - rev(tmp))) } ), compress=FALSE) my_var @ Use the \Rfunction{tileGenome} function to create a set of bins along the genome. <>= bins <- tileGenome(seqinfo(Scerevisiae), tilewidth=100, cut.last.tile.in.chrom=TRUE) @ Compute the binned average for \Rcode{my\_var}: <>= binnedAverage(bins, my_var, "binned_var") @ The bin size can be modified with the \Rcode{tilewidth} argument to \Rfunction{tileGenome}. See \Rcode{?binnedAverage} for additional examples. %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \section{Session Information} <>= sessionInfo() @ %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \bibliography{GenomicRanges} \bibliographystyle{plainnat} \end{document} GenomicRanges/inst/doc/GenomicRangesHOWTOs.pdf0000644000175400017540000057334013032312064022271 0ustar00biocbuildbiocbuild%PDF-1.5 % 154 0 obj << /Length 2330 /Filter /FlateDecode >> stream x[Ys6~ׯ[8U!SH]kGR*PI搇HpHlkA~!{'q+_$ܡgN b\ϕQN5iK+; xZ2 ai&t6׹F"op~]nH*1{U%_W~e޺WpK3h ʍJZʠV tBj44~ohS*%W9g;ܪDSCm$Q /,OaOca52Ð3URrDt ɨT6RqH4SmhMU{UD0H窷\;[bT# YPu֘eON;x\q6r< #o,;M$>%ct7s}(Nrq3[G: `\܁V`kagUi_[< ᘎc8|bEuB7Q<0rPcGkLR89cdlݎ}ŜfQr l졸^QY*mPݩ:1xqIN aaR#K6Jpv uٚ} Qbޘ}5wo֣EYv|mB,xmBl}AF|ζ];6W= @ "#nMaeeIQjkU؀ Cw[>lwd,n,+ʡ-+n͎qch%2|ɭlcN [a1 I5r7vfq#?Wy4 CYv*sbkۢ۶<q0ƲZSt9G@;J #rP M0;FIQ"V&|:7&L?RʪE+a*+p*]}{ 0FeN#*=k}[h_Lm)6o#S˰ 1U)OsXO%?`zuddFiühEiyKZ3JgˡpGV֦EcK|/drjeq&\5Ie׌޼t}# Ҁ,]ۆ 1"_: 4=)6b\59jolJ7[[mټfJ=IA7rt·pL$}/m`טܭ S% A=5̢󻅬v;Bv8$D,_\ z'l?chdߡB5NjBNS: J9#( [N*OSJγc] dfI;xZ M8j+> stream xڽZnH}W&C0`g&%0D;ܑHI%~jQ_ĝHh^OUWW5%I%-ȑ)(&'qHE\(EZ[R pCE21t3pC{r\D7I^ n{o moD"d5Ũr+RI60SKNr@^hsx 1Z ҧWsqsY>M@LX x'"0h^""^`!`PK tE9 L))AR=Ba2A5PYJ%KpA$SVM$A҇mu2Tw o`TiKx&.^gwa,H>!<}<Ȯs P~NisP^t崯:/'/$-՗/6Ol$]MWRsMY3].ʺ߂޶e[/;*Y9YoVp\̪f ^#kY /EU7掖itx׿_ڽ|Rzju&qxtu,ß麚[P}ۢjFkT* v6tUlꎦoU55lWOwJZe}m#f=2l +cnGkF{Y?XnhѷU7ekf|0ͻc, K|v~=1OuW.[ 2! 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In the \software{Bioconductor} package hierarchy, it builds upon the \Rpackage{IRanges} (infrastructure) package and provides support for the \Rpackage{BSgenome} (infrastructure), \Rpackage{Rsamtools} (I/O), \Rpackage{ShortRead} (I/O \& QA), \Rpackage{rtracklayer} (I/O), and \Rpackage{GenomicFeatures} (infrastructure) packages, and many other \software{Bioconductor} packages. This package lays a foundation for genomic analysis by introducing three classes (\Rclass{GRanges}, \Rclass{GPos}, and \Rclass{GRangesList}), which are used to represent genomic ranges, genomic positions, and groups of genomic ranges. This vignette focuses on the \Rclass{GRanges} and \Rclass{GRangesList} classes and their associated methods. The \Rpackage{GenomicRanges} package is available at bioconductor.org and can be downloaded via \Rfunction{biocLite}: <>= source("https://bioconductor.org/biocLite.R") biocLite("GenomicRanges") @ <>= library(GenomicRanges) @ %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \section{\Rclass{GRanges}: Genomic Ranges} The \Rclass{GRanges} class represents a collection of genomic ranges that each have a single start and end location on the genome. It can be used to store the location of genomic features such as contiguous binding sites, transcripts, and exons. These objects can be created by using the \Rfunction{GRanges} constructor function. For example, <>= gr <- GRanges(seqnames = Rle(c("chr1", "chr2", "chr1", "chr3"), c(1, 3, 2, 4)), ranges = IRanges(1:10, end = 7:16, names = head(letters, 10)), strand = Rle(strand(c("-", "+", "*", "+", "-")), c(1, 2, 2, 3, 2)), score = 1:10, GC = seq(1, 0, length=10)) gr @ \noindent creates a \Rclass{GRanges} object with 10 genomic ranges. The output of the \Rclass{GRanges} \Rmethod{show} method separates the information into a left and right hand region that are separated by \Rcode{|} symbols. The genomic coordinates (seqnames, ranges, and strand) are located on the left-hand side and the metadata columns (annotation) are located on the right. For this example, the metadata is comprised of \Rcode{score} and \Rcode{GC} information, but almost anything can be stored in the metadata portion of a \Rclass{GRanges} object. The components of the genomic coordinates within a \Rclass{GRanges} object can be extracted using the \Rmethod{seqnames}, \Rmethod{ranges}, and \Rmethod{strand} accessor functions. <>= seqnames(gr) ranges(gr) strand(gr) @ Stored annotations for these coordinates can be extracted as a \Rclass{DataFrame} object using the \Rmethod{mcols} accessor. <>= mcols(gr) mcols(gr)$score @ Finally, the total lengths of the various sequences that the ranges are aligned to can also be stored in the \Rclass{GRanges} object. So if this is data from \textit{Homo sapiens}, we can set the values as: <>= seqlengths(gr) <- c(249250621,243199373,198022430) @ And then retrieves as: <>= seqlengths(gr) @ Methods for accessing the \Rmethod{length} and \Rmethod{names} have also been defined. <>= names(gr) length(gr) @ \subsection{Splitting and combining \Rclass{GRanges} objects} \Rclass{GRanges} objects can be devided into groups using the \Rmethod{split} method. This produces a \Rclass{GRangesList} object, a class that will be discussed in detail in the next section. <>= sp <- split(gr, rep(1:2, each=5)) sp @ If you then grab the components of this list, they can also be merged by using the \Rmethod{c} and \Rmethod{append} methods. <>= c(sp[[1]], sp[[2]]) @ \subsection{Subsetting \Rclass{GRanges} objects} The expected subsetting operations are also available for \Rclass{GRanges} objects. <>= gr[2:3] @ A second argument to the \Rmethod{[} subset operator can be used to specify which metadata columns to extract from the \Rclass{GRanges} object. For example, <>= gr[2:3, "GC"] @ You can also assign into elements of the \Rclass{GRanges} object. Here is an example where the 2nd row of a \Rclass{GRanges} object is replaced with the 1st row of \Robject{gr}. <>= singles <- split(gr, names(gr)) grMod <- gr grMod[2] <- singles[[1]] head(grMod, n=3) @ Here is a second example where the metadata for score from the 3rd element is replaced with the score from the 2nd row etc. <>= grMod[2,1] <- singles[[3]][,1] head(grMod, n=3) @ There are also methods to repeat, reverse, or select specific portions of \Rclass{GRanges} objects. <>= rep(singles[[2]], times = 3) rev(gr) head(gr,n=2) tail(gr,n=2) window(gr, start=2,end=4) gr[IRanges(start=c(2,7), end=c(3,9))] @ \subsection{Basic interval operations for \Rclass{GRanges} objects} Basic interval characteristics of \Rclass{GRanges} objects can be extracted using the \Rmethod{start}, \Rmethod{end}, \Rmethod{width}, and \Rmethod{range} methods. <>= g <- gr[1:3] g <- append(g, singles[[10]]) start(g) end(g) width(g) range(g) @ The \Rclass{GRanges} class also has many methods for manipulating the intervals. For example, the \Rmethod{flank} method can be used to recover regions flanking the set of ranges represented by the \Rclass{GRanges} object. So to get a \Rclass{GRanges} object containing the ranges that include the 10 bases upstream of the ranges: <>= flank(g, 10) @ And to include the downstream bases: <>= flank(g, 10, start=FALSE) @ Similar to \Rmethod{flank}, there are also operations to \Rmethod{resize} and \Rmethod{shift} our \Rclass{GRanges} object. The \Rmethod{shift} method will move the ranges by a specific number of base pairs, and the \Rmethod{resize} method will extend the ranges by a specified width. <>= shift(g, 5) resize(g, 30) @ The \Rmethod{reduce} will align the ranges and merge overlapping ranges to produce a simplified set. <>= reduce(g) @ Sometimes you may be interested in the spaces or the qualities of the spaces between the ranges represented by your \Rclass{GRanges} object. The \Rmethod{gaps} method will help you calculate the spaces between a reduced version of your ranges: <>= gaps(g) @ And sometimes you also may want to know how many quantitatively unique fragments your ranges could possibly represent. For this task there is the \Rmethod{disjoin} method. <>= disjoin(g) @ One of the most powerful methods for looking at \Rclass{GRanges} objects is the \Rmethod{coverage} method. The \Rmethod{coverage} method quantifies the degree of overlap for all the ranges in a \Rclass{GRanges} object. <>= coverage(g) @ \subsection{Interval set operations for \Rclass{GRanges} objects} There are also operations for calculating relationships between different \Rclass{GRanges} objects. Here are some examples for how you can calculate the \Rmethod{union}, the \Rmethod{intersect} and the asymmetric difference (using \Rmethod{setdiff}). <>= g2 <- head(gr, n=2) union(g, g2) intersect(g, g2) setdiff(g, g2) @ In addition, there is similar set of operations that act at the level of the individual ranges within each \Rclass{GRanges}. These operations all begin with a ``p", which is short for parallel. A requirement for this set of operations is that the number of elements in each \Rclass{GRanges} object has to be the same, and that both of the objects have to have the same seqnames and strand assignments throughout. <>= g3 <- g[1:2] ranges(g3[1]) <- IRanges(start=5, end=12) punion(g2, g3) pintersect(g2, g3) psetdiff(g2, g3) @ For even more information on the \Rcode{GRanges} classes be sure to consult the manual page. <>= ?GRanges @ %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \section{\Rclass{GRangesList}: Groups of Genomic Ranges} Some important genomic features, such as spliced transcripts that are are comprised of exons, are inherently compound structures. Such a feature makes much more sense when expressed as a compound object such as a \Rclass{GRangesList}. Whenever genomic features consist of multiple ranges that are grouped by a parent feature, they can be represented as a \Rclass{GRangesList} object. Consider the simple example of the two transcript \Rfunction{GRangesList} below created using the \Rfunction{GRangesList} constructor. <>= gr1 <- GRanges(seqnames = "chr2", ranges = IRanges(3, 6), strand = "+", score = 5L, GC = 0.45) gr2 <- GRanges(seqnames = c("chr1", "chr1"), ranges = IRanges(c(7,13), width = 3), strand = c("+", "-"), score = 3:4, GC = c(0.3, 0.5)) grl <- GRangesList("txA" = gr1, "txB" = gr2) grl @ The \Rmethod{show} method for a \Rclass{GRangesList} object displays it as a named list of \Rclass{GRanges} objects, where the names of this list are considered to be the names of the grouping feature. In the example above, the groups of individual exon ranges are represented as separate \Rclass{GRanges} objects which are further organized into a list structure where each element name is a transcript name. Many other combinations of grouped and labeled \Rclass{GRanges} objects are possible of course, but this example is expected to be a common arrangement. \subsection{Basic \Rclass{GRangesList} accessors} Just as with \Rclass{GRanges} object, the components of the genomic coordinates within a \Rclass{GRangesList} object can be extracted using simple accessor methods. Not surprisingly, the \Rclass{GRangesList} objects have many of the same accessors as \Rclass{GRanges} objects. The difference is that many of these methods return a list since the input is now essentially a list of \Rclass{GRanges} objects. Here are a few examples: <>= seqnames(grl) ranges(grl) strand(grl) @ The \Rmethod{length} and \Rmethod{names} methods will return the length or names of the list and the \Rmethod{seqlengths} method will return the set of sequence lengths. <>= length(grl) names(grl) seqlengths(grl) @ The \Rmethod{elementNROWS} method returns a list of integers corresponding to the result of calling \Rmethod{NROW} on each individual \Rclass{GRanges} object contained by the \Rclass{GRangesList}. This is a faster alternative to calling \Rmethod{lapply} on the \Rclass{GRangesList}. <>= elementNROWS(grl) @ You can also use \Rmethod{isEmpty} to test if a \Rclass{GRangesList} object contains anything. <>= isEmpty(grl) @ Finally, in the context of a \Rclass{GRangesList} object, the \Rmethod{mcols} method performs a similar operation to what it does on a \Rclass{GRanges} object. However, this metadata now refers to information at the list level instead of the level of the individual \Rclass{GRanges} objects. <>= mcols(grl) <- c("Transcript A","Transcript B") mcols(grl) @ \subsection{Combining \Rclass{GRangesList} objects} \Rclass{GRangesList} objects can be unlisted to combine the separate \Rclass{GRanges} objects that they contain as an expanded \Rclass{GRanges}. <>= ul <- unlist(grl) @ You can also append values together useing \Rmethod{append} or \Rmethod{c}. % <>= % grl2 <- shift(grl,10) % names(grl2) <- c("shiftTxA","shiftTxB") % append(grl, grl2) % c(grl, grl2) % @ \subsection{Basic interval operations for \Rclass{GRangesList} objects} For interval operations, many of the same methods exist for \Rclass{GRangesList} objects that exist for \Rclass{GRanges} objects. <>= start(grl) end(grl) width(grl) @ And as with \Rclass{GRanges} objects, you can also shift all the \Rclass{GRanges} objects in a \Rclass{GRangesList} object, or calculate the coverage. Both of these operations are also carried out across each \Rclass{GRanges} list member. <>= shift(grl, 20) coverage(grl) @ \subsection{Subsetting \Rclass{GRangesList} objects} As you might guess, the subsetting of a \Rclass{GRangesList} object is quite different from subsetting on a \Rclass{GRanges} object in that it acts as if you are subseting a list. If you try out the following you will notice that the standard conventions have been followed. <>= grl[1] grl[[1]] grl["txA"] grl$txB @ But in addition to this, when subsetting a \Rclass{GRangesList}, you can also pass in a second parameter (as with a \Rclass{GRanges} object) to again specify which of the metadata columns you wish to select. <>= grl[1, "score"] grl["txB", "GC"] @ The \Rmethod{head}, \Rmethod{tail}, \Rmethod{rep}, \Rmethod{rev}, and \Rmethod{window} methods all behave as you would expect them to for a list object. For example, the elements referred to by \Rmethod{window} are now list elements instead of \Rclass{GRanges} elements. <>= rep(grl[[1]], times = 3) rev(grl) head(grl, n=1) tail(grl, n=1) window(grl, start=1, end=1) grl[IRanges(start=2, end=2)] @ \subsection{Looping over \Rclass{GRangesList} objects} For \Rclass{GRangesList} objects there is also a family of \Rmethod{apply} methods. These include \Rmethod{lapply}, \Rmethod{sapply}, \Rmethod{mapply}, \Rmethod{endoapply}, \Rmethod{mendoapply}, \Rmethod{Map}, and \Rmethod{Reduce}. The different looping methods defined for \Rclass{GRangesList} objects are useful for returning different kinds of results. The standard \Rmethod{lapply} and \Rmethod{sapply} behave according to convention, with the \Rmethod{lapply} method returning a list and \Rmethod{sapply} returning a more simplified output. <>= lapply(grl, length) sapply(grl, length) @ As with \Rclass{IRanges} objects, there is also a multivariate version of \Rmethod{sapply}, called \Rmethod{mapply}, defined for \Rclass{GRangesList} objects. And, if you don't want the results simplified, you can call the \Rmethod{Map} method, which does the same things as \Rmethod{mapply} but without simplifying the output. <>= grl2 <- shift(grl, 10) names(grl2) <- c("shiftTxA", "shiftTxB") mapply(c, grl, grl2) Map(c, grl, grl2) @ Sometimes, you may not want to get back a simplified output or a list. Sometimes you will want to get back a modified version of the \Rclass{GRangesList} that you originally passed in. This is conceptually similar to the mathematical notion of an endomorphism. This is achieved using the \Rmethod{endoapply} method, which will return the results as a \Rclass{GRangesList} object. <>= endoapply(grl,rev) @ And, there is also a multivariate version of the \Rmethod{endoapply} method in the form of the \Rmethod{mendoapply} method. <>= mendoapply(c,grl,grl2) @ Finally, the \Rmethod{Reduce} method will allow the \Rclass{GRanges} objects to be collapsed across the whole of the \Rclass{GRangesList} object. % Again, this seems like a sub-optimal example to me. <>= Reduce(c,grl) @ For even more information on the \Rcode{GRangesList} classes be sure to consult the manual page. <>= ?GRangesList @ %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \section{Interval overlaps involving \Rclass{GRanges} and \Rclass{GRangesList} objects} Interval overlapping is the process of comparing the ranges in two objects to determine if and when they overlap. As such, it is perhaps the most common operation performed on \Rclass{GRanges} and \Rclass{GRangesList} objects. To this end, the \Rpackage{GenomicRanges} package provides a family of interval overlap functions. The most general of these functions is \Rfunction{findOverlaps}, which takes a query and a subject as inputs and returns a \Rclass{Hits} object containing the index pairings for the overlapping elements. <>= mtch <- findOverlaps(gr, grl) as.matrix(mtch) @ \noindent As suggested in the sections discussing the nature of the \Rclass{GRanges} and \Rclass{GRangesList} classes, the index in the above matrix of hits for a \Rclass{GRanges} object is a single range while for a \Rclass{GRangesList} object it is the set of ranges that define a "feature". Another function in the overlaps family is \Rfunction{countOverlaps}, which tabulates the number of overlaps for each element in the query. <>= countOverlaps(gr, grl) @ A third function in this family is \Rfunction{subsetByOverlaps}, which extracts the elements in the query that overlap at least one element in the subject. <>= subsetByOverlaps(gr,grl) @ Finally, you can use the \Rcode{select} argument to get the index of the first overlapping element in the subject for each element in the query. <>= findOverlaps(gr, grl, select="first") findOverlaps(grl, gr, select="first") @ %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \section{Session Information} All of the output in this vignette was produced under the following conditions: \begin{small} <>= sessionInfo() @ \end{small} \end{document} GenomicRanges/inst/doc/GenomicRangesIntroduction.pdf0000644000175400017540000060234613032312064023666 0ustar00biocbuildbiocbuild%PDF-1.5 % 75 0 obj << /Length 2201 /Filter /FlateDecode >> stream xrF`a Z1O r*۵*ں%9@$D1I٥Obdٮ p=<3]*_δժ a\Tfۢ:M<7>[>uKsl |ٛuެq;qhkvᇹ 4_f5>k]4PAJM mf*V nWv q6-d;9w{> c.X:#p5x: @UtL54fFL>]f %svT@+eYbNŬ ةEzW 6f 1[9(58\#5r.~nFX5S:\Us %mFzE^颌8 ԜuJUyA[q-'-%^\kђۨi՚3DFVew~X,@YQ]-w/ 6e4p&|xKG,!cҥr2INȄf/u(OqߕO!&uY}2-8uZPb=kno'$ olU05|Fr*ENV^'9>;z]M68XH4ukĤxR C@[2ZWO` NUVR[/*!j ; Ν8V)xLNi}-/cRT^,dXn|ӟy?Q="c7bA$G)8Vyc?W񔃨>5Y-T ?`hC·$瑠 -jg鿂AMO楕BXj^%iof[͔D0[u%W,r2*ܸF(7_4w]ČÄ6uRnH5FU<B6ڐɃ͡TbY! cK֒&nUgHe+qFIrAeObiaq2nim0;\+ lO 9Uߑ٧)>JLjN5>]G53wEUt!4O&,k\ 3"޷H6 ᪯bDD.괼cj!MkAH,CuXܹN_&@n^xPGLn6T E !}#m ҧh[C`%Գ.5x!VzD3&wka.E$K7 A=:}g*б-T"iew{yK,#Ś` N /z.K4e&'`>t~V[S ;pcA` e~52fQT|:4u>%^V10T$*!g 'Щ+*rfILe0g.  kDuhP>P{.FH3"0jz+N8ƈQӮdXyl!!ްzLǠbߋ{;lGΕ 9B.ؐϓ#oUL;DH_H8|ず.ܖ_R []aJ{ɟ"GLr)K*$/.]M ~||{\WAzOZrŨcr&p(hR=;ONFN .->]$X;Ԋ3!M`}q=/0[[غ7xz*jhd ij-1HTA`b|KCa; o ¸= l897сdW|mE)(}y;$a1V@9loL4"e5?szV"\1Cփ?<kD: endstream endobj 92 0 obj << /Length 2160 /Filter /FlateDecode >> stream xZK8W$o"Hޙvj3=Mu%;EN^=T/v?jE ~U(F-n׋zth;]->ej?PW!*0$/em-jYe^u]˸`n e^ukc \wU\I m ĪuS9 T&,!R3U$Ҽ3ρRɓ5L8q?HKfxZXL;Ps\q]T:3C;HLIjGH%C;2mRfeqδ́ڃyS Y7\JյLk)]Q,FG/O b3AghqMIO9/KcdmxoF\ ֨Mx$G<,6w Tr0-ߑ _c쇣fߑd80Z8)Wa0*v'j]e_o`~:6U j}L1, ,iӾ5oFBJ) rZ%,ѮZc a+G: lTcU^JEݖ`w=R4fm"e.V^YS7L;GFb~ n3=|/yǖp>Vׇ]0Ȗ*&-}Sfg;&W~H6a&mA7.#XY)j_NzqoO`')ظGK[h+sy;볨ؿXwI;J6\1fV1~ b7>Y@vDD)n΋8!^n'6 },mT Wwy킘Իo&vG={ De+O$gUt5mDw.kX|tʆE fMӆߤm$(5JO~#lEI(}Z.V]|PUEg-^R+Rr>Ojs͚=gcp q] D j>!8,v}+Y9Siݥ7_BTU|[܅ @B/4ݧ. ;2Jyۛ%sҝE_h]ãZF:u1Fj|ʢ$^ڑ}AU- w1XU y p?Y0\u$sjm]@MԸrF%@!=듹[<쩺3 ރ(~Н}"X=W>Z8zQ|՟[,*j}%݆o7ʠ:P4ma158g:8?.WRM^Q\4O.kfB_"TaLL?6m,w{Fpi+9-HهJٕ؛.Rzts*kLesݣTATnyi<sB%=fl9!vH³_U#Id @ǑBd`%E\yϓt *sƲìkӺ(mhdnHc]=eovkȰ(ko(n2t,Mڻ"؊ڌN0UOt٢2W6vhw8u^pGGor/R|#n#?mVB*P1;[zQxsnF#"JƔͽDBxCwyk#3~݈uyx&,E䩘xu-*_*s?5`#ADm) G0TpAHס mXg\GtTescY_QSyWy8t]@P !*u7>XecF:qޥEU0}\WS&H:27!0׭)ejDE#}v=+&sJ!֨3ѹyguݺwf@E^> stream xڭWIo7WAr% Mh/KX2dMN}gH %nnWԬ.d1^LJä5ge-&l6u)LD_'| jc0(JS@[P縮s]pEx L#3'Q!47Re; - mc9?`an-Wn)i?s$ÁHl%;g WII#yM4d~Fk|?zFޠOB|NڶTdۯݵ0뀖fVdi]*})[[g{RۋDT˄z0 QQes5-K22kF0YZYݩsؑ%NAəT=7b/+j/4Z :TkL`)c/ Dp?o"Hݒ$C8 %PqX V*]q #|$9*_{}I2d`ȌȡW2 b6w>HIdNՉQ0a<1T {Y$*'G&:'G×G[ɟ㘣)Dwпgʄ.rTUD5QC ZaRJyT#|4kRi x [f{kJPgae05&k|uF6"]4<.TXe;5[/ʜ*rCUu딅jֻaU8biq縂&MԬ~tqrCZ2}jZeJ9qgED/iv hyݓYLv7X Ș|dL\aGu/@saٽKL <>/PtݾMm(4wSVP{ȣ<*'u*Yo [a)+6gbl,c5YoFgGY09#UVњL)*GSy=M{NqvgCךvHBYѷ >v 1cL75K69<Bb(߃…,d-: zc.Vm _ӛx:xE<]xwn0Xs/82̉6D* BĢνտlb endstream endobj 100 0 obj << /Length 1483 /Filter /FlateDecode >> stream xYKs6 arqŧ6v;-A~$q&SL~ (1d=ngmRϓWo)=Yhr>25#c+V[>GqɹPşKo\]KYjoR7ﴊe0%Ũ1ֶhW@jzt\Q\lKkt3aܒRH4W7L6@8H8^B*,\Q3@ ]>;v`'Pc=-1uΓ6<[phTnM96ЮIvwc27 k܁Cƥ\.:A-6}VZKd:sGn6Hl[%e1szh; ۭ}Άms`7Cst@MS-fá33)~3nzȺ"BnY#hg'!} 2~UB?֐rC6(8chbr`h?A~U㮿3!#Xq]Cw ?Թ^l` S؏)D"Cx=`x miIfU߱XӄbM-$D1aI;P"Ild#Q .1[z0q 㕪-%eU5ҭ"u%m!aN ݇óA*yqJ? 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3, 2, 4)), IRanges(1:10, width=10:1, names=head(letters, 10)), Rle(strand(c("-", "+", "*", "+", "-")), c(1, 2, 2, 3, 2)), score=1:10, GC=seq(1, 0, length=10)) ir <- IRanges(c(11:13, 2, 7:6), width=3) mcols(ir) <- DataFrame(id=letters[1:6], score=3:-2) x <- GRanges(c("chr1:1-1000", "chr2:2000-3000"), score=c(0.45, 0.1), a1=c(5L, 7L), a2=c(6, 8)) mcols(x)$score[2] <- NA y <- GRanges(c("chr2:150-151", "chr1:1-10", "chr2:2000-3000"), score=c(0.7, 0.82, 0.1), b1=c(0L, 5L, 1L), b2=c(1, -2, 1)) ################################################### ### code chunk number 2: inner_outer_mcols ################################################### mcols(grl)$id <- paste0("ID", seq_along(grl)) grl ################################################### ### code chunk number 3: inner_outer_mcols ################################################### mcols(grl) # outer mcols mcols(unlist(grl, use.names=FALSE)) # inner mcols ################################################### ### code chunk number 4: Ten_things_slides.Rnw:84-85 ################################################### gr ################################################### ### code chunk number 5: Ten_things_slides.Rnw:95-96 ################################################### invertStrand(gr) ################################################### ### code chunk number 6: Ten_things_slides.Rnw:106-107 ################################################### grl ################################################### ### code chunk number 7: Ten_things_slides.Rnw:117-118 ################################################### invertStrand(grl) ################################################### ### code chunk number 8: Ten_things_slides.Rnw:131-135 ################################################### cvg <- Rle(c(0L, 2L, 5L, 1L, 0L), c(10, 6, 3, 4, 15)) cvg i <- IRanges(c(16, 19, 9), width=5, names=letters[1:3]) i ################################################### ### code chunk number 9: Ten_things_slides.Rnw:143-144 ################################################### extractList(cvg, i) ################################################### ### code chunk number 10: Ten_things_slides.Rnw:154-157 ################################################### i <- IntegerList(c(25:20), NULL, seq(from=2, to=length(cvg), by=2)) i extractList(cvg, i) ################################################### ### code chunk number 11: Ten_things_slides.Rnw:168-171 ################################################### ir ir2 <- reduce(ir, with.revmap=TRUE) ir2 ################################################### ### code chunk number 12: Ten_things_slides.Rnw:180-186 ################################################### revmap <- mcols(ir2)$revmap extractList(mcols(ir)$id, revmap) extractList(mcols(ir)$score, revmap) mcols(ir2) <- DataFrame(id=extractList(mcols(ir)$id, revmap), score=extractList(mcols(ir)$score, revmap)) ir2 ################################################### ### code chunk number 13: Ten_things_slides.Rnw:199-202 ################################################### sliding_query <- IRanges(1:6, width=0) sliding_query countOverlaps(sliding_query, IRanges(3, 4)) ################################################### ### code chunk number 14: Ten_things_slides.Rnw:209-210 ################################################### countOverlaps(sliding_query, IRanges(3, 4), minoverlap=0) ################################################### ### code chunk number 15: Ten_things_slides.Rnw:223-227 ################################################### library(Biostrings) library(hgu95av2probe) probes <- DNAStringSet(hgu95av2probe) probes ################################################### ### code chunk number 16: Ten_things_slides.Rnw:236-237 ################################################### replaceAt(probes, at=IRanges(3, 4), value="-++-") ################################################### ### code chunk number 17: Ten_things_slides.Rnw:246-247 ################################################### replaceAt(probes, at=IRanges(3, 4), value="") ################################################### ### code chunk number 18: Ten_things_slides.Rnw:256-257 ################################################### replaceAt(probes, at=IRanges(4, 3), value="-++-") ################################################### ### code chunk number 19: Ten_things_slides.Rnw:267-269 ################################################### midx <- vmatchPattern("VCGTT", probes, fixed=FALSE) replaceAt(probes, at=midx, value="-++-") ################################################### ### code chunk number 20: GRanges_as_a_subscript_1 ################################################### cvg <- RleList(chr1=101:120, chr2=2:-8, chr3=31:40) gr ################################################### ### code chunk number 21: GRanges_as_a_subscript_2 ################################################### cvg[gr] ################################################### ### code chunk number 22: Ten_things_slides.Rnw:304-310 ################################################### library(BSgenome.Mmusculus.UCSC.mm10) genome <- BSgenome.Mmusculus.UCSC.mm10 library(TxDb.Mmusculus.UCSC.mm10.knownGene) txdb <- TxDb.Mmusculus.UCSC.mm10.knownGene ex <- exons(txdb, columns=c("exon_id", "tx_name", "gene_id")) v <- Views(genome, ex) ################################################### ### code chunk number 23: Ten_things_slides.Rnw:319-320 ################################################### v ################################################### ### code chunk number 24: Ten_things_slides.Rnw:329-331 ################################################### af <- alphabetFrequency(v, baseOnly=TRUE) head(af) ################################################### ### code chunk number 25: Ten_things_slides.Rnw:341-351 ################################################### library(Rsamtools) library(RNAseqData.HNRNPC.bam.chr14) fl <- RNAseqData.HNRNPC.bam.chr14_BAMFILES[1] sbp <- ScanBamParam(which=GRanges("chr14", IRanges(1, 53674770))) pp <- PileupParam(distinguish_nucleotides=FALSE, distinguish_strands=FALSE, min_mapq=13, min_base_quality=10, min_nucleotide_depth=4) res <- pileup(fl, scanBamParam=sbp, pileupParam=pp) ################################################### ### code chunk number 26: Ten_things_slides.Rnw:359-361 ################################################### dim(res) head(res) ################################################### ### code chunk number 27: Ten_things_slides.Rnw:372-374 ################################################### x y ################################################### ### code chunk number 28: Ten_things_slides.Rnw:384-385 ################################################### merge(x, y) ################################################### ### code chunk number 29: Ten_things_slides.Rnw:395-396 ################################################### merge(x, y, all=TRUE) GenomicRanges/inst/doc/Ten_things_slides.Rnw0000644000175400017540000002152613032312063022177 0ustar00biocbuildbiocbuild%\VignetteIndexEntry{10 Things You Didn't Know (slides from BioC 2016)} %\VignetteDepends{GenomicRanges, Biostrings, Rsamtools, BSgenome, hgu95av2probe} \SweaveOpts{keep.source=TRUE, eps=FALSE, width=9, height=3} \documentclass[9pt]{beamer} \usepackage{slides} \renewcommand\Rclass[1]{{\texttt{#1}\index{#1 (class)}}} \title{10 things (maybe) you didn't know about GenomicRanges, Biostrings, and Rsamtools} \author{Herv\'e Pag\`es\\ \href{mailto:hpages@fredhutch.org}{hpages@fredhutch.org}} \date{June 2016} \begin{document} <>= options(width=80) library(GenomicRanges) library(Biostrings) library(Rsamtools) library(BSgenome) library(hgu95av2probe) example(GRangesList) gr <- GRanges(Rle(c("chr2", "chr2", "chr1", "chr3"), c(1, 3, 2, 4)), IRanges(1:10, width=10:1, names=head(letters, 10)), Rle(strand(c("-", "+", "*", "+", "-")), c(1, 2, 2, 3, 2)), score=1:10, GC=seq(1, 0, length=10)) ir <- IRanges(c(11:13, 2, 7:6), width=3) mcols(ir) <- DataFrame(id=letters[1:6], score=3:-2) x <- GRanges(c("chr1:1-1000", "chr2:2000-3000"), score=c(0.45, 0.1), a1=c(5L, 7L), a2=c(6, 8)) mcols(x)$score[2] <- NA y <- GRanges(c("chr2:150-151", "chr1:1-10", "chr2:2000-3000"), score=c(0.7, 0.82, 0.1), b1=c(0L, 5L, 1L), b2=c(1, -2, 1)) @ \maketitle %\frame{\tableofcontents} %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \begin{frame}[fragile] \frametitle{1. {\it Inner} vs {\it outer} metadata columns} \begin{exampleblock}{} {\small <>= mcols(grl)$id <- paste0("ID", seq_along(grl)) grl @ } \end{exampleblock} \end{frame} \begin{frame}[fragile] \frametitle{1. {\it Inner} vs {\it outer} metadata columns} \begin{exampleblock}{} {\small <>= mcols(grl) # outer mcols mcols(unlist(grl, use.names=FALSE)) # inner mcols @ } \end{exampleblock} \end{frame} %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \begin{frame}[fragile] \frametitle{2. invertStrand()} Works out-of-the-box on any object that has a strand() getter and setter ==> no need to implement specific methods. \begin{exampleblock}{} {\small <<>>= gr @ } \end{exampleblock} \end{frame} \begin{frame}[fragile] \frametitle{2. invertStrand()} \begin{exampleblock}{} {\small <<>>= invertStrand(gr) @ } \end{exampleblock} \end{frame} \begin{frame}[fragile] \frametitle{2. invertStrand()} \begin{exampleblock}{} {\small <<>>= grl @ } \end{exampleblock} \end{frame} \begin{frame}[fragile] \frametitle{2. invertStrand()} \begin{exampleblock}{} {\small <<>>= invertStrand(grl) @ } \end{exampleblock} \end{frame} %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \begin{frame}[fragile] \frametitle{3. extractList()} Extract groups of elements from a vector-like object and return them in a list-like object. \begin{exampleblock}{} <<>>= cvg <- Rle(c(0L, 2L, 5L, 1L, 0L), c(10, 6, 3, 4, 15)) cvg i <- IRanges(c(16, 19, 9), width=5, names=letters[1:3]) i @ \end{exampleblock} \end{frame} \begin{frame}[fragile] \frametitle{3. extractList()} \begin{exampleblock}{} <<>>= extractList(cvg, i) @ \end{exampleblock} \end{frame} \begin{frame}[fragile] \frametitle{3. extractList()} \begin{exampleblock}{} \Rcode{i} can be an IntegerList object: {\small <<>>= i <- IntegerList(c(25:20), NULL, seq(from=2, to=length(cvg), by=2)) i extractList(cvg, i) @ } \end{exampleblock} \end{frame} %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \begin{frame}[fragile] \frametitle{4. 'with.revmap' arg for reduce() and (now) disjoin()} \begin{exampleblock}{} <<>>= ir ir2 <- reduce(ir, with.revmap=TRUE) ir2 @ \end{exampleblock} \end{frame} \begin{frame}[fragile] \frametitle{4. 'with.revmap' arg for reduce() and disjoin()} \begin{exampleblock}{} {\small <<>>= revmap <- mcols(ir2)$revmap extractList(mcols(ir)$id, revmap) extractList(mcols(ir)$score, revmap) mcols(ir2) <- DataFrame(id=extractList(mcols(ir)$id, revmap), score=extractList(mcols(ir)$score, revmap)) ir2 @ } \end{exampleblock} \end{frame} %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \begin{frame}[fragile] \frametitle{5. Zero-width ranges} \Rcode{findOverlaps}/\Rcode{countOverlaps} support zero-width ranges. \begin{exampleblock}{} {\small <<>>= sliding_query <- IRanges(1:6, width=0) sliding_query countOverlaps(sliding_query, IRanges(3, 4)) @ } \end{exampleblock} But you have to specify \Rcode{minoverlap=0} for this to work (default is 1). \begin{exampleblock}{} {\small <<>>= countOverlaps(sliding_query, IRanges(3, 4), minoverlap=0) @ } \end{exampleblock} \end{frame} %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \begin{frame}[fragile] \frametitle{6. Biostrings::replaceAt()} Perform multiple substitutions at arbitrary positions in a set of sequences. \begin{exampleblock}{} <<>>= library(Biostrings) library(hgu95av2probe) probes <- DNAStringSet(hgu95av2probe) probes @ \end{exampleblock} \end{frame} \begin{frame}[fragile] \frametitle{6. Biostrings::replaceAt()} Replace 3rd and 4th nucleotides by pattern \Rcode{-++-}. \begin{exampleblock}{} <<>>= replaceAt(probes, at=IRanges(3, 4), value="-++-") @ \end{exampleblock} \end{frame} \begin{frame}[fragile] \frametitle{6. Biostrings::replaceAt()} If supplied pattern is empty, then performs deletions. \begin{exampleblock}{} <<>>= replaceAt(probes, at=IRanges(3, 4), value="") @ \end{exampleblock} \end{frame} \begin{frame}[fragile] \frametitle{6. Biostrings::replaceAt()} If \Rcode{at} is a zero-with range, then performs insertions. \begin{exampleblock}{} <<>>= replaceAt(probes, at=IRanges(4, 3), value="-++-") @ \end{exampleblock} \end{frame} \begin{frame}[fragile] \frametitle{6. Biostrings::replaceAt()} Use it in combination with \Rcode{vmatchPattern} to replace all the occurences of a given pattern with another pattern: \begin{exampleblock}{} <<>>= midx <- vmatchPattern("VCGTT", probes, fixed=FALSE) replaceAt(probes, at=midx, value="-++-") @ \end{exampleblock} \end{frame} %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \begin{frame}[fragile] \frametitle{7. GRanges as a subscript} \begin{exampleblock}{} {\small <>= cvg <- RleList(chr1=101:120, chr2=2:-8, chr3=31:40) gr @ } \end{exampleblock} \end{frame} \begin{frame}[fragile] \frametitle{7. GRanges as a subscript} \begin{exampleblock}{} {\scriptsize <>= cvg[gr] @ } \end{exampleblock} \end{frame} %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \begin{frame}[fragile] \frametitle{8. BSgenomeViews objects} \begin{exampleblock}{} <<>>= library(BSgenome.Mmusculus.UCSC.mm10) genome <- BSgenome.Mmusculus.UCSC.mm10 library(TxDb.Mmusculus.UCSC.mm10.knownGene) txdb <- TxDb.Mmusculus.UCSC.mm10.knownGene ex <- exons(txdb, columns=c("exon_id", "tx_name", "gene_id")) v <- Views(genome, ex) @ \end{exampleblock} \end{frame} \begin{frame}[fragile] \frametitle{8. BSgenomeViews objects} \begin{exampleblock}{} {\scriptsize <<>>= v @ } \end{exampleblock} \end{frame} \begin{frame}[fragile] \frametitle{8. BSgenomeViews objects} \begin{exampleblock}{} <<>>= af <- alphabetFrequency(v, baseOnly=TRUE) head(af) @ \end{exampleblock} \end{frame} %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \begin{frame}[fragile] \frametitle{9. Pile-up statistics on a BAM file with Rsamtools::pileup()} \begin{exampleblock}{} <<>>= library(Rsamtools) library(RNAseqData.HNRNPC.bam.chr14) fl <- RNAseqData.HNRNPC.bam.chr14_BAMFILES[1] sbp <- ScanBamParam(which=GRanges("chr14", IRanges(1, 53674770))) pp <- PileupParam(distinguish_nucleotides=FALSE, distinguish_strands=FALSE, min_mapq=13, min_base_quality=10, min_nucleotide_depth=4) res <- pileup(fl, scanBamParam=sbp, pileupParam=pp) @ \end{exampleblock} \end{frame} \begin{frame}[fragile] \frametitle{9. Pile-up statistics on a BAM file with Rsamtools::pileup()} \begin{exampleblock}{} <<>>= dim(res) head(res) @ \end{exampleblock} \end{frame} %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \begin{frame}[fragile] \frametitle{10. Merging 2 GRanges objects (added this week)} \begin{exampleblock}{} {\small <<>>= x y @ } \end{exampleblock} \end{frame} \begin{frame}[fragile] \frametitle{10. Merging 2 GRanges objects} \begin{exampleblock}{} {\small <<>>= merge(x, y) @ } \end{exampleblock} \end{frame} \begin{frame}[fragile] \frametitle{10. Merging 2 GRanges objects} \begin{exampleblock}{} {\small <<>>= merge(x, y, all=TRUE) @ } \end{exampleblock} \end{frame} %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \end{document} GenomicRanges/inst/doc/Ten_things_slides.pdf0000644000175400017540000041612713032312064022210 0ustar00biocbuildbiocbuild%PDF-1.5 % 35 0 obj << /Length 912 /Filter /FlateDecode >> stream xWKO1W̭1} =U ;cKZPU(R#zNDC/,d,2u#C(.~9"MCdkoQNoIڒsbQt10o.8@Un )7%H[4X=б-%ZbYY\+/>5$+dtKH+E:^|[,!6aٶd/5_+}aη՟uHSpHtI} } |uI8KBϺ=R7cs"UaUU L%/3>ц(>}w:S''Mw)/%$x3~: Dkݬ|bq͈j>J+I^*41wߎo.gaΖs:_Yd ̲2f$e=,eׯǟVÄ6Z9zdx%3"GZ_Ry)=wa^fYmSȘs\Hd)=6DZؼgsX0{ԫ%"I[so9]y-XZ-ֱ~k;}#=An۾~6 endstream endobj 66 0 obj << /Length 1414 /Filter /FlateDecode >> stream xXKo7WEk{ic$ŲojPBcIn.>v%c;uS.<>d挳kp2LֲҲuf[BY_ HsOIYǹ C# `RJyT]͚蚍nq@R5s{X}(TVI` 섍NPi?^v(?T{[6/2 (3&lEpD=Lݏߊ83Rh+_+!ʁHz1%d @ 9D8(.US2OxA 2(rMf6k sO>z dtCمb8I+SetbNJ{1wv&ki$iQX|6+S¿ FQ?D^kxyn=2''  Bj:+%*qLGҐH)z\-od#͛Ed7[4";&T 9-na"(aI?M伆{԰ެ w{C(]mY' VmP1nDG]lzY.c^ <1Do/Nj20,-R[B f| JשvCX\:II^~Fkt!#kpLKc&K-ܧ1E{<>|@ n%Uؘp>ʆC mޮC!:Z/͡wOރU%I1bbZ |J8S|u nfl9gw䓔-ML𰌹OG;k?Lk t+`W%ܑ^%A!NT)TÁ-87^ka/t[)-yeg;dyETJqbRDwe/eW^l%&kvj'Goךl-9 ׇB]UtC (@Zuٳ EĹ"?OvyNZF endstream endobj 94 0 obj << /Length 1147 /Filter /FlateDecode >> stream xWYo7~ׯ  2I b[om%k\ڕ4q $C]2>MNHLYɽ2L:AZ>g=s0,4l}vI'c\ֳ>1mQN( >0W0ŵw&lf#"7-\ @RL" Ȟc69&NYۢ}Q!x֡4_W{ ՞|žw58<8(b,x{4#IcS(.49FB4y1U B)cFH@ʢ'PL Yhc@D@n!PNQeX}ЁϽ" هRhKipxJREx2)`?^LorĈʋXkXĂI#%HMqJjVRˣH(󢤭P% Zty[(jfJˮ޽S^$.<;XSY%mgbb_ƻ}A o"Hme_Y/sz-g@,9M*)D?u+bO.KSSe@+9`O} _4j.Zղy1࠼'o"67B3i479>{͑C Ku$3P;:J˵ݨHYݣ0%JBvt8"fRrڮqFR:2-j|.;a8>ϕƂI+X\Hpv FЁ(2&OzIɌf`GLr(< IRgja$Zl+l1*0KeL]"# f $U[J#R^,s9}HI:"!.kÒd_Jٳz.nbwP-Ӗ#Ym-Jp}4\: bFև 0NX΅X Ė09T{9"/f<&aE㙛eFyl>+e/Syʸ֭QͧRdo% !.IaRspHZck-W_=3"R)H]M".2i!MFʊ|@T_8|MRGU endstream endobj 119 0 obj << /Length 1440 /Filter /FlateDecode >> stream xَG}mn<$ (w<f|zMH||bBHY]uwR,B;mv.gGLނyʊR&\y !1a&rҺ ݚE>gGHD VV(#%@B!HK8y&ٷAXi%&FZ 0hX?~QVk@F#E@ oep`ttu j  Tz]%Hd;"N,Jzzop6FL-?;!S)3$gF?"tsHB{ 2]nɗ_~R,>+`^_`RZ #*/:'E".(42:B27Rq w S<%]~IUhТ]`Eđ+rz.0/Fd[g\)Qisyb3Xa|wpYEawu~i_{! @=\.'SNq؁'WjU#!%ϧՃnK6`Cb3e(%1A\.04BYZr&^T;nѣFpoq]hr+ie`}&8PE Y3;3pnl6N?]l$V5u?eZcP374& RQYRe=RC p ao:X$֘O(w6yJ1Bo.0V)z e:ZΙQY:t>qNUwsMP}Դ"QY#͔+@Szm/g ':kFwUv>nn&8NO8&< "A:5ziUDWe{^#_l޳Uh!IMW[بF/Ak_vTͬ7JFM.9-Sn_JJB^\ i>h}^;Ԙֲ@;0|0[LH_"@jB Ce&o+=ެ>7ޗ @ ߞ 4$jbe> stream xWKo7WB,o-ZYAȲKew]IۨAj@$g8ϏZ%l"fp~@`JaIҲLay0ҰfcZֳ}:>Sfx3Sr.F9~%TZ! fqga[}{joQ^G(kKX6pa)&ܬRbj*!^he*|y .N|@Bw5©atӻ@ jp!JJwEs1|baƻz~ekA}ir 6TwVY%].lϩ @(+9̪J0jI 4Դzrvlo4 .$^r`7xL xv]5O[UB'|[aD`z}X{ |7ϕƣv&PuBn7vod`r6O^hqD +52"fsy`-/lFV6xD {\袛H<&p-r/);OĵLӚiNI5EIg#i>lɚle o?P]T>;ac7QGq C7 ZD8pۢ+AJ0۝^rJrԢދj98Ws):h<'Otz?I=ϖ8`O.WR|:z:Sy;96CYhvIX8q*ocQE֢3ZX-xBQ8Eq) + + + +GXzWetEs}۽잝L6^ҩcJ`7-GI1 &Iju$rm0s$ɮ#C{ʏȰV~$F‘ $! $E`ʑZ#"Mk#5.DDGu "ihhzRZTOb[rޑ':zEC;GXXk?0GGbaXN]OlwHj_0㛝̭"LdžK#K1$T?h t4~a,7nfNrC&̌iuhs/'6mYXA`PX۞/0OZXtM/޼7WvxƹϿuseO{U}$J (xB^X.'Uo/NttG4}a:R `^@OP#^M-m!y6^Wnx6^7A4A%(JP(AQDE%hA 5 hF5@jƔbC{)U=Q\?/D5M5I2$bRʐ2$ef[dEɊ%+JV(YQE)v p{B5x_ @51Pj|9TTcCհCauCh'/ﴴt[L/f1Swe=cيg+^7{jjZ݁1 >xO؇}} c`>a6Om( \v!#S(CIl=IbQ[ϒz$'<Jl=QbGJ*W> stream xWKo7W>4hXM]d\Krs un5`rf`s&؛#q-")+WI'9H6-b=úH6svMƤg]}C&DL*U0LqkVGj6!a2ϥ " N*T)$.asvƎHk5A'/\ECP!˲FqBd8ˁ `qqeaR?1 sܣ!=`LQ\v'1P8t#ϭJWȜ8QgWiܸ9} 2P˧wd+ +sV:5P { "Mfi'_Hj?I}}Ej?^/7J>G(Z. lRRo[)1@mUe ^he _YCg#MAzBCрV0hM"#ͬ^( .zΑﷃFI2\xw`qK[|7"bb_Ƈ}A o+k8Me_/sz-/g@,9M&!Db$pGʁSt@((*9a >qjI_DthcyJ#G~xqRSIba4M+ɏkU8j6KM:8~w1?d> stream xWr7+paT GC\*._DS\E%nl3F(\Uq4XȌ0r>bG-C #& ʅ!ۖLG}rMG M3r,2sqсd0`)H%r!{d$w| dC3[tVJ"@3^ 2N*w MEa{DvQt$JL 74v [ s| EN4Ra:)h9%bHnqp9O#G TEedLA?6 N:d6؇=D,zPv鸔N6`29g;?,[agɼB'aQطW$S FQ?DY 0nxT" O PqFHPed7cJ"/l!H8\[^CG7%,)afRimӥw x5$ISx ;ܚo2 2>nXY϶=ޔ_\wkAt}9B)tMېOtΊ(SV瀳.%nQ8>`ǬD?%n#hf步N~t5$9BkЊj TJj~«_i_F |~cņzXG |2J+Ktf)~YvTݓG: `RwFK+\wCWk:ozi.Pa(Q"_347&Z.f8lBAa'Vm1YB_DeG>`[.*4"dcL;LjK Jp`b{# Fjl!0%a&x{_g1_BΌNa08Gؠ|YKiq[j7Akᩁ]Ota=^Vonq!gUM$ f ItU(I 0(E&ED疀8,]^G&XF7H>׋;dvޯ3\xR `] a{<=D&'`ߧ*OW2zq7L\su^CFٖ}lvYd0m4v:dQξ5w ɚ_pk7x68d[oUl, [*Ehnf{`'n=^O} ۵$ endstream endobj 222 0 obj << /Length 1293 /Filter /FlateDecode >> stream xWKoG WQy?$q ?K%/9q8Tp8$Hά$2c;#xFIM C)g#嚤 du2eI/҈s0`)H%zvGf{d$w|$rG3[tVJ"@3Ȟc2;FMHzyq`SQ?TyY52I58Vw,*fo$ Ji VzDMg7bIE s$b8eQB:32 b`qPM*;44=xR N6`2j䘳&OV ,.fmOHj_n-7J>'( k /1όG̒IüA q3*,pLI]8#[I_8\[^y<S&BUS;_5IҔ9`FB0Yp a؃g me =ޗ_zׂ}9B)ܒ0a_nCfP҅ib*)+u5[e7{! 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5ustar00biocbuildbiocbuildGenomicRanges/inst/scripts/timing_overlaps.R0000644000175400017540000000744613001230267022317 0ustar00biocbuildbiocbuild### ========================================================================= ### Timing findOverlaps() and summarizeOverlaps() ### ------------------------------------------------------------------------- library(RNAseqData.HNRNPC.bam.chr14) bamfile <- RNAseqData.HNRNPC.bam.chr14_BAMFILES[1] library(GenomicAlignments) reads <- readGAlignments(bamfile) reads_grl <- grglist(reads) library(TxDb.Hsapiens.UCSC.hg19.knownGene) txdb <- TxDb.Hsapiens.UCSC.hg19.knownGene exbygene <- exonsBy(txdb, by="gene") ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ### Timing findOverlaps,GRangesList,GRangesList method ### system.time(hits1 <- findOverlaps(exbygene, reads_grl)) #digest(hits1) # "42f770fcf1a2f27f2ceeb18249244677" # With BioC 3.0 / R 3.1 # --------------------- # First time: # user system elapsed # 1.999 0.020 2.022 # Subsequent times: # user system elapsed # 0.852 0.000 0.854 # With BioC 3.1/ R 3.2 # -------------------- # First time: # user system elapsed # 0.797 0.008 0.806 # Subsequent times: # user system elapsed # 0.141 0.000 0.141 system.time(hits2 <- findOverlaps(exbygene, reads_grl, ignore.strand=TRUE)) #digest(hits2) # "3f1a62b338d431ef2705602ce6dfbf9a" # With BioC 3.0 / R 3.1 # --------------------- # First time: # user system elapsed # 3.605 0.020 3.629 # Subsequent times: # user system elapsed # 1.152 0.000 1.154 # With BioC 3.1/ R 3.2 # -------------------- # First time: # user system elapsed # 1.244 0.008 1.254 # Subsequent times: # user system elapsed # 0.170 0.000 0.171 ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ### Timing findOverlaps,GRangesList,GAlignments method ### system.time(hits3 <- findOverlaps(exbygene, reads)) #digest(hits3) # "42f770fcf1a2f27f2ceeb18249244677" # With BioC 3.0 / R 3.1 # --------------------- # First time: # user system elapsed # 3.844 0.016 3.864 # Subsequent times: # user system elapsed # 1.093 0.000 1.095 # With BioC 3.1/ R 3.2 # -------------------- # First time: # user system elapsed # 1.792 0.008 1.802 # Subsequent times: # user system elapsed # 0.366 0.000 0.367 system.time(hits4 <- findOverlaps(exbygene, reads, ignore.strand=TRUE)) #digest(hits4) # "3f1a62b338d431ef2705602ce6dfbf9a" # With BioC 3.0 / R 3.1 # --------------------- # First time: # user system elapsed # 5.075 0.004 5.084 # Subsequent times: # user system elapsed # 2.642 0.012 2.658 # With BioC 3.1/ R 3.2 # -------------------- # First time: # user system elapsed # 1.766 0.004 1.771 # Subsequent times: # user system elapsed # 0.396 0.000 0.397 ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ### Timing summarizeOverlaps,GRangesList,GAlignments method ### system.time(se1 <- summarizeOverlaps(exbygene, reads)) #digest(assay(se1)) # "7e58dc4eb0c56fbf75ff86c09dbafb90" # With BioC 3.0 / R 3.1 # --------------------- # First time: # user system elapsed # 3.815 0.012 3.831 # Subsequent times: # user system elapsed # 1.361 0.000 1.363 # With BioC 3.1/ R 3.2 # -------------------- # First time: # user system elapsed # 1.783 0.004 1.789 # Subsequent times: # user system elapsed # 0.436 0.004 0.441 system.time(se2 <- summarizeOverlaps(exbygene, reads, ignore.strand=TRUE)) #digest(assay(se2)) # "7d473582932ee5704acbddd1ffc5c146" # With BioC 3.0 / R 3.1 # --------------------- # First time: # user system elapsed # 5.545 0.020 5.570 # Subsequent times: # user system elapsed # 2.614 0.000 2.617 # With BioC 3.1/ R 3.2 # -------------------- # First time: # user system elapsed # 2.489 0.004 2.495 # Subsequent times: # user system elapsed # 0.490 0.008 0.498 GenomicRanges/inst/unitTests/0000755000175400017540000000000013001230267017272 5ustar00biocbuildbiocbuildGenomicRanges/inst/unitTests/test_GNCList-class.R0000644000175400017540000001370213001230267023025 0ustar00biocbuildbiocbuild### findOverlaps_GNCList <- GenomicRanges:::findOverlaps_GNCList ### We need some of the helper functions defined for the NCList unit tests ### in IRanges. source(system.file("unitTests", "test_NCList-class.R", package="IRanges")) .get_query_overlaps2 <- function(query, subject, maxgap, min_overlap_score, type, ignore.strand) { ok <- .get_query_overlaps(query, subject, maxgap, min_overlap_score, type) ok <- ok & seqnames(query) == seqnames(subject) if (ignore.strand || as.logical(strand(query) == "*")) return(ok) ok & (strand(subject) == "*" | strand(query) == strand(subject)) } ### Redefine the .findOverlaps_naive() function we got from sourcing ### test_NCList-class.R above. .findOverlaps_naive <- function(query, subject, maxgap=0L, minoverlap=1L, type=c("any", "start", "end", "within", "extend", "equal"), select=c("all", "first", "last", "arbitrary", "count"), ignore.strand=FALSE) { type <- match.arg(type) if (type == "any") { min_overlap_score <- .min_overlap_score(maxgap, minoverlap) } else { min_overlap_score <- minoverlap } select <- match.arg(select) hits_per_query <- lapply(seq_along(query), function(i) which(.get_query_overlaps2(query[i], subject, maxgap, min_overlap_score, type, ignore.strand))) hits <- .make_Hits_from_q2s(hits_per_query, length(subject)) selectHits(hits, select=select) } test_GNCList <- function() { x <- GRanges(Rle(c("chrM", "chr1", "chrM", "chr1"), 4:1), IRanges(1:10, width=5, names=LETTERS[1:10]), strand=rep(c("+", "-"), 5), score=seq(0.7, by=0.045, length.out=10)) gnclist <- GNCList(x) checkTrue(is(gnclist, "GNCList")) checkTrue(validObject(gnclist, complete=TRUE)) checkIdentical(granges(x), granges(gnclist)) checkIdentical(x, granges(gnclist, use.mcols=TRUE)) checkIdentical(length(x), length(gnclist)) checkIdentical(names(x), names(gnclist)) checkIdentical(seqnames(x), seqnames(gnclist)) checkIdentical(start(x), start(gnclist)) checkIdentical(end(x), end(gnclist)) checkIdentical(width(x), width(gnclist)) checkIdentical(ranges(x), ranges(gnclist)) checkIdentical(ranges(x, use.names=FALSE), ranges(gnclist, use.names=FALSE)) checkIdentical(ranges(x, use.mcols=TRUE), ranges(gnclist, use.mcols=TRUE)) checkIdentical(strand(x), strand(gnclist)) checkIdentical(seqinfo(x), seqinfo(gnclist)) checkIdentical(x, as(gnclist, "GRanges")) checkIdentical(x[-6], as(gnclist[-6], "GRanges")) } test_findOverlaps_GNCList <- function() { q_ranges <- IRanges(-3:7, width=3) s_ranges <- IRanges(rep.int(1:6, 6:1), c(0:5, 1:5, 2:5, 3:5, 4:5, 5)) query <- GRanges( Rle(c("chr1", "chr2", "chrM"), rep(length(q_ranges), 3)), rep(q_ranges, 3), strand=Rle(c("+", "+", "-"), rep(length(q_ranges), 3))) subject <- GRanges( Rle(c("chr1", "chr2", "chrM"), rep(length(s_ranges), 3)), rep(s_ranges, 3), strand=Rle(c("+", "-", "*"), rep(length(s_ranges), 3))) for (ignore.strand in c(FALSE, TRUE)) { target0 <- .findOverlaps_naive(query, subject, ignore.strand=ignore.strand) current <- findOverlaps_GNCList(query, GNCList(subject), ignore.strand=ignore.strand) checkTrue(.compare_hits(target0, current)) current <- findOverlaps_GNCList(GNCList(query), subject, ignore.strand=ignore.strand) checkTrue(.compare_hits(target0, current)) current <- findOverlaps_GNCList(query, subject, ignore.strand=ignore.strand) checkTrue(.compare_hits(target0, current)) ## Shuffle query and/or subject elements. permute_input <- function(q_perm, s_perm) { q_revperm <- integer(length(q_perm)) q_revperm[q_perm] <- seq_along(q_perm) s_revperm <- integer(length(s_perm)) s_revperm[s_perm] <- seq_along(s_perm) target <- remapHits(target0, Lnodes.remapping=q_revperm, new.nLnode=length(q_perm), Rnodes.remapping=s_revperm, new.nRnode=length(s_perm)) current <- findOverlaps_GNCList(query[q_perm], GNCList(subject[s_perm]), ignore.strand=ignore.strand) checkTrue(.compare_hits(target, current)) current <- findOverlaps_GNCList(GNCList(query[q_perm]), subject[s_perm], ignore.strand=ignore.strand) checkTrue(.compare_hits(target, current)) current <- findOverlaps_GNCList(query[q_perm], subject[s_perm], ignore.strand=ignore.strand) checkTrue(.compare_hits(target, current)) } q_perm <- rev(seq_along(query)) s_perm <- rev(seq_along(subject)) permute_input(q_perm, seq_along(subject)) # reverse query permute_input(seq_along(query), s_perm) # reverse subject permute_input(q_perm, s_perm) # reverse both set.seed(97) for (i in 1:17) { ## random permutations q_perm <- sample(length(query)) s_perm <- sample(length(subject)) permute_input(q_perm, seq_along(subject)) permute_input(seq_along(query), s_perm) permute_input(q_perm, s_perm) } } } GenomicRanges/inst/unitTests/test_GRanges-class.R0000644000175400017540000004462313001230267023116 0ustar00biocbuildbiocbuild### .TARGET_names <- letters[1:10] .TARGET_seqlevels <- c("chr1", "chr2", "chr3", "chrX") .TARGET_seqnames <- Rle(factor(c("chr1", "chr2", "chr1", "chr3"), levels=.TARGET_seqlevels), c(1, 3, 2, 4)) .TARGET_start <- 1:10 .TARGET_end <- rep(10L, 10) .TARGET_ranges <- IRanges(.TARGET_start, .TARGET_end, names=.TARGET_names) .TARGET_strand <- Rle(strand(c("-", "+", "*", "+", "-")), c(1, 2, 2, 3, 2)) .TARGET_mcols <- DataFrame(score=1:10, GC=seq(1, 0, length=10)) .TARGET_seqlengths <- c(120L, NA, 80L, 50L) names(.TARGET_seqlengths) <- .TARGET_seqlevels .TARGET_seqinfo <- Seqinfo(seqnames=.TARGET_seqlevels, seqlengths=.TARGET_seqlengths) .make_TARGET_GRanges <- function() { new("GRanges", seqnames=.TARGET_seqnames, ranges=.TARGET_ranges, strand= .TARGET_strand, elementMetadata=.TARGET_mcols, seqinfo=.TARGET_seqinfo) } test_GRanges_construction <- function() { checkException(GRanges(letters), silent = TRUE) checkException(GRanges(ranges = IRanges(1:10, 1:10)), silent = TRUE) checkException(GRanges(letters, IRanges(1:10, 1:10)), silent = TRUE) checkException(GRanges(letters, IRanges(1:26, 1:26), strand = letters), silent = TRUE) checkException(GRanges(letters, IRanges(1:26, 1:26), score = 1:10), silent = TRUE) checkException(GRanges(letters, IRanges(1:26, 1:26), start = 1:26), silent = TRUE) checkException(GRanges(letters, IRanges(1:26, 1:26), end = 1:26), silent = TRUE) checkException(GRanges(letters, IRanges(1:26, 1:26), width = 1:26), silent = TRUE) checkException(GRanges(letters, IRanges(1:26, 1:26), element = letters), silent = TRUE) checkException(GRanges(c(letters, NA), IRanges(1:27, 1:27)), silent = TRUE) checkTrue(validObject(new("GRanges"))) checkTrue(validObject(GRanges())) checkTrue(validObject(GRanges(letters, IRanges(1:26, 1:26)))) checkTrue(validObject(GRanges(letters, IRanges(1:26, 1:26), score = 1:26))) checkTrue(validObject(GRanges(factor(letters), IRanges(1:26, 1:26)))) checkTrue(validObject(GRanges(1:10, IRanges(1:10, 1:10)))) current_seqnames <- S4Vectors:::decodeRle(.TARGET_seqnames) current_strand <- Rle(c("-", "+", "*", "+", "-"), c(1, 2, 2, 3, 2)) current <- GRanges(seqnames=current_seqnames, ranges=.TARGET_ranges, strand=current_strand, seqlengths=.TARGET_seqlengths, score=1:10, GC=seq(1, 0, length=10)) checkIdentical(.make_TARGET_GRanges(), current) ## Call with unnamed 'seqnames', 'ranges', and 'strand' args. current <- GRanges(current_seqnames, .TARGET_ranges, current_strand, seqlengths=.TARGET_seqlengths, score=1:10, GC=seq(1, 0, length=10)) checkIdentical(.make_TARGET_GRanges(), current) ## Call with unnamed metadata cols. score <- 1:10 GC <- seq(1, 0, length=10) current <- GRanges(.TARGET_seqnames, .TARGET_ranges, .TARGET_strand, seqlengths=.TARGET_seqlengths, score, GC) checkIdentical(.make_TARGET_GRanges(), current) ## Call with 'c' metadata col. current <- GRanges(seqnames=.TARGET_seqnames, ranges=.TARGET_ranges, strand=.TARGET_strand, c=LETTERS[1:10]) checkIdentical(DataFrame(c=LETTERS[1:10]), mcols(current)) seqinfo <- Seqinfo(letters, rep(1000L, length(letters))) checkIdentical(seqinfo(GRanges(seqinfo=seqinfo)), seqinfo) checkIdentical(seqinfo(GRanges(seqlengths = seqlengths(seqinfo))), seqinfo) } ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ### Accessors. ### test_GRanges_length <- function() { checkIdentical(length(.TARGET_ranges), length(.make_TARGET_GRanges())) } test_GRanges_names <- function() { ## names() getter gr0 <- GRanges() gr1 <- .make_TARGET_GRanges() checkIdentical(NULL, names(gr0)) checkIdentical(.TARGET_names, names(gr1)) ## names() setter names(gr0) <- names(gr0) # no-op checkIdentical(GRanges(), gr0) names(gr1) <- names(gr1) # no-op checkIdentical(.make_TARGET_GRanges(), gr1) checkException(names(gr1) <- letters, silent = TRUE) names(gr1) <- NULL checkIdentical(NULL, names(gr1)) } test_GRanges_seqnames <- function() { ## seqnames() getter gr0 <- GRanges() gr1 <- .make_TARGET_GRanges() checkIdentical(Rle(factor()), seqnames(gr0)) checkIdentical(.TARGET_seqnames, seqnames(gr1)) ## seqnames() setter seqnames(gr0) <- seqnames(gr0) # no-op checkIdentical(GRanges(), gr0) seqnames(gr1) <- seqnames(gr1) # no-op checkIdentical(.make_TARGET_GRanges(), gr1) checkException(seqnames(gr0) <- NULL, silent=TRUE) checkException(seqnames(gr1) <- NULL, silent=TRUE) checkException(seqnames(gr1) <- letters, silent=TRUE) } test_GRanges_ranges <- function() { ## ranges() getter gr0 <- GRanges() gr1 <- .make_TARGET_GRanges() checkIdentical(IRanges(), ranges(gr0)) checkIdentical(.TARGET_ranges, ranges(gr1)) ## ranges() setter ranges(gr0) <- ranges(gr0) # no-op checkIdentical(GRanges(), gr0) ranges(gr1) <- ranges(gr1) # no-op checkIdentical(.make_TARGET_GRanges(), gr1) checkException(ranges(gr0) <- NULL, silent=TRUE) checkException(ranges(gr1) <- NULL, silent=TRUE) checkException(ranges(gr1) <- IRanges(1:26, 1:26), silent=TRUE) val <- IRanges(1:length(gr1), width=10) ranges(gr1) <- val checkIdentical(ranges(gr1), val) } test_GRanges_start <- function() { ## start() getter gr0 <- GRanges() gr1 <- .make_TARGET_GRanges() checkIdentical(integer(), start(gr0)) checkIdentical(.TARGET_start, start(gr1)) ## start() setter start(gr0) <- start(gr0) # no-op checkIdentical(GRanges(), gr0) start(gr1) <- start(gr1) # no-op checkIdentical(.make_TARGET_GRanges(), gr1) #checkException(start(gr0) <- NULL, silent=TRUE) this actually works! checkException(suppressWarnings(start(gr1) <- letters), silent=TRUE) #checkException(start(gr1) <- 1:26, silent=TRUE) this actually works! start(gr1) <- as.numeric(seq_len(length(gr1))) checkIdentical(seq_len(length(gr1)), start(gr1)) } test_GRanges_end <- function() { ## end() getter gr0 <- GRanges() gr1 <- .make_TARGET_GRanges() checkIdentical(integer(), end(gr0)) checkIdentical(.TARGET_end, end(gr1)) ## end() setter end(gr0) <- end(gr0) # no-op checkIdentical(GRanges(), gr0) end(gr1) <- end(gr1) # no-op checkIdentical(.make_TARGET_GRanges(), gr1) #checkException(end(gr0) <- NULL, silent=TRUE) this actually works! checkException(suppressWarnings(end(gr1) <- letters), silent=TRUE) #checkException(end(gr1) <- 1:26, silent=TRUE) this actually works! end(gr1) <- as.numeric(10L + seq_len(length(gr1))) checkIdentical(10L + seq_len(length(gr1)), end(gr1)) } test_GRanges_width <- function() { ## width() getter gr0 <- GRanges() gr1 <- .make_TARGET_GRanges() checkIdentical(integer(), width(gr0)) checkIdentical(.TARGET_end - .TARGET_start + 1L, width(gr1)) ## width() setter width(gr0) <- width(gr0) # no-op checkIdentical(GRanges(), gr0) width(gr1) <- width(gr1) # no-op checkIdentical(.make_TARGET_GRanges(), gr1) #checkException(width(gr0) <- NULL, silent=TRUE) this actually works! checkException(suppressWarnings(width(gr1) <- letters), silent=TRUE) #checkException(width(gr1) <- 1:26, silent=TRUE) this actually works! width(gr1) <- as.numeric(10L + seq_len(length(gr1))) checkIdentical(10L + seq_len(length(gr1)), width(gr1)) } test_GRanges_strand <- function() { ## strand() getter gr0 <- GRanges() gr1 <- .make_TARGET_GRanges() checkIdentical(Rle(strand()), strand(gr0)) checkIdentical(.TARGET_strand, strand(gr1)) ## strand() setter strand(gr0) <- strand(gr0) # no-op checkIdentical(GRanges(), gr0) strand(gr1) <- strand(gr1) # no-op checkIdentical(.make_TARGET_GRanges(), gr1) checkException(strand(gr0) <- NULL, silent=TRUE) checkException(strand(gr1) <- NULL, silent=TRUE) checkException(strand(gr1) <- letters, silent=TRUE) val <- Rle(strand("+"), length(gr1)) strand(gr1) <- val checkIdentical(val, strand(gr1)) strand(gr1) <- "*" checkIdentical(Rle(strand("*"), length(gr1)), strand(gr1)) } test_GRanges_mcols <- function() { ## mcols() getter gr0 <- GRanges() gr1 <- .make_TARGET_GRanges() checkIdentical(DataFrame(), mcols(gr0)) checkIdentical(.TARGET_mcols, mcols(gr1)) ## mcols() setter mcols(gr0) <- mcols(gr0) # no-op checkIdentical(GRanges(), gr0) mcols(gr1) <- mcols(gr1) # no-op checkIdentical(.make_TARGET_GRanges(), gr1) checkException(mcols(gr1) <- DataFrame(score=letters), silent=TRUE) mcols(gr1) <- NULL checkIdentical(S4Vectors:::make_zero_col_DataFrame(length(gr1)), mcols(gr1)) val <- DataFrame(x=1:length(gr1), y = head(letters, length(gr1))) mcols(gr1) <- val checkTrue(validObject(gr1)) checkIdentical(val, mcols(gr1)) rownames(val) <- names(gr1) checkIdentical(val, mcols(gr1, use.names=TRUE)) mcols(gr1) <- val checkTrue(validObject(gr1)) checkIdentical(val, mcols(gr1, use.names=TRUE)) rownames(val) <- NULL checkIdentical(val, mcols(gr1)) } test_GRanges_seqlevels <- function() { ## seqlevels() getter gr0 <- GRanges() gr1 <- .make_TARGET_GRanges() checkIdentical(character(), seqlevels(gr0)) checkIdentical(.TARGET_seqlevels, seqlevels(gr1)) ## seqlevels() setter seqlevels(gr0) <- seqlevels(gr0) # no-op checkIdentical(GRanges(), gr0) seqlevels(gr1) <- seqlevels(gr1) # no-op checkIdentical(.make_TARGET_GRanges(), gr1) val <- seqlevels(gr1) val <- sub("^chr", "Chr", val) seqlevels(gr1) <- val checkIdentical(val, seqlevels(gr1)) } test_GRanges_seqlengths <- function() { ## seqlengths() getter gr0 <- GRanges() gr1 <- .make_TARGET_GRanges() checkIdentical(setNames(integer(), character()), seqlengths(gr0)) checkIdentical(.TARGET_seqlengths, seqlengths(gr1)) ## seqlengths() setter seqlengths(gr0) <- seqlengths(gr0) # no-op checkIdentical(GRanges(), gr0) seqlengths(gr1) <- seqlengths(gr1) # no-op checkIdentical(.make_TARGET_GRanges(), gr1) checkException(seqlengths(gr0) <- NULL, silent=TRUE) checkException(seqlengths(gr1) <- NULL, silent=TRUE) checkException(seqlengths(gr1) <- 1:10, silent=TRUE) val <- seqlengths(gr1) val[] <- c(10L, 20L, 30L, 10L) seqlengths(gr1) <- val checkIdentical(val, seqlengths(gr1)) } ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ### Coercion. ### test_GRanges_coercion <- function() { ## -- From GRanges to character/factor/data.frame -- ## ## no strand, no score gr <- GRanges(seqnames = factor(c("chr2", "chr11", "chr1"), levels=c("chr1", "chr2", "chr11")), ranges = IRanges(1:-1, c(4:5, -2), names=head(letters, 3))) target1 <- c(a="chr2:1-4", b="chr11:0-5", c="chr1:-1--2") checkIdentical(target1, as.character(gr)) target2 <- factor(target1, levels=target1[c(3, 1, 2)]) checkIdentical(target2, as.factor(gr)) target3 <- data.frame(seqnames = factor(c("chr2", "chr11", "chr1"), levels=c("chr1", "chr2", "chr11")), start = 1:-1, end = c(4:5, -2L), width = c(4L, 6L, 0L), strand = strand(rep("*", 3)), row.names = head(letters, 3), stringsAsFactors = FALSE) checkIdentical(target3, as.data.frame(gr)) ## strand, no score strand(gr) <- strand(c("+", "-", "*")) target1s <- c(a="chr2:1-4:+", b="chr11:0-5:-", c="chr1:-1--2:*") checkIdentical(target1s, as.character(gr)) checkIdentical(target1, as.character(gr, ignore.strand=TRUE)) target2s <- factor(target1s, levels=target1s[c(3, 1, 2)]) checkIdentical(target2s, as.factor(gr)) target3$strand <- strand(c("+", "-", "*")) checkIdentical(target3, as.data.frame(gr)) ## strand, score mcols(gr)$score <- c(10L, 2L, NA) checkIdentical(target1s, as.character(gr)) checkIdentical(target2s, as.factor(gr)) target3$score <- c(10L, 2L, NA) checkIdentical(target3, as.data.frame(gr)) ## no strand, score gr <- unstrand(gr) checkIdentical(target1, as.character(gr)) checkIdentical(target2, as.factor(gr)) target3$strand <- strand("*") checkIdentical(target3, as.data.frame(gr)) ## -- From GRanges to character/factor (continued) -- ## checkIdentical(as.character(gr), as(gr, "character")) checkIdentical(as.factor(gr), as(gr, "factor")) set.seed(555) gr2 <- sample(gr, 100, replace=TRUE) current <- as.factor(gr2) checkTrue(all(as.factor(as.character(gr2)) == current)) checkIdentical(unname(as.character(sort(unique(gr2)))), levels(current)) strand(gr2) <- c("*", "-", "+", "-") current <- as.factor(gr2) checkTrue(all(as.factor(as.character(gr2)) == current)) checkIdentical(unname(as.character(sort(unique(gr2)))), levels(current)) ## -- table() -- ## current <- table(gr2) # same as table(as.factor(gr2)) but much faster target <- table(as.factor(gr2)) dimnames(current) <- unname(dimnames(current)) dimnames(target) <- unname(dimnames(target)) checkIdentical(target, current) ## -- From character/factor to GRanges -- ## x <- c(a="chrX:21-3.5e+03", b="chr1: +21 \t-+30.5:-", c="1:-15--3:*", d="chr..Y:-21..-3:", e="chr1-X: \t 21 .. \t+30 \t\t:+") current <- as(x, "GRanges") target <- GRanges(c("chrX", "chr1", "1", "chr..Y", "chr1-X"), IRanges(c( 21, 21, -15, -21, 21), c(3500, 30, -3, -3, 30), names=letters[1:5]), c("*", "-", "*", "*", "+")) checkIdentical(target, current) checkIdentical(target, as(x, "GenomicRanges")) checkIdentical(unname(target), as(unname(x), "GRanges")) f <- as.factor(x) checkIdentical(target, as(f, "GRanges")) checkIdentical(target, as(f, "GenomicRanges")) checkIdentical(unname(target), as(unname(f), "GRanges")) ## -- Going back and forth between character/factor and GRanges -- ## ## this looses the metadata(), mcols(), and seqinfo() current <- as(as.character(gr2), "GRanges") target <- gr2 mcols(target) <- NULL seqlevels(target) <- seqlevels(current) checkIdentical(target, current) checkIdentical(target, as(as.factor(gr2), "GRanges")) current <- as(as.character(gr2, ignore.strand=TRUE), "GRanges") checkIdentical(unstrand(target), current) ## x <- as.character(gr2) checkIdentical(x, as.character(as(x, "GRanges"))) } ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ### Subsetting. ### test_GRanges_subsetting <- function() { ## [ gr <- .make_TARGET_GRanges() checkException(gr[1000], silent = TRUE) checkException(gr["bad"], silent = TRUE) checkIdentical(gr, gr[]) checkIdentical(as.data.frame(gr)[c(1,3,5),], as.data.frame(gr[c(1,3,5)])) checkIdentical(as.data.frame(gr)[c(1,3,5),-7], as.data.frame(gr[c(1,3,5),"score"])) checkIdentical(as.data.frame(gr)[c(1,3,5),-7], as.data.frame(gr[c(1,3,5),1])) checkIdentical(gr, gr[Rle(TRUE)]) ## [<- gr <- .make_TARGET_GRanges() gr[] <- rev(gr) revgr <- rev(.make_TARGET_GRanges()) names(revgr) <- rev(names(revgr)) checkIdentical(gr, revgr) ## window gr <- .make_TARGET_GRanges() checkIdentical(gr[1:3], window(gr, 1, 3)) ## [ by IRanges gr <- .make_TARGET_GRanges() checkIdentical(gr[1:3], gr[IRanges(1, 3)]) checkIdentical(gr[c(1:3, 1:3)], gr[IRanges(c(1,1), c(3,3))]) ## [<- by IRanges gr1 <- .make_TARGET_GRanges() gr1[1:3] <- .make_TARGET_GRanges()[4:6] gr2 <- .make_TARGET_GRanges() gr2[IRanges(1, 3)] <- .make_TARGET_GRanges()[4:6] checkIdentical(gr1, gr2) } ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ### Combine and split. ### test_GRanges_combine <- function() { gr1 <- .make_TARGET_GRanges() gr2 <- .make_TARGET_GRanges() ######################################################################### ## An unremarkable combination gc1 <- c(gr1, gr2) checkEquals(start(gc1), c(start(gr1), start(gr2))) checkEquals(end(gc1), c(end(gr1), end(gr2))) ## Check the combined data frames -- the rownaming is different when ## combining using these two strategies, so ignore them for now. vc1 <- as.data.frame(mcols(gc1)) rownames(vc1) <- NULL vc.orig <- as.data.frame(rbind(mcols(gr1), mcols(gr2))) rownames(vc.orig) <- NULL checkIdentical(vc1, vc.orig) ######################################################################### ## Combining GRanges objects with differing metadata columns colnames(mcols(gr1))[1] <- 'illegal' checkException(c(gr1, gr2), silent=TRUE) ## Ignore mcols gc2 <- c(gr1, gr2, ignore.mcols=TRUE) em2 <- mcols(gc2) checkIdentical(nrow(em2), length(gc2)) checkIdentical(ncol(em2), 0L) ######################################################################### ## More testing gr <- .make_TARGET_GRanges() gr2 <- gr names(gr2) <- NULL checkException(c(GRanges(), RangedData()), silent = TRUE) checkException(c(gr, gr[,-1]), silent = TRUE) checkIdentical(as.data.frame(c(gr, gr2), row.names=NULL), rbind(as.data.frame(gr, row.names=NULL), as.data.frame(gr2, row.names=NULL))) checkIdentical(as.data.frame(c(gr2, gr), row.names=NULL), rbind(as.data.frame(gr2, row.names=NULL), as.data.frame(gr, row.names=NULL))) } test_GRanges_split <- function() { gr <- .make_TARGET_GRanges() checkException(split(gr, NULL), silent = TRUE) checkIdentical(split(gr, rep(c("a", "b"), each=5)), GRangesList(a = head(gr, 5), b = tail(gr, 5))) } GenomicRanges/inst/unitTests/test_GRangesList-class.R0000644000175400017540000001326013001230267023743 0ustar00biocbuildbiocbuildmake_test_GRangesList <- function() { GRangesList( a = GRanges( seqnames = Rle(factor(c("chr1", "chr2", "chr1", "chr3")), c(1, 3, 2, 4)), ranges = IRanges(1:10, width = 10:1, names = head(letters, 10)), strand = Rle(strand(c("-", "+", "*", "+", "-")), c(1, 2, 2, 3, 2)), seqinfo = Seqinfo(seqnames = paste("chr", 1:3, sep="")), score = 1:10, GC = seq(1, 0, length=10)), b = GRanges( seqnames = Rle(factor(c("chr2", "chr4", "chr5")), c(3, 6, 4)), ranges = IRanges(1:13, width = 13:1, names = tail(letters, 13)), strand = Rle(strand(c("-", "+", "-")), c(4, 5, 4)), seqinfo = Seqinfo(seqnames = paste("chr", c(2L, 4:5), sep="")), score = 1:13, GC = seq(0, 1, length=13)) ) } test_GRangesList_construction <- function() { checkException(GRangesList(IRangesList()), silent = TRUE) checkTrue(validObject(new("GRangesList"))) checkTrue(validObject(GRangesList())) checkTrue(validObject(GRangesList(GRanges()))) checkTrue(validObject(GRangesList(a = GRanges()))) checkTrue(validObject(make_test_GRangesList())) } test_GRangesList_coercion <- function() { ## as.data.frame gr1 <- GRanges(seqnames = c(1,1,2), ranges = IRanges(1:3,4:6, names = head(letters,3)), strand = strand(c("+", "-", "*")), score = c(10L,2L,NA)) gr2 <- GRanges(seqnames = c("chr1", "chr2"), ranges = IRanges(1:2,1:2, names = tail(letters,2)), strand = strand(c("*", "*")), score = 12:13) grl <- GRangesList(a = gr1, b = gr2) df <- data.frame(group = togroup(PartitioningByWidth(grl)), group_name = rep(c("a","b"), c(3, 2)), seqnames = factor(c(1,1,2,"chr1","chr2")), start = c(1:3,1:2), end = c(4:6,1:2), width = c(4L, 4L, 4L, 1L, 1L), strand = strand(c("+", "-", "*", "*", "*")), score = c(10L,2L,NA,12:13), stringsAsFactors = FALSE) checkIdentical(as.data.frame(grl), df) } test_GRangesList_accessors <- function() { grl <- make_test_GRangesList() checkIdentical(seqnames(grl), RleList(lapply(grl, seqnames), compress=TRUE)) checkIdentical(ranges(grl), IRangesList(lapply(grl, ranges))) checkIdentical(strand(grl), RleList(lapply(grl, strand), compress=TRUE)) checkIdentical(seqlengths(grl), seqlengths(grl@unlistData)) checkIdentical(mcols(grl, level="within"), SplitDataFrameList(lapply(grl, mcols))) } test_GRangesList_RangesList <- function() { grl <- make_test_GRangesList() checkIdentical(start(grl), IntegerList(lapply(grl, start))) checkIdentical(end(grl), IntegerList(lapply(grl, end))) checkIdentical(width(grl), IntegerList(lapply(grl, width))) ## start checkException(start(GRangesList()) <- NULL, silent = TRUE) checkException(start(make_test_GRangesList()) <- 1:26, silent = TRUE) grl <- make_test_GRangesList() orig <- start(grl) start(grl) <- orig + 1L checkIdentical(start(grl), orig + 1L) ## end checkException(end(GRangesList()) <- NULL, silent = TRUE) checkException(end(make_test_GRangesList()) <- 1:26, silent = TRUE) grl <- make_test_GRangesList() orig <- end(grl) end(grl) <- orig + 1L checkIdentical(end(grl), orig + 1L) ## width checkException(width(GRangesList()) <- NULL, silent = TRUE) checkException(width(make_test_GRangesList()) <- 1:26, silent = TRUE) grl <- make_test_GRangesList() orig <- width(grl) width(grl) <- orig + 1L checkIdentical(width(grl), orig + 1L) } test_GRangesList_Vector <- function() { grl <- make_test_GRangesList() checkIdentical(grl, grl[]) checkIdentical(grl[,"score"], GRangesList(lapply(grl, function(x) x[,"score"]))) checkIdentical(grl[seqnames(grl) == "chr2",], GRangesList(lapply(grl, function(x) x[seqnames(x) == "chr2",]))) checkIdentical(grl[seqnames(grl) == "chr2", "score"], GRangesList(lapply(grl, function(x) x[seqnames(x) == "chr2", "score"]))) checkIdentical(GRangesList(), c(GRangesList(), GRangesList())) checkIdentical(grl, c(grl, GRangesList())) checkIdentical(grl, c(GRangesList(), grl)) GRL <- local({ x <- grl names(x) <- toupper(names(grl)) for (i in seq_len(length(x))) names(x[[i]]) <- toupper(names(grl[[i]])) x }) res <- c(grl, GRL) checkTrue(validObject(res)) ## [ checkIdentical(grl, grl[Rle(TRUE)]) checkIdentical(grl, res[seq_len(length(grl))]) checkIdentical(GRL, res[-seq_len(length(grl))]) ## checkException(c(grl, grl), "c() check for duplicated names", TRUE) checkIdentical(grl, local({ x <- grl x[IRanges(1, 1)] <- grl[IRanges(1, 1)] x }), "[ by IRanges") checkIdentical(grl, local({ x <- grl; x[1] <- grl[1]; x }), "[<- 1") checkIdentical(grl, local({ x <- grl; x[1:2] <- grl[1:2]; x }), "[<- 2") checkIdentical(grl, local({ x <- grl; x[[1]] <- grl[[1]] names(x) <- names(grl) x }), "[[<-, replace") checkIdentical(grl, local({ x <- grl; x[["b"]] <- grl[[2]] names(x) <- names(grl) x }), "[[<-, replace char") checkIdentical(grl, local({ x <- grl[1]; x[[2]] <- grl[[2]] names(x) <- names(grl) x }), "[[<-, extend-by-1") checkIdentical(grl, local({ x <- grl[1]; x[["b"]] <- grl[[2]] names(x) <- names(grl) x }), "[[<-, extend-by-1 char") } GenomicRanges/inst/unitTests/test_GenomicRanges-comparison.R0000644000175400017540000000334113001230267025346 0ustar00biocbuildbiocbuild### make_test_GRanges <- function() { ## TODO: Make a GRanges object that is more challenging to check whether ## it is sorted. GRanges( Rle(factor(c("chr1", "chr2", "chr1", "chr3"), levels=paste0("chr", 1:5)), c(1, 3, 2, 4)), IRanges(1:10, width=10:1, names=head(letters, 10)), strand=Rle(strand(c("-", "+", "*", "+", "-")), c(1, 2, 2, 3, 2)), score=1:10, GC=seq(1, 0, length=10) ) } test_is.unsorted <- function() { gr <- make_test_GRanges() ## Warning expected checkException(S4Vectors:::errorIfWarning(is.unsorted(gr, na.rm=TRUE)), silent=TRUE) checkTrue(is.unsorted(gr)) checkTrue(!is.unsorted(sort(gr))) ## is.unsorted() should return TRUE if 'strictly=TRUE' and 'gr' contains ## duplicate ranges, even if these are sorted. dup_gr <- sort(c(gr[1], gr[1])) checkTrue(!is.unsorted(dup_gr)) checkTrue(is.unsorted(dup_gr, strictly=TRUE)) ## Ranges that differ in only seqnames, strand, start, or width x <- c(gr[1], gr[1]) xx <- x seqnames(xx) <- factor(c("chr2", "chr1"), levels=seqlevels(xx)) checkTrue(is.unsorted(xx)) checkTrue(!is.unsorted(rev(xx))) xx <- x strand(xx) <- c("-", "+") checkTrue(is.unsorted(xx)) checkTrue(!is.unsorted(rev(xx))) xx <- x start(xx) <- c(2, 1) checkTrue(is.unsorted(xx)) checkTrue(!is.unsorted(rev(xx))) xx <- x width(xx) <- c(10, 7) checkTrue(is.unsorted(xx)) checkTrue(!is.unsorted(rev(xx))) ## Ignore the strand checkTrue(is.unsorted(gr, ignore.strand=TRUE)) gr2 <- sort(gr, ignore.strand=TRUE) checkTrue(is.unsorted(gr2)) checkTrue(!is.unsorted(gr2, ignore.strand=TRUE)) } GenomicRanges/inst/unitTests/test_coverage-methods.R0000644000175400017540000001144213001230267023712 0ustar00biocbuildbiocbuildmake_test_GRanges <- function() { new("GRanges", seqnames = Rle(factor(c("chr1", "chr2", "chr1", "chr3")), c(1, 3, 2, 4)), ranges = IRanges(1:10, width = 10:1, names = head(letters, 10)), strand = Rle(strand(c("-", "+", "*", "+", "-")), c(1, 2, 2, 3, 2)), seqinfo = Seqinfo(seqnames = paste("chr", 1:3, sep="")), elementMetadata = DataFrame(score = 1:10, GC = seq(1, 0, length=10))) } make_test_GRangesList <- function() { GRangesList( a = new("GRanges", seqnames = Rle(factor(c("chr1", "chr2", "chr1", "chr3")), c(1, 3, 2, 4)), ranges = IRanges(1:10, width = 10:1, names = head(letters, 10)), strand = Rle(strand(c("-", "+", "*", "+", "-")), c(1, 2, 2, 3, 2)), seqinfo = Seqinfo(seqnames = paste("chr", 1:3, sep="")), elementMetadata = DataFrame(score = 1:10, GC = seq(1, 0, length=10))), b = new("GRanges", seqnames = Rle(factor(c("chr2", "chr4", "chr5")), c(3, 6, 4)), ranges = IRanges(1:13, width = 13:1, names = tail(letters, 13)), strand = Rle(strand(c("-", "+", "-")), c(4, 5, 4)), seqinfo = Seqinfo(seqnames = paste("chr", c(2L, 4:5), sep="")), elementMetadata = DataFrame(score = 1:13, GC = seq(0, 1, length=13)))) } test_coverage_GRanges <- function() { gr <- make_test_GRanges() target <- RleList(chr1=Rle(1:3, c(4, 1, 5)), chr2=Rle(0:3, c(1, 1, 1, 7)), chr3=Rle(0:4, c(6, 1, 1, 1, 1)), compress=FALSE) checkIdentical(target, coverage(gr)) width <- c(chr1=10, chr2=20, chr3=30) target <- RleList(chr1=Rle(1:3, c(4, 1, 5)), chr2=Rle(c(0:3, 0L), c(1, 1, 1, 7, 10)), chr3=Rle(c(0:4, 0L), c(6, 1, 1, 1, 1, 20)), compress=FALSE) checkIdentical(target, coverage(gr, width=width)) weight <- list(chr1=1L, chr2=10L, chr3=100L) target <- RleList(chr1=Rle(1:3, c(4, 1, 5)), chr2=Rle(10L * 0:3, c(1, 1, 1, 7)), chr3=Rle(100L * 0:4, c(6, 1, 1, 1, 1)), compress=FALSE) checkIdentical(target, coverage(gr, weight=weight)) shift <- list(chr1=0, chr2=1, chr3=2) target <- RleList(chr1=Rle(1:3, c(4, 1, 5)), chr2=Rle(0:3, c(2, 1, 1, 7)), chr3=Rle(0:4, c(8, 1, 1, 1, 1)), compress=FALSE) checkIdentical(target, coverage(gr, shift=shift)) ## with circular sequences gr <- GRanges(seqnames=c("A", "B"), ranges=IRanges(start=5:6, width=7)) gr@seqinfo <- Seqinfo(seqnames=c("A", "B"), seqlengths=c(10, NA), isCircular=c(TRUE, FALSE)) target <- RleList(A=Rle(c(1L, 0L, 1L), c(1, 3, 6)), B=Rle(c(0L, 1L), c(5, 7)), compress=FALSE) checkIdentical(target, coverage(gr)) } test_coverage_GRangesList <- function() { grl <- make_test_GRangesList() target <- RleList(chr1=Rle(1:3, c(4, 1, 5)), chr2=Rle(c(1L, 3L, 5L, 6L, 3L), c(1, 1, 1, 7, 3)), chr3=Rle(0:4, c(6, 1, 1, 1, 1)), chr4=Rle(0:6, c(3, 1, 1, 1, 1, 1, 5)), chr5=Rle(0:4, c(9, 1, 1, 1, 1)), compress=FALSE) checkIdentical(target, coverage(grl)) width <- c(chr1=10, chr2=20, chr3=30, chr4=40, chr5=50) target <- RleList(chr1=Rle(1:3, c(4, 1, 5)), chr2=Rle(c(1L, 3L, 5L, 6L, 3L, 0L), c(1, 1, 1, 7, 3, 7)), chr3=Rle(c(0:4, 0L), c(6, 1, 1, 1, 1, 20)), chr4=Rle(c(0:6, 0L), c(3, 1, 1, 1, 1, 1, 5, 27)), chr5=Rle(c(0:4, 0L), c(9, 1, 1, 1, 1, 37)), compress=FALSE) checkIdentical(target, coverage(grl, width=width)) weight <- list(chr1=1L, chr2=10L, chr3=100L, chr4=1000L, chr5=10000L) target <- RleList(chr1=Rle(1:3, c(4, 1, 5)), chr2=Rle(10L * c(1L, 3L, 5L, 6L, 3L), c(1, 1, 1, 7, 3)), chr3=Rle(100L * 0:4, c(6, 1, 1, 1, 1)), chr4=Rle(1000L * 0:6, c(3, 1, 1, 1, 1, 1, 5)), chr5=Rle(10000L * 0:4, c(9, 1, 1, 1, 1)), compress=FALSE) checkIdentical(target, coverage(grl, weight=weight)) shift <- list(chr1=0, chr2=1, chr3=2, chr4=3, chr5=4) target <- RleList(chr1=Rle(1:3, c(4, 1, 5)), chr2=Rle(c(0L, 1L, 3L, 5L, 6L, 3L), c(1, 1, 1, 1, 7, 3)), chr3=Rle(0:4, c(8, 1, 1, 1, 1)), chr4=Rle(0:6, c(6, 1, 1, 1, 1, 1, 5)), chr5=Rle(0:4, c(13, 1, 1, 1, 1)), compress=FALSE) checkIdentical(target, coverage(grl, shift=shift)) } GenomicRanges/inst/unitTests/test_findOverlaps-methods.R0000644000175400017540000003210513001230267024552 0ustar00biocbuildbiocbuildmake_subject <- function() { new("GRanges", seqnames = Rle(factor(c("chr1", "chr2", "chr1", "chr3")), c(1, 3, 2, 4)), ranges = IRanges(1:10, width = 10:1), strand = Rle(strand(c("-", "+", "+", "-", "-", "-")), c(1, 2, 1, 1, 3, 2)), seqinfo = Seqinfo(seqnames = paste("chr", 1:3, sep="")), elementMetadata = DataFrame(score = 1:10, GC = seq(1, 0, length=10))) } make_query <- function() { GRangesList(nomatch = GRanges(seqnames = "chr1", ranges = IRanges(start=5, end=10), strand = "+"), onematch = GRanges(seqnames = "chr3", ranges = IRanges(start=2, end=7), strand = "-"), twomatch = GRanges(seqnames = "chr1", ranges = IRanges(start=1, end=5), strand = "-")) } .checkHits <- function(q_hits, s_hits, q_len, s_len, current, select) { target <- Hits(q_hits, s_hits, q_len, s_len, sort.by.query=TRUE) checkIdentical(t(selectHits(target, select=select)), t(unname(current))) } test_findOverlaps_no_overlaps_returns_empty_matches <- function() { query <- make_query() subject <- make_subject() ranges(subject) <- shift(ranges(subject), 1000L) ## select = "all" for (type in c("any", "start", "end")) { current <- findOverlaps(query, subject, type = type, select = "all") .checkHits(integer(0), integer(0), 3, 10, current, select="all") ans <- countOverlaps(query, subject, type = type) checkIdentical(structure(c(0L, 0L, 0L), names=c("nomatch", "onematch", "twomatch")), ans) ans <- subsetByOverlaps(query, subject, type = type) checkIdentical(query[integer(0)], ans) } ## select = "first" expect <- rep(NA_integer_, length(query)) for (type in c("any", "start", "end")) { ans <- findOverlaps(query, subject, type = type, select = "first") checkIdentical(expect, ans) } } test_findOverlaps_empty_query <- function() { query <- new("GRangesList") subject <- make_subject() ## select = "all" for (type in c("any", "start", "end")) { current <- findOverlaps(query, subject, type = type, select = "all") .checkHits(integer(0), integer(0), 0, 10, current, select="all") ans <- countOverlaps(query, subject, type = type) checkIdentical(integer(0), ans) ans <- subsetByOverlaps(query, subject, type = type) checkIdentical(query, ans) } ## select = "first" expect <- integer() for (type in c("any", "start", "end")) { ans <- findOverlaps(query, subject, type = type, select = "first") checkIdentical(expect, ans) } } test_findOverlaps_empty_subject <- function() { query <- make_query() subject <- new("GRanges") ## select = "all" for (type in c("any", "start", "end")) { current <- findOverlaps(query, subject, type = type, select = "all") .checkHits(integer(0), integer(0), 3, 0, current, select="all") ans <- countOverlaps(query, subject, type = type) checkIdentical(structure(c(0L, 0L, 0L), names=c("nomatch", "onematch", "twomatch")), ans) ans <- subsetByOverlaps(query, subject, type = type) checkIdentical(query[integer(0)], ans) } ## select = "first" expect <- rep(NA_integer_, length(query)) for (type in c("any", "start", "end")) { ans <- findOverlaps(query, subject, type = type, select = "first") checkIdentical(expect, ans) } } test_findOverlaps_zero_one_two_matches <- function() { query <- make_query() subject <- make_subject() ## select = "all" ansAny <- findOverlaps(query, subject, type="any", select="all") ansStart <- findOverlaps(query, subject, type="start", select="all") ansEnd <- findOverlaps(query, subject, type="end", select="all") .checkHits(c(2, 3, 3), c(7, 1, 5), 3, 10, ansAny, select="all") .checkHits(3, 1, 3, 10, ansStart, select="all") .checkHits(integer(0), integer(0), 3, 10, ansEnd, select="all") countsAny <- countOverlaps(query, subject, type="any") countsStart <- countOverlaps(query, subject, type="start") countsEnd <- countOverlaps(query, subject, type="end") .checkHits(c(2, 3, 3), c(7, 1, 5), 3, 10, countsAny, select="count") .checkHits(3, 1, 3, 10, countsStart, select="count") .checkHits(integer(0), integer(0), 3, 10, countsEnd, select="count") subsetAny <- subsetByOverlaps(query, subject, type="any") subsetStart <- subsetByOverlaps(query, subject, type="start") subsetEnd <- subsetByOverlaps(query, subject, type="end") checkIdentical(query[countsAny > 0], subsetAny) checkIdentical(query[countsStart > 0], subsetStart) checkIdentical(query[countsEnd > 0], subsetEnd) ## select = "first" ansAny <- findOverlaps(query, subject, type="any", select="first") ansStart <- findOverlaps(query, subject, type="start", select="first") ansEnd <- findOverlaps(query, subject, type="end", select="first") .checkHits(c(2, 3, 3), c(7, 1, 5), 3, 10, ansAny, select="first") .checkHits(3, 1, 3, 10, ansStart, select="first") .checkHits(integer(0), integer(0), 3, 10, ansEnd, select="first") } test_findOverlaps_multimatch_within_one_query <- function() { query <- make_query() query[[3L]] <- c(query[[3L]], query[[3L]]) subject <- make_subject() ## select = "all" ansAny <- findOverlaps(query, subject, type="any", select="all") ansStart <- findOverlaps(query, subject, type="start", select="all") ansEnd <- findOverlaps(query, subject, type="end", select="all") .checkHits(c(2, 3, 3), c(7, 1, 5), 3, 10, ansAny, select="all") .checkHits(3, 1, 3, 10, ansStart, select="all") .checkHits(integer(0), integer(0), 3, 10, ansEnd, select="all") countsAny <- countOverlaps(query, subject, type="any") countsStart <- countOverlaps(query, subject, type="start") countsEnd <- countOverlaps(query, subject, type="end") .checkHits(c(2, 3, 3), c(7, 1, 5), 3, 10, countsAny, select="count") .checkHits(3, 1, 3, 10, countsStart, select="count") .checkHits(integer(0), integer(0), 3, 10, countsEnd, select="count") subsetAny <- subsetByOverlaps(query, subject, type="any") subsetStart <- subsetByOverlaps(query, subject, type="start") subsetEnd <- subsetByOverlaps(query, subject, type="end") checkIdentical(query[countsAny > 0], subsetAny) checkIdentical(query[countsStart > 0], subsetStart) checkIdentical(query[countsEnd > 0], subsetEnd) ## select = "first" ansAny <- findOverlaps(query, subject, type="any", select="first") ansStart <- findOverlaps(query, subject, type="start", select="first") ansEnd <- findOverlaps(query, subject, type="end", select="first") .checkHits(c(2, 3, 3), c(7, 1, 5), 3, 10, ansAny, select="first") .checkHits(3, 1, 3, 10, ansStart, select="first") .checkHits(integer(0), integer(0), 3, 10, ansEnd, select="first") } test_findOverlaps_either_strand <- function() { query <- make_query() subject <- make_subject() query@unlistData@strand <- Rle(strand(c("*", "*", "-"))) ## select = "all" ansAny <- findOverlaps(query, subject, type="any", select="all") ansStart <- findOverlaps(query, subject, type="start", select="all") ansEnd <- findOverlaps(query, subject, type="end", select="all") .checkHits(c(1, 1, 1, 2, 3, 3), c(1, 5, 6, 7, 1, 5), 3, 10, ansAny, select="all") .checkHits(c(1, 3), c(5, 1), 3, 10, ansStart, select="all") .checkHits(c(1, 1, 1), c(1, 5, 6), 3, 10, ansEnd, select="all") countsAny <- countOverlaps(query, subject, type = "any") countsStart <- countOverlaps(query, subject, type = "start") countsEnd <- countOverlaps(query, subject, type = "end") .checkHits(c(1, 1, 1, 2, 3, 3), c(1, 5, 6, 7, 1, 5), 3, 10, countsAny, select="count") .checkHits(c(1, 3), c(5, 1), 3, 10, countsStart, select="count") .checkHits(c(1, 1, 1), c(1, 5, 6), 3, 10, countsEnd, select="count") subsetAny <- subsetByOverlaps(query, subject, type = "any") subsetStart <- subsetByOverlaps(query, subject, type = "start") subsetEnd <- subsetByOverlaps(query, subject, type = "end") checkIdentical(query[countsAny > 0], subsetAny) checkIdentical(query[countsStart > 0], subsetStart) checkIdentical(query[countsEnd > 0], subsetEnd) # select = "first" ansAny <- findOverlaps(query, subject, type="any", select="first") ansStart <- findOverlaps(query, subject, type="start", select="first") ansEnd <- findOverlaps(query, subject, type="end", select="first") .checkHits(c(1, 1, 1, 2, 3, 3), c(1, 5, 6, 7, 1, 5), 3, 10, ansAny, select="first") .checkHits(c(1, 3), c(5, 1), 3, 10, ansStart, select="first") .checkHits(c(1, 1, 1), c(1, 5, 6), 3, 10, ansEnd, select="first") } test_findOverlaps_minoverlap_GRanges_GRangesList <- function() { query <- make_subject() subject <- make_query() current <- findOverlaps(query, subject, minoverlap = 5) .checkHits(1, 3, 10, 3, current, select="all") current <- findOverlaps(query, subject, minoverlap = 6) .checkHits(integer(0), integer(0), 10, 3, current, select="all") } test_findOverlaps_minoverlap_GRangesList_GRanges <- function() { subject <- make_subject() query <- make_query() current <- findOverlaps(query, subject, minoverlap = 5) .checkHits(3, 1, 3, 10, current, select="all") current <- findOverlaps(query, subject, minoverlap = 6) .checkHits(integer(0), integer(0), 3, 10, current, select="all") } test_findOverlaps_minoverlap_GRangesList_GRangesList <- function() { query <- make_query() subject <- GRangesList("g1" = make_subject()) current <- findOverlaps(query, subject, minoverlap = 1) .checkHits(c(2, 3), c(1, 1), 3, 1, current, select="all") query <- make_query() subject <- GRangesList("g1" = make_subject()) current <- findOverlaps(query, subject, minoverlap = 6) .checkHits(3, 1, 3, 1, current, select="all") query <- make_query() subject <- GRangesList("g1" = make_subject()) current <- findOverlaps(query, subject, minoverlap = 7) .checkHits(integer(0), integer(0), 3, 1, current, select="all") current <- findOverlaps(subject, query, minoverlap = 6) .checkHits(1, 3, 1, 3, current, select="all") } test_findOverlaps_with_circular_sequences <- function() { gr <- GRanges(seqnames=rep.int("A", 4), ranges=IRanges(start=c(2, 4, 6, 8), width=3)) ## With A of length 9 --> no overlap between last and first ranges. gr@seqinfo <- Seqinfo(seqnames="A", seqlengths=9, isCircular=TRUE) current0 <- findOverlaps(gr, gr) target0_q_hits <- c(1L, 1L, 2L, 2L, 2L, 3L, 3L, 3L, 4L, 4L) target0_s_hits <- c(1L, 2L, 1L, 2L, 3L, 2L, 3L, 4L, 3L, 4L) .checkHits(target0_q_hits, target0_s_hits, 4, 4, current0, select="all") ## With A of length 8 --> last and first ranges do overlap. gr@seqinfo <- Seqinfo(seqnames="A", seqlengths=8, isCircular=TRUE) current1 <- findOverlaps(gr, gr) .checkHits(c(1, target0_q_hits, 4), c(4, target0_s_hits, 1), 4, 4, current1, select="all") ## With A of length 8 and minoverlap=2 --> no overlap between last ## and first ranges. current2 <- findOverlaps(gr, gr, minoverlap=2) .checkHits(1:4, 1:4, 4, 4, current2, select="all") ## With A of length 7 and minoverlap=2 --> last and first ranges ## do overlap. gr@seqinfo <- Seqinfo(seqnames="A", seqlengths=7, isCircular=TRUE) current3 <- findOverlaps(gr, gr, minoverlap=2) .checkHits(c(1, 1:4, 4), c(4, 1:4, 1), 4, 4, current3, select="all") ## type = "within" q0 <- GRanges("A", IRanges(c(11, 5, 4, 11, 11, 4), c(30, 30, 30, 50, 51, 51))) s0 <- GRanges("A", IRanges(5, width=46)) s0@seqinfo <- Seqinfo(seqnames="A", seqlengths=100, isCircular=TRUE) ## sanity check with linear shift fo0 <- findOverlaps(q0, s0, type="within") expected <- c(1L, 2L, 4L) checkIdentical(queryHits(fo0), expected) A=90 q1 <- shift(q0, A) s1 <- shift(s0, A) fo1 <- findOverlaps(q1, s1, type="within") checkIdentical(queryHits(fo1), expected) ## circular shift n1=-1; n2=0 q2 <- shift(q0, A + 100 * n1) s2 <- shift(s0, A + 100 * n2) fo1 <- findOverlaps(q1, s1, type="within") checkIdentical(queryHits(fo1), expected) ## With A of length 8 --> range 3 is within range 2 gr <- GRanges(seqnames=rep.int("A", 4), ranges=IRanges(start=c(2, 4, 6, 8), width=c(3, 3, 3, 5))) gr@seqinfo <- Seqinfo(seqnames="A", seqlengths=8, isCircular=TRUE) current4 <- findOverlaps(gr, gr, type="within") .checkHits(c(1, 1:4), c(1, 4, 2, 3, 4), 4, 4, current4, select="all") ## With A of length 9 --> range 3 is not within range 2 gr@seqinfo <- Seqinfo(seqnames="A", seqlengths=9, isCircular=TRUE) current5 <- findOverlaps(gr, gr, type="within") .checkHits(1:4, 1:4, 4, 4, current5, select="all") } GenomicRanges/inst/unitTests/test_inter-range-methods.R0000644000175400017540000001200613001230267024327 0ustar00biocbuildbiocbuildmake_test_GRanges <- function() GRanges(Rle(factor(c("chr1", "chr2", "chr1", "chr3")), c(1, 3, 2, 4)), IRanges(1:10, width=10:1, names=head(letters, 10)), Rle(c("-", "+", "*", "+", "-"), c(1, 2, 2, 3, 2)), score=1:10, GC=seq(1, 0, length=10), seqinfo=Seqinfo(paste("chr", 1:3, sep=""))) test_range_GenomicRanges <- function() { gr <- make_test_GRanges() current <- range(gr) target <- GRanges(Rle(c("chr1", "chr2", "chr3"), c(3, 2, 2)), IRanges(start=c(6, 1, 5, 2, 4, 7, 9), end=10), c("+", "-", "*", "+", "*", "+", "-")) checkTrue(validObject(current, complete=TRUE)) checkIdentical(target, current) current <- range(gr, ignore.strand=TRUE) target <- GRanges(c("chr1", "chr2", "chr3"), IRanges(start=c(1, 2, 7), end=10), c("*", "*", "*")) checkTrue(validObject(current, complete=TRUE)) checkIdentical(target, current) } test_range_GRangesList <- function() { gr <- make_test_GRanges() grl <- GRangesList(gr, shift(rev(gr), 5 * seq_along(gr))) for (ignore.strand in c(FALSE, TRUE)) { current <- range(grl, ignore.strand=TRUE) target <- endoapply(grl, range, ignore.strand=TRUE) checkTrue(validObject(current, complete=TRUE)) checkIdentical(target, current) } } test_reduce_GenomicRanges <- function() { gr <- make_test_GRanges() current <- reduce(gr) target <- GRanges(Rle(c("chr1", "chr2", "chr3"), c(3, 2, 2)), IRanges(start=c(6, 1, 5, 2, 4, 7, 9), end=10), c("+", "-", "*", "+", "*", "+", "-")) checkTrue(validObject(current, complete=TRUE)) checkIdentical(target, current) current <- reduce(gr, with.revmap=TRUE) mcols(target)$revmap <- IntegerList(6, 1, 5, 2:3, 4, 7:8, 9:10) checkIdentical(target, current) } test_reduce_GRangesList <- function() { gr <- make_test_GRanges() grl <- GRangesList(gr, shift(rev(gr), 5 * seq_along(gr))) for (with.revmap in c(FALSE, TRUE)) { for (ignore.strand in c(FALSE, TRUE)) { current <- reduce(grl, with.revmap=with.revmap, ignore.strand=ignore.strand) target <- endoapply(grl, reduce, with.revmap=with.revmap, ignore.strand=ignore.strand) checkTrue(validObject(current, complete=TRUE)) checkIdentical(target, current) } } } test_gaps_GenomicRanges <- function() { gr <- make_test_GRanges() current <- gaps(gr, start=1, end=10) target <- GRanges(Rle(c("chr1", "chr2", "chr3"), c(2, 3, 3)), IRanges(start=1, end=c(5, 4, 1, 10, 3, 6, 8, 10)), c("+", "*", "+", "-", "*", "+", "-", "*")) checkTrue(validObject(current, complete=TRUE)) checkIdentical(target, current) } test_disjoin_GenomicRanges <- function() { gr <- make_test_GRanges() current <- disjoin(gr) target <- GRanges(Rle(c("chr1", "chr2", "chr3"), c(3, 3, 4)), IRanges(start=c(6, 1, 5, 2, 3, 4, 7, 8, 9, 10), end=c(10, 10, 10, 2, 10, 10, 7, 10, 9, 10)), c("+", "-", "*", "+", "+", "*", "+", "+", "-", "-")) checkTrue(validObject(current, complete=TRUE)) checkIdentical(target, current) gr <- GRanges(Rle(c("chr1", "chr3"), c(2, 2)), IRanges(c(8, 6, 8, 6), c(11, 15, 11, 15), names=c("k", "l", "m", "n")), c("-", "-", "+", "*"), score=11:14, GC=c(.2, .3, .3, .1)) current <- disjoin(gr) target <- GRanges(Rle(c("chr1", "chr3"), c(3, 2)), IRanges(c(6, 8, 12, 8, 6), c(7, 11, 15, 11, 15)), Rle(c("-", "+", "*"), c(3, 1, 1))) checkTrue(validObject(current, complete=TRUE)) checkIdentical(target, current) current <- disjoin(gr, with.revmap=TRUE) mcols(target)$revmap <- IntegerList(2, 1:2, 2, 3, 4) checkIdentical(target, current) } test_disjoin_GRangesList <- function() { grl <- GRangesList(make_test_GRanges(), GRanges("1", IRanges(1, 10), score=21, GC=.21), GRanges(), GRanges(Rle(c("chr1", "chr3"), c(2, 2)), IRanges(c(8, 6, 8, 6), c(11, 15, 11, 15), names=c("k", "l", "m", "n")), strand(c("-", "-","+","*")), score=41:44, GC=c(.41, .42, .43, .44))) for (with.revmap in c(FALSE, TRUE)) { for (ignore.strand in c(FALSE, TRUE)) { current <- disjoin(grl, with.revmap=with.revmap, ignore.strand=ignore.strand) target <- endoapply(grl, disjoin, with.revmap=with.revmap, ignore.strand=ignore.strand) checkTrue(validObject(current, complete=TRUE)) checkIdentical(target, current) } } } GenomicRanges/inst/unitTests/test_intra-range-methods.R0000644000175400017540000002457113001230267024335 0ustar00biocbuildbiocbuildmake_test_GRanges <- function() { new("GRanges", seqnames = Rle(factor(c("chr1", "chr2", "chr1", "chr3")), c(1, 3, 2, 4)), ranges = IRanges(1:10, width = 10:1, names = head(letters, 10)), strand = Rle(strand(c("-", "+", "*", "+", "-")), c(1, 2, 2, 3, 2)), seqinfo = Seqinfo(seqnames = paste("chr", 1:3, sep="")), elementMetadata = DataFrame(score = 1:10, GC = seq(1, 0, length=10))) } make_test_GRangesList <- function() { GRangesList( a = new("GRanges", seqnames = Rle(factor(c("chr1", "chr2", "chr1", "chr3")), c(1, 3, 2, 4)), ranges = IRanges(1:10, width = 10:1, names = head(letters, 10)), strand = Rle(strand(c("-", "+", "*", "+", "-")), c(1, 2, 2, 3, 2)), seqinfo = Seqinfo(seqnames = paste("chr", 1:3, sep="")), elementMetadata = DataFrame(score = 1:10, GC = seq(1, 0, length=10))), b = new("GRanges", seqnames = Rle(factor(c("chr2", "chr4", "chr5")), c(3, 6, 4)), ranges = IRanges(1:13, width = 13:1, names = tail(letters, 13)), strand = Rle(strand(c("-", "+", "-")), c(4, 5, 4)), seqinfo = Seqinfo(seqnames = paste("chr", c(2L, 4:5), sep="")), elementMetadata = DataFrame(score = 1:13, GC = seq(0, 1, length=13)))) } test_shift_GenomicRanges <- function() { ## empty, reversibility, recycling 'x' gr <- make_test_GRanges() checkIdentical(shift(GRanges(), 10), GRanges()) checkIdentical(gr, shift(shift(gr, 10), -10)) x <- 1:2 checkIdentical(start(shift(gr[1:4], x)), start(gr[1:4]) + x) ## no seqlength or circularity checkIdentical(start(gr) + 10L, start(shift(gr, 10))) checkIdentical(width(gr), width(shift(gr, 10))) gr <- GRanges("chrA", IRanges(20, 30)) checkIdentical(IRanges(8, 18), ranges(shift(gr, -12))) checkIdentical(IRanges(98, 108), ranges(shift(gr, 78))) ## seqlength and circularity combos gr <- GRanges("chr1", IRanges(5, width=6)) isCircular(gr) <- TRUE checkIdentical(start(shift(gr, -10)), -5L) seqlengths(gr) <- 20 isCircular(gr) <- NA warn <- FALSE res <- withCallingHandlers({ shift(gr, -10) }, warning=function(w) { warn <<- TRUE invokeRestart("muffleWarning") }) checkTrue(warn == TRUE) checkIdentical(start(res), -5L) isCircular(gr) <- FALSE warn <- FALSE res <- withCallingHandlers({ shift(gr, -10) }, warning=function(w) { warn <<- TRUE invokeRestart("muffleWarning") }) checkTrue(warn == TRUE) checkIdentical(start(res), -5L) } test_shift_GRangesList <- function() { grl <- make_test_GRangesList() shifted <- shift(grl, 10) checkIdentical(start(grl) + 10L, start(shifted)) } test_resize_GenomicRanges <- function() { gr <- make_test_GRanges() checkException(resize(gr, 10, fix = "middle"), silent = TRUE) checkException(resize(gr, 10, fix = rep("end", 3)), silent = TRUE) resized <- resize(gr, 10) checkIdentical(rep(10L, length(gr)), width(resized)) checkIdentical(c(1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 1L, 1L), start(resized)) checkIdentical(ranges(resize(gr, 10, fix = "center")), IRanges(rep(1:5, each=2), width = 10, names = head(letters, 10))) checkIdentical(ranges(resize(gr, 10, fix = c("start", "end"))), IRanges(c(1L, 1L, 3L, 1L, 5L, 1L, 7L, 1L, 1L, 10L), width = 10, names = head(letters, 10))) ## No-ops. checkIdentical(gr, resize(gr, width=width(gr))) checkIdentical(gr, resize(gr, width=width(gr), fix="end")) checkIdentical(gr, resize(gr, width=width(gr), fix="center")) } test_resize_GRangesList <- function() { grl <- make_test_GRangesList() target <- endoapply(grl, resize, width=5) current <- resize(grl, width=5) checkIdentical(target, current) ## No-ops. checkIdentical(grl, resize(grl, width=width(grl))) checkIdentical(grl, resize(grl, width=width(grl), fix="end")) checkIdentical(grl, resize(grl, width=width(grl), fix="center")) } test_flank_GenomicRanges <- function() { checkIdentical(flank(GRanges(), 10), GRanges()) gr_seqnames <- c("chr1", "chr2", "chr1", "chrM") gr_ranges <- IRanges(21:24, width=10) gr_strand <- strand(c("+", "-", "*", "-")) gr <- GRanges(gr_seqnames, gr_ranges, gr_strand) ## NO warning expected. S4Vectors:::errorIfWarning(current <- flank(gr, 10)) checkTrue(S4Vectors:::errorIfWarning(validObject(current))) target_ranges <- IRanges(c(11, 32, 13, 34), width=10) target <- GRanges(gr_seqnames, target_ranges, gr_strand) checkIdentical(target, current) ## NO warning expected. S4Vectors:::errorIfWarning(current <- flank(gr, 10, start=FALSE)) checkTrue(S4Vectors:::errorIfWarning(validObject(current))) target_ranges <- IRanges(c(31, 12, 33, 14), width=10) target <- GRanges(gr_seqnames, target_ranges, gr_strand) checkIdentical(target, current) ## NO warning expected. S4Vectors:::errorIfWarning(current <- flank(gr, 30)) checkTrue(S4Vectors:::errorIfWarning(validObject(current))) target_ranges <- IRanges(c(-9, 32, -7, 34), width=30) target <- GRanges(gr_seqnames, target_ranges, gr_strand) checkIdentical(target, current) ## NO warning expected. S4Vectors:::errorIfWarning(current <- flank(gr, 30, start=FALSE)) checkTrue(S4Vectors:::errorIfWarning(validObject(current))) target_ranges <- IRanges(c(31, -8, 33, -6), width=30) target <- GRanges(gr_seqnames, target_ranges, gr_strand) checkIdentical(target, current) seqlengths(gr) <- c(chr1=60, chr2=50, chrM=35) ## Warning expected. checkException(S4Vectors:::errorIfWarning( current <- flank(gr, 10) ), silent=TRUE) suppressWarnings(current <- flank(gr, 10)) checkException(S4Vectors:::errorIfWarning( validObject(current) ), silent=TRUE) checkTrue(suppressWarnings(validObject(current))) target_ranges <- IRanges(c(11, 32, 13, 34), width=10) checkIdentical(target_ranges, ranges(current)) isCircular(gr) <- c(chr1=NA, chr2=FALSE, chrM=TRUE) ## NO warning expected. S4Vectors:::errorIfWarning(current <- flank(gr, 10)) checkTrue(S4Vectors:::errorIfWarning(validObject(current))) target_ranges <- IRanges(c(11, 32, 13, 34), width=10) checkIdentical(target_ranges, ranges(current)) ## Warning expected. checkException(S4Vectors:::errorIfWarning( current <- flank(gr, 20) ), silent=TRUE) suppressWarnings(current <- flank(gr, 20)) checkException(S4Vectors:::errorIfWarning( validObject(current) ), silent=TRUE) checkTrue(suppressWarnings(validObject(current))) target_ranges <- IRanges(c(1, 32, 3, 34), width=20) checkIdentical(target_ranges, ranges(current)) } test_promoters_GenomicRanges <- function() { checkTrue(length(promoters(GRanges())) == 0) ## upstream / downstream gr <- GRanges("chr1", IRanges(c(5, 10), width=1), "+") target <- GRanges("chr1", IRanges(c(5, 10), width=0), "+") current <- promoters(gr, 0, 0) checkIdentical(target, current) strand(gr) <- c("+", "-") target <- IRanges(c(3, 11), width=2) current <- ranges(promoters(gr, 2, 0)) checkIdentical(target, current) target <- IRanges(c(5, 9), width=2) current <- ranges(promoters(gr, 0, 2)) checkIdentical(target, current) gr <- GRanges("chr1", IRanges(0, width=6), "+") target <- GRanges("chr1", IRanges(-3, 2), "+") current <- promoters(gr, 3, 3) checkIdentical(target, current) checkTrue(validObject(current) == TRUE) gr <- GRanges("chr1", IRanges(rep(10, 3), width=6), c("+", "-", "*")) target <- GRanges("chr1", IRanges(c(7, 13, 7), c(12, 18, 12)), c("+", "-", "*")) current <- suppressWarnings(promoters(gr, 3, 3)) checkIdentical(target, current) ## treat "*" as "+" gr <- GRanges("chr1", IRanges(5, width=6), "+") target <- GRanges("chr1", IRanges(2, 7), "+") current <- promoters(gr, 3, 3) checkIdentical(target, current) strand(gr) <- "*" strand(target) <- "*" current <- suppressWarnings(promoters(gr, 3, 3)) checkIdentical(target, current) ## metadata gr <- GRanges("chr1", IRanges(0, width=6), names="A", strand="+", score=99) current <- promoters(gr, 3, 3) checkIdentical(mcols(gr), mcols(current)) checkIdentical(names(gr), names(current)) checkIdentical(seqinfo(gr), seqinfo(current)) } test_restrict_GenomicRanges <- function() { gr <- make_test_GRanges() st <- structure(c(4,5), names = c("chr1", "chr2")) en <- structure(c(8,9), names = c("chr2", "chr3")) res <- restrict(gr, start = st, end = en) checkIdentical(mcols(gr), mcols(res)) checkIdentical(seqnames(gr), seqnames(res)) checkIdentical(seqinfo(gr), seqinfo(res)) target <- IRanges(start=c(4, 5, 5, 5, 5, 6, 7, 8, 9, 10), end = c(10, 8, 8, 8, 10, 10, 9, 9, 9, 9), names=letters[1:10]) checkIdentical(ranges(res), target) } test_trim_GenomicRanges <- function() { checkIdentical(trim(GRanges()), GRanges()) gr_seqnames <- c("chr1", "chr2", "chr1", "chrM") gr_ranges <- IRanges(0:3, width=30) ## NO warning expected. S4Vectors:::errorIfWarning(gr <- GRanges(gr_seqnames, gr_ranges)) checkTrue(S4Vectors:::errorIfWarning(validObject(gr))) checkIdentical(trim(gr), gr) gr_seqlengths <- c(chr1=50, chr2=NA, chrM=NA) ## Warning expected. checkException(S4Vectors:::errorIfWarning( seqlengths(gr) <- gr_seqlengths ), silent=TRUE) suppressWarnings(seqlengths(gr) <- gr_seqlengths) checkException(S4Vectors:::errorIfWarning( validObject(gr) ), silent=TRUE) checkTrue(suppressWarnings(validObject(gr))) gr <- trim(gr) checkTrue(S4Vectors:::errorIfWarning(validObject(gr))) target_ranges <- IRanges(c(1, 1, 2, 3), width=c(29, 30, 30, 30)) checkIdentical(target_ranges, ranges(gr)) isCircular(gr) <- c(chr1=FALSE, chr2=FALSE, chrM=TRUE) ## NO warning expected. gr_seqlengths <- c(chr1=50, chr2=NA, chrM=15) S4Vectors:::errorIfWarning(seqlengths(gr) <- gr_seqlengths) checkTrue(S4Vectors:::errorIfWarning(validObject(gr))) checkIdentical(trim(gr), gr) } GenomicRanges/inst/unitTests/test_makeGRangesFromDataFrame.R0000644000175400017540000001424713001230267025241 0ustar00biocbuildbiocbuild### test_find_GRanges_cols <- function() { find_GRanges_cols <- GenomicRanges:::.find_GRanges_cols df_colnames <- c("chrom", "start", "end") target <- c(seqnames=1L, start=2L, end=3L, width=NA_integer_, strand=NA_integer_) current <- find_GRanges_cols(df_colnames) checkIdentical(target, current) df_colnames <- c("stop", "start", "Chr") target <- c(seqnames=3L, start=2L, end=1L, width=NA_integer_, strand=NA_integer_) current <- find_GRanges_cols(df_colnames) checkIdentical(target, current) df_colnames <- c("stop", "width", "start", "Chr") target <- c(seqnames=4L, start=3L, end=1L, width=2L, strand=NA_integer_) current <- find_GRanges_cols(df_colnames) checkIdentical(target, current) df_colnames <- c("strand", "STOP", "START", "chromosome_name") target <- c(seqnames=4L, start=3L, end=2L, width=NA_integer_, strand=1L) current <- find_GRanges_cols(df_colnames) checkIdentical(target, current) df_colnames <- c("Seqnames", "strand", "start", "end") target <- c(seqnames=1L, start=3L, end=4L, width=NA_integer_, strand=2L) current <- find_GRanges_cols(df_colnames) checkIdentical(target, current) df_colnames <- c("Seqnames", "strand", "start", "end") target <- c(seqnames=1L, start=3L, end=4L, width=NA_integer_, strand=NA_integer_) current <- find_GRanges_cols(df_colnames, ignore.strand=TRUE) checkIdentical(target, current) df_colnames <- c("seqname", "start", "end") target <- c(seqnames=1L, start=2L, end=3L, width=NA_integer_, strand=NA_integer_) current <- find_GRanges_cols(df_colnames) checkIdentical(target, current) df_colnames <- c("chrom", "strand", "txStart", "txEnd") target <- c(seqnames=1L, start=3L, end=4L, width=NA_integer_, strand=2L) current <- find_GRanges_cols(df_colnames) checkIdentical(target, current) df_colnames <- c("chrom", "strand", "txStart", "txEnd", "txChrom") target <- c(seqnames=5L, start=3L, end=4L, width=NA_integer_, strand=2L) current <- find_GRanges_cols(df_colnames) checkIdentical(target, current) df_colnames <- c("strand", "txStart", "txEnd", "txChrom", "txStrand") target <- c(seqnames=4L, start=2L, end=3L, width=NA_integer_, strand=5L) current <- find_GRanges_cols(df_colnames) checkIdentical(target, current) df_colnames <- c("chrom", "txStrand", "start", "end") target <- c(seqnames=1L, start=3L, end=4L, width=NA_integer_, strand=NA_integer_) current <- find_GRanges_cols(df_colnames) checkIdentical(target, current) df_colnames <- c("stop", "txEnd", "txStart", "CHR", "start") target <- c(seqnames=4L, start=5L, end=1L, width=NA_integer_, strand=NA_integer_) current <- find_GRanges_cols(df_colnames) checkIdentical(target, current) df_colnames <- c("txEnd", "txStart", "chromosome_name") target <- c(seqnames=3L, start=2L, end=1L, width=NA_integer_, strand=NA_integer_) current <- find_GRanges_cols(df_colnames) checkIdentical(target, current) df_colnames <- c("tx_end", "tx_start", "chrom", "tx_chrom") target <- c(seqnames=4L, start=2L, end=1L, width=NA_integer_, strand=NA_integer_) current <- find_GRanges_cols(df_colnames) checkIdentical(target, current) df_colnames <- c("chrom", "strand", "exon_chrom_start", "exon_chrom_end") target <- c(seqnames=1L, start=3L, end=4L, width=NA_integer_, strand=2L) current <- find_GRanges_cols(df_colnames) checkIdentical(target, current) df_colnames <- c("chrom", "start", "end", "end") checkException(find_GRanges_cols(df_colnames), silent=TRUE) df_colnames <- c("chrom", "start", "End", "end") checkException(find_GRanges_cols(df_colnames), silent=TRUE) df_colnames <- c("chrom", "start", "end", "stop") checkException(find_GRanges_cols(df_colnames), silent=TRUE) target <- c(seqnames=1L, start=2L, end=4L, width=NA_integer_, strand=NA_integer_) current <- find_GRanges_cols(df_colnames, end.field="stop") checkIdentical(target, current) checkException(find_GRanges_cols(df_colnames, end.field=4), silent=TRUE) df_colnames <- c("chrom", "start", "start", "stop") checkException(find_GRanges_cols(df_colnames), silent=TRUE) df_colnames <- c("chrom", "start", "end", "CHR") checkException(find_GRanges_cols(df_colnames), silent=TRUE) target <- c(seqnames=4L, start=2L, end=3L, width=NA_integer_, strand=NA_integer_) current <- find_GRanges_cols(df_colnames, seqnames.field="chr") checkIdentical(target, current) df_colnames <- c("chrom", "start", "end", "chromosome_name") checkException(find_GRanges_cols(df_colnames), silent=TRUE) df_colnames <- c("chrom", "tx_start", "tx_end", "exon_start") checkException(find_GRanges_cols(df_colnames), silent=TRUE) target <- c(seqnames=1L, start=2L, end=3L, width=NA_integer_, strand=NA_integer_) current <- find_GRanges_cols(df_colnames, start.field="tx_start", end.field="tx_end") checkIdentical(target, current) df_colnames <- c("chrom", "tx_start", "tx_end", "exon_stop") checkException(find_GRanges_cols(df_colnames), silent=TRUE) df_colnames <- c("chrom", "tx_start", "tx_end", "tx_end") checkException(find_GRanges_cols(df_colnames), silent=TRUE) df_colnames <- c("chrom", "tx_start", "tx_end", "tx_stop") checkException(find_GRanges_cols(df_colnames), silent=TRUE) df_colnames <- c("chrom", "tx_start", "start", "end") target <- c(seqnames=1L, start=3L, end=4L, width=NA_integer_, strand=NA_integer_) current <- find_GRanges_cols(df_colnames) checkIdentical(target, current) df_colnames <- c("chrom", "strand", "start", "end", "STRAND") checkException(find_GRanges_cols(df_colnames), silent=TRUE) target <- c(seqnames=1L, start=3L, end=4L, width=NA_integer_, strand=NA_integer_) current <- find_GRanges_cols(df_colnames, ignore.strand=TRUE) checkIdentical(target, current) } GenomicRanges/inst/unitTests/test_makeGRangesListFromDataFrame.R0000644000175400017540000001275413001230267026076 0ustar00biocbuildbiocbuild## .TARGET_names <- paste0(letters[1:10], 1L:10L) .TARGET_seqlevels <- c("chr1", "chr2", "chr3", "chrX") .TARGET_seqnames <- Rle(factor(c("chr1", "chr3", "chr1", "chrX"), levels = .TARGET_seqlevels), c(2, 4, 2, 2)) .TARGET_start <- 11:20 .TARGET_end <- 12:21 .TARGET_ranges <- IRanges(.TARGET_start, .TARGET_end, names = .TARGET_names) .TARGET_strand <- Rle(strand(c("-", "+", "*", "+", "-")), c(1, 2, 3, 2, 2)) .TARGET_mcols <- DataFrame(score = 10:19, GC = seq(1, 0, length = 10)) .TARGET_classifier <- c("a", "a", "b", "b", "c", "c", "d", "d", "a", "b") .TARGET_classifierF <- factor(c("a", "a", "b", "b", "c", "c", "d", "d", "a", "b")) dfr <- data.frame(seqnames = .TARGET_seqnames, .TARGET_ranges, strand = .TARGET_strand, .TARGET_mcols, .TARGET_classifier) dfac <- data.frame(seqnames = .TARGET_seqnames, .TARGET_ranges, strand = .TARGET_strand, .TARGET_mcols, .TARGET_classifierF) DF <- DataFrame(seqnames = .TARGET_seqnames, start = .TARGET_start, end = .TARGET_end, width = width(.TARGET_ranges), names = .TARGET_names, strand = .TARGET_strand, .TARGET_mcols, .TARGET_classifier) DFac <- DataFrame(seqnames = .TARGET_seqnames, start = .TARGET_start, end = .TARGET_end, width = width(.TARGET_ranges), names = .TARGET_names, strand = .TARGET_strand, .TARGET_mcols, .TARGET_classifierF) .make_TARGET_GRangesList_from_dataframe <- function() { makeGRangesListFromDataFrame(dfr, split.field = ".TARGET_classifier", names.field = "names", keep.extra.columns = TRUE) } .make_TARGET_GRangesList_from_DataFrame <- function() { makeGRangesListFromDataFrame(DF, split.field = ".TARGET_classifier", names.field = "names", keep.extra.columns = TRUE) } test_makeGRangesListFromDataFrame <- function() { checkException( makeGRangesListFromDataFrame(dfr, split.field = ".TARGET_classifier", names.field = "Bad_Field"), silent = TRUE) checkException( makeGRangesListFromDataFrame(dfr, split.field = "Bad_Field"), silent = TRUE) checkTrue(validObject( makeGRangesListFromDataFrame(dfr, split.field = ".TARGET_classifier") )) checkTrue(validObject( makeGRangesListFromDataFrame(DF, split.field = ".TARGET_classifier") )) checkTrue(validObject( .make_TARGET_GRangesList_from_dataframe() )) checkTrue(validObject( .make_TARGET_GRangesList_from_DataFrame() )) # Test with factor in data.frame checkTrue(validObject( makeGRangesListFromDataFrame( dfac, split.field = ".TARGET_classifierF", names.field = "names", keep.extra.columns = TRUE) )) # Test with factor in DataFrame checkTrue(validObject( makeGRangesListFromDataFrame( DFac, split.field = ".TARGET_classifierF", names.field = "names", keep.extra.columns = TRUE) )) # Test GRangesList length to split.field unique/level length checkTrue(identical(length(unique(.TARGET_classifier)), length(.make_TARGET_GRangesList_from_dataframe()))) checkTrue(identical(length(levels(.TARGET_classifierF)), length(.make_TARGET_GRangesList_from_dataframe()))) checkTrue(identical(length(unique(.TARGET_classifier)), length(.make_TARGET_GRangesList_from_DataFrame()))) checkTrue(identical(length(levels(.TARGET_classifierF)), length(.make_TARGET_GRangesList_from_DataFrame()))) # Names check with and without keep.extra.columns checkTrue(identical( Reduce(intersect, lapply(.make_TARGET_GRangesList_from_dataframe(), function(x) names(mcols(x)) )), c("score", "GC") )) checkTrue(identical( Reduce(intersect, lapply(.make_TARGET_GRangesList_from_DataFrame(), function(x) names(mcols(x)) )), c("score", "GC") )) checkTrue(identical( Reduce(intersect, lapply( makeGRangesListFromDataFrame(dfr, split.field = ".TARGET_classifier", names.field = "names"), function(x) names(mcols(x)) )), character(0L) )) checkTrue(identical( Reduce(intersect, lapply( makeGRangesListFromDataFrame(DF, split.field = ".TARGET_classifier", names.field = "names"), function(x) names(mcols(x)) )), character(0L) )) } GenomicRanges/inst/unitTests/test_nearest-methods.R0000644000175400017540000003124313001230267023561 0ustar00biocbuildbiocbuildquiet <- suppressWarnings test_GenomicRanges_findNearest0 <- function() { .findNearest <- GenomicRanges:::.GenomicRanges_findNearest0 sentinel <- c(0, 20) subject <- c(5, 15) hits <- .findNearest(10, subject, sentinel, TRUE) checkIdentical(2L, subjectHits(hits)) hits <- .findNearest(10, subject, sentinel, FALSE) checkIdentical(1L, subjectHits(hits)) hits <- .findNearest(5, subject, sentinel, TRUE) checkIdentical(2L, subjectHits(hits)) hits <- .findNearest(15, subject, sentinel, FALSE) checkIdentical(1L, subjectHits(hits)) hits <- .findNearest(5, subject, sentinel, FALSE) checkIdentical(integer(), subjectHits(hits)) hits <- .findNearest(15, subject, sentinel, TRUE) checkIdentical(integer(), subjectHits(hits)) subject <- c(15, 5) hits <- .findNearest(10, subject, sentinel, TRUE) checkIdentical(1L, subjectHits(hits)) hits <- .findNearest(10, subject, sentinel, FALSE) checkIdentical(2L, subjectHits(hits)) } test_GenomicRanges_precede_follow <- function() { ## query on "+" query <- GRanges("A", IRanges(c(1, 5, 10, 15, 20), width=1), "+") subject <- GRanges("A", IRanges(c(5, 15), width=1), "+") hits <- precede(query, subject) checkIdentical(c(1L, 2L, 2L, NA_integer_, NA_integer_), hits) hits <- follow(query, subject) checkIdentical(c(NA_integer_, NA_integer_, 1L, 1L, 2L), hits) subject <- GRanges("A", IRanges(c(5, 15), width=1), "-") hits <- precede(query, subject) checkIdentical(rep(NA_integer_, length(query)), hits) hits <- follow(query, subject) checkIdentical(rep(NA_integer_, length(query)), hits) subject <- GRanges("A", IRanges(c(5, 15), width=1), "*") hits <- precede(query, subject) checkIdentical(c(1L, 2L, 2L, NA_integer_, NA_integer_), hits) hits <- follow(query, subject) checkIdentical(c(NA_integer_, NA_integer_, 1L, 1L, 2L), hits) ## query on "-" query <- GRanges("A", IRanges(c(1, 5, 10, 15, 20), width=1), "-") subject <- GRanges("A", IRanges(c(5, 15), width=1), "-") hits <- precede(query, subject) checkIdentical(c(NA_integer_, NA_integer_, 1L, 1L, 2L), hits) hits <- follow(query, subject) checkIdentical(c(1L, 2L, 2L, NA_integer_, NA_integer_), hits) subject <- GRanges("A", IRanges(c(5, 15), width=1), "+") hits <- precede(query, subject) checkIdentical(rep(NA_integer_, length(query)), hits) hits <- follow(query, subject) checkIdentical(rep(NA_integer_, length(query)), hits) subject <- GRanges("A", IRanges(c(5, 15), width=1), "*") hits <- precede(query, subject) checkIdentical(c(NA_integer_, NA_integer_, 1L, 1L, 2L), hits) hits <- follow(query, subject) checkIdentical(c(1L, 2L, 2L, NA_integer_, NA_integer_), hits) ## query on "*" -> acts like ignore.strand=TRUE query <- GRanges("A", IRanges(c(1, 5, 10, 15, 20), width=1), "*") subject <- GRanges("A", IRanges(c(5, 15), width=1), "+") hits <- precede(query, subject) checkIdentical(c(1L, 2L, 2L, NA_integer_, NA_integer_), hits) hits <- follow(query, subject) checkIdentical(c(NA_integer_, NA_integer_, 1L, 1L, 2L), hits) subject <- GRanges("A", IRanges(c(5, 15), width=1), "-") hits <- precede(query, subject) checkIdentical(c(1L, 2L, 2L, NA_integer_, NA_integer_), hits) hits <- follow(query, subject) checkIdentical(c(NA_integer_, NA_integer_, 1L, 1L, 2L), hits) subject <- GRanges("A", IRanges(c(5, 15), width=1), "*") hits <- precede(query, subject) checkIdentical(c(1L, 2L, 2L, NA_integer_, NA_integer_), hits) hits <- follow(query, subject) checkIdentical(c(NA_integer_, NA_integer_, 1L, 1L, 2L), hits) } test_GenomicRanges_precede_follow_ties <- function() { query <- GRanges("A", IRanges(10, width=1), c("+", "-", "*")) subject <- GRanges("A", IRanges(c(5, 5, 5, 15, 15, 15), width=1), rep(c("+", "-", "*"), 2)) checkIdentical(c(4L, 2L, 4L), precede(query, subject)) checkIdentical(c(1L, 4L, 1L), precede(query, rev(subject))) } test_GenomicRanges_precede_follow_0width <- function() { a <- GRanges("A", IRanges(100, width=0), "*") b <- GRanges("A", IRanges(20, 25), "+") checkIdentical(NA_integer_, precede(a, b)) checkIdentical(1L, precede(b, a)) checkIdentical(1L, follow(a, b)) checkIdentical(NA_integer_, follow(b, a)) checkIdentical(1L, nearest(a, b)) checkIdentical(1L, nearest(b, a)) } test_GenomicRanges_ignore_strand <- function() { query <- GRanges("A", IRanges(10, width=1), c("+", "-", "*")) subject <- GRanges("A", IRanges(c(5, 5, 5, 15, 15, 15), width=1), rep(c("+", "-", "*"), 2)) checkIdentical(c(4L, 4L, 4L), precede(query, subject, ignore.strand=TRUE)) checkIdentical(c(1L, 1L, 1L), precede(query, rev(subject),ignore.strand=TRUE)) } test_GenomicRanges_nearest <- function() { ## adjacent r <- IRanges(c(1,6), c(5,10)) g <- GRanges("chr1", r, "+") checkEquals(follow(r), follow(g)) checkEquals(precede(r), precede(g)) checkEquals(nearest(r), nearest(g)) checkEquals(follow(r,r), follow(g,g)) checkEquals(precede(r,r), precede(g,g)) checkEquals(nearest(r,r), nearest(g,g)) g <- GRanges("chr1", r, "-") checkEquals(follow(r), precede(g)) checkEquals(precede(r), follow(g)) checkEquals(nearest(r), nearest(g)) checkEquals(follow(r,r), precede(g,g)) checkEquals(precede(r,r), follow(g,g)) checkEquals(nearest(r,r), nearest(g,g)) g <- GRanges("chr1", r, "*") checkEquals(nearest(r), nearest(g)) checkEquals(nearest(r,r), nearest(g,g)) ## separated by 1 r <- IRanges(c(1,7), c(5,11)) g <- GRanges("chr1", r, "+") checkEquals(follow(r), follow(g)) checkEquals(precede(r), precede(g)) checkEquals(nearest(r), nearest(g)) checkEquals(nearest(r,r), nearest(g,g)) g <- GRanges("chr1", r, "-") checkEquals(follow(r), precede(g)) checkEquals(precede(r), follow(g)) checkEquals(nearest(r), nearest(g)) checkEquals(nearest(r,r), nearest(g,g)) g <- GRanges("chr1", r, "*") checkEquals(nearest(r), nearest(g)) checkEquals(nearest(r,r), nearest(g,g)) ## separated by > 1 r <- IRanges(c(1,5,10), c(2,7,12)) g <- GRanges("chr1", r, "+") checkEquals(precede(r), precede(g)) checkEquals(follow(r), follow(g)) checkEquals(nearest(r), nearest(g)) g <- GRanges("chr1", r, "-") checkEquals(follow(r), precede(g)) checkEquals(precede(r), follow(g)) checkEquals(nearest(r), nearest(g)) ## overlapping r <- IRanges(c(1,4,8), c(6,10,12)) g <- GRanges("chr1", r, "+") checkEquals(nearest(r), nearest(g)) checkEquals(nearest(r, r), nearest(g, g)) checkEquals(nearest(r, rev(r)), nearest(g, rev(g))) g <- GRanges("chr1", r, "-") checkEquals(nearest(r), nearest(g)) checkEquals(nearest(r, r), nearest(g, g)) checkEquals(nearest(r, rev(r)), nearest(g, rev(g))) g <- GRanges("chr1", r, "*") checkEquals(nearest(r), nearest(g)) checkEquals(nearest(r, r), nearest(g, g)) checkEquals(nearest(r, rev(r)), nearest(g, rev(g))) q <- GRanges("chr1", IRanges(1, 15), "+") s <- GRanges("chr1", IRanges(c(1, 1, 10), c(5, 15, 15)), "+") target <- nearest(q, s, select="arbitrary") checkEquals(3, target) strand(q) <- "-" strand(s) <- "-" target <- nearest(q, s, select="arbitrary") checkEquals(3, target) target1 <- nearest(ranges(q), ranges(s), select="all") target2 <- nearest(q, s, select="all") checkEquals(target1, target2) ## select = 'all' q <- GRanges("chr1", IRanges(1, 1)) s <- GRanges("chr1", IRanges(5, 5), strand="-") target1 <- nearest(ranges(q), ranges(s), select="all") target2 <- nearest(q, s, select="all") checkEquals(target1, target2) ## ignore.strand q <- GRanges("chr1", IRanges(5, width=1), "+") s <- GRanges("chr1", IRanges(c(10, 8), width=1), "-") res <- nearest(q, s, ignore.strand=FALSE) checkEquals(res, NA_integer_) res <- nearest(q, s, ignore.strand=TRUE) checkEquals(res, 2L) q <- GRanges("chr1", IRanges(5, 5)) s <- GRanges("chr1", IRanges(c(6,7), c(6,7)), c("+", "-")) checkEquals(nearest(q, s), 1L) q <- GRanges("chr1", IRanges(105, 105), "-") s <- GRanges("chr1", IRanges(c(1,120), c(100, 125)), c("-", "-")) pos <- nearest(q, s, ignore.strand=TRUE) neg <- nearest(q, s, ignore.strand=FALSE) checkEquals(pos, neg) } test_GenomicRanges_distance <- function() { ## empty checkException(quiet(distance(GRanges())), silent=TRUE) checkIdentical(quiet(distance(GRanges(), GRanges())), integer()) g1 <- GRanges(seqnames = c(rep("chr1", 3), rep("chr2", 2)), ranges = IRanges(rep(1, 5), width=3), strand = c("+", "-", "*", "*", "*")) g2 <- GRanges(seqnames = c(rep("chr1", 3), rep("chr2", 2)), ranges = IRanges(rep(5, 5), width=3), strand = c("+", "-", "*", "-", "+")) current <- quiet(distance(g1, g2)) target <- rep(1L, length(current)) checkIdentical(current, target) strand(g2[1]) <- "-" current <- quiet(distance(g1[1], g2[1])) checkTrue(is.na(current)) seqnames(g2[4]) <- factor("chr1", levels=c("chr1", "chr2")) current <- quiet(distance(g1[4], g2[4])) checkTrue(is.na(current)) ## adjacent, overlap, separated by 1 query <- GRanges("A", IRanges(c(1, 3, 9), c(2, 7, 10))) subject <- GRanges("A", IRanges(c(3, 5, 12), c(3, 6, 12))) checkIdentical(quiet(distance(query, subject)), c(0L, 0L, 1L)) ## recycling checkIdentical(quiet(distance(query[1:2], subject)), c(0L, 0L, 9L)) ## zero-width target <- abs(-3:3) current <- sapply(-3:3, function(i) quiet(distance(shift(IRanges(4,3), i), IRanges(4,3)))) checkIdentical(current, target) query <- GRanges("A", IRanges(4,3)) subject <- GRanges("A", IRanges(3,4)) current <- quiet(distance(query, subject)) checkIdentical(current, 0L) } test_GenomicRanges_distanceToNearest <- function() { target <- Hits(distance=integer(0), sort.by.query=TRUE) current <- quiet(distanceToNearest(GRanges())) checkIdentical(current, target) x <- GRanges("chr1", IRanges(c(1, 5, 10), width=1)) subject <- GRanges("chr1", IRanges(c(3, 12), width=1)) current <- quiet(distanceToNearest(x, subject)) target <- c(1L, 1L, 2L) checkIdentical(target, subjectHits(current)) ## strand strand(x) <- "+" strand(subject) <- c("+", "-") current <- quiet(distanceToNearest(x, subject)) target <- c(1L, 1L, 1L) checkIdentical(target, subjectHits(current)) current <- quiet(distanceToNearest(x, subject, ignore.strand=TRUE)) target <- c(1L, 1L, 2L) checkIdentical(target, subjectHits(current)) ## no self-hits / self-hits current <- quiet(distanceToNearest(x)) target <- c(2L, 1L, 2L) checkIdentical(target, subjectHits(current)) current <- quiet(distanceToNearest(x, x)) target <- c(1L, 2L, 3L) checkIdentical(target, subjectHits(current)) ## ranges start at 0 x <- GRanges("chr1", IRanges(0, width=1)) subject <- GRanges("chr1", IRanges(1, width=1)) current <- distanceToNearest(x, subject) iranges <- distanceToNearest(ranges(x), ranges(subject)) checkIdentical(subjectHits(iranges), subjectHits(current)) checkIdentical(mcols(iranges)$distance, mcols(current)$distance) current <- distanceToNearest(x, x) checkIdentical(1L, subjectHits(current)) checkIdentical(0L, mcols(current)$distance) current <- distanceToNearest(subject, x) iranges <- distanceToNearest(ranges(subject), ranges(x)) checkIdentical(subjectHits(iranges), subjectHits(current)) checkIdentical(mcols(iranges)$distance, mcols(current)$distance) x <- GRanges("chr1", IRanges(c(0, 10, 99), width=1)) subject <- GRanges("chr1", IRanges(15, width=1)) current <- distanceToNearest(x, subject) iranges <- distanceToNearest(ranges(x), ranges(subject)) checkIdentical(subjectHits(iranges), subjectHits(current)) checkIdentical(mcols(iranges)$distance, mcols(current)$distance) ## NA handling x <- GRanges(c("chr1", "chr2"), IRanges(c(1, 5), width=1)) current <- quiet(distanceToNearest(x, x[1])) checkIdentical(1L, queryHits(current)) checkIdentical(1L, subjectHits(current)) checkIdentical(0L, mcols(current)$distance) ## select = 'all' vs 'arbitrary' x <- GRanges("chr1", IRanges(2, width=1)) subject <- GRanges("chr1", IRanges(c(8, 10, 8), width=1)) current <- distanceToNearest(x, subject, select="all") checkTrue(is(current, "Hits")) checkIdentical(length(current), 2L) current <- distanceToNearest(x, GRanges(), select="all") checkIdentical(length(current), 0L) checkIdentical(queryLength(current), 1L) } GenomicRanges/inst/unitTests/test_setops-methods.R0000644000175400017540000002530113001230267023433 0ustar00biocbuildbiocbuild### make_test_GRanges <- function() { new("GRanges", seqnames = Rle(factor(c("chr1", "chr2", "chr1", "chr3"), levels = paste("chr", 1:5, sep = "")), c(1, 3, 2, 4)), ranges = IRanges(1:10, width = 10:1, names = head(letters, 10)), strand = Rle(strand(c("-", "+", "*", "+", "-")), c(1, 2, 2, 3, 2)), seqinfo = Seqinfo(seqnames = paste("chr", 1:5, sep="")), elementMetadata = DataFrame(score = 1:10, GC = seq(1, 0, length=10))) } make_test_GRangesList <- function() { suppressWarnings(GRangesList( a = new("GRanges", seqnames = Rle(factor(c("chr1", "chr2", "chr1", "chr3")), c(1, 3, 2, 4)), ranges = IRanges(1:10, width = 10:1, names = head(letters, 10)), strand = Rle(strand(c("-", "+", "*", "+", "-")), c(1, 2, 2, 3, 2)), seqinfo = Seqinfo(seqnames = paste("chr", 1:3, sep="")), elementMetadata = DataFrame(score = 1:10, GC = seq(1, 0, length=10))), b = new("GRanges", seqnames = Rle(factor(c("chr2", "chr4", "chr5")), c(3, 6, 4)), ranges = IRanges(1:13, width = 13:1, names = tail(letters, 13)), strand = Rle(strand(c("-", "+", "-")), c(4, 5, 4)), seqinfo = Seqinfo(seqnames = paste("chr", c(2L, 4:5), sep="")), elementMetadata = DataFrame(score = 1:13, GC = seq(0, 1, length=13))))) } test_union <- function() { gr <- make_test_GRanges() grlist <- make_test_GRangesList() checkException(union(gr, grlist), silent = TRUE) checkException(union(grlist, gr), silent = TRUE) expect <- gr mcols(expect) <- NULL expect <- reduce(gr) checkIdentical(union(gr, gr), expect) checkIdentical(union(grlist[[1]], grlist[[2]]), reduce(unlist(grlist))) } test_intersect <- function() { gr <- make_test_GRanges() grlist <- make_test_GRangesList() checkException(intersect(gr, grlist), silent = TRUE) checkException(intersect(grlist, gr), silent = TRUE) expect <- gr mcols(expect) <- NULL expect <- reduce(gr) checkIdentical(intersect(gr, gr), expect) checkIdentical(union(grlist[[1]], grlist[[2]]), reduce(unlist(grlist))) gr1 <- GRanges(seqnames = c("chr1", "chr2", "chr3", "chr4"), ranges = IRanges(1:4, width = 10), strand = c("-", "+", "+", "+")) gr2 <- GRanges(seqnames = c("chr1", "chr2", "chr5", "chr6"), ranges = IRanges(1:4, width = 10), strand = c("-", "+", "+", "+")) expect <- GRanges(seqnames = factor(c("chr1", "chr2"), levels = paste("chr", 1:6, sep = "")), ranges = IRanges(1:2, width = 10), strand = c("-", "+")) checkIdentical(suppressWarnings(intersect(gr1, gr2)), expect) ## intersect,GRangesList,GRangesList gr <- make_test_GRanges() grlist <- make_test_GRangesList() expect <- reduce(grlist) mcols(expect) <- NULL checkIdentical(intersect(grlist, grlist), expect) } test_setdiff <- function() { gr <- make_test_GRanges() grlist <- make_test_GRangesList() checkException(setdiff(gr, grlist), silent = TRUE) checkException(setdiff(grlist, gr), silent = TRUE) expect <- GRanges(seqlengths=seqlengths(gr)) checkIdentical(setdiff(gr, gr), expect) checkIdentical(setdiff(grlist[[1]], grlist[[2]]), reduce(grlist[[1]])) gr1 <- GRanges(seqnames = c("chr1", "chr2", "chr3", "chr4"), ranges = IRanges(1:4, width = 10), strand = c("-", "+", "+", "+")) gr2 <- GRanges(seqnames = c("chr1", "chr2", "chr5", "chr6"), ranges = IRanges(1:4, width = 10), strand = c("-", "+", "+", "+")) expect <- GRanges(seqnames = factor(c("chr3", "chr4"), levels = paste("chr", 1:6, sep = "")), ranges = IRanges(3:4, width = 10), strand = c("+", "+")) checkIdentical(suppressWarnings(setdiff(gr1, gr2)), expect) } test_punion <- function() { gr <- make_test_GRanges() grlist <- make_test_GRangesList() expect <- gr mcols(expect) <- NULL checkIdentical(punion(gr, gr), expect) } test_pintersect <- function() { ## pintersect,GRanges,GRanges gr0 <- GRanges() current <- pintersect(gr0, gr0, drop.nohit.ranges=TRUE) target <- gr0 checkIdentical(target, current) current <- pintersect(gr0, gr0) mcols(target)$hit <- logical(0) checkIdentical(target, current) x <- GRanges("chr1", IRanges(c( 4, 13, 2, 9, 16, 9), width=1, names=letters[1:6])) current <- pintersect(x, x) target <- x mcols(target)$hit <- TRUE checkIdentical(target, current) checkIdentical(target, pintersect(x, unname(x))) checkIdentical(unname(target), pintersect(unname(x), x)) current <- suppressWarnings(pintersect(x, GRanges("chrX", ranges(x)))) target <- x mcols(target)$hit <- FALSE width(target) <- 0 checkIdentical(target, current) current <- suppressWarnings(pintersect(x, GRanges("chrX", ranges(x)), drop.nohit.ranges=TRUE)) checkIdentical(x[0], current) y <- x strand(y) <- Rle(strand(c("*", "-", "+")), c(2, 2, 2)) target <- y mcols(target)$hit <- TRUE checkIdentical(target, pintersect(y, y)) checkIdentical(target, pintersect(y, x)) checkIdentical(target, pintersect(x, y)) current <- pintersect(x, y, strict.strand=TRUE) target <- x mcols(target)$hit <- as.logical(strand(x) == strand(y)) width(target)[!mcols(target)$hit] <- 0 checkIdentical(target, current) current <- pintersect(y, x, strict.strand=TRUE) strand(target) <- strand(y) checkIdentical(target, current) strand(x) <- rep(Rle(strand(c("*", "+", "-"))), 2) current <- pintersect(x, y) target <- x mcols(target)$hit <- as.logical(strand(x) == strand(y) | strand(x) == "*" | strand(y) == "*") width(target)[!mcols(target)$hit] <- 0 disambig_strand_idx <- strand(target) == "*" & mcols(target)$hit strand(target)[disambig_strand_idx] <- strand(y)[disambig_strand_idx] checkIdentical(target, current) current <- pintersect(x, y, ignore.strand=TRUE) target <- x mcols(target)$hit <- TRUE checkIdentical(target, current) current <- pintersect(y, x, ignore.strand=TRUE) target <- y mcols(target)$hit <- TRUE checkIdentical(target, current) x <- GRanges("chr1", IRanges( c( 2, 7, 7, 4, 13, 2, 9, 12, 9, 4, 8, 7, 5, 20), c( 6, 8, 12, 10, 12, 2, 12, 18, 8, 12, 11, 11, 11, 20))) y <- GRanges("chr1", IRanges(1, 20)) current <- pintersect(x, y) target <- x mcols(target)$hit <- TRUE checkIdentical(target, current) y <- GRanges("chr1", IRanges(7, 11), strand="+") current <- pintersect(x, y) target <- x ranges(target) <- IRanges( c( 7, 7, 7, 7, 13, 2, 9, 12, 9, 7, 8, 7, 7, 20), c( 6, 8, 11, 10, 12, 1, 11, 11, 8, 11, 11, 11, 11, 19)) mcols(target)$hit <- TRUE nohit_idx <- c(5, 6, 14) mcols(target)$hit <- !(1:14 %in% nohit_idx) strand(target)[mcols(target)$hit] <- strand(y) checkIdentical(target, current) current <- pintersect(x, y, drop.nohit.ranges=TRUE) target <- target[mcols(target)$hit] mcols(target)$hit <- NULL checkIdentical(target, current) ## pintersect,GRangesList,GRanges and pintersect,GRanges,GRangesList x <- GRangesList(x, rev(x)) current <- pintersect(x, y) target <- mendoapply(pintersect, x, as(y, "GRangesList")) checkIdentical(target, current) current <- pintersect(x, y, drop.nohit.ranges=TRUE) target <- mendoapply(pintersect, x, as(y, "GRangesList"), MoreArgs=list(drop.nohit.ranges=TRUE)) checkIdentical(target, current) y <- GRanges("chr1", IRanges(c(1, 7), c(20, 11))) strand(y) <- c("+", "-") current <- pintersect(x, y) target <- mendoapply(pintersect, x, as(y, "GRangesList")) checkIdentical(target, current) checkIdentical(target, pintersect(y, x)) current <- pintersect(x, y, drop.nohit.ranges=TRUE) target <- mendoapply(pintersect, x, as(y, "GRangesList"), MoreArgs=list(drop.nohit.ranges=TRUE)) checkIdentical(target, current) strand(x[[1]]) <- c("+", "-") current <- pintersect(x, y) target <- mendoapply(pintersect, x, as(y, "GRangesList")) checkIdentical(target, current) checkIdentical(target, pintersect(y, x)) current <- pintersect(x, y, drop.nohit.ranges=TRUE) target <- mendoapply(pintersect, x, as(y, "GRangesList"), MoreArgs=list(drop.nohit.ranges=TRUE)) checkIdentical(target, current) checkIdentical(target, pintersect(y, x, drop.nohit.ranges=TRUE)) current <- pintersect(x, y, strict.strand=TRUE) target <- mendoapply(pintersect, x, as(y, "GRangesList"), MoreArgs=list(strict.strand=TRUE)) checkIdentical(target, current) checkIdentical(target, pintersect(y, x, strict.strand=TRUE)) current <- pintersect(x, y, drop.nohit.ranges=TRUE, strict.strand=TRUE) target <- mendoapply(pintersect, x, as(y, "GRangesList"), MoreArgs=list(drop.nohit.ranges=TRUE, strict.strand=TRUE)) checkIdentical(target, current) checkIdentical(target, pintersect(y, x, drop.nohit.ranges=TRUE, strict.strand=TRUE)) current <- reduce(pintersect(x, y, strict.strand=TRUE), drop.empty.ranges=TRUE) target <- mendoapply(intersect, x, as(y, "GRangesList")) checkIdentical(target, current) current <- reduce(pintersect(x, y, ignore.strand=TRUE), drop.empty.ranges=TRUE, ignore.strand=TRUE) target <- mendoapply(intersect, x, as(y, "GRangesList"), MoreArgs=list(ignore.strand=TRUE)) checkIdentical(target, current) } test_psetdiff <- function() { gr <- make_test_GRanges() grlist <- make_test_GRangesList() checkException(psetdiff(grlist, gr), silent = TRUE) expect <- gr width(expect) <- 0L mcols(expect) <- NULL checkIdentical(psetdiff(gr, gr), expect) expect <- gr mcols(expect) <- NULL strand(expect) <- Rle(c("-", "+", "-"), c(1, 7, 2)) end(expect)[5:6] <- c(5, 5) checkIdentical(psetdiff(gr, rev(gr)), expect) expect <- GRangesList(a = GRanges(), b = GRanges(factor("chr2", levels = paste("chr", 1:5, sep = "")), IRanges(2, 10), "+")) checkIdentical(psetdiff(gr[1:2], grlist[1:2]), expect) } GenomicRanges/man/0000755000175400017540000000000013032300275015067 5ustar00biocbuildbiocbuildGenomicRanges/man/DelegatingGenomicRanges-class.Rd0000644000175400017540000000154513001230267023172 0ustar00biocbuildbiocbuild\name{DelegatingGenomicRanges-class} \docType{class} % Class: \alias{class:DelegatingGenomicRanges} \alias{DelegatingGenomicRanges-class} \alias{class:DelegatingGenomicRanges} \alias{DelegatingGenomicRanges-class} % Accessors: \alias{seqnames,DelegatingGenomicRanges-method} \alias{ranges,DelegatingGenomicRanges-method} \alias{strand,DelegatingGenomicRanges-method} \alias{seqinfo,DelegatingGenomicRanges-method} % Coercion: \title{DelegatingGenomicRanges objects} \description{ The \code{DelegatingGenomicRanges} class implements the virtual \code{GenomicRanges} class using a delegate \code{GenomicRanges}. This enables developers to create \code{GenomicRanges} subclasses that add specialized columns or other structure, while remaining agnostic to how the data are actually stored. See the Extending GenomicRanges vignette. } \author{M. Lawrence} GenomicRanges/man/GNCList-class.Rd0000644000175400017540000001161713001230267017731 0ustar00biocbuildbiocbuild\name{GNCList-class} \docType{class} % GNCList objects: \alias{class:GNCList} \alias{GNCList-class} \alias{GNCList} \alias{granges,GNCList-method} \alias{length,GNCList-method} \alias{names,GNCList-method} \alias{seqnames,GNCList-method} \alias{start,GNCList-method} \alias{end,GNCList-method} \alias{width,GNCList-method} \alias{ranges,GNCList-method} \alias{strand,GNCList-method} \alias{seqinfo,GNCList-method} \alias{coerce,GNCList,GRanges-method} \alias{coerce,GenomicRanges,GNCList-method} \title{GNCList objects} \description{ The GNCList class is a container for storing the Nested Containment List representation of a vector of genomic ranges (typically represented as a \link{GRanges} object). To preprocess a \link{GRanges} object, simply call the \code{GNCList} constructor function on it. The resulting GNCList object can then be used for efficient overlap-based operations on the genomic ranges. } \usage{ GNCList(x) } \arguments{ \item{x}{ The \link{GRanges} (or more generally \link{GenomicRanges}) object to preprocess. } } \details{ The \pkg{IRanges} package also defines the \code{\link[IRanges]{NCList}} and \code{\link[IRanges]{NCLists}} constructors and classes for preprocessing and representing a \link[IRanges]{Ranges} or \link[IRanges]{RangesList} object as a data structure based on Nested Containment Lists. Note that GNCList objects (introduced in BioC 3.1) are replacements for GIntervalTree objects (BioC < 3.1). See \code{?\link[IRanges]{NCList}} in the \pkg{IRanges} package for some important differences between the new algorithm based on Nested Containment Lists and the old algorithm based on interval trees. In particular, the new algorithm supports preprocessing of a \link{GenomicRanges} object with ranges defined on circular sequences (e.g. on the mitochnodrial chromosome). See below for some examples. } \value{ A GNCList object. } \author{H. Pagès} \references{ Alexander V. Alekseyenko and Christopher J. Lee -- Nested Containment List (NCList): a new algorithm for accelerating interval query of genome alignment and interval databases. Bioinformatics (2007) 23 (11): 1386-1393. doi: 10.1093/bioinformatics/btl647 } \seealso{ \itemize{ \item The \code{\link[IRanges]{NCList}} and \code{\link[IRanges]{NCLists}} constructors and classs defined in the \pkg{IRanges} package. \item \code{\link{findOverlaps}} for finding/counting interval overlaps between two \emph{range-based} objects. \item \link{GRanges} objects. } } \examples{ ## The examples below are for illustration purpose only and do NOT ## reflect typical usage. This is because, for a one time use, it is ## NOT advised to explicitely preprocess the input for findOverlaps() ## or countOverlaps(). These functions will take care of it and do a ## better job at it (by preprocessing only what's needed when it's ## needed, and release memory as they go). ## --------------------------------------------------------------------- ## PREPROCESS QUERY OR SUBJECT ## --------------------------------------------------------------------- query <- GRanges(Rle(c("chrM", "chr1", "chrM", "chr1"), 4:1), IRanges(1:10, width=5), strand=rep(c("+", "-"), 5)) subject <- GRanges(Rle(c("chr1", "chr2", "chrM"), 3:1), IRanges(6:1, width=5), strand="+") ## Either the query or the subject of findOverlaps() can be preprocessed: ppsubject <- GNCList(subject) hits1a <- findOverlaps(query, ppsubject) hits1a hits1b <- findOverlaps(query, ppsubject, ignore.strand=TRUE) hits1b ppquery <- GNCList(query) hits2a <- findOverlaps(ppquery, subject) hits2a hits2b <- findOverlaps(ppquery, subject, ignore.strand=TRUE) hits2b ## Note that 'hits1a' and 'hits2a' contain the same hits but not ## necessarily in the same order. stopifnot(identical(sort(hits1a), sort(hits2a))) ## Same for 'hits1b' and 'hits2b'. stopifnot(identical(sort(hits1b), sort(hits2b))) ## --------------------------------------------------------------------- ## WITH CIRCULAR SEQUENCES ## --------------------------------------------------------------------- seqinfo <- Seqinfo(c("chr1", "chr2", "chrM"), seqlengths=c(100, 50, 10), isCircular=c(FALSE, FALSE, TRUE)) seqinfo(query) <- seqinfo[seqlevels(query)] seqinfo(subject) <- seqinfo[seqlevels(subject)] ppsubject <- GNCList(subject) hits3 <- findOverlaps(query, ppsubject) hits3 ## Circularity introduces more hits: stopifnot(all(hits1a \%in\% hits3)) new_hits <- setdiff(hits3, hits1a) new_hits # 1 new hit query[queryHits(new_hits)] subject[subjectHits(new_hits)] # positions 11:13 on chrM are the same # as positions 1:3 ## Sanity checks: stopifnot(identical(new_hits, Hits(9, 6, 10, 6, sort.by.query=TRUE))) ppquery <- GNCList(query) hits4 <- findOverlaps(ppquery, subject) stopifnot(identical(sort(hits3), sort(hits4))) } \keyword{classes} \keyword{methods} GenomicRanges/man/GPos-class.Rd0000644000175400017540000002512713001230267017337 0ustar00biocbuildbiocbuild\name{GPos-class} \docType{class} \alias{class:GPos} \alias{GPos-class} \alias{GPos} \alias{length,GPos-method} \alias{names,GPos-method} \alias{names<-,GPos-method} \alias{seqnames,GPos-method} \alias{pos} \alias{pos,GPos-method} \alias{start,GPos-method} \alias{end,GPos-method} \alias{width,GPos-method} \alias{ranges,GPos-method} \alias{strand,GPos-method} \alias{seqinfo,GPos-method} \alias{seqinfo<-,GPos-method} \alias{coerce,GenomicRanges,GPos-method} \alias{as.data.frame,GPos-method} \alias{extractROWS,GPos-method} \alias{show,GPos-method} \alias{c,GPos-method} \title{GPos objects} \description{ The GPos class is a container for storing a set of genomic \emph{positions}, that is, genomic ranges of width 1. Even though a \link{GRanges} object can be used for that, using a GPos object can be much more memory-efficient, especially when the object contains long runs of adjacent positions. } \usage{ GPos(pos_runs) # constructor function } \arguments{ \item{pos_runs}{ A \link{GRanges} object (or any other \link{GenomicRanges} derivative) where each range is interpreted as a run of adjacent genomic positions. If \code{pos_runs} is not a \link{GenomicRanges} object, \code{GPos()} first tries to coerce it to one with \code{as(pos_runs, "GenomicRanges", strict=FALSE)}. } } \value{ A GPos object. } \section{Accessors}{ \subsection{Getters}{ GPos objects support the same set of getters as \link{GRanges} objects (i.e. \code{seqnames()}, \code{start()}, \code{end()}, \code{ranges()}, \code{strand()}, \code{mcols()}, \code{seqinfo()}, etc...), plus the \code{pos()} getter which is equivalent to \code{start()} or \code{end()}. See \code{?GRanges} for the list of getters supported by \link{GRanges} objects. Note that a GPos object cannot hold names i.e. \code{names()} always returns \code{NULL} on it. } \subsection{Setters}{ Like \link{GRanges} objects, GPos objects support the following setters: \itemize{ \item The \code{mcols()} and \code{metadata()} setters. \item The family of setters that operate on the seqinfo component of an object: \code{\link[GenomeInfoDb]{seqlevels}()}, \code{\link[GenomeInfoDb]{seqlevelsStyle}()}, \code{\link[GenomeInfoDb]{seqlengths}()}, \code{\link[GenomeInfoDb]{isCircular}()}, \code{\link[GenomeInfoDb]{genome}()}, and \code{\link[GenomeInfoDb]{seqinfo}()}. These setters are defined and documented in the \pkg{GenomeInfoDb} package. } However, there is no \code{seqnames()}, \code{pos()}, or \code{strand()} setter for GPos objects at the moment (although they might be added in the future). } } \section{Coercion}{ From GenomicRanges to GPos: A \link{GenomicRanges} object \code{x} in which all the ranges have a width of 1 can be coerced to a GPos object with \code{as(x, "GPos")}. The names on \code{x} are not propagated (a warning is issued if \code{x} has names on it). From GPos to GRanges: A GPos object \code{x} can be coerced to a \link{GRanges} object with \code{as(x, "GRanges")}. However be aware that the resulting object can use thousands times (or more) memory than \code{x}! See "MEMORY USAGE" in the Examples section below. From GPos to ordinary R objects: Like with a \link{GRanges} object, \code{as.character()}, \code{as.factor()}, and \code{as.data.frame()} work with a GPos object \code{x}. Note that \code{as.data.frame(x)} returns a data frame with a \code{pos} column (containing \code{pos(x)}) instead of the \code{start}, \code{end}, and \code{width} columns that one gets when \code{x} is a \link{GRanges} object. } \section{Subsetting}{ A GPos object can be subsetted exactly like a \link{GRanges} object. } \section{Combining}{ GPos objects can be combined (a.k.a. appended) with \code{c()} or \code{append()}. } \section{Splitting and Relisting}{ Like with a \link{GRanges} object, \code{split()} and \code{relist()} work with a GPos object \code{x}. Note that they return a \link{GenomicRangesList} object instead of a \link{GRangesList} object. } \note{ Like for any \link[S4Vectors]{Vector} derivative, the length of a GPos object cannot exceed \code{.Machine$integer.max} (i.e. 2^31 on most platforms). \code{GPos()} will return an error if \code{pos_runs} contains too many genomic positions. } \author{ Hervé Pagès; based on ideas borrowed from Georg Stricker \email{georg.stricker@in.tum.de} and Julien Gagneur \email{gagneur@in.tum.de} } \seealso{ \itemize{ \item \link{GRanges} objects. \item The \code{\link[GenomeInfoDb]{seqinfo}} accessor and family in the \pkg{GenomeInfoDb} package for accessing/modifying the seqinfo component of an object. \item \link{GenomicRanges-comparison} for comparing and ordering genomic positions. \item \link[GenomicRanges]{findOverlaps-methods} for finding overlapping genomic ranges and/or positions. \item \link[GenomicRanges]{nearest-methods} for finding the nearest genomic range/position neighbor. \item The \code{\link[BSgenome]{snpsBySeqname}}, \code{\link[BSgenome]{snpsByOverlaps}}, and \code{\link[BSgenome]{snpsById}} methods for \link[BSgenome]{SNPlocs} objects defined in the \pkg{BSgenome} package for extractors that return a GPos object. \item \link[SummarizedExperiment]{SummarizedExperiment} objects in the \pkg{SummarizedExperiment} package. } } \examples{ ## --------------------------------------------------------------------- ## BASIC EXAMPLES ## --------------------------------------------------------------------- ## Example 1: gpos1 <- GPos(c("chr1:44-53", "chr1:5-10", "chr2:2-5")) gpos1 length(gpos1) seqnames(gpos1) pos(gpos1) # same as 'start(gpos1)' and 'end(gpos1)' strand(gpos1) as.character(gpos1) as.data.frame(gpos1) as(gpos1, "GRanges") as.data.frame(as(gpos1, "GRanges")) gpos1[9:17] ## Example 2: pos_runs <- GRanges("chrI", IRanges(c(1, 6, 12, 17), c(5, 10, 16, 20)), strand=c("+", "-", "-", "+")) gpos2 <- GPos(pos_runs) gpos2 strand(gpos2) ## Example 3: gpos3A <- gpos3B <- GPos(c("chrI:1-1000", "chrI:1005-2000")) npos <- length(gpos3A) mcols(gpos3A)$sample <- Rle("sA") sA_counts <- sample(10, npos, replace=TRUE) mcols(gpos3A)$counts <- sA_counts mcols(gpos3B)$sample <- Rle("sB") sB_counts <- sample(10, npos, replace=TRUE) mcols(gpos3B)$counts <- sB_counts gpos3 <- c(gpos3A, gpos3B) gpos3 ## Example 4: library(BSgenome.Scerevisiae.UCSC.sacCer2) genome <- BSgenome.Scerevisiae.UCSC.sacCer2 gpos4 <- GPos(seqinfo(genome)) gpos4 # all the positions along the genome are represented mcols(gpos4)$dna <- do.call("c", unname(as.list(genome))) gpos4 ## Note however that, like for any Vector derivative, the length of a ## GPos object cannot exceed '.Machine$integer.max' (i.e. 2^31 on most ## platforms) so the above only works with a "small" genome. ## For example it doesn't work with the Human genome: library(TxDb.Hsapiens.UCSC.hg19.knownGene) \dontrun{ GPos(seqinfo(TxDb.Hsapiens.UCSC.hg19.knownGene)) # error! } ## You can use isSmallGenome() to check upfront whether the genome is ## "small" or not. isSmallGenome(genome) isSmallGenome(TxDb.Hsapiens.UCSC.hg19.knownGene) ## --------------------------------------------------------------------- ## MEMORY USAGE ## --------------------------------------------------------------------- ## Coercion to GRanges works... gr4 <- as(gpos4, "GRanges") gr4 ## ... but is generally not a good idea: object.size(gpos4) object.size(gr4) # 6951 times bigger than the GPos object! ## Shuffling the order of the positions impacts memory usage: gpos4r <- rev(gpos4) object.size(gpos4r) # significantly gpos4s <- sample(gpos4) object.size(gpos4s) # even worse! ## AN IMPORTANT NOTE: In the worst situations, GPos still performs as ## good as a GRanges object. object.size(as(gpos4r, "GRanges")) # same size as 'gpos4r' object.size(as(gpos4s, "GRanges")) # same size as 'gpos4s' ## Best case scenario is when the object is strictly sorted (i.e. ## positions are in strict ascending order). ## This can be checked with: is.unsorted(gpos4, strict=TRUE) # 'gpos4' is strictly sorted ## --------------------------------------------------------------------- ## USING MEMORY-EFFICIENT METADATA COLUMNS ## --------------------------------------------------------------------- ## In order to keep memory usage as low as possible, it is recommended ## to use a memory-efficient representation of the metadata columns that ## we want to set on the object. Rle's are particularly well suited for ## this, especially if the metadata columns contain long runs of ## identical values. This is the case for example if we want to use a ## GPos object to represent the coverage of sequencing reads along a ## genome. ## Example 5: library(pasillaBamSubset) library(Rsamtools) # for the BamFile() constructor function bamfile1 <- BamFile(untreated1_chr4()) bamfile2 <- BamFile(untreated3_chr4()) gpos5 <- GPos(seqinfo(bamfile1)) library(GenomicAlignments) # for "coverage" method for BamFile objects cov1 <- unlist(coverage(bamfile1), use.names=FALSE) cov2 <- unlist(coverage(bamfile2), use.names=FALSE) mcols(gpos5) <- DataFrame(cov1, cov2) gpos5 object.size(gpos5) # lightweight ## Keep only the positions where coverage is at least 10 in one of the ## 2 samples: gpos5[mcols(gpos5)$cov1 >= 10 | mcols(gpos5)$cov2 >= 10] ## --------------------------------------------------------------------- ## USING A GPos OBJECT IN A SummarizedExperiment OBJECT ## --------------------------------------------------------------------- ## Because the GPos class extends the GenomicRanges virtual class, a GPos ## object can be used as the rowRanges component of a SummarizedExperiment ## object. ## As a 1st example, we show how the counts for samples sA and sB in ## 'gpos3' can be stored in a SummarizedExperiment object where the rows ## correspond to unique genomic positions and the columns to samples: library(SummarizedExperiment) counts <- cbind(sA=sA_counts, sB=sB_counts) mcols(gpos3A) <- NULL rse3 <- SummarizedExperiment(list(counts=counts), rowRanges=gpos3A) rse3 rowRanges(rse3) head(assay(rse3)) ## Finally we show how the coverage data from Example 5 can be easily ## stored in a lightweight SummarizedExperiment object: cov <- mcols(gpos5) mcols(gpos5) <- NULL rse5 <- SummarizedExperiment(list(cov=cov), rowRanges=gpos5) rse5 rowRanges(rse5) assay(rse5) ## Keep only the positions where coverage is at least 10 in one of the ## 2 samples: rse5[assay(rse5)$cov1 >= 10 | assay(rse5)$cov2 >= 10] } \keyword{methods} \keyword{classes} GenomicRanges/man/GRanges-class.Rd0000644000175400017540000006514313001230267020017 0ustar00biocbuildbiocbuild\name{GRanges-class} \docType{class} % Class: \alias{class:GenomicRanges} \alias{GenomicRanges-class} \alias{GenomicRanges} \alias{class:GRanges} \alias{GRanges-class} \alias{GRanges} \alias{GenomicRangesORGRangesList-class} \alias{GenomicRangesORmissing-class} % Constructors: \alias{GRanges} \alias{updateObject,GRanges-method} % Coercion: \alias{as.character,GenomicRanges-method} \alias{as.factor,GenomicRanges-method} \alias{as.data.frame,GenomicRanges-method} \alias{coerce,GenomicRanges,RangesList-method} \alias{coerce,GenomicRanges,RangedData-method} \alias{granges,GenomicRanges-method} \alias{coerce,GenomicRanges,GRanges-method} \alias{coerce,character,GRanges-method} \alias{coerce,factor,GRanges-method} \alias{coerce,RangesList,GRanges-method} \alias{coerce,RangedData,GRanges-method} \alias{coerce,Seqinfo,GRanges-method} \alias{coerce,Seqinfo,RangesList-method} \alias{coerce,GenomicRanges,Grouping-method} \alias{coerce,ANY,GenomicRanges-method} % Accessors: \alias{seqnames,GRanges-method} \alias{seqnames,RangedData-method} \alias{seqnames<-,GenomicRanges-method} \alias{ranges,GRanges-method} \alias{ranges<-,GenomicRanges-method} \alias{strand,GRanges-method} \alias{strand<-,GenomicRanges,ANY-method} \alias{names,GenomicRanges-method} \alias{names<-,GenomicRanges-method} \alias{seqinfo,GRanges-method} \alias{seqinfo,List-method} \alias{seqinfo,RangedData-method} \alias{seqinfo,RangesList-method} \alias{seqinfo<-,GenomicRanges-method} \alias{seqinfo<-,List-method} \alias{seqinfo<-,RangedData-method} \alias{score,GenomicRanges-method} \alias{score<-,GenomicRanges-method} % Ranges methods: \alias{start,GenomicRanges-method} \alias{start<-,GenomicRanges-method} \alias{end,GenomicRanges-method} \alias{end<-,GenomicRanges-method} \alias{width,GenomicRanges-method} \alias{width<-,GenomicRanges-method} % Vector methods: \alias{length,GenomicRanges-method} \alias{[<-,GRanges-method} \alias{[,GenomicRanges,ANY-method} \alias{[<-,GenomicRanges,ANY,ANY,ANY-method} \alias{[,List,GenomicRanges-method} \alias{[,list,GenomicRanges-method} \alias{c,GenomicRanges-method} \alias{window,GenomicRanges-method} % $ and $<- methods: \alias{$,GenomicRanges-method} \alias{$<-,GenomicRanges-method} % displaying: \alias{summary.GenomicRanges} \alias{summary,GenomicRanges-method} \alias{show,GenomicRanges-method} \title{GRanges objects} \description{ The GRanges class is a container for the genomic locations and their associated annotations. } \details{ GRanges is a vector of genomic locations and associated annotations. Each element in the vector is comprised of a sequence name, an interval, a \link{strand}, and optional metadata columns (e.g. score, GC content, etc.). This information is stored in four components: \describe{ \item{\code{seqnames}}{a 'factor' \link[S4Vectors]{Rle} object containing the sequence names.} \item{\code{ranges}}{an \link[IRanges]{IRanges} object containing the ranges.} \item{\code{strand}}{a 'factor' \link[S4Vectors]{Rle} object containing the \link{strand} information.} \item{\code{mcols}}{a \link[S4Vectors]{DataFrame} object containing the metadata columns. Columns cannot be named \code{"seqnames"}, \code{"ranges"}, \code{"strand"}, \code{"seqlevels"}, \code{"seqlengths"}, \code{"isCircular"}, \code{"start"}, \code{"end"}, \code{"width"}, or \code{"element"}.} \item{\code{seqinfo}}{a \link{Seqinfo} object containing information about the set of genomic sequences present in the GRanges object.} } } \section{Constructor}{ \describe{ \item{}{ \code{GRanges(seqnames=NULL, ranges=NULL, strand=NULL, ..., seqlengths=NULL, seqinfo=NULL)}: Creates a GRanges object. \describe{ \item{\code{seqnames}}{ \code{NULL}, or an \link[S4Vectors]{Rle} object, character vector, or factor containing the sequence names. } \item{\code{ranges}}{ \code{NULL}, or an \link[IRanges]{IRanges} object containing the ranges. } \item{\code{strand}}{ \code{NULL}, or an \link[S4Vectors]{Rle} object, character vector, or factor containing the strand information. } \item{\code{...}}{ Optional metadata columns. These columns cannot be named \code{"start"}, \code{"end"}, \code{"width"}, or \code{"element"}. } \item{\code{seqlengths}}{ \code{NULL}, or an integer vector named with \code{levels(seqnames)} and containing the lengths (or NA) for each level in \code{levels(seqnames)}. } \item{\code{seqinfo}}{ \code{NULL}, or a \link[GenomeInfoDb]{Seqinfo} object containing allowed sequence names, lengths (or NA), and circularity flag, for each level in \code{levels(seqnames)}. } } If \code{ranges} is not supplied and/or NULL then the constructor proceeds in 2 steps: \enumerate{ \item An initial GRanges object is created with \code{as(seqnames, "GRanges")}. \item Then this GRanges object is updated according to whatever non-NULL remaining arguments were passed to the call to \code{GRanges()}. } As a consequence of this behavior, \code{GRanges(x)} is equivalent to \code{as(x, "GRanges")}. } } } \section{Coercion}{ In the code snippets below, \code{x} is a GRanges object. \describe{ \item{}{ \code{as(from, "GRanges")}: Creates a GRanges object from a character vector, a factor, or a RangedData, or RangesList object. When \code{from} is a character vector (or a factor), each element in it must represent a genomic range in format \code{chr1:2501-2800} (unstranded range) or \code{chr1:2501-2800:+} (stranded range). \code{..} is also supported as a separator between the start and end positions. Strand can be \code{+}, \code{-}, \code{*}, or missing. The names on \code{from} are propagated to the returned GRanges object. See \code{as.character()} and \code{as.factor()} below for the reverse transformations. Coercing a data.frame or DataFrame into a GRanges object is also supported. See \code{\link{makeGRangesFromDataFrame}} for the details. } \item{}{ \code{as(from, "RangedData")}: Creates a RangedData object from a GRanges object. The \code{strand} and metadata columns become columns in the result. The \code{seqlengths(from)}, \code{isCircular(from)}, and \code{genome(from)} vectors are stored in the metadata columns of \code{ranges(rd)}. } \item{}{ \code{as(from, "RangesList")}: Creates a RangesList object from a GRanges object. The \code{strand} and metadata columns become \emph{inner} metadata columns (i.e. metadata columns on the ranges). The \code{seqlengths(from)}, \code{isCircular(from)}, and \code{genome(from)} vectors become the metadata columns. } \item{}{ \code{as.character(x, ignore.strand=FALSE)}: Turn GRanges object \code{x} into a character vector where each range in \code{x} is represented by a string in format \code{chr1:2501-2800:+}. If \code{ignore.strand} is TRUE or if \emph{all} the ranges in \code{x} are unstranded (i.e. their strand is set to \code{*}), then all the strings in the output are in format \code{chr1:2501-2800}. The names on \code{x} are propagated to the returned character vector. Its metadata (\code{metadata(x)}) and metadata columns (\code{mcols(x)}) are ignored. See \code{as(from, "GRanges")} above for the reverse transformation. } \item{}{ \code{as.factor(x)}: Equivalent to \preformatted{ factor(as.character(x), levels=as.character(sort(unique(x)))) } See \code{as(from, "GRanges")} above for the reverse transformation. Note that \code{table(x)} is supported on a GRanges object. It is equivalent to, but much faster than, \code{table(as.factor(x))}. } \item{}{ \code{as.data.frame(x, row.names = NULL, optional = FALSE, ...)}: Creates a data.frame with columns \code{seqnames} (factor), \code{start} (integer), \code{end} (integer), \code{width} (integer), \code{strand} (factor), as well as the additional metadata columns stored in \code{mcols(x)}. Pass an explicit \code{stringsAsFactors=TRUE/FALSE} argument via \code{\ldots} to override the default conversions for the metadata columns in \code{mcols(x)}. } \item{}{ \code{as(from, "Grouping")}: Creates a \code{\link[IRanges]{ManyToOneGrouping}} object that groups \code{from} by seqname, strand, start and end (same as the default sort order). This makes it convenient, for example, to aggregate a GenomicRanges object by range. } } In the code snippets below, \code{x} is a \link[GenomeInfoDb]{Seqinfo} object. \describe{ \item{}{ \code{as(x, "GRanges")}, \code{as(x, "GenomicRanges")}, \code{as(x, "RangesList")}: Turns \link[GenomeInfoDb]{Seqinfo} object \code{x} (with no \code{NA} lengths) into a GRanges or RangesList. } } } \section{Accessors}{ In the following code snippets, \code{x} is a GRanges object. \describe{ \item{}{ \code{length(x)}: Get the number of elements. } \item{}{ \code{seqnames(x)}, \code{seqnames(x) <- value}: Get or set the sequence names. \code{value} can be an \link[S4Vectors]{Rle} object, a character vector, or a factor. } \item{}{ \code{ranges(x)}, \code{ranges(x) <- value}: Get or set the ranges. \code{value} can be a Ranges object. } \item{}{ \code{names(x)}, \code{names(x) <- value}: Get or set the names of the elements. } \item{}{ \code{strand(x)}, \code{strand(x) <- value}: Get or set the strand. \code{value} can be an Rle object, character vector, or factor. } \item{}{ \code{mcols(x, use.names=FALSE)}, \code{mcols(x) <- value}: Get or set the metadata columns. If \code{use.names=TRUE} and the metadata columns are not \code{NULL}, then the names of \code{x} are propagated as the row names of the returned \link{DataFrame} object. When setting the metadata columns, the supplied value must be \code{NULL} or a data.frame-like object (i.e. \link{DataTable} or data.frame) object holding element-wise metadata. } \item{}{ \code{elementMetadata(x)}, \code{elementMetadata(x) <- value}, \code{values(x)}, \code{values(x) <- value}: Alternatives to \code{mcols} functions. Their use is discouraged. } \item{}{ \code{seqinfo(x)}, \code{seqinfo(x) <- value}: Get or set the information about the underlying sequences. \code{value} must be a \link{Seqinfo} object. } \item{}{ \code{seqlevels(x)}, \code{seqlevels(x, force=FALSE) <- value}: Get or set the sequence levels. \code{seqlevels(x)} is equivalent to \code{seqlevels(seqinfo(x))} or to \code{levels(seqnames(x))}, those 2 expressions being guaranteed to return identical character vectors on a GRanges object. \code{value} must be a character vector with no NAs. See \code{?\link{seqlevels}} for more information. } \item{}{ \code{seqlengths(x)}, \code{seqlengths(x) <- value}: Get or set the sequence lengths. \code{seqlengths(x)} is equivalent to \code{seqlengths(seqinfo(x))}. \code{value} can be a named non-negative integer or numeric vector eventually with NAs. } \item{}{ \code{isCircular(x)}, \code{isCircular(x) <- value}: Get or set the circularity flags. \code{isCircular(x)} is equivalent to \code{isCircular(seqinfo(x))}. \code{value} must be a named logical vector eventually with NAs. } \item{}{ \code{genome(x)}, \code{genome(x) <- value}: Get or set the genome identifier or assembly name for each sequence. \code{genome(x)} is equivalent to \code{genome(seqinfo(x))}. \code{value} must be a named character vector eventually with NAs. } \item{}{ \code{seqlevelsStyle(x)}, \code{seqlevelsStyle(x) <- value}: Get or set the seqname style for \code{x}. See the \link[GenomeInfoDb]{seqlevelsStyle} generic getter and setter in the \pkg{GenomeInfoDb} package for more information. } \item{}{ \code{score(x), score(x) <- value}: Get or set the \dQuote{score} column from the element metadata. } \item{}{ \code{granges(x, use.mcols=FALSE)}: Gets a \code{GRanges} with only the range information from \code{x}, unless \code{use.mcols} is \code{TRUE}, in which case the metadata columns are also returned. Those columns will include any "extra column slots" if \code{x} is a specialized \code{GenomicRanges} derivative. } } } \section{Ranges methods}{ In the following code snippets, \code{x} is a GRanges object. \describe{ \item{}{ \code{start(x)}, \code{start(x) <- value}: Get or set \code{start(ranges(x))}. } \item{}{ \code{end(x)}, \code{end(x) <- value}: Get or set \code{end(ranges(x))}. } \item{}{ \code{width(x)}, \code{width(x) <- value}: Get or set \code{width(ranges(x))}. } } } \section{Splitting and Combining}{ In the code snippets below, \code{x} is a GRanges object. \describe{ \item{}{ \code{append(x, values, after = length(x))}: Inserts the \code{values} into \code{x} at the position given by \code{after}, where \code{x} and \code{values} are of the same class. } \item{}{ \code{c(x, ...)}: Combines \code{x} and the GRanges objects in \code{...} together. Any object in \code{...} must belong to the same class as \code{x}, or to one of its subclasses, or must be \code{NULL}. The result is an object of the same class as \code{x}. } \item{}{ \code{c(x, ..., ignore.mcols=FALSE)} If the \code{GRanges} objects have metadata columns (represented as one \link{DataFrame} per object), each such \link{DataFrame} must have the same columns in order to combine successfully. In order to circumvent this restraint, you can pass in an \code{ignore.mcols=TRUE} argument which will combine all the objects into one and drop all of their metadata columns. } \item{}{ \code{split(x, f, drop=FALSE)}: Splits \code{x} according to \code{f} to create a \link{GRangesList} object. If \code{f} is a list-like object then \code{drop} is ignored and \code{f} is treated as if it was \code{rep(seq_len(length(f)), sapply(f, length))}, so the returned object has the same shape as \code{f} (it also receives the names of \code{f}). Otherwise, if \code{f} is not a list-like object, empty list elements are removed from the returned object if \code{drop} is \code{TRUE}. } } } \section{Subsetting}{ In the code snippets below, \code{x} is a GRanges object. \describe{ \item{}{ \code{x[i, j]}, \code{x[i, j] <- value}: Get or set elements \code{i} with optional metadata columns \code{mcols(x)[,j]}, where \code{i} can be missing; an NA-free logical, numeric, or character vector; or a 'logical' Rle object. } \item{}{ \code{x[i, j] <- value}: Replaces elements \code{i} and optional metadata columns \code{j} with \code{value}. } \item{}{ \code{head(x, n = 6L)}: If \code{n} is non-negative, returns the first n elements of the GRanges object. If \code{n} is negative, returns all but the last \code{abs(n)} elements of the GRanges object. } \item{}{ \code{rep(x, times, length.out, each)}: Repeats the values in \code{x} through one of the following conventions: \describe{ \item{\code{times}}{Vector giving the number of times to repeat each element if of length \code{length(x)}, or to repeat the whole vector if of length 1.} \item{\code{length.out}}{Non-negative integer. The desired length of the output vector.} \item{\code{each}}{Non-negative integer. Each element of \code{x} is repeated \code{each} times.} } } \item{}{ \code{subset(x, subset)}: Returns a new object of the same class as \code{x} made of the subset using logical vector \code{subset}, where missing values are taken as \code{FALSE}. } \item{}{ \code{tail(x, n = 6L)}: If \code{n} is non-negative, returns the last n elements of the GRanges object. If \code{n} is negative, returns all but the first \code{abs(n)} elements of the GRanges object. } \item{}{ \code{window(x, start = NA, end = NA, width = NA, frequency = NULL, delta = NULL, ...)}: Extracts the subsequence window from the GRanges object using: \describe{ \item{\code{start}, \code{end}, \code{width}}{The start, end, or width of the window. Two of the three are required.} \item{\code{frequency}, \code{delta}}{Optional arguments that specify the sampling frequency and increment within the window.} } In general, this is more efficient than using \code{"["} operator. } \item{}{ \code{window(x, start = NA, end = NA, width = NA, keepLength = TRUE) <- value}: Replaces the subsequence window specified on the left (i.e. the subsequence in \code{x} specified by \code{start}, \code{end} and \code{width}) by \code{value}. \code{value} must either be of class \code{class(x)}, belong to a subclass of \code{class(x)}, be coercible to \code{class(x)}, or be \code{NULL}. If \code{keepLength} is \code{TRUE}, the elements of \code{value} are repeated to create a GRanges object with the same number of elements as the width of the subsequence window it is replacing. If \code{keepLength} is \code{FALSE}, this replacement method can modify the length of \code{x}, depending on how the length of the left subsequence window compares to the length of \code{value}. } \item{}{ \code{x$name}, \code{x$name <- value}: Shortcuts for \code{mcols(x)$name} and \code{mcols(x)$name <- value}, respectively. Provided as a convenience, for GRanges objects *only*, and as the result of strong popular demand. Note that those methods are not consistent with the other \code{$} and \code{$<-} methods in the IRanges/GenomicRanges infrastructure, and might confuse some users by making them believe that a GRanges object can be manipulated as a data.frame-like object. Therefore we recommend using them only interactively, and we discourage their use in scripts or packages. For the latter, use \code{mcols(x)$name} and \code{mcols(x)$name <- value}, instead of \code{x$name} and \code{x$name <- value}, respectively. } } Note that a GRanges object can be used to as a subscript to subset a list-like object that has names on it. In that case, the names on the list-like object are interpreted as sequence names. In the code snippets below, \code{x} is a list or \link{List} object with names on it, and the subscript \code{gr} is a GRanges object with all its seqnames being valid \code{x} names. \describe{ \item{}{ \code{x[gr]}: Return an object of the same class as \code{x} and \emph{parallel} to \code{gr}. More precisely, it's conceptually doing: \preformatted{ lapply(gr, function(gr1) x[[seqnames(gr1)]][ranges(gr1)]) } } } } \section{Other methods}{ \describe{ \item{}{ \code{show(x)}: By default the \code{show} method displays 5 head and 5 tail elements. This can be changed by setting the global options \code{showHeadLines} and \code{showTailLines}. If the object length is less than (or equal to) the sum of these 2 options plus 1, then the full object is displayed. Note that these options also affect the display of \link[GenomicAlignments]{GAlignments} and \link[GenomicAlignments]{GAlignmentPairs} objects (defined in the \pkg{GenomicAlignments} package), as well as other objects defined in the \pkg{IRanges} and \pkg{Biostrings} packages (e.g. \link[IRanges]{IRanges} and \link[Biostrings]{DNAStringSet} objects). } } } \author{P. Aboyoun and H. Pagès} \seealso{ \itemize{ \item \code{\link{makeGRangesFromDataFrame}} for making a GRanges object from a data.frame or \link[S4Vectors]{DataFrame} object. \item \code{\link[GenomeInfoDb]{seqinfo}} for accessing/modifying information about the underlying sequences of a GRanges object. \item The \link{GPos} class, a memory-efficient container for storing genomic \emph{positions}, that is, genomic ranges of width 1. \item \link{GenomicRanges-comparison} for comparing and ordering genomic ranges. \item \link[GenomicRanges]{findOverlaps-methods} for finding/counting overlapping genomic ranges. \item \link[GenomicRanges]{intra-range-methods} and \link[GenomicRanges]{inter-range-methods} for intra range and inter range transformations of a GRanges object. \item \link[GenomicRanges]{coverage-methods} for computing the coverage of a GRanges object. \item \link[GenomicRanges]{setops-methods} for set operations on GRanges objects. \item \link[GenomicRanges]{nearest-methods} for finding the nearest genomic range neighbor. \item \code{\link{absoluteRanges}} for transforming genomic ranges into \emph{absolute} ranges (i.e. into ranges on the sequence obtained by virtually concatenating all the sequences in a genome). \item \code{\link{tileGenome}} for putting tiles on a genome. \item \link{genomicvars} for manipulating genomic variables. \item \link{GRangesList} objects. \item \link[IRanges]{Ranges} objects in the \pkg{IRanges} package. \item \link[S4Vectors]{Vector}, \link[S4Vectors]{Rle}, and \link[S4Vectors]{DataFrame} objects in the \pkg{S4Vectors} package. } } \examples{ ## --------------------------------------------------------------------- ## CONSTRUCTION ## --------------------------------------------------------------------- ## Specifying the bare minimum i.e. seqnames and ranges only. The ## GRanges object will have no names, no strand information, and no ## metadata columns: gr0 <- GRanges(Rle(c("chr2", "chr2", "chr1", "chr3"), c(1, 3, 2, 4)), IRanges(1:10, width=10:1)) gr0 ## Specifying names, strand, metadata columns. They can be set on an ## existing object: names(gr0) <- head(letters, 10) strand(gr0) <- Rle(strand(c("-", "+", "*", "+", "-")), c(1, 2, 2, 3, 2)) mcols(gr0)$score <- 1:10 mcols(gr0)$GC <- seq(1, 0, length=10) gr0 ## ... or specified at construction time: gr <- GRanges(Rle(c("chr2", "chr2", "chr1", "chr3"), c(1, 3, 2, 4)), IRanges(1:10, width=10:1, names=head(letters, 10)), Rle(strand(c("-", "+", "*", "+", "-")), c(1, 2, 2, 3, 2)), score=1:10, GC=seq(1, 0, length=10)) stopifnot(identical(gr0, gr)) ## Specifying the seqinfo. It can be set on an existing object: seqinfo <- Seqinfo(paste0("chr", 1:3), c(1000, 2000, 1500), NA, "mock1") seqinfo(gr0) <- merge(seqinfo(gr0), seqinfo) seqlevels(gr0) <- seqlevels(seqinfo) ## ... or specified at construction time: gr <- GRanges(Rle(c("chr2", "chr2", "chr1", "chr3"), c(1, 3, 2, 4)), IRanges(1:10, width=10:1, names=head(letters, 10)), Rle(strand(c("-", "+", "*", "+", "-")), c(1, 2, 2, 3, 2)), score=1:10, GC=seq(1, 0, length=10), seqinfo=seqinfo) stopifnot(identical(gr0, gr)) ## --------------------------------------------------------------------- ## COERCION ## --------------------------------------------------------------------- ## From GRanges: as.character(gr) as.factor(gr) as.data.frame(gr) ## From character to GRanges: x1 <- "chr2:56-125" as(x1, "GRanges") as(rep(x1, 4), "GRanges") x2 <- c(A=x1, B="chr1:25-30:-") as(x2, "GRanges") ## From data.frame to GRanges: df <- data.frame(chrom="chr2", start=11:15, end=20:24) gr3 <- as(df, "GRanges") ## Alternatively, coercion to GRanges can be done by just calling the ## GRanges() constructor on the object to coerce: gr1 <- GRanges(x1) # same as as(x1, "GRanges") gr2 <- GRanges(x2) # same as as(x2, "GRanges") gr3 <- GRanges(df) # same as as(df, "GRanges") ## Sanity checks: stopifnot(identical(as(x1, "GRanges"), gr1)) stopifnot(identical(as(x2, "GRanges"), gr2)) stopifnot(identical(as(df, "GRanges"), gr3)) ## --------------------------------------------------------------------- ## SUMMARIZING ELEMENTS ## --------------------------------------------------------------------- table(seqnames(gr)) table(strand(gr)) sum(width(gr)) table(gr) summary(mcols(gr)[,"score"]) ## The number of lines displayed in the 'show' method are controlled ## with two global options: longGR <- sample(gr, 25, replace=TRUE) longGR options(showHeadLines=7) options(showTailLines=2) longGR ## Revert to default values options(showHeadLines=NULL) options(showTailLines=NULL) ## --------------------------------------------------------------------- ## INVERTING THE STRAND ## --------------------------------------------------------------------- invertStrand(gr) ## --------------------------------------------------------------------- ## RENAMING THE UNDERLYING SEQUENCES ## --------------------------------------------------------------------- seqlevels(gr) seqlevels(gr) <- sub("chr", "Chrom", seqlevels(gr)) gr seqlevels(gr) <- sub("Chrom", "chr", seqlevels(gr)) # revert ## --------------------------------------------------------------------- ## COMBINING OBJECTS ## --------------------------------------------------------------------- gr2 <- GRanges(seqnames=Rle(c('chr1', 'chr2', 'chr3'), c(3, 3, 4)), IRanges(1:10, width=5), strand='-', score=101:110, GC=runif(10), seqinfo=seqinfo) gr3 <- GRanges(seqnames=Rle(c('chr1', 'chr2', 'chr3'), c(3, 4, 3)), IRanges(101:110, width=10), strand='-', score=21:30, seqinfo=seqinfo) some.gr <- c(gr, gr2) ## all.gr <- c(gr, gr2, gr3) ## (This would fail) all.gr <- c(gr, gr2, gr3, ignore.mcols=TRUE) ## --------------------------------------------------------------------- ## USING A GRANGES OBJECT AS A SUBSCRIPT TO SUBSET ANOTHER OBJECT ## --------------------------------------------------------------------- ## Subsetting *by* a GRanges subscript is supported only if the object ## to subset is a named list-like object: x <- RleList(chr1=101:120, chr2=2:-8, chr3=31:40) x[gr] } GenomicRanges/man/GRangesList-class.Rd0000644000175400017540000003371313001230267020651 0ustar00biocbuildbiocbuild\name{GRangesList-class} \docType{class} % Class \alias{class:GRangesList} \alias{GRangesList-class} \alias{GRangesList} % Constructors: \alias{GRangesList} \alias{makeGRangesListFromFeatureFragments} \alias{updateObject,GRangesList-method} % Accessor methods: \alias{seqnames,GRangesList-method} \alias{seqnames<-,GRangesList-method} \alias{ranges,GRangesList-method} \alias{ranges<-,GRangesList-method} \alias{start,GRangesList-method} \alias{start<-,GRangesList-method} \alias{end,GRangesList-method} \alias{end<-,GRangesList-method} \alias{width,GRangesList-method} \alias{width<-,GRangesList-method} \alias{strand,GRangesList-method} \alias{strand<-,GRangesList,ANY-method} \alias{strand<-,GRangesList,character-method} \alias{elementMetadata,GRangesList-method} \alias{elementMetadata<-,GRangesList-method} \alias{seqinfo,GRangesList-method} \alias{seqinfo<-,GRangesList-method} \alias{score,GRangesList-method} \alias{score<-,GRangesList-method} % Coercion: \alias{coerce,GRangesList,CompressedIRangesList-method} \alias{coerce,GRangesList,IRangesList-method} \alias{coerce,GRangesList,RangesList-method} \alias{coerce,GRanges,GRangesList-method} \alias{coerce,RangedDataList,GRangesList-method} % Subsetting: \alias{[,GRangesList,ANY-method} \alias{[<-,GRangesList-method} \alias{[<-,GRangesList,ANY-method} \alias{[<-,GRangesList,ANY,ANY,ANY-method} \alias{[[<-,GRangesList-method} \alias{[[<-,GRangesList,ANY-method} \alias{[[<-,GRangesList,ANY,ANY-method} % extractList() and family: \alias{relistToClass,GRanges-method} % show method: \alias{show,GRangesList-method} \title{GRangesList objects} \description{ The GRangesList class is a container for storing a collection of GRanges objects. It is derived from GenomicRangesList. } \section{Constructors}{ \describe{ \item{}{ \code{GRangesList(...)}: Creates a GRangesList object using GRanges objects supplied in \code{\ldots}. } \item{}{ \code{makeGRangesListFromFeatureFragments(seqnames=Rle(factor()), fragmentStarts=list(), fragmentEnds=list(), fragmentWidths=list(), strand=character(0), sep=",")}: Constructs a GRangesList object from a list of fragmented features. See the Examples section below. } } } \section{Accessors}{ In the following code snippets, \code{x} is a GRanges object. \describe{ \item{}{ \code{length(x)}: Get the number of list elements. } \item{}{ \code{names(x)}, \code{names(x) <- value}: Get or set the names on \code{x}. } \item{}{ \code{elementNROWS(x)}: Get a vector of the \code{length} of each of the list element. } \item{}{ \code{isEmpty(x)}: Returns a logical indicating either if the GRangesList has no elements or if all its elements are empty. } \item{}{ \code{seqnames(x)}, \code{seqnames(x) <- value}: Get or set the sequence names in the form of an RleList. \code{value} can be an RleList or CharacterList object. } \item{}{ \code{ranges(x, use.mcols=FALSE)}, \code{ranges(x) <- value}: Get or set the ranges in the form of a CompressedIRangesList. \code{value} can be a RangesList object. } \item{}{ \code{start(x)}, \code{start(x) <- value}: Get or set \code{start(ranges(x))}. } \item{}{ \code{end(x)}, \code{end(x) <- value}: Get or set \code{end(ranges(x))}. } \item{}{ \code{width(x)}, \code{width(x) <- value}: Get or set \code{width(ranges(x))}. } \item{}{ \code{strand(x)}, \code{strand(x) <- value}: Get or set the strand in the form of an RleList. \code{value} can be an RleList, CharacterList or single character. \code{value} as a single character converts all ranges in \code{x} to the same \code{value}; for selective strand conversion (i.e., mixed \dQuote{+} and \dQuote{-}) use RleList or CharacterList. } \item{}{ \code{mcols(x, use.names=FALSE)}, \code{mcols(x) <- value}: Get or set the metadata columns. \code{value} can be \code{NULL}, or a data.frame-like object (i.e. \link{DataFrame} or data.frame) holding element-wise metadata. } \item{}{ \code{elementMetadata(x)}, \code{elementMetadata(x) <- value}, \code{values(x)}, \code{values(x) <- value}: Alternatives to \code{mcols} functions. Their use is discouraged. } \item{}{ \code{seqinfo(x)}, \code{seqinfo(x) <- value}: Get or set the information about the underlying sequences. \code{value} must be a \link{Seqinfo} object. } \item{}{ \code{seqlevels(x)}, \code{seqlevels(x, force=FALSE) <- value}: Get or set the sequence levels. \code{seqlevels(x)} is equivalent to \code{seqlevels(seqinfo(x))} or to \code{levels(seqnames(x))}, those 2 expressions being guaranteed to return identical character vectors on a GRangesList object. \code{value} must be a character vector with no NAs. See \code{?\link{seqlevels}} for more information. } \item{}{ \code{seqlengths(x)}, \code{seqlengths(x) <- value}: Get or set the sequence lengths. \code{seqlengths(x)} is equivalent to \code{seqlengths(seqinfo(x))}. \code{value} can be a named non-negative integer or numeric vector eventually with NAs. } \item{}{ \code{isCircular(x)}, \code{isCircular(x) <- value}: Get or set the circularity flags. \code{isCircular(x)} is equivalent to \code{isCircular(seqinfo(x))}. \code{value} must be a named logical vector eventually with NAs. } \item{}{ \code{genome(x)}, \code{genome(x) <- value}: Get or set the genome identifier or assembly name for each sequence. \code{genome(x)} is equivalent to \code{genome(seqinfo(x))}. \code{value} must be a named character vector eventually with NAs. } \item{}{ \code{seqlevelsStyle(x)}, \code{seqlevelsStyle(x) <- value}: Get or set the seqname style for \code{x}. See the \link[GenomeInfoDb]{seqlevelsStyle} generic getter and setter in the \pkg{GenomeInfoDb} package for more information. } \item{}{ \code{score(x), score(x) <- value}: Get or set the \dQuote{score} metadata column. } } } \section{Coercion}{ In the code snippets below, \code{x} is a GRangesList object. \describe{ \item{}{ \code{as.data.frame(x, row.names = NULL, optional = FALSE, ..., value.name = "value", use.outer.mcols = FALSE, group_name.as.factor = FALSE)}: Coerces \code{x} to a \code{data.frame}. See as.data.frame on the \code{List} man page for details (?\code{List}). } \item{}{\code{as.list(x, use.names = TRUE)}: Creates a list containing the elements of \code{x}. } \item{}{\code{as(x, "IRangesList")}: Turns \code{x} into an \link[IRanges]{IRangesList} object. } \item{}{\code{as(from, "GRangesList")}: Creates a GRangesList object from a \link[IRanges]{RangedDataList} object. } } } \section{Subsetting}{ In the following code snippets, \code{x} is a GRangesList object. \describe{ \item{}{ \code{x[i, j]}, \code{x[i, j] <- value}: Get or set elements \code{i} with optional metadata columns \code{mcols(x)[,j]}, where \code{i} can be missing; an NA-free logical, numeric, or character vector; a 'logical' Rle object, or an AtomicList object. } \item{}{ \code{x[[i]]}, \code{x[[i]] <- value}: Get or set element \code{i}, where \code{i} is a numeric or character vector of length 1. } \item{}{ \code{x$name}, \code{x$name <- value}: Get or set element \code{name}, where \code{name} is a name or character vector of length 1. } \item{}{ \code{head(x, n = 6L)}: If \code{n} is non-negative, returns the first n elements of the GRangesList object. If \code{n} is negative, returns all but the last \code{abs(n)} elements of the GRangesList object. } \item{}{ \code{rep(x, times, length.out, each)}: Repeats the values in \code{x} through one of the following conventions: \describe{ \item{\code{times}}{Vector giving the number of times to repeat each element if of length \code{length(x)}, or to repeat the whole vector if of length 1.} \item{\code{length.out}}{Non-negative integer. The desired length of the output vector.} \item{\code{each}}{Non-negative integer. Each element of \code{x} is repeated \code{each} times.} } } \item{}{ \code{subset(x, subset)}: Returns a new object of the same class as \code{x} made of the subset using logical vector \code{subset}, where missing values are taken as \code{FALSE}. } \item{}{ \code{tail(x, n = 6L)}: If \code{n} is non-negative, returns the last n elements of the GRanges object. If \code{n} is negative, returns all but the first \code{abs(n)} elements of the GRanges object. } } } \section{Combining}{ In the code snippets below, \code{x} is a GRangesList object. \describe{ \item{}{ \code{c(x, ...)}: Combines \code{x} and the GRangesList objects in \code{...} together. Any object in \code{...} must belong to the same class as \code{x}, or to one of its subclasses, or must be \code{NULL}. The result is an object of the same class as \code{x}. } \item{}{ \code{append(x, values, after = length(x))}: Inserts the \code{values} into \code{x} at the position given by \code{after}, where \code{x} and \code{values} are of the same class. } \item{}{\code{unlist(x, recursive = TRUE, use.names = TRUE)}: Concatenates the elements of \code{x} into a single GRanges object. } } } \section{Looping}{ In the code snippets below, \code{x} is a GRangesList object. \describe{ \item{}{ \code{endoapply(X, FUN, ...)}: Similar to \code{\link[base]{lapply}}, but performs an endomorphism, i.e. returns an object of \code{class(X)}. } \item{}{ \code{lapply(X, FUN, ...)}: Like the standard \code{\link[base]{lapply}} function defined in the base package, the \code{lapply} method for GRangesList objects returns a list of the same length as \code{X}, with each element being the result of applying \code{FUN} to the corresponding element of \code{X}. } \item{}{ \code{Map(f, ...)}: Applies a function to the corresponding elements of given GRangesList objects. } \item{}{ \code{mapply(FUN, ..., MoreArgs = NULL, SIMPLIFY = TRUE, USE.NAMES = TRUE)}: Like the standard \code{\link[base]{mapply}} function defined in the base package, the \code{mapply} method for GRangesList objects is a multivariate version of \code{sapply}. } \item{}{ \code{mendoapply(FUN, ..., MoreArgs = NULL)}: Similar to \code{\link[base]{mapply}}, but performs an endomorphism across multiple objects, i.e. returns an object of \code{class(list(...)[[1]])}. } \item{}{ \code{Reduce(f, x, init, right = FALSE, accumulate = FALSE)}: Uses a binary function to successively combine the elements of \code{x} and a possibly given initial value. \describe{ \item{f}{A binary argument function.} \item{init}{An R object of the same kind as the elements of \code{x}.} \item{right}{A logical indicating whether to proceed from left to right (default) or from right to left.} \item{nomatch}{The value to be returned in the case when "no match" (no element satisfying the predicate) is found.} } } \item{}{ \code{sapply(X, FUN, ..., simplify=TRUE, USE.NAMES=TRUE)}: Like the standard \code{\link[base:lapply]{sapply}} function defined in the base package, the \code{sapply} method for GRangesList objects is a user-friendly version of \code{lapply} by default returning a vector or matrix if appropriate. } } } \author{P. Aboyoun & H. Pagès} \seealso{ \link{GRanges-class}, \code{\link[GenomeInfoDb]{seqinfo}}, \link[S4Vectors]{Vector-class}, \link[IRanges]{RangesList-class}, \link[IRanges]{RleList-class}, \link[IRanges]{DataFrameList-class}, \link[GenomicRanges]{intra-range-methods}, \link[GenomicRanges]{inter-range-methods}, \link{coverage-methods}, \link{setops-methods}, \link{findOverlaps-methods} } \examples{ ## Construction with GRangesList(): gr1 <- GRanges(seqnames = "chr2", ranges = IRanges(3, 6), strand = "+", score = 5L, GC = 0.45) gr2 <- GRanges(seqnames = c("chr1", "chr1"), ranges = IRanges(c(7,13), width = 3), strand = c("+", "-"), score = 3:4, GC = c(0.3, 0.5)) gr3 <- GRanges(seqnames = c("chr1", "chr2"), ranges = IRanges(c(1, 4), c(3, 9)), strand = c("-", "-"), score = c(6L, 2L), GC = c(0.4, 0.1)) grl <- GRangesList("gr1" = gr1, "gr2" = gr2, "gr3" = gr3) grl ## Summarizing elements: elementNROWS(grl) table(seqnames(grl)) ## Extracting subsets: grl[seqnames(grl) == "chr1", ] grl[seqnames(grl) == "chr1" & strand(grl) == "+", ] ## Renaming the underlying sequences: seqlevels(grl) seqlevels(grl) <- sub("chr", "Chrom", seqlevels(grl)) grl ## Coerce to IRangesList (seqnames and strand information is lost): as(grl, "IRangesList") ## isDisjoint(): isDisjoint(grl) ## disjoin(): disjoin(grl) # metadata columns and order NOT preserved ## Construction with makeGRangesListFromFeatureFragments(): filepath <- system.file("extdata", "feature_frags.txt", package="GenomicRanges") featfrags <- read.table(filepath, header=TRUE, stringsAsFactors=FALSE) grl2 <- with(featfrags, makeGRangesListFromFeatureFragments(seqnames=targetName, fragmentStarts=targetStart, fragmentWidths=blockSizes, strand=strand)) names(grl2) <- featfrags$RefSeqID grl2 } GenomicRanges/man/GenomicRanges-comparison.Rd0000644000175400017540000001574113001230267022256 0ustar00biocbuildbiocbuild\name{GenomicRanges-comparison} \alias{GenomicRanges-comparison} \alias{pcompare} \alias{pcompare,GenomicRanges,GenomicRanges-method} \alias{duplicated,GenomicRanges-method} \alias{duplicated.GenomicRanges} \alias{match,GenomicRanges,GenomicRanges-method} \alias{selfmatch,GenomicRanges-method} \alias{is.unsorted,GenomicRanges-method} \alias{order,GenomicRanges-method} \alias{sort,GenomicRanges-method} \alias{sort.GenomicRanges} \alias{rank,GenomicRanges-method} \title{Comparing and ordering genomic ranges} \description{ Methods for comparing and ordering the elements in one or more \link{GenomicRanges} objects. } \usage{ ## duplicated() ## ------------ \S4method{duplicated}{GenomicRanges}(x, incomparables=FALSE, fromLast=FALSE, method=c("auto", "quick", "hash")) ## match() & selfmatch() ## --------------------- \S4method{match}{GenomicRanges,GenomicRanges}(x, table, nomatch=NA_integer_, incomparables=NULL, method=c("auto", "quick", "hash"), ignore.strand=FALSE) \S4method{selfmatch}{GenomicRanges}(x, method=c("auto", "quick", "hash"), ignore.strand=FALSE) ## order() and related methods ## ---------------------------- \S4method{is.unsorted}{GenomicRanges}(x, na.rm=FALSE, strictly=FALSE, ignore.strand=FALSE) \S4method{order}{GenomicRanges}(..., na.last=TRUE, decreasing=FALSE, method=c("shell", "radix")) \S4method{sort}{GenomicRanges}(x, decreasing=FALSE, ignore.strand=FALSE, by) \S4method{rank}{GenomicRanges}(x, na.last=TRUE, ties.method=c("average", "first", "random", "max", "min")) ## Generalized parallel comparison of 2 GenomicRanges objects ## ---------------------------------------------------------- \S4method{pcompare}{GenomicRanges,GenomicRanges}(x, y) } \arguments{ \item{x, table, y}{ \link{GenomicRanges} objects. } \item{incomparables}{ Not supported. } \item{fromLast, method, nomatch}{ See \code{?`\link[IRanges]{Ranges-comparison}`} in the \pkg{IRanges} package for a description of these arguments. } \item{ignore.strand}{ Whether or not the strand should be ignored when comparing 2 genomic ranges. } \item{na.rm}{ Ignored. } \item{strictly}{ Logical indicating if the check should be for \emph{strictly} increasing values. } \item{...}{ One or more \link{GenomicRanges} objects. The \link{GenomicRanges} objects after the first one are used to break ties. } \item{na.last}{ Ignored. } \item{decreasing}{ \code{TRUE} or \code{FALSE}. } \item{ties.method}{ A character string specifying how ties are treated. Only \code{"first"} is supported for now. } \item{by}{ An optional formula that is resolved against \code{as.env(x)}; the resulting variables are passed to \code{order} to generate the ordering permutation. } } \details{ Two elements of a \link{GenomicRanges} object (i.e. two genomic ranges) are considered equal iff they are on the same underlying sequence and strand, and have the same start and width. \code{duplicated()} and \code{unique()} on a \link{GenomicRanges} object are conforming to this. The "natural order" for the elements of a \link{GenomicRanges} object is to order them (a) first by sequence level, (b) then by strand, (c) then by start, (d) and finally by width. This way, the space of genomic ranges is totally ordered. Note that the \code{reduce} method for \link{GenomicRanges} uses this "natural order" implicitly. Also, note that, because we already do (c) and (d) for regular ranges (see \code{?`\link[IRanges]{Ranges-comparison}`}), genomic ranges that belong to the same underlying sequence and strand are ordered like regular ranges. \code{is.unsorted()}, \code{order()}, \code{sort()}, and \code{rank()} on a \link{GenomicRanges} object behave accordingly to this "natural order". \code{==}, \code{!=}, \code{<=}, \code{>=}, \code{<} and \code{>} on \link{GenomicRanges} objects also behave accordingly to this "natural order". } \author{H. Pagès, \code{is.unsorted} contributed by Pete Hickey} \seealso{ \itemize{ \item The \link{GenomicRanges} class. \item \link[IRanges]{Ranges-comparison} in the \pkg{IRanges} package for comparing and ordering genomic ranges. \item \link[GenomicRanges]{findOverlaps-methods} for finding overlapping genomic ranges. \item \link[GenomicRanges]{intra-range-methods} and \link[GenomicRanges]{inter-range-methods} for intra range and inter range transformations of a \link{GRanges} object. \item \link[GenomicRanges]{setops-methods} for set operations on \link{GenomicRanges} objects. } } \examples{ gr0 <- GRanges( Rle(c("chr1", "chr2", "chr1", "chr3"), c(1, 3, 2, 4)), IRanges(c(1:9,7L), end=10), strand=Rle(strand(c("-", "+", "*", "+", "-")), c(1, 2, 2, 3, 2)), seqlengths=c(chr1=11, chr2=12, chr3=13) ) gr <- c(gr0, gr0[7:3]) names(gr) <- LETTERS[seq_along(gr)] ## --------------------------------------------------------------------- ## A. ELEMENT-WISE (AKA "PARALLEL") COMPARISON OF 2 GenomicRanges OBJECTS ## --------------------------------------------------------------------- gr[2] == gr[2] # TRUE gr[2] == gr[5] # FALSE gr == gr[4] gr >= gr[3] ## --------------------------------------------------------------------- ## B. duplicated(), unique() ## --------------------------------------------------------------------- duplicated(gr) unique(gr) ## --------------------------------------------------------------------- ## C. match(), %in% ## --------------------------------------------------------------------- table <- gr[1:7] match(gr, table) match(gr, table, ignore.strand=TRUE) gr \%in\% table ## --------------------------------------------------------------------- ## D. findMatches(), countMatches() ## --------------------------------------------------------------------- findMatches(gr, table) countMatches(gr, table) findMatches(gr, table, ignore.strand=TRUE) countMatches(gr, table, ignore.strand=TRUE) gr_levels <- unique(gr) countMatches(gr_levels, gr) ## --------------------------------------------------------------------- ## E. order() AND RELATED METHODS ## --------------------------------------------------------------------- is.unsorted(gr) order(gr) sort(gr) is.unsorted(sort(gr)) is.unsorted(gr, ignore.strand=TRUE) gr2 <- sort(gr, ignore.strand=TRUE) is.unsorted(gr2) # TRUE is.unsorted(gr2, ignore.strand=TRUE) # FALSE ## TODO: Broken. Please fix! #sort(gr, by = ~ seqnames + start + end) # equivalent to (but slower than) above score(gr) <- rev(seq_len(length(gr))) ## TODO: Broken. Please fix! #sort(gr, by = ~ score) rank(gr) ## --------------------------------------------------------------------- ## F. GENERALIZED ELEMENT-WISE COMPARISON OF 2 GenomicRanges OBJECTS ## --------------------------------------------------------------------- gr3 <- GRanges(c(rep("chr1", 12), "chr2"), IRanges(c(1:11, 6:7), width=3)) strand(gr3)[12] <- "+" gr4 <- GRanges("chr1", IRanges(5, 9)) pcompare(gr3, gr4) rangeComparisonCodeToLetter(pcompare(gr3, gr4)) } \keyword{methods} GenomicRanges/man/GenomicRangesList-class.Rd0000644000175400017540000000354513001230267022044 0ustar00biocbuildbiocbuild\name{GenomicRangesList-class} \docType{class} % Class: \alias{class:GenomicRangesList} \alias{class:SimpleGenomicRangesList} \alias{GenomicRangesList-class} \alias{SimpleGenomicRangesList-class} % Constructors: \alias{GenomicRangesList} % Coercion \alias{coerce,RangedDataList,GenomicRangesList-method} \alias{coerce,GenomicRangesList,RangedDataList-method} \title{GenomicRangesList objects} \description{ A \code{GenomicRangesList} is a \code{\link[S4Vectors]{List}} of \code{\linkS4class{GenomicRanges}}. It is a virtual class; \code{SimpleGenomicRangesList} is the basic implementation. The subclass \code{\linkS4class{GRangesList}} provides special behavior and is particularly efficient for storing a large number of elements. } \section{Constructor}{ \describe{ \item{}{ \code{GenomicRangesList(\dots)}: Constructs a \code{SimpleGenomicRangesList} with elements taken from the arguments in \dots. If the only argument is a list, the elements are taken from that list. } } } \section{Coercion}{ \describe{ \item{}{ \code{as(from, "GenomicRangesList")}: Supported \code{from} types include: \describe{ \item{RangedDataList}{Each element of \code{from} is coerced to a \code{GenomicRanges}.} } } \item{}{ \code{as(from, "RangedDataList")}: Supported \code{from} types include: \describe{ \item{GenomicRangesList}{Each element of \code{from} is coerced to a \code{RangedData}.} } } } } \author{Michael Lawrence} \seealso{ \code{\linkS4class{GRangesList}}, which differs from \code{SimpleGenomicRangesList} in that the \code{GRangesList} treats its elements as single, compound ranges, particularly in overlap operations. \code{SimpleGenomicRangesList} is just a barebones list for now, without that compound semantic. } GenomicRanges/man/absoluteRanges.Rd0000644000175400017540000001141413001230267020334 0ustar00biocbuildbiocbuild\name{absoluteRanges} \alias{absoluteRanges} \alias{relativeRanges} \alias{isSmallGenome} \title{Transform genomic ranges into "absolute" ranges} \description{ \code{absoluteRanges} transforms the genomic ranges in \code{x} into \emph{absolute} ranges i.e. into ranges counted from the beginning of the virtual sequence obtained by concatenating all the sequences in the underlying genome (in the order reported by \code{seqlevels(x)}). \code{relativeRanges} performs the reverse transformation. NOTE: These functions only work on \emph{small} genomes. See Details section below. } \usage{ absoluteRanges(x) relativeRanges(x, seqlengths) ## Related utility: isSmallGenome(seqlengths) } \arguments{ \item{x}{ For \code{absoluteRanges}: a \link{GenomicRanges} object with ranges defined on a \emph{small} genome (see Details section below). For \code{relativeRanges}: a \link{Ranges} object. } \item{seqlengths}{ An object holding sequence lengths. This can be a named integer (or numeric) vector with no duplicated names as returned by \code{\link[GenomeInfoDb]{seqlengths}()}, or any object from which sequence lengths can be extracted with \code{\link[GenomeInfoDb]{seqlengths}()}. For \code{relativeRanges}, \code{seqlengths} must describe a \emph{small} genome (see Details section below). } } \details{ Because \code{absoluteRanges} returns the \emph{absolute} ranges in an \link[IRanges]{IRanges} object, and because an \link[IRanges]{IRanges} object cannot hold ranges with an end > \code{.Machine$integer.max} (i.e. >= 2^31 on most platforms), \code{absoluteRanges} cannot be used if the size of the underlying genome (i.e. the total length of the sequences in it) is > \code{.Machine$integer.max}. Utility function \code{isSmallGenome} is provided as a mean for the user to check upfront whether the genome is \emph{small} (i.e. its size is <= \code{.Machine$integer.max}) or not, and thus compatible with \code{absoluteRanges} or not. \code{relativeRanges} applies the same restriction by looking at the \code{seqlengths} argument. } \value{ An \link[IRanges]{IRanges} object for \code{absoluteRanges}. A \link{GRanges} object for \code{relativeRanges}. \code{absoluteRanges} and \code{relativeRanges} both return an object that is \emph{parallel} to the input object (i.e. same length and names). \code{isSmallGenome} returns TRUE if the total length of the underlying sequences is <= \code{.Machine$integer.max} (e.g. Fly genome), FALSE if not (e.g. Human genome), or NA if it cannot be computed (because some sequence lengths are NA). } \author{ H. Pagès } \seealso{ \itemize{ \item \link{GRanges} objects. \item \link[IRanges]{IRanges} objects in the \pkg{IRanges} package. \item \link{Seqinfo} objects and the \code{\link{seqlengths}} getter in the \pkg{GenomeInfoDb} package. \item \link{genomicvars} for manipulating genomic variables. \item The \code{\link{tileGenome}} function for putting tiles on a genome. } } \examples{ ## --------------------------------------------------------------------- ## TOY EXAMPLE ## --------------------------------------------------------------------- gr <- GRanges(Rle(c("chr2", "chr1", "chr3", "chr1"), 4:1), IRanges(1:10, width=5), seqinfo=Seqinfo(c("chr1", "chr2", "chr3"), c(100, 50, 20))) ar <- absoluteRanges(gr) ar gr2 <- relativeRanges(ar, seqlengths(gr)) gr2 ## Sanity check: stopifnot(all(gr == gr2)) ## --------------------------------------------------------------------- ## ON REAL DATA ## --------------------------------------------------------------------- ## With a "small" genome library(TxDb.Dmelanogaster.UCSC.dm3.ensGene) txdb <- TxDb.Dmelanogaster.UCSC.dm3.ensGene ex <- exons(txdb) ex isSmallGenome(ex) ## Note that because isSmallGenome() can return NA (see Value section ## above), its result should always be wrapped inside isTRUE() when ## used in an if statement: if (isTRUE(isSmallGenome(ex))) { ar <- absoluteRanges(ex) ar ex2 <- relativeRanges(ar, seqlengths(ex)) ex2 # original strand is not restored ## Sanity check: strand(ex2) <- strand(ex) # restore the strand stopifnot(all(ex == ex2)) } ## With a "big" genome (but we can reduce it) library(TxDb.Hsapiens.UCSC.hg19.knownGene) txdb <- TxDb.Hsapiens.UCSC.hg19.knownGene ex <- exons(txdb) isSmallGenome(ex) \dontrun{ absoluteRanges(ex) # error! } ## However, if we are only interested in some chromosomes, we might ## still be able to use absoluteRanges(): seqlevels(ex, force=TRUE) <- paste0("chr", 1:10) isSmallGenome(ex) # TRUE! ar <- absoluteRanges(ex) ex2 <- relativeRanges(ar, seqlengths(ex)) ## Sanity check: strand(ex2) <- strand(ex) stopifnot(all(ex == ex2)) } \keyword{manip} GenomicRanges/man/constraint.Rd0000644000175400017540000003356013001230267017550 0ustar00biocbuildbiocbuild\name{Constraints} \alias{Constraints} \alias{class:Constraint} \alias{Constraint-class} \alias{Constraint} \alias{class:ConstraintORNULL} \alias{ConstraintORNULL-class} \alias{ConstraintORNULL} \alias{constraint} \alias{constraint<-} \alias{checkConstraint} \title{Enforcing constraints thru Constraint objects} \description{ Attaching a Constraint object to an object of class A (the "constrained" object) is meant to be a convenient/reusable/extensible way to enforce a particular set of constraints on particular instances of A. THIS IS AN EXPERIMENTAL FEATURE AND STILL VERY MUCH A WORK-IN-PROGRESS! } \details{ For the developer, using constraints is an alternative to the more traditional approach that consists in creating subclasses of A and implementing specific validity methods for each of them. However, using constraints offers the following advantages over the traditional approach: \itemize{ \item The traditional approach often tends to lead to a proliferation of subclasses of A. \item Constraints can easily be re-used across different classes without the need to create any new class. \item Constraints can easily be combined. } All constraints are implemented as concrete subclasses of the Constraint class, which is a virtual class with no slots. Like the Constraint virtual class itself, concrete Constraint subclasses cannot have slots. Here are the 7 steps typically involved in the process of putting constraints on objects of class A: \enumerate{ \item Add a slot named \code{constraint} to the definition of class A. The type of this slot must be ConstraintORNULL. Note that any subclass of A will inherit this slot. \item Implements the \code{constraint()} accessors (getter and setter) for objects of class A. This is done by implementing the \code{"constraint"} method (getter) and replacement method (setter) for objects of class A (see the examples below). As a convenience to the user, the setter should also accept the name of a constraint (i.e. the name of its class) in addition to an instance of that class. Note that those accessors will work on instances of any subclass of A. \item Modify the validity method for class A so it also returns the result of \code{checkConstraint(x, constraint(x))} (append this result to the result returned by the validity method). \item Testing: Create \code{x}, an instance of class A (or subclass of A). By default there is no constraint on it (\code{constraint(x)} is \code{NULL}). \code{validObject(x)} should return \code{TRUE}. \item Create a new constraint (MyConstraint) by extending the Constraint class, typically with \code{setClass("MyConstraint", contains="Constraint")}. This constraint is not enforcing anything yet so you could put it on \code{x} (with \code{constraint(x) <- "MyConstraint"}), but not much would happen. In order to actually enforce something, a \code{"checkConstraint"} method for signature \code{c(x="A", constraint="MyConstraint")} needs to be implemented. \item Implement a \code{"checkConstraint"} method for signature \code{c(x="A", constraint="MyConstraint")}. Like validity methods, \code{"checkConstraint"} methods must return \code{NULL} or a character vector describing the problems found. Like validity methods, they should never fail (i.e. they should never raise an error). Note that, alternatively, an existing constraint (e.g. SomeConstraint) can be adapted to work on objects of class A by just defining a new \code{"checkConstraint"} method for signature \code{c(x="A", constraint="SomeConstraint")}. Also, stricter constraints can be built on top of existing constraints by extending one or more existing constraints (see the examples below). \item Testing: Try \code{constraint(x) <- "MyConstraint"}. It will or will not work depending on whether \code{x} satisfies the constraint or not. In the former case, trying to modify \code{x} in a way that breaks the constraint on it will also raise an error. } } \note{ WARNING: This note is not true anymore as the \code{constraint} slot has been temporarily removed from \link{GenomicRanges} objects (starting with package GenomicRanges >= 1.7.9). Currently, only \link{GenomicRanges} objects can be constrained, that is: \itemize{ \item they have a \code{constraint} slot; \item they have \code{constraint()} accessors (getter and setter) for this slot; \item their validity method has been modified so it also returns the result of \code{checkConstraint(x, constraint(x))}. } More classes in the GenomicRanges and IRanges packages will support constraints in the near future. } \author{H. Pagès} \seealso{ \code{\link{setClass}}, \code{\link{is}}, \code{\link{setMethod}}, \code{\link{showMethods}}, \code{\link{validObject}}, \link{GenomicRanges-class} } \examples{ ## The examples below show how to define and set constraints on ## GenomicRanges objects. Note that this is how the constraint() ## setter is defined for GenomicRanges objects: #setReplaceMethod("constraint", "GenomicRanges", # function(x, value) # { # if (isSingleString(value)) # value <- new(value) # if (!is(value, "ConstraintORNULL")) # stop("the supplied 'constraint' must be a ", # "Constraint object, a single string, or NULL") # x@constraint <- value # validObject(x) # x # } #) #selectMethod("constraint", "GenomicRanges") # the getter #selectMethod("constraint<-", "GenomicRanges") # the setter ## We'll use the GRanges instance 'gr' created in the GRanges examples ## to test our constraints: example(GRanges, echo=FALSE) gr #constraint(gr) ## --------------------------------------------------------------------- ## EXAMPLE 1: The HasRangeTypeCol constraint. ## --------------------------------------------------------------------- ## The HasRangeTypeCol constraint checks that the constrained object ## has a unique "rangeType" metadata column and that this column ## is a 'factor' Rle with no NAs and with the following levels ## (in this order): gene, transcript, exon, cds, 5utr, 3utr. setClass("HasRangeTypeCol", contains="Constraint") ## Like validity methods, "checkConstraint" methods must return NULL or ## a character vector describing the problems found. They should never ## fail i.e. they should never raise an error. setMethod("checkConstraint", c("GenomicRanges", "HasRangeTypeCol"), function(x, constraint, verbose=FALSE) { x_mcols <- mcols(x) idx <- match("rangeType", colnames(x_mcols)) if (length(idx) != 1L || is.na(idx)) { msg <- c("'mcols(x)' must have exactly 1 column ", "named \"rangeType\"") return(paste(msg, collapse="")) } rangeType <- x_mcols[[idx]] .LEVELS <- c("gene", "transcript", "exon", "cds", "5utr", "3utr") if (!is(rangeType, "Rle") || S4Vectors:::anyMissing(runValue(rangeType)) || !identical(levels(rangeType), .LEVELS)) { msg <- c("'mcols(x)$rangeType' must be a ", "'factor' Rle with no NAs and with levels: ", paste(.LEVELS, collapse=", ")) return(paste(msg, collapse="")) } NULL } ) #\dontrun{ #constraint(gr) <- "HasRangeTypeCol" # will fail #} checkConstraint(gr, new("HasRangeTypeCol")) # with GenomicRanges >= 1.7.9 levels <- c("gene", "transcript", "exon", "cds", "5utr", "3utr") rangeType <- Rle(factor(c("cds", "gene"), levels=levels), c(8, 2)) mcols(gr)$rangeType <- rangeType #constraint(gr) <- "HasRangeTypeCol" # OK checkConstraint(gr, new("HasRangeTypeCol")) # with GenomicRanges >= 1.7.9 ## Use is() to check whether the object has a given constraint or not: #is(constraint(gr), "HasRangeTypeCol") # TRUE #\dontrun{ #mcols(gr)$rangeType[3] <- NA # will fail #} mcols(gr)$rangeType[3] <- NA checkConstraint(gr, new("HasRangeTypeCol")) # with GenomicRanges >= 1.7.9 ## --------------------------------------------------------------------- ## EXAMPLE 2: The GeneRanges constraint. ## --------------------------------------------------------------------- ## The GeneRanges constraint is defined on top of the HasRangeTypeCol ## constraint. It checks that all the ranges in the object are of type ## "gene". setClass("GeneRanges", contains="HasRangeTypeCol") ## The checkConstraint() generic will check the HasRangeTypeCol constraint ## first, and, only if it's statisfied, it will then check the GeneRanges ## constraint. setMethod("checkConstraint", c("GenomicRanges", "GeneRanges"), function(x, constraint, verbose=FALSE) { rangeType <- mcols(x)$rangeType if (!all(rangeType == "gene")) { msg <- c("all elements in 'mcols(x)$rangeType' ", "must be equal to \"gene\"") return(paste(msg, collapse="")) } NULL } ) #\dontrun{ #constraint(gr) <- "GeneRanges" # will fail #} checkConstraint(gr, new("GeneRanges")) # with GenomicRanges >= 1.7.9 mcols(gr)$rangeType[] <- "gene" ## This replace the previous constraint (HasRangeTypeCol): #constraint(gr) <- "GeneRanges" # OK checkConstraint(gr, new("GeneRanges")) # with GenomicRanges >= 1.7.9 #is(constraint(gr), "GeneRanges") # TRUE ## However, 'gr' still indirectly has the HasRangeTypeCol constraint ## (because the GeneRanges constraint extends the HasRangeTypeCol ## constraint): #is(constraint(gr), "HasRangeTypeCol") # TRUE #\dontrun{ #mcols(gr)$rangeType[] <- "exon" # will fail #} mcols(gr)$rangeType[] <- "exon" checkConstraint(gr, new("GeneRanges")) # with GenomicRanges >= 1.7.9 ## --------------------------------------------------------------------- ## EXAMPLE 3: The HasGCCol constraint. ## --------------------------------------------------------------------- ## The HasGCCol constraint checks that the constrained object has a ## unique "GC" metadata column, that this column is of type numeric, ## with no NAs, and that all the values in that column are >= 0 and <= 1. setClass("HasGCCol", contains="Constraint") setMethod("checkConstraint", c("GenomicRanges", "HasGCCol"), function(x, constraint, verbose=FALSE) { x_mcols <- mcols(x) idx <- match("GC", colnames(x_mcols)) if (length(idx) != 1L || is.na(idx)) { msg <- c("'mcols(x)' must have exactly ", "one column named \"GC\"") return(paste(msg, collapse="")) } GC <- x_mcols[[idx]] if (!is.numeric(GC) || S4Vectors:::anyMissing(GC) || any(GC < 0) || any(GC > 1)) { msg <- c("'mcols(x)$GC' must be a numeric vector ", "with no NAs and with values between 0 and 1") return(paste(msg, collapse="")) } NULL } ) ## This replace the previous constraint (GeneRanges): #constraint(gr) <- "HasGCCol" # OK checkConstraint(gr, new("HasGCCol")) # with GenomicRanges >= 1.7.9 #is(constraint(gr), "HasGCCol") # TRUE #is(constraint(gr), "GeneRanges") # FALSE #is(constraint(gr), "HasRangeTypeCol") # FALSE ## --------------------------------------------------------------------- ## EXAMPLE 4: The HighGCRanges constraint. ## --------------------------------------------------------------------- ## The HighGCRanges constraint is defined on top of the HasGCCol ## constraint. It checks that all the ranges in the object have a GC ## content >= 0.5. setClass("HighGCRanges", contains="HasGCCol") ## The checkConstraint() generic will check the HasGCCol constraint ## first, and, if it's statisfied, it will then check the HighGCRanges ## constraint. setMethod("checkConstraint", c("GenomicRanges", "HighGCRanges"), function(x, constraint, verbose=FALSE) { GC <- mcols(x)$GC if (!all(GC >= 0.5)) { msg <- c("all elements in 'mcols(x)$GC' ", "must be >= 0.5") return(paste(msg, collapse="")) } NULL } ) #\dontrun{ #constraint(gr) <- "HighGCRanges" # will fail #} checkConstraint(gr, new("HighGCRanges")) # with GenomicRanges >= 1.7.9 mcols(gr)$GC[6:10] <- 0.5 #constraint(gr) <- "HighGCRanges" # OK checkConstraint(gr, new("HighGCRanges")) # with GenomicRanges >= 1.7.9 #is(constraint(gr), "HighGCRanges") # TRUE #is(constraint(gr), "HasGCCol") # TRUE ## --------------------------------------------------------------------- ## EXAMPLE 5: The HighGCGeneRanges constraint. ## --------------------------------------------------------------------- ## The HighGCGeneRanges constraint is the combination (AND) of the ## GeneRanges and HighGCRanges constraints. setClass("HighGCGeneRanges", contains=c("GeneRanges", "HighGCRanges")) ## No need to define a method for this constraint: the checkConstraint() ## generic will automatically check the GeneRanges and HighGCRanges ## constraints. #constraint(gr) <- "HighGCGeneRanges" # OK checkConstraint(gr, new("HighGCGeneRanges")) # with GenomicRanges >= 1.7.9 #is(constraint(gr), "HighGCGeneRanges") # TRUE #is(constraint(gr), "HighGCRanges") # TRUE #is(constraint(gr), "HasGCCol") # TRUE #is(constraint(gr), "GeneRanges") # TRUE #is(constraint(gr), "HasRangeTypeCol") # TRUE ## See how all the individual constraints are checked (from less ## specific to more specific constraints): #checkConstraint(gr, constraint(gr), verbose=TRUE) checkConstraint(gr, new("HighGCGeneRanges"), verbose=TRUE) # with # GenomicRanges # >= 1.7.9 ## See all the "checkConstraint" methods: showMethods("checkConstraint") } \keyword{methods} \keyword{classes} GenomicRanges/man/coverage-methods.Rd0000644000175400017540000001034513001230267020614 0ustar00biocbuildbiocbuild\name{coverage-methods} \alias{coverage-methods} \alias{coverage} \alias{coverage,GenomicRanges-method} \alias{coverage,GRangesList-method} \title{Coverage of a GRanges or GRangesList object} \description{ \code{\link[IRanges]{coverage}} methods for \link{GRanges} and \link{GRangesList} objects. NOTE: The \code{\link[IRanges]{coverage}} generic function and methods for \link[IRanges]{Ranges} and \link[IRanges]{RangesList} objects are defined and documented in the \pkg{IRanges} package. Methods for \link[GenomicAlignments]{GAlignments} and \link[GenomicAlignments]{GAlignmentPairs} objects are defined and documented in the \pkg{GenomicAlignments} package. } \usage{ \S4method{coverage}{GenomicRanges}(x, shift=0L, width=NULL, weight=1L, method=c("auto", "sort", "hash")) \S4method{coverage}{GRangesList}(x, shift=0L, width=NULL, weight=1L, method=c("auto", "sort", "hash")) } \arguments{ \item{x}{ A \link{GRanges} or \link{GRangesList} object. } \item{shift}{ A numeric vector or a list-like object. If numeric, it must be parallel to \code{x} (recycled if necessary). If a list-like object, it must have 1 list element per seqlevel in \code{x}, and its names must be exactly \code{seqlevels(x)}. Alternatively, \code{shift} can also be specified as a single string naming a metadata column in \code{x} (i.e. a column in \code{mcols(x)}) to be used as the \code{shift} vector. See \code{?\link[IRanges]{coverage}} in the \pkg{IRanges} package for more information about this argument. } \item{width}{ Either \code{NULL} (the default), or an integer vector. If \code{NULL}, it is replaced with \code{seqlengths(x)}. Otherwise, the vector must have the length and names of \code{seqlengths(x)} and contain NAs or non-negative integers. See \code{?\link[IRanges]{coverage}} in the \pkg{IRanges} package for more information about this argument. } \item{weight}{ A numeric vector or a list-like object. If numeric, it must be parallel to \code{x} (recycled if necessary). If a list-like object, it must have 1 list element per seqlevel in \code{x}, and its names must be exactly \code{seqlevels(x)}. Alternatively, \code{weight} can also be specified as a single string naming a metadata column in \code{x} (i.e. a column in \code{mcols(x)}) to be used as the \code{weight} vector. See \code{?\link[IRanges]{coverage}} in the \pkg{IRanges} package for more information about this argument. } \item{method}{ See \code{?\link[IRanges]{coverage}} in the \pkg{IRanges} package for a description of this argument. } } \details{ When \code{x} is a \link{GRangesList} object, \code{coverage(x, ...)} is equivalent to \code{coverage(unlist(x), ...)}. } \value{ A named \link[IRanges]{RleList} object with one coverage vector per seqlevel in \code{x}. } \author{H. Pagès and P. Aboyoun} \seealso{ \itemize{ \item \code{\link[IRanges]{coverage}} in the \pkg{IRanges} package. \item \link[GenomicAlignments]{coverage-methods} in the \pkg{GenomicAlignments} package. \item \link[IRanges]{RleList} objects in the \pkg{IRanges} package. \item \link{GRanges} and \link{GRangesList} objects. } } \examples{ ## Coverage of a GRanges object: gr <- GRanges( seqnames=Rle(c("chr1", "chr2", "chr1", "chr3"), c(1, 3, 2, 4)), ranges=IRanges(1:10, end=10), strand=Rle(strand(c("-", "+", "*", "+", "-")), c(1, 2, 2, 3, 2)), seqlengths=c(chr1=11, chr2=12, chr3=13)) cvg <- coverage(gr) pcvg <- coverage(gr[strand(gr) == "+"]) mcvg <- coverage(gr[strand(gr) == "-"]) scvg <- coverage(gr[strand(gr) == "*"]) stopifnot(identical(pcvg + mcvg + scvg, cvg)) ## Coverage of a GRangesList object: gr1 <- GRanges(seqnames="chr2", ranges=IRanges(3, 6), strand = "+") gr2 <- GRanges(seqnames=c("chr1", "chr1"), ranges=IRanges(c(7,13), width=3), strand=c("+", "-")) gr3 <- GRanges(seqnames=c("chr1", "chr2"), ranges=IRanges(c(1, 4), c(3, 9)), strand=c("-", "-")) grl <- GRangesList(gr1=gr1, gr2=gr2, gr3=gr3) stopifnot(identical(coverage(grl), coverage(unlist(grl)))) } \keyword{methods} \keyword{utilities} GenomicRanges/man/findOverlaps-methods.Rd0000644000175400017540000001602713001230267021460 0ustar00biocbuildbiocbuild\name{findOverlaps-methods} \alias{findOverlaps-methods} \alias{findOverlaps} \alias{findOverlaps,GenomicRanges,GenomicRanges-method} \alias{findOverlaps,GRangesList,GenomicRanges-method} \alias{findOverlaps,GenomicRanges,GRangesList-method} \alias{findOverlaps,GRangesList,GRangesList-method} \alias{findOverlaps,RangedData,GenomicRanges-method} \alias{countOverlaps} \alias{countOverlaps,GenomicRanges,GenomicRanges-method} \alias{overlapsAny} \alias{subsetByOverlaps} \title{Finding overlapping genomic ranges} \description{ Various methods for finding/counting overlaps between objects containing genomic ranges. This man page describes the methods that operate on \link{GenomicRanges} and \link{GRangesList} objects. NOTE: The \code{\link[IRanges]{findOverlaps}} generic function and methods for \link[IRanges]{Ranges} and \link[IRanges]{RangesList} objects are defined and documented in the \pkg{IRanges} package. The methods for \link[GenomicAlignments]{GAlignments}, \link[GenomicAlignments]{GAlignmentPairs}, and \link[GenomicAlignments]{GAlignmentsList} objects are defined and documented in the \pkg{GenomicAlignments} package. \link{GenomicRanges} and \link{GRangesList} objects also support \code{countOverlaps}, \code{overlapsAny}, and \code{subsetByOverlaps} thanks to the default methods defined in the \pkg{IRanges} package and to the \code{findOverlaps} and \code{countOverlaps} methods defined in this package and documented below. } \usage{ \S4method{findOverlaps}{GenomicRanges,GenomicRanges}(query, subject, maxgap=0L, minoverlap=1L, type=c("any", "start", "end", "within", "equal"), select=c("all", "first", "last", "arbitrary"), ignore.strand=FALSE) \S4method{countOverlaps}{GenomicRanges,GenomicRanges}(query, subject, maxgap=0L, minoverlap=1L, type=c("any", "start", "end", "within", "equal"), ignore.strand=FALSE) } \arguments{ \item{query, subject}{ A \link{GRanges} or \link{GRangesList} object. } \item{maxgap, minoverlap, type}{ See \code{\link[IRanges]{findOverlaps}} in the \pkg{IRanges} package for a description of these arguments. } \item{select}{ When \code{select} is \code{"all"} (the default), the results are returned as a \link{Hits} object. Otherwise the returned value is an integer vector parallel to \code{query} (i.e. same length) containing the first, last, or arbitrary overlapping interval in \code{subject}, with \code{NA} indicating intervals that did not overlap any intervals in \code{subject}. } \item{ignore.strand}{ When set to \code{TRUE}, the strand information is ignored in the overlap calculations. } } \details{ When the query and the subject are \link{GRanges} or \link{GRangesList} objects, \code{findOverlaps} uses the triplet (sequence name, range, strand) to determine which features (see paragraph below for the definition of feature) from the \code{query} overlap which features in the \code{subject}, where a strand value of \code{"*"} is treated as occurring on both the \code{"+"} and \code{"-"} strand. An overlap is recorded when a feature in the \code{query} and a feature in the \code{subject} have the same sequence name, have a compatible pairing of strands (e.g. \code{"+"}/\code{"+"}, \code{"-"}/\code{"-"}, \code{"*"}/\code{"+"}, \code{"*"}/\code{"-"}, etc.), and satisfy the interval overlap requirements. In the context of \code{findOverlaps}, a feature is a collection of ranges that are treated as a single entity. For \link{GRanges} objects, a feature is a single range; while for \link{GRangesList} objects, a feature is a list element containing a set of ranges. In the results, the features are referred to by number, which run from 1 to \code{length(query)}/\code{length(subject)}. } \value{ For \code{findOverlaps} either a \link[S4Vectors]{Hits} object when \code{select="all"} or an integer vector otherwise. For \code{countOverlaps} an integer vector containing the tabulated query overlap hits. } \author{P. Aboyoun, S. Falcon, M. Lawrence, and H. Pagès} \seealso{ \itemize{ \item The \link{Hits} class for representing a set of hits between 2 vector-like objects. \item The \code{\link[IRanges]{findOverlaps}} generic function defined in the \pkg{IRanges} package. \item The \link{GNCList} constructor and class for preprocessing and representing a \link{GenomicRanges} or object as a data structure based on Nested Containment Lists. \item The \link{GRanges} and \link{GRangesList} classes. } } \examples{ ## --------------------------------------------------------------------- ## BASIC EXAMPLES ## --------------------------------------------------------------------- ## GRanges object: gr <- GRanges( seqnames=Rle(c("chr1", "chr2", "chr1", "chr3"), c(1, 3, 2, 4)), ranges=IRanges(1:10, width=10:1, names=head(letters,10)), strand=Rle(strand(c("-", "+", "*", "+", "-")), c(1, 2, 2, 3, 2)), score=1:10, GC=seq(1, 0, length=10) ) gr ## GRangesList object: gr1 <- GRanges(seqnames="chr2", ranges=IRanges(4:3, 6), strand="+", score=5:4, GC=0.45) gr2 <- GRanges(seqnames=c("chr1", "chr1"), ranges=IRanges(c(7,13), width=3), strand=c("+", "-"), score=3:4, GC=c(0.3, 0.5)) gr3 <- GRanges(seqnames=c("chr1", "chr2"), ranges=IRanges(c(1, 4), c(3, 9)), strand=c("-", "-"), score=c(6L, 2L), GC=c(0.4, 0.1)) grl <- GRangesList("gr1"=gr1, "gr2"=gr2, "gr3"=gr3) ## Overlapping two GRanges objects: table(!is.na(findOverlaps(gr, gr1, select="arbitrary"))) countOverlaps(gr, gr1) findOverlaps(gr, gr1) subsetByOverlaps(gr, gr1) countOverlaps(gr, gr1, type="start") findOverlaps(gr, gr1, type="start") subsetByOverlaps(gr, gr1, type="start") findOverlaps(gr, gr1, select="first") findOverlaps(gr, gr1, select="last") findOverlaps(gr1, gr) findOverlaps(gr1, gr, type="start") findOverlaps(gr1, gr, type="within") findOverlaps(gr1, gr, type="equal") ## --------------------------------------------------------------------- ## MORE EXAMPLES ## --------------------------------------------------------------------- table(!is.na(findOverlaps(gr, gr1, select="arbitrary"))) countOverlaps(gr, gr1) findOverlaps(gr, gr1) subsetByOverlaps(gr, gr1) ## Overlaps between a GRanges and a GRangesList object: table(!is.na(findOverlaps(grl, gr, select="first"))) countOverlaps(grl, gr) findOverlaps(grl, gr) subsetByOverlaps(grl, gr) countOverlaps(grl, gr, type="start") findOverlaps(grl, gr, type="start") subsetByOverlaps(grl, gr, type="start") findOverlaps(grl, gr, select="first") table(!is.na(findOverlaps(grl, gr1, select="first"))) countOverlaps(grl, gr1) findOverlaps(grl, gr1) subsetByOverlaps(grl, gr1) countOverlaps(grl, gr1, type="start") findOverlaps(grl, gr1, type="start") subsetByOverlaps(grl, gr1, type="start") findOverlaps(grl, gr1, select="first") ## Overlaps between two GRangesList objects: countOverlaps(grl, rev(grl)) findOverlaps(grl, rev(grl)) subsetByOverlaps(grl, rev(grl)) } \keyword{methods} \keyword{utilities} GenomicRanges/man/genomicvars.Rd0000644000175400017540000002020713001230267017673 0ustar00biocbuildbiocbuild\name{genomicvars} \alias{genomicvars} \alias{genomicvariables} \alias{coerce,RleList,GRanges-method} \alias{coerce,RleViewsList,GRanges-method} \alias{bindAsGRanges} \alias{mcolAsRleList} \alias{binnedAverage} \title{Manipulating genomic variables} \description{ A \emph{genomic variable} is a variable defined along a genome. Here are 2 ways a genomic variable is generally represented in Bioconductor: \enumerate{ \item as a named \link[IRanges]{RleList} object with one list element per chromosome; \item as a metadata column on a \emph{disjoint} GRanges object. } This man page documents tools for switching from one form to the other. } \usage{ bindAsGRanges(...) mcolAsRleList(x, varname) binnedAverage(bins, numvar, varname) } \arguments{ \item{...}{ One or more genomic variables in the form of named RleList objects. } \item{x}{ A \emph{disjoint} \link{GRanges} object with metadata columns on it. A \link{GRanges} object is said to be \emph{disjoint} if it contains ranges that do not overlap with each other. This can be tested with \code{isDisjoint}. See \code{?`\link{isDisjoint,GenomicRanges-method}`} for more information about the \code{isDisjoint} method for \link{GRanges} objects. } \item{varname}{ The name of the genomic variable. For \code{mcolAsRleList} this must be the name of the metadata column on \code{x} to be turned into an \link[IRanges]{RleList} object. For \code{binnedAverage} this will be the name of the metadata column that contains the binned average in the returned object. } \item{bins}{ A \link{GRanges} object representing the genomic bins. Typically obtained by calling \code{\link{tileGenome}} with \code{cut.last.tile.in.chrom=TRUE}. } \item{numvar}{ A named \link[IRanges]{RleList} object representing a numerical variable defined along the genome covered by \code{bins} (which is the genome described by \code{seqinfo(bins)}). } } \details{ \code{bindAsGRanges} allows to switch the representation of one or more genomic variables from the \emph{named RleList} form to the \emph{metadata column on a disjoint GRanges object} form by binding the supplied named RleList objects together and putting them on the same \link{GRanges} object. This transformation is lossless. \code{mcolAsRleList} performs the opposite transformation and is also lossless (however the circularity flags and genome information in \code{seqinfo(x)} won't propagate). It works for any metadata column on \code{x} that can be put in \link[S4Vectors]{Rle} form i.e. that is an atomic vector or a factor. \code{binnedAverage} computes the binned average of a numerical variable defined along a genome. } \value{ For \code{bindAsGRanges}: a \link{GRanges} object with 1 metadata column per supplied genomic variable. For \code{mcolAsRleList}: a named \link[IRanges]{RleList} object with 1 list element per seqlevel in \code{x}. For \code{binnedAverage}: the \link{GRanges} object \code{bins} with an additional metadata column named \code{varname} containing the binned average. } \author{ H. Pagès } \seealso{ \itemize{ \item \link[IRanges]{RleList} objects in the \pkg{IRanges} package. \item \link{coverage,GenomicRanges-method} for computing the coverage of a \link{GRanges} object. \item The \code{\link{tileGenome}} function for putting tiles on a genome. \item \link{GRanges} objects and \link{isDisjoint,GenomicRanges-method} for the \code{isDisjoint} method for \link{GenomicRanges} objects. } } \examples{ ## --------------------------------------------------------------------- ## A. TWO WAYS TO REPRESENT A GENOMIC VARIABLE ## ----------------------------------------------------------------- ## 1) As a named RleList object ## ---------------------------- ## Let's create a genomic variable in the "named RleList" form: library(BSgenome.Scerevisiae.UCSC.sacCer2) set.seed(55) my_var <- RleList( lapply(seqlengths(Scerevisiae), function(seqlen) { tmp <- sample(50L, seqlen, replace=TRUE) %/% 50L Rle(cumsum(tmp - rev(tmp))) } ), compress=FALSE) my_var ## 2) As a metadata column on a disjoint GRanges object ## ---------------------------------------------------- gr1 <- bindAsGRanges(my_var=my_var) gr1 gr2 <- GRanges(c("chrI:1-150", "chrI:211-285", "chrI:291-377", "chrV:51-60"), score=c(0.4, 8, -10, 2.2), id=letters[1:4], seqinfo=seqinfo(Scerevisiae)) gr2 ## Going back to the "named RleList" form: mcolAsRleList(gr1, "my_var") score <- mcolAsRleList(gr2, "score") score id <- mcolAsRleList(gr2, "id") id bindAsGRanges(score=score, id=id) ## Bind 'my_var', 'score', and 'id' together: gr3 <- bindAsGRanges(my_var=my_var, score=score, id=id) ## Sanity checks: stopifnot(identical(my_var, mcolAsRleList(gr3, "my_var"))) stopifnot(identical(score, mcolAsRleList(gr3, "score"))) stopifnot(identical(id, mcolAsRleList(gr3, "id"))) gr2b <- bindAsGRanges(score=score, id=id) seqinfo(gr2b) <- seqinfo(gr2) stopifnot(identical(gr2, gr2b)) ## --------------------------------------------------------------------- ## B. BIND TOGETHER THE COVERAGES OF SEVERAL BAM FILES ## --------------------------------------------------------------------- library(pasillaBamSubset) library(GenomicAlignments) untreated1_cvg <- coverage(BamFile(untreated1_chr4())) untreated3_cvg <- coverage(BamFile(untreated3_chr4())) all_cvg <- bindAsGRanges(untreated1=untreated1_cvg, untreated3=untreated3_cvg) ## Keep regions with coverage: all_cvg[with(mcols(all_cvg), untreated1 + untreated3 >= 1)] ## Plot the coverage profiles with the Gviz package: library(Gviz) plotNumvars <- function(numvars, region, name="numvars", ...) { stopifnot(is(numvars, "GRanges")) stopifnot(is(region, "GRanges"), length(region) == 1L) gtrack <- GenomeAxisTrack() dtrack <- DataTrack(numvars, chromosome=as.character(seqnames(region)), name=name, groups=colnames(mcols(numvars)), type="l", ...) plotTracks(list(gtrack, dtrack), from=start(region), to=end(region)) } plotNumvars(all_cvg, GRanges("chr4:1-25000"), "coverage", col=c("red", "blue")) plotNumvars(all_cvg, GRanges("chr4:1.03e6-1.08e6"), "coverage", col=c("red", "blue")) ## Sanity checks: stopifnot(identical(untreated1_cvg, mcolAsRleList(all_cvg, "untreated1"))) stopifnot(identical(untreated3_cvg, mcolAsRleList(all_cvg, "untreated3"))) ## --------------------------------------------------------------------- ## C. COMPUTE THE BINNED AVERAGE OF A NUMERICAL VARIABLE DEFINED ALONG A ## GENOME ## --------------------------------------------------------------------- ## In some applications (e.g. visualization), there is the need to compute ## the average of a genomic variable for a set of predefined fixed-width ## regions (sometimes called "bins"). ## Let's use tileGenome() to create such a set of bins: bins1 <- tileGenome(seqinfo(Scerevisiae), tilewidth=100, cut.last.tile.in.chrom=TRUE) ## Compute the binned average for 'my_var' and 'score': bins1 <- binnedAverage(bins1, my_var, "binned_var") bins1 bins1 <- binnedAverage(bins1, score, "binned_score") bins1 ## Binned average in "named RleList" form: binned_var1 <- mcolAsRleList(bins1, "binned_var") binned_var1 stopifnot(all.equal(mean(my_var), mean(binned_var1))) # sanity check mcolAsRleList(bins1, "binned_score") ## With bigger bins: bins2 <- tileGenome(seqinfo(Scerevisiae), tilewidth=50000, cut.last.tile.in.chrom=TRUE) bins2 <- binnedAverage(bins2, my_var, "binned_var") bins2 <- binnedAverage(bins2, score, "binned_score") bins2 binned_var2 <- mcolAsRleList(bins2, "binned_var") binned_var2 stopifnot(all.equal(mean(my_var), mean(binned_var2))) # sanity check mcolAsRleList(bins2, "binned_score") ## Not surprisingly, the "binned" variables are much more compact in ## memory than the original variables (they contain much less runs): object.size(my_var) object.size(binned_var1) object.size(binned_var2) } \keyword{manip} GenomicRanges/man/inter-range-methods.Rd0000644000175400017540000002272013001761644021244 0ustar00biocbuildbiocbuild\name{inter-range-methods} \alias{inter-range-methods} \alias{range} \alias{range,GenomicRanges-method} \alias{range,GPos-method} \alias{range,GRangesList-method} \alias{reduce} \alias{reduce,GenomicRanges-method} \alias{reduce,GRangesList-method} \alias{reduce,GenomicRangesList-method} \alias{gaps} \alias{gaps,GenomicRanges-method} \alias{disjoin} \alias{disjoin,GenomicRanges-method} \alias{disjoin,GRangesList-method} \alias{isDisjoint} \alias{isDisjoint,GenomicRanges-method} \alias{isDisjoint,GPos-method} \alias{isDisjoint,GRangesList-method} \alias{disjointBins} \alias{disjointBins,GenomicRanges-method} \title{Inter range transformations of a GRanges or GRangesList object} \description{ This man page documents \emph{inter range transformations} of a \link{GenomicRanges} object (i.e. of an object that belongs to the \link{GenomicRanges} class or one of its subclasses, this includes for example \link{GRanges} objects), or a \link{GRangesList} object. See \code{?`\link[IRanges]{intra-range-methods}`} and \code{?`\link[IRanges]{inter-range-methods}`} in the \pkg{IRanges} package for a quick introduction to \emph{intra range} and \emph{inter range transformations}. See \code{?`\link[GenomicRanges]{intra-range-methods}`} for \emph{intra range transformations} of a \link{GenomicRanges} object or \link{GRangesList} object. } \usage{ \S4method{range}{GenomicRanges}(x, ..., ignore.strand=FALSE, na.rm=FALSE) \S4method{range}{GRangesList}(x, ..., ignore.strand=FALSE, na.rm=FALSE) \S4method{reduce}{GenomicRanges}(x, drop.empty.ranges=FALSE, min.gapwidth=1L, with.revmap=FALSE, with.inframe.attrib=FALSE, ignore.strand=FALSE) \S4method{reduce}{GRangesList}(x, drop.empty.ranges=FALSE, min.gapwidth=1L, with.revmap=FALSE, with.inframe.attrib=FALSE, ignore.strand=FALSE) \S4method{gaps}{GenomicRanges}(x, start=1L, end=seqlengths(x)) \S4method{disjoin}{GenomicRanges}(x, with.revmap=FALSE, ignore.strand=FALSE) \S4method{disjoin}{GRangesList}(x, with.revmap=FALSE, ignore.strand=FALSE) \S4method{isDisjoint}{GenomicRanges}(x, ignore.strand=FALSE) \S4method{isDisjoint}{GRangesList}(x, ignore.strand=FALSE) \S4method{disjointBins}{GenomicRanges}(x, ignore.strand=FALSE) } \arguments{ \item{x}{A \link{GenomicRanges} object.} \item{drop.empty.ranges, min.gapwidth, with.revmap, with.inframe.attrib, start, end}{ See \code{?`\link[IRanges]{inter-range-methods}`} in the \pkg{IRanges} package. } \item{ignore.strand}{ \code{TRUE} or \code{FALSE}. Whether the strand of the input ranges should be ignored or not. See details below. } \item{...}{ For \code{range}, additional \code{GenomicRanges} objects to consider. Ignored otherwise. } \item{na.rm}{Ignored.} } \details{ \subsection{On a GRanges object}{ \code{range} returns an object of the same type as \code{x} containing range bounds for each distinct (seqname, strand) pairing. The names (\code{names(x)}) and the metadata columns in \code{x} are dropped. \code{reduce} returns an object of the same type as \code{x} containing reduced ranges for each distinct (seqname, strand) pairing. The names (\code{names(x)}) and the metadata columns in \code{x} are dropped. See \code{?\link[IRanges]{reduce}} for more information about range reduction and for a description of the optional arguments. \code{gaps} returns an object of the same type as \code{x} containing complemented ranges for each distinct (seqname, strand) pairing. The names (\code{names(x)}) and the columns in \code{x} are dropped. For the start and end arguments of this gaps method, it is expected that the user will supply a named integer vector (where the names correspond to the appropriate seqlevels). See \code{?\link[IRanges]{gaps}} for more information about range complements and for a description of the optional arguments. \code{disjoin} returns an object of the same type as \code{x} containing disjoint ranges for each distinct (seqname, strand) pairing. The names (\code{names(x)}) and the metadata columns in \code{x} are dropped. If \code{with.revmap=TRUE}, a metadata column that maps the ouput ranges to the input ranges is added to the returned object. See \code{?\link[IRanges]{disjoin}} for more information. \code{isDisjoint} returns a logical value indicating whether the ranges in \code{x} are disjoint (i.e. non-overlapping). \code{disjointBins} returns bin indexes for the ranges in \code{x}, such that ranges in the same bin do not overlap. If \code{ignore.strand=FALSE}, the two features cannot overlap if they are on different strands. } \subsection{On a GRangesList object}{ When they are supported on GRangesList object \code{x}, the above inter range transformations will apply the transformation to each of the list elements in \code{x} and return a list-like object \emph{parallel} to \code{x} (i.e. with 1 list element per list element in \code{x}). If \code{x} has names on it, they're propagated to the returned object. } } \author{H. Pagès and P. Aboyoun} \seealso{ \itemize{ \item The \link{GenomicRanges} and \link{GRanges} classes. \item The \link[IRanges]{Ranges} class in the \pkg{IRanges} package. \item The \link[IRanges]{inter-range-methods} man page in the \pkg{IRanges} package. \item \link{GenomicRanges-comparison} for comparing and ordering genomic ranges. \item \code{\link[S4Vectors]{endoapply}} in the \pkg{S4Vectors} package. } } \examples{ gr <- GRanges( seqnames=Rle(paste("chr", c(1, 2, 1, 3), sep=""), c(1, 3, 2, 4)), ranges=IRanges(1:10, width=10:1, names=letters[1:10]), strand=Rle(strand(c("-", "+", "*", "+", "-")), c(1, 2, 2, 3, 2)), score=1:10, GC=seq(1, 0, length=10) ) gr gr1 <- GRanges(seqnames="chr2", ranges=IRanges(3, 6), strand="+", score=5L, GC=0.45) gr2 <- GRanges(seqnames="chr1", ranges=IRanges(c(10, 7, 19), width=5), strand=c("+", "-", "+"), score=3:5, GC=c(0.3, 0.5, 0.66)) gr3 <- GRanges(seqnames=c("chr1", "chr2"), ranges=IRanges(c(1, 4), c(3, 9)), strand=c("-", "-"), score=c(6L, 2L), GC=c(0.4, 0.1)) grl <- GRangesList(gr1=gr1, gr2=gr2, gr3=gr3) grl ## --------------------------------------------------------------------- ## range() ## --------------------------------------------------------------------- ## On a GRanges object: range(gr) ## On a GRangesList object: range(grl) range(grl, ignore.strand=TRUE) # --------------------------------------------------------------------- ## reduce() ## --------------------------------------------------------------------- reduce(gr) gr2 <- reduce(gr, with.revmap=TRUE) revmap <- mcols(gr2)$revmap # an IntegerList ## Use the mapping from reduced to original ranges to group the original ## ranges by reduced range: relist(gr[unlist(revmap)], revmap) ## Or use it to split the DataFrame of original metadata columns by ## reduced range: relist(mcols(gr)[unlist(revmap), ], revmap) # a SplitDataFrameList ## [For advanced users] Use this reverse mapping to compare the reduced ## ranges with the ranges they originate from: expanded_gr2 <- rep(gr2, elementNROWS(revmap)) reordered_gr <- gr[unlist(revmap)] codes <- pcompare(expanded_gr2, reordered_gr) ## All the codes should translate to "d", "e", "g", or "h" (the 4 letters ## indicating that the range on the left contains the range on the right): alphacodes <- rangeComparisonCodeToLetter(pcompare(expanded_gr2, reordered_gr)) stopifnot(all(alphacodes \%in\% c("d", "e", "g", "h"))) ## On a big GRanges object with a lot of seqlevels: mcols(gr) <- NULL biggr <- c(gr, GRanges("chr1", IRanges(c(4, 1), c(5, 2)), strand="+")) seqlevels(biggr) <- paste0("chr", 1:2000) biggr <- rep(biggr, 25000) set.seed(33) seqnames(biggr) <- sample(factor(seqlevels(biggr), levels=seqlevels(biggr)), length(biggr), replace=TRUE) biggr2 <- reduce(biggr, with.revmap=TRUE) revmap <- mcols(biggr2)$revmap expanded_biggr2 <- rep(biggr2, elementNROWS(revmap)) reordered_biggr <- biggr[unlist(revmap)] codes <- pcompare(expanded_biggr2, reordered_biggr) alphacodes <- rangeComparisonCodeToLetter(pcompare(expanded_biggr2, reordered_biggr)) stopifnot(all(alphacodes \%in\% c("d", "e", "g", "h"))) table(alphacodes) ## On a GRangesList object: reduce(grl) # Doesn't really reduce anything but note the reordering # of the inner elements in the 2nd and 3rd list elements: # the ranges are reordered by sequence name first (which # should appear in the same order as in 'seqlevels(grl)'), # and then by strand. reduce(grl, ignore.strand=TRUE) # 2nd list element got reduced ## --------------------------------------------------------------------- ## gaps() ## --------------------------------------------------------------------- gaps(gr, start=1, end=10) ## --------------------------------------------------------------------- ## disjoin(), isDisjoint(), disjointBins() ## --------------------------------------------------------------------- disjoin(gr) disjoin(gr, with.revmap=TRUE) disjoin(gr, with.revmap=TRUE, ignore.strand=TRUE) isDisjoint(gr) stopifnot(isDisjoint(disjoin(gr))) disjointBins(gr) stopifnot(all(sapply(split(gr, disjointBins(gr)), isDisjoint))) ## On a GRangesList object: disjoin(grl) # doesn't really disjoin anything but note the reordering disjoin(grl, with.revmap=TRUE) } \keyword{utilities} GenomicRanges/man/intra-range-methods.Rd0000644000175400017540000002046013001230267021227 0ustar00biocbuildbiocbuild\name{intra-range-methods} \alias{intra-range-methods} \alias{shift} \alias{shift,GenomicRanges-method} \alias{shift,GPos-method} \alias{shift,GRangesList-method} \alias{narrow} \alias{narrow,GenomicRanges-method} \alias{narrow,GRangesList-method} \alias{resize} \alias{resize,GenomicRanges-method} \alias{resize,GRangesList-method} \alias{flank} \alias{flank,GenomicRanges-method} \alias{flank,GRangesList-method} \alias{promoters} \alias{promoters,GenomicRanges-method} \alias{promoters,GRangesList-method} \alias{restrict} \alias{restrict,GenomicRanges-method} \alias{restrict,GRangesList-method} \alias{trim,GenomicRanges-method} \alias{Ops,GenomicRanges,numeric-method} \title{Intra range transformations of a GRanges or GRangesList object} \description{ This man page documents \emph{intra range transformations} of a \link{GenomicRanges} object (i.e. of an object that belongs to the \link{GenomicRanges} class or one of its subclasses, this includes for example \link{GRanges} objects), or a \link{GRangesList} object. See \code{?`\link[IRanges]{intra-range-methods}`} and \code{?`\link[IRanges]{inter-range-methods}`} in the \pkg{IRanges} package for a quick introduction to \emph{intra range} and \emph{inter range transformations}. \emph{Intra range} methods for \link[GenomicAlignments]{GAlignments} and \link[GenomicAlignments]{GAlignmentsList} objects are defined and documented in the \pkg{GenomicAlignments} package. See \code{?`\link[GenomicRanges]{inter-range-methods}`} for \emph{inter range transformations} of a \link{GenomicRanges} or \link{GRangesList} object. } \usage{ \S4method{shift}{GenomicRanges}(x, shift=0L, use.names=TRUE) \S4method{shift}{GRangesList}(x, shift=0L, use.names=TRUE) \S4method{narrow}{GenomicRanges}(x, start=NA, end=NA, width=NA, use.names=TRUE) \S4method{resize}{GenomicRanges}(x, width, fix="start", use.names=TRUE, ignore.strand=FALSE) \S4method{resize}{GRangesList}(x, width, fix="start", use.names=TRUE, ignore.strand=FALSE) \S4method{flank}{GenomicRanges}(x, width, start=TRUE, both=FALSE, use.names=TRUE, ignore.strand=FALSE) \S4method{flank}{GRangesList}(x, width, start=TRUE, both=FALSE, use.names=TRUE, ignore.strand=FALSE) \S4method{promoters}{GenomicRanges}(x, upstream=2000, downstream=200, ...) \S4method{promoters}{GRangesList}(x, upstream=2000, downstream=200, ...) \S4method{restrict}{GenomicRanges}(x, start=NA, end=NA, keep.all.ranges=FALSE, use.names=TRUE) \S4method{restrict}{GRangesList}(x, start=NA, end=NA, keep.all.ranges=FALSE, use.names=TRUE) \S4method{trim}{GenomicRanges}(x, use.names=TRUE) } \arguments{ \item{x}{A \link{GenomicRanges} or \link{GRangesList} object.} \item{shift, use.names, start, end, width, both, fix, keep.all.ranges, upstream, downstream}{ See \code{?`\link[IRanges]{intra-range-methods}`}. } \item{ignore.strand}{ \code{TRUE} or \code{FALSE}. Whether the strand of the input ranges should be ignored or not. See details below. } \item{\dots}{ Additional arguments to methods. } } \details{ \itemize{ \item{}{\code{shift} behaves like the \code{shift} method for \link[IRanges]{Ranges} objects. See \code{?`\link[IRanges]{intra-range-methods}`} for the details. } \item(){\code{narrow} on a \link{GenomicRanges} object behaves like on a \link[IRanges]{Ranges} object. See \code{?`\link[IRanges]{intra-range-methods}`} for the details. A major difference though is that it returns a \link{GenomicRanges} object instead of a \link[IRanges]{Ranges} object. The returned object is \emph{parallel} (i.e. same length and names) to the original object \code{x}. } \item{}{\code{resize} returns an object of the same type and length as \code{x} containing intervals that have been resized to width \code{width} based on the \code{strand(x)} values. Elements where \code{strand(x) == "+"} or \code{strand(x) == "*"} are anchored at \code{start(x)} and elements where \code{strand(x) == "-"} are anchored at the \code{end(x)}. The \code{use.names} argument determines whether or not to keep the names on the ranges. } \item{}{\code{flank} returns an object of the same type and length as \code{x} containing intervals of width \code{width} that flank the intervals in \code{x}. The \code{start} argument takes a logical indicating whether \code{x} should be flanked at the "start" (\code{TRUE}) or the "end" (\code{FALSE}), which for \code{strand(x) != "-"} is \code{start(x)} and \code{end(x)} respectively and for \code{strand(x) == "-"} is \code{end(x)} and \code{start(x)} respectively. The \code{both} argument takes a single logical value indicating whether the flanking region \code{width} positions extends \emph{into} the range. If \code{both=TRUE}, the resulting range thus straddles the end point, with \code{width} positions on either side. } \item{}{ \code{promoters} returns an object of the same type and length as \code{x} containing promoter ranges. Promoter ranges extend around the transcription start site (TSS) which is defined as \code{start(x)}. The \code{upsteam} and \code{downstream} arguments define the number of nucleotides in the 5' and 3' direction, respectively. The full range is defined as, (start(x) - upstream) to (start(x) + downstream - 1). Ranges on the \code{*} strand are treated the same as those on the \code{+} strand. When no seqlengths are present in \code{x}, it is possible to have non-positive start values in the promoter ranges. This occurs when (TSS - \code{upstream}) < 1. In the equal but opposite case, the \code{end} values of the ranges may extend beyond the chromosome end when (TSS + \code{downstream} + 1) > 'chromosome end'. When \code{seqlengths} are not \code{NA} the promoter ranges are kept within the bounds of the defined \code{seqlengths}. } \item{}{\code{restrict} returns an object of the same type and length as \code{x} containing restricted ranges for distinct seqnames. The \code{start} and \code{end} arguments can be a named numeric vector of seqnames for the ranges to be resticted or a numeric vector or length 1 if the restriction operation is to be applied to all the sequences in \code{x}. See \code{?`\link[IRanges]{intra-range-methods}`} for more information about range restriction and for a description of the optional arguments. } \item{}{\code{trim} trims out-of-bound ranges located on non-circular sequences whose length is not NA. } } } \author{P. Aboyoun and V. Obenchain } \seealso{ \itemize{ \item \link{GenomicRanges}, \link{GRanges}, and \link{GRangesList} objects. \item The \link[IRanges]{intra-range-methods} man page in the \pkg{IRanges} package. \item The \link[IRanges]{Ranges} class in the \pkg{IRanges} package. \item \code{\link[S4Vectors]{endoapply}} in the \pkg{S4Vectors} package. } } \examples{ ## --------------------------------------------------------------------- ## A. ON A GRanges OBJECT ## --------------------------------------------------------------------- gr <- GRanges( seqnames=Rle(paste("chr", c(1, 2, 1, 3), sep=""), c(1, 3, 2, 4)), ranges=IRanges(1:10, width=10:1, names=letters[1:10]), strand=Rle(strand(c("-", "+", "*", "+", "-")), c(1, 2, 2, 3, 2)), score=1:10, GC=seq(1, 0, length=10) ) gr shift(gr, 1) narrow(gr[-10], start=2, end=-2) resize(gr, width=10) flank(gr, width=10) restrict(gr, start=3, end=7) gr <- GRanges("chr1", IRanges(rep(10, 3), width=6), c("+", "-", "*")) promoters(gr, 2, 2) ## --------------------------------------------------------------------- ## B. ON A GRangesList OBJECT ## --------------------------------------------------------------------- gr1 <- GRanges("chr2", IRanges(3, 6)) gr2 <- GRanges(c("chr1", "chr1"), IRanges(c(7,13), width=3), strand=c("+", "-")) gr3 <- GRanges(c("chr1", "chr2"), IRanges(c(1, 4), c(3, 9)), strand="-") grl <- GRangesList(gr1= gr1, gr2=gr2, gr3=gr3) grl resize(grl, width=20) flank(grl, width=20) restrict(grl, start=3) } \keyword{methods} \keyword{utilities} GenomicRanges/man/makeGRangesFromDataFrame.Rd0000644000175400017540000002105113001230267022131 0ustar00biocbuildbiocbuild\name{makeGRangesFromDataFrame} \alias{makeGRangesFromDataFrame} \alias{coerce,data.frame,GRanges-method} \alias{coerce,DataFrame,GRanges-method} \title{Make a GRanges object from a data.frame or DataFrame} \description{ \code{makeGRangesFromDataFrame} takes a data-frame-like object as input and tries to automatically find the columns that describe genomic ranges. It returns them as a \link{GRanges} object. \code{makeGRangesFromDataFrame} is also the workhorse behind the coercion method from data.frame (or \link[S4Vectors]{DataFrame}) to \link{GRanges}. } \usage{ makeGRangesFromDataFrame(df, keep.extra.columns=FALSE, ignore.strand=FALSE, seqinfo=NULL, seqnames.field=c("seqnames", "seqname", "chromosome", "chrom", "chr", "chromosome_name", "seqid"), start.field="start", end.field=c("end", "stop"), strand.field="strand", starts.in.df.are.0based=FALSE) } \arguments{ \item{df}{ A data.frame or \link[S4Vectors]{DataFrame} object. If not, then the function first tries to turn \code{df} into a data frame with \code{as.data.frame(df)}. } \item{keep.extra.columns}{ \code{TRUE} or \code{FALSE} (the default). If \code{TRUE}, the columns in \code{df} that are not used to form the genomic ranges of the returned \link{GRanges} object are then returned as metadata columns on the object. Otherwise, they are ignored. If \code{df} has a \code{width} column, then it's always ignored. } \item{ignore.strand}{ \code{TRUE} or \code{FALSE} (the default). If \code{TRUE}, then the strand of the returned \link{GRanges} object is set to \code{"*"}. } \item{seqinfo}{ Either \code{NULL}, or a \link{Seqinfo} object, or a character vector of seqlevels, or a named numeric vector of sequence lengths. When not \code{NULL}, it must be compatible with the genomic ranges in \code{df} i.e. it must include at least the sequence levels represented in \code{df}. } \item{seqnames.field}{ A character vector of recognized names for the column in \code{df} that contains the chromosome name (a.k.a. sequence name) associated with each genomic range. Only the first name in \code{seqnames.field} that is found in \code{colnames(df)} is used. If no one is found, then an error is raised. } \item{start.field}{ A character vector of recognized names for the column in \code{df} that contains the start positions of the genomic ranges. Only the first name in \code{start.field} that is found in \code{colnames(df)} is used. If no one is found, then an error is raised. } \item{end.field}{ A character vector of recognized names for the column in \code{df} that contains the end positions of the genomic ranges. Only the first name in \code{start.field} that is found in \code{colnames(df)} is used. If no one is found, then an error is raised. } \item{strand.field}{ A character vector of recognized names for the column in \code{df} that contains the strand associated with each genomic range. Only the first name in \code{strand.field} that is found in \code{colnames(df)} is used. If no one is found or if \code{ignore.strand} is \code{TRUE}, then the strand of the returned \link{GRanges} object is set to \code{"*"}. } \item{starts.in.df.are.0based}{ \code{TRUE} or \code{FALSE} (the default). If \code{TRUE}, then the start positions of the genomic ranges in \code{df} are considered to be \emph{0-based} and are converted to \emph{1-based} in the returned \link{GRanges} object. This feature is intended to make it more convenient to handle input that contains data obtained from resources using the "0-based start" convention. A notorious example of such resource is the UCSC Table Browser (\url{http://genome.ucsc.edu/cgi-bin/hgTables}). } } \value{ A \link{GRanges} object with one element per row in the input. If the \code{seqinfo} argument was supplied, the returned object will have exactly the seqlevels specified in \code{seqinfo} and in the same order. Otherwise, the seqlevels are ordered according to the output of the \code{\link[GenomeInfoDb]{rankSeqlevels}} function (except if \code{df} contains the seqnames in the form of a factor-Rle, in which case the levels of the factor-Rle become the seqlevels of the returned object and with no re-ordering). If \code{df} has non-automatic row names (i.e. \code{rownames(df)} is not \code{NULL} and is not \code{seq_len(nrow(df))}), then they will be used to set names on the returned \link{GRanges} object. } \note{ Coercing data.frame or \link[S4Vectors]{DataFrame} \code{df} into a \link{GRanges} object (with \code{as(df, "GRanges")}), or calling \code{GRanges(df)}, are both equivalent to calling \code{makeGRangesFromDataFrame(df, keep.extra.columns=TRUE)}. } \author{ H. Pagès, based on a proposal by Kasper Daniel Hansen } \seealso{ \itemize{ \item \link{GRanges} objects. \item \link[GenomeInfoDb]{Seqinfo} objects and the \code{\link[GenomeInfoDb]{rankSeqlevels}} function in the \pkg{GenomeInfoDb} package. \item The \code{\link{makeGRangesListFromFeatureFragments}} function for making a \link{GRangesList} object from a list of fragmented features. \item The \code{\link[rtracklayer]{getTable}} function in the \pkg{rtracklayer} package for an R interface to the UCSC Table Browser. \item \link[S4Vectors]{DataFrame} objects in the \pkg{S4Vectors} package. } } \examples{ ## --------------------------------------------------------------------- ## BASIC EXAMPLES ## --------------------------------------------------------------------- df <- data.frame(chr="chr1", start=11:15, end=12:16, strand=c("+","-","+","*","."), score=1:5) df makeGRangesFromDataFrame(df) # strand value "." is replaced with "*" ## The strand column is optional: df <- data.frame(chr="chr1", start=11:15, end=12:16, score=1:5) makeGRangesFromDataFrame(df) gr <- makeGRangesFromDataFrame(df, keep.extra.columns=TRUE) gr2 <- as(df, "GRanges") # equivalent to the above stopifnot(identical(gr, gr2)) gr2 <- GRanges(df) # equivalent to the above stopifnot(identical(gr, gr2)) makeGRangesFromDataFrame(df, ignore.strand=TRUE) makeGRangesFromDataFrame(df, keep.extra.columns=TRUE, ignore.strand=TRUE) makeGRangesFromDataFrame(df, seqinfo=paste0("chr", 4:1)) makeGRangesFromDataFrame(df, seqinfo=c(chrM=NA, chr1=500, chrX=100)) makeGRangesFromDataFrame(df, seqinfo=Seqinfo(paste0("chr", 4:1))) ## --------------------------------------------------------------------- ## ABOUT AUTOMATIC DETECTION OF THE seqnames/start/end/strand COLUMNS ## --------------------------------------------------------------------- ## Automatic detection of the seqnames/start/end/strand columns is ## case insensitive: df <- data.frame(ChRoM="chr1", StarT=11:15, stoP=12:16, STRAND=c("+","-","+","*","."), score=1:5) makeGRangesFromDataFrame(df) ## It also ignores a common prefix between the start and end columns: df <- data.frame(seqnames="chr1", tx_start=11:15, tx_end=12:16, strand=c("+","-","+","*","."), score=1:5) makeGRangesFromDataFrame(df) ## The common prefix between the start and end columns is used to ## disambiguate between more than one seqnames column: df <- data.frame(chrom="chr1", tx_start=11:15, tx_end=12:16, tx_chr="chr2", score=1:5) makeGRangesFromDataFrame(df) ## --------------------------------------------------------------------- ## 0-BASED VS 1-BASED START POSITIONS ## --------------------------------------------------------------------- if (require(rtracklayer)) { session <- browserSession() genome(session) <- "sacCer2" query <- ucscTableQuery(session, "Most Conserved") df <- getTable(query) ## A common pitfall is to forget that the UCSC Table Browser uses the ## "0-based start" convention: gr0 <- makeGRangesFromDataFrame(df, keep.extra.columns=TRUE) head(gr0) min(start(gr0)) ## The start positions need to be converted into 1-based positions, ## to adhere to the convention used in Bioconductor: gr1 <- makeGRangesFromDataFrame(df, keep.extra.columns=TRUE, starts.in.df.are.0based=TRUE) head(gr1) } } \keyword{manip} GenomicRanges/man/makeGRangesListFromDataFrame.Rd0000644000175400017540000000602113001230267022765 0ustar00biocbuildbiocbuild\name{makeGRangesListFromDataFrame} \alias{makeGRangesListFromDataFrame} \title{Make a GRangesList object from a data.frame or DataFrame} \description{ \code{makeGRangesListFromDataFrame} extends the \link[GenomicRanges]{makeGRangesFromDataFrame} functionality from \code{GenomicRanges}. It can take a data-frame-like object as input and tries to automatically find the columns that describe the genomic ranges. It returns a \link{GRangesList} object. This is different from the \code{makeGRangesFromDataFrame} function by requiring a \code{split.field}. The \code{split.field} acts like the "f" argument in the \code{\link[S4Vectors]{split}} function. This factor must be of the same length as the number of rows in the \code{DataFrame} argument. The \code{split.field} may also be a character vector. } \usage{ makeGRangesListFromDataFrame(df, split.field = NULL, names.field = NULL, ...) } \arguments{ \item{df}{ A \code{DataFrame} or \code{data.frame} class object } \item{split.field}{ A character string of a recognized column name in \code{df} that contains the grouping. This column defines how the rows of \code{df} are split and is typically a \code{factor} or \code{character} vector. When \code{split.field} is not provided the \code{df} will be split by the number of rows. } \item{names.field}{ An optional single \code{character} string indicating the name of the column in \code{df} that designates the names for the ranges in the elements of the \link{GRangesList}. } \item{...}{ Additional arguments passed on to \link{makeGRangesFromDataFrame} } } \value{ A \linkS4class{GRangesList} of the same length as the number of levels or unique character strings in the \code{df} column indicated by \code{split.field}. When \code{split.field} is not provided the \code{df} is split by row and the resulting \linkS4class{GRangesList} has the same length as nrow(df). Names on the individual ranges are taken from the \code{names.field} argument. Names on the outer list elements of the \linkS4class{GRangesList} are propagated from \code{split.field}. } \author{ M. Ramos } \seealso{ \itemize{ \item \link{makeGRangesFromDataFrame} } } \examples{ ## --------------------------------------------------------------------- ## BASIC EXAMPLES ## --------------------------------------------------------------------- df <- data.frame(chr="chr1", start=11:15, end=12:16, strand=c("+","-","+","*","."), score=1:5, specimen = c("a", "a", "b", "b", "c"), gene_symbols = paste0("GENE", letters[1:5])) df grl <- makeGRangesListFromDataFrame(df, split.field = "specimen", names.field = "gene_symbols") grl names(grl) ## Keep metadata columns makeGRangesListFromDataFrame(df, split.field = "specimen", keep.extra.columns = TRUE) } GenomicRanges/man/nearest-methods.Rd0000644000175400017540000002144713032300275020470 0ustar00biocbuildbiocbuild\name{nearest-methods} \alias{nearest-methods} \alias{precede} \alias{precede,GenomicRanges,GenomicRanges-method} \alias{precede,GenomicRanges,missing-method} \alias{follow} \alias{follow,GenomicRanges,GenomicRanges-method} \alias{follow,GenomicRanges,missing-method} \alias{nearest} \alias{nearest,GenomicRanges,GenomicRanges-method} \alias{nearest,GenomicRanges,missing-method} \alias{distance} \alias{distance,GenomicRanges,GenomicRanges-method} \alias{distanceToNearest} \alias{distanceToNearest,GenomicRanges,GenomicRanges-method} \alias{distanceToNearest,GenomicRanges,missing-method} \title{Finding the nearest genomic range neighbor} \description{ The \code{nearest}, \code{precede}, \code{follow}, \code{distance} and \code{distanceToNearest} methods for \code{\linkS4class{GenomicRanges}} objects and subclasses. } \usage{ \S4method{precede}{GenomicRanges,GenomicRanges}(x, subject, select=c("arbitrary", "all"), ignore.strand=FALSE) \S4method{precede}{GenomicRanges,missing}(x, subject, select=c("arbitrary", "all"), ignore.strand=FALSE) \S4method{follow}{GenomicRanges,GenomicRanges}(x, subject, select=c("arbitrary", "all"), ignore.strand=FALSE) \S4method{follow}{GenomicRanges,missing}(x, subject, select=c("arbitrary", "all"), ignore.strand=FALSE) \S4method{nearest}{GenomicRanges,GenomicRanges}(x, subject, select=c("arbitrary", "all"), ignore.strand=FALSE) \S4method{nearest}{GenomicRanges,missing}(x, subject, select=c("arbitrary", "all"), ignore.strand=FALSE) \S4method{distanceToNearest}{GenomicRanges,GenomicRanges}(x, subject, ignore.strand=FALSE, ...) \S4method{distanceToNearest}{GenomicRanges,missing}(x, subject, ignore.strand=FALSE, ...) \S4method{distance}{GenomicRanges,GenomicRanges}(x, y, ignore.strand=FALSE, ...) } \arguments{ \item{x}{The query \link{GenomicRanges} instance. } \item{subject}{The subject \link{GenomicRanges} instance within which the nearest neighbors are found. Can be missing, in which case \code{x} is also the subject. } \item{y}{For the \code{distance} method, a \code{GRanges} instance. Cannot be missing. If \code{x} and \code{y} are not the same length, the shortest will be recycled to match the length of the longest. } \item{select}{Logic for handling ties. By default, all methods select a single interval (arbitrary for \code{nearest}, the first by order in \code{subject} for \code{precede}, and the last for \code{follow}). When \code{select="all"} a \link[S4Vectors]{Hits} object is returned with all matches for \code{x}. If \code{x} does not have a match in \code{subject} the \code{x} is not included in the \link[S4Vectors]{Hits} object. } \item{ignore.strand}{ A \code{logical} indicating if the strand of the input ranges should be ignored. When \code{TRUE}, strand is set to \code{'+'}. } \item{...}{Additional arguments for methods.} } \details{ \itemize{ \item{nearest: }{ Performs conventional nearest neighbor finding. Returns an integer vector containing the index of the nearest neighbor range in \code{subject} for each range in \code{x}. If there is no nearest neighbor \code{NA} is returned. For details of the algorithm see the man page in IRanges, ?\code{nearest}. } \item{precede: }{ For each range in \code{x}, \code{precede} returns the index of the range in \code{subject} that is directly preceded by the range in \code{x}. Overlapping ranges are excluded. \code{NA} is returned when there are no qualifying ranges in \code{subject}. } \item{follow: }{ The opposite of \code{precede}, \code{follow} returns the index of the range in \code{subject} that is directly followed by the range in \code{x}. Overlapping ranges are excluded. \code{NA} is returned when there are no qualifying ranges in \code{subject}. } \item{Orientation and Strand: }{ The relevant orientation for \code{precede} and \code{follow} is 5' to 3', consistent with the direction of translation. Because positional numbering along a chromosome is from left to right and transcription takes place from 5' to 3', \code{precede} and \code{follow} can appear to have `opposite' behavior on the \code{+} and \code{-} strand. Using positions 5 and 6 as an example, 5 precedes 6 on the \code{+} strand but follows 6 on the \code{-} strand. Below we outline the priority when ranges on multiple strands are compared. Ranges of strand \code{*} treat all ranges as if on the \code{+} strand; this is the behavior as \code{ignore.strand=TRUE}. \itemize{ \item{}{x on + strand can match to ranges on both + and * strands. In the case of a tie the first range by order is chosen. } \item{}{x on - strand can match to ranges on both - and * strands. In the case of a tie the first range by order is chosen. } \item{}{x on * treats all ranges as if on the \code{+} strand. In the case of a tie the first range by order is chosen. } } } \item{distanceToNearest: }{Returns the distance for each range in \code{x} to its nearest neighbor in the \code{subject}. } \item{distance: }{ Returns the distance for each range in \code{x} to the range in \code{y}. The behavior of \code{distance} has changed in Bioconductor 2.12. See the man page \code{?distance} in IRanges for details. } } } \value{ For \code{nearest}, \code{precede} and \code{follow}, an integer vector of indices in \code{subject}, or a \link[S4Vectors]{Hits} if \code{select="all"}. For \code{distanceToNearest}, a \link[S4Vectors]{Hits} object with a column for the \code{query} index (queryHits), \code{subject} index (subjectHits) and the \code{distance} between the pair. For \code{distance}, an integer vector of distances between the ranges in \code{x} and \code{y}. } \author{P. Aboyoun and V. Obenchain } \seealso{ \itemize{ \item The \link{GenomicRanges} and \link{GRanges} classes. \item The \link[IRanges]{Ranges} class in the \pkg{IRanges} package. \item The \link[S4Vectors]{Hits} class in the \pkg{S4Vectors} package. \item The \link[IRanges]{nearest-methods} man page in the \pkg{IRanges} package. \item \link[GenomicRanges]{findOverlaps-methods} for finding just the overlapping ranges. \item The \link[GenomicFeatures]{nearest-methods} man page in the \pkg{GenomicFeatures} package. } } \examples{ ## ----------------------------------------------------------- ## precede() and follow() ## ----------------------------------------------------------- query <- GRanges("A", IRanges(c(5, 20), width=1), strand="+") subject <- GRanges("A", IRanges(rep(c(10, 15), 2), width=1), strand=c("+", "+", "-", "-")) precede(query, subject) follow(query, subject) strand(query) <- "-" precede(query, subject) follow(query, subject) ## ties choose first in order query <- GRanges("A", IRanges(10, width=1), c("+", "-", "*")) subject <- GRanges("A", IRanges(c(5, 5, 5, 15, 15, 15), width=1), rep(c("+", "-", "*"), 2)) precede(query, subject) precede(query, rev(subject)) ## ignore.strand=TRUE treats all ranges as '+' precede(query[1], subject[4:6], select="all", ignore.strand=FALSE) precede(query[1], subject[4:6], select="all", ignore.strand=TRUE) ## ----------------------------------------------------------- ## nearest() ## ----------------------------------------------------------- ## When multiple ranges overlap an "arbitrary" range is chosen query <- GRanges("A", IRanges(5, 15)) subject <- GRanges("A", IRanges(c(1, 15), c(5, 19))) nearest(query, subject) ## select="all" returns all hits nearest(query, subject, select="all") ## Ranges in 'x' will self-select when 'subject' is present query <- GRanges("A", IRanges(c(1, 10), width=5)) nearest(query, query) ## Ranges in 'x' will not self-select when 'subject' is missing nearest(query) ## ----------------------------------------------------------- ## distance(), distanceToNearest() ## ----------------------------------------------------------- ## Adjacent, overlap, separated by 1 query <- GRanges("A", IRanges(c(1, 2, 10), c(5, 8, 11))) subject <- GRanges("A", IRanges(c(6, 5, 13), c(10, 10, 15))) distance(query, subject) ## recycling distance(query[1], subject) ## zero-width ranges zw <- GRanges("A", IRanges(4,3)) stopifnot(distance(zw, GRanges("A", IRanges(3,4))) == 0L) sapply(-3:3, function(i) distance(shift(zw, i), GRanges("A", IRanges(4,3)))) query <- GRanges(c("A", "B"), IRanges(c(1, 5), width=1)) distanceToNearest(query, subject) ## distance() with GRanges and TxDb see the ## ?'distance,GenomicRanges,TxDb-method' man ## page in the GenomicFeatures package. } \keyword{utilities} GenomicRanges/man/phicoef.Rd0000644000175400017540000000205613001230267016775 0ustar00biocbuildbiocbuild\name{phicoef} \alias{phicoef} \title{Calculate the "phi coefficient" between two binary variables} \description{ The \code{phicoef} function calculates the "phi coefficient" between two binary variables. } \usage{ phicoef(x, y=NULL) } \arguments{ \item{x, y}{ Two logical vectors of the same length. If \code{y} is not supplied, \code{x} must be a 2x2 integer matrix (or an integer vector of length 4) representing the contingency table of two binary variables. } } \value{ The "phi coefficient" between the two binary variables. This is a single numeric value ranging from -1 to +1. } \references{ \url{http://en.wikipedia.org/wiki/Phi_coefficient} } \author{ H. Pagès } \examples{ set.seed(33) x <- sample(c(TRUE, FALSE), 100, replace=TRUE) y <- sample(c(TRUE, FALSE), 100, replace=TRUE) phicoef(x, y) phicoef(rep(x, 10), c(rep(x, 9), y)) stopifnot(phicoef(table(x, y)) == phicoef(x, y)) stopifnot(phicoef(y, x) == phicoef(x, y)) stopifnot(phicoef(x, !y) == - phicoef(x, y)) stopifnot(phicoef(x, x) == 1) } \keyword{manip} GenomicRanges/man/range-squeezers.Rd0000644000175400017540000000721513001230267020502 0ustar00biocbuildbiocbuild\name{range-squeezers} \alias{range-squeezers} \alias{granges} \alias{grglist} \alias{rglist} \alias{grglist,Pairs-method} \alias{rglist,Pairs-method} \title{Squeeze the ranges out of a range-based object} \description{ S4 generic functions for squeezing the ranges out of a range-based object. \code{granges} returns them in a \link{GRanges} object, \code{grglist} in a \link{GRangesList} object, and \code{rglist} in a \link[IRanges]{RangesList} object. } \usage{ granges(x, use.names=TRUE, use.mcols=FALSE, ...) grglist(x, use.names=TRUE, use.mcols=FALSE, ...) rglist(x, use.names=TRUE, use.mcols=FALSE, ...) } \arguments{ \item{x}{ A range-based object e.g. a \link[SummarizedExperiment]{RangedSummarizedExperiment}, \link[GenomicAlignments]{GAlignments}, \link[GenomicAlignments]{GAlignmentPairs}, \link[GenomicAlignments]{GAlignmentsList} or a \link[S4Vectors:Pairs-class]{Pairs} object containing ranges. } \item{use.names}{ \code{TRUE} (the default) or \code{FALSE}. Whether or not the names on \code{x} (accessible with \code{names(x)}) should be propagated to the returned object. } \item{use.mcols}{ \code{TRUE} or \code{FALSE} (the default). Whether or not the metadata columns on \code{x} (accessible with \code{mcols(x)}) should be propagated to the returned object. } \item{...}{ Additional arguments, for use in specific methods. } } \details{ The \pkg{GenomicRanges}, \pkg{SummarizedExperiment}, and \pkg{GenomicAlignments} packages define and document methods for various types of range-based objects (e.g. for \link[SummarizedExperiment]{RangedSummarizedExperiment}, \link[GenomicAlignments]{GAlignments}, \link[GenomicAlignments]{GAlignmentPairs}, and \link[GenomicAlignments]{GAlignmentsList} objects). Other Bioconductor packages might as well. Note that these functions can be seen as a specific kind of \emph{object getters} as well as functions performing coercion. For some objects (e.g. \link[GenomicAlignments]{GAlignments} and \link[GenomicAlignments]{GAlignmentPairs} objects defined in the \pkg{GenomicAlignments} package), \code{as(x, "GRanges")}, \code{as(x, "GRangesList")}, and \code{as(x, "RangesList")}, are equivalent to \code{granges(x, use.names=TRUE, use.mcols=TRUE)}, \code{grglist(x, use.names=TRUE, use.mcols=TRUE)}, and \code{rglist(x, use.names=TRUE, use.mcols=TRUE)}, respectively. } \value{ A \link{GRanges} object for \code{granges}. A \link{GRangesList} object for \code{grglist}. A \link[IRanges]{RangesList} object for \code{rglist}. If \code{x} is a vector-like object (e.g. \link[GenomicAlignments]{GAlignments}), the returned object is expected to be \emph{parallel} to \code{x}, that is, the i-th element in the output corresponds to the i-th element in the input. If \code{use.names} is TRUE, then the names on \code{x} (if any) are propagated to the returned object. If \code{use.mcols} is TRUE, then the metadata columns on \code{x} (if any) are propagated to the returned object. } \author{H. Pagès} \seealso{ \itemize{ \item \link{GRanges} and \link{GRangesList} objects. \item \link[IRanges]{RangesList} objects in the \pkg{IRanges} package. \item \link[SummarizedExperiment]{RangedSummarizedExperiment} objects in the \pkg{SummarizedExperiment} packages. \item \link[GenomicAlignments]{GAlignments}, \link[GenomicAlignments]{GAlignmentPairs}, and \link[GenomicAlignments]{GAlignmentsList} objects in the \pkg{GenomicAlignments} package. } } \examples{ ## See ?GAlignments in the GenomicAlignments package for some ## examples. } \keyword{methods} GenomicRanges/man/setops-methods.Rd0000644000175400017540000002104613001230267020336 0ustar00biocbuildbiocbuild\name{setops-methods} \alias{setops-methods} \alias{union} \alias{union,GenomicRanges,GenomicRanges-method} \alias{union,GRangesList,GRangesList-method} \alias{union,GenomicRanges,Vector-method} \alias{union,Vector,GenomicRanges-method} \alias{intersect} \alias{intersect,GenomicRanges,GenomicRanges-method} \alias{intersect,GRangesList,GRangesList-method} \alias{intersect,GenomicRanges,Vector-method} \alias{intersect,Vector,GenomicRanges-method} \alias{setdiff} \alias{setdiff,GenomicRanges,GenomicRanges-method} \alias{setdiff,GRangesList,GRangesList-method} \alias{setdiff,GenomicRanges,Vector-method} \alias{setdiff,Vector,GenomicRanges-method} \alias{punion} \alias{punion,GRanges,GRanges-method} \alias{punion,GRanges,GRangesList-method} \alias{punion,GRangesList,GRanges-method} \alias{pintersect} \alias{pintersect,GRanges,GRanges-method} \alias{pintersect,GRanges,GRangesList-method} \alias{pintersect,GRangesList,GRanges-method} \alias{psetdiff} \alias{psetdiff,GRanges,GRanges-method} \alias{psetdiff,GRanges,GRangesList-method} \alias{pgap} \alias{pgap,GRanges,GRanges-method} \title{Set operations on genomic ranges} \description{ Performs set operations on \link{GRanges} and \link{GRangesList} objects. NOTE: The \code{\link[IRanges]{punion}}, \code{\link[IRanges]{pintersect}}, \code{\link[IRanges]{psetdiff}}, and \code{\link[IRanges]{pgap}} generic functions and methods for \link[IRanges]{Ranges} objects are defined and documented in the \pkg{IRanges} package. } \usage{ ## Vector-wise set operations ## -------------------------- \S4method{union}{GenomicRanges,GenomicRanges}(x, y, ignore.strand=FALSE) \S4method{intersect}{GenomicRanges,GenomicRanges}(x, y, ignore.strand=FALSE) \S4method{setdiff}{GenomicRanges,GenomicRanges}(x, y, ignore.strand=FALSE) ## Element-wise (aka "parallel") set operations ## -------------------------------------------- \S4method{punion}{GRanges,GRanges}(x, y, fill.gap=FALSE, ignore.strand=FALSE) \S4method{pintersect}{GRanges,GRanges}(x, y, drop.nohit.ranges=FALSE, ignore.strand=FALSE, strict.strand=FALSE) \S4method{psetdiff}{GRanges,GRanges}(x, y, ignore.strand=FALSE) } \arguments{ \item{x, y}{ For \code{union}, \code{intersect}, and \code{setdiff}: 2 \link{GenomicRanges} objects or 2 \link{GRangesList} objects. For \code{punion} and \code{pintersect}: 2 \link{GRanges} objects, or 1 \link{GRanges} object and 1 \link{GRangesList} object. For \code{psetdiff}: \code{x} must be a \link{GRanges} object and \code{y} can be a \link{GRanges} or \link{GRangesList} object. For \code{pgap}: 2 \link{GRanges} objects. In addition, for the \emph{parallel} operations, \code{x} and \code{y} must be of equal length (i.e. \code{length(x) == length(y)}). } \item{fill.gap}{ Logical indicating whether or not to force a union by using the rule \code{start = min(start(x), start(y)), end = max(end(x), end(y))}. } \item{ignore.strand}{ For set operations: If set to TRUE, then the strand of \code{x} and \code{y} is set to \code{"*"} prior to any computation. For parallel set operations: If set to TRUE, the strand information is ignored in the computation and the result has the strand information of \code{x}. } \item{drop.nohit.ranges}{ If TRUE then elements in \code{x} that don't intersect with their corresponding element in \code{y} are removed from the result (so the returned object is no more parallel to the input). If FALSE (the default) then nothing is removed and a \code{hit} metadata column is added to the returned object to indicate elements in \code{x} that intersect with the corresponding element in \code{y}. For those that don't, the reported intersection is a zero-width range that has the same start as \code{x}. } \item{strict.strand}{ If set to FALSE (the default), features on the \code{"*"} strand are treated as occurring on both the \code{"+"} and \code{"-"} strand. If set to TRUE, the strand of intersecting elements must be strictly the same. } } \details{ The \code{pintersect} methods involving \link{GRanges} and/or \link{GRangesList} objects use the triplet (sequence name, range, strand) to determine the element by element intersection of features, where a strand value of \code{"*"} is treated as occurring on both the \code{"+"} and \code{"-"} strand (unless \code{strict.strand} is set to TRUE, in which case the strand of intersecting elements must be strictly the same). The \code{psetdiff} methods involving \link{GRanges} and/or \link{GRangesList} objects use the triplet (sequence name, range, strand) to determine the element by element set difference of features, where a strand value of \code{"*"} is treated as occurring on both the \code{"+"} and \code{"-"} strand. } \value{ For \code{union}, \code{intersect}, and \code{setdiff}: a \link{GRanges} object if \code{x} and \code{y} are \link{GenomicRanges} objects, and a \link{GRangesList} object if they are \link{GRangesList} objects. For \code{punion} and \code{pintersect}: when \code{x} or \code{y} is not a \link{GRanges} object, an object of the same class as this non-\link{GRanges} object. Otherwise, a \link{GRanges} object. For \code{psetdiff}: either a \link{GRanges} object when both \code{x} and \code{y} are \link{GRanges} objects, or a \link{GRangesList} object when \code{y} is a \link{GRangesList} object. For \code{pgap}: a \link{GRanges} object. } \author{P. Aboyoun and H. Pagès} \seealso{ \itemize{ \item \link[IRanges]{setops-methods} in the \pkg{IRanges} package for set operations on \link[IRanges]{Ranges} and \link[IRanges]{RangesList} objects. \item \link[GenomicRanges]{findOverlaps-methods} for finding/counting overlapping genomic ranges. \item \link[GenomicRanges]{intra-range-methods} and \link[GenomicRanges]{inter-range-methods} for \emph{intra range} and \emph{inter range} transformations of a \link{GRanges} object. \item \link{GRanges} and \link{GRangesList} objects. \item \code{\link[S4Vectors]{mendoapply}} in the \pkg{S4Vectors} package. } } \examples{ ## --------------------------------------------------------------------- ## A. SET OPERATIONS ## --------------------------------------------------------------------- x <- GRanges("chr1", IRanges(c(2, 9) , c(7, 19)), strand=c("+", "-")) y <- GRanges("chr1", IRanges(5, 10), strand="-") union(x, y) union(x, y, ignore.strand=TRUE) intersect(x, y) intersect(x, y, ignore.strand=TRUE) setdiff(x, y) setdiff(x, y, ignore.strand=TRUE) ## With 2 GRangesList objects: gr1 <- GRanges(seqnames="chr2", ranges=IRanges(3, 6)) gr2 <- GRanges(seqnames=c("chr1", "chr1"), ranges=IRanges(c(7,13), width = 3), strand=c("+", "-")) gr3 <- GRanges(seqnames=c("chr1", "chr2"), ranges=IRanges(c(1, 4), c(3, 9)), strand=c("-", "-")) grlist <- GRangesList(gr1=gr1, gr2=gr2, gr3=gr3) union(grlist, shift(grlist, 3)) intersect(grlist, shift(grlist, 3)) setdiff(grlist, shift(grlist, 3)) ## Sanity checks: grlist2 <- shift(grlist, 3) stopifnot(identical( union(grlist, grlist2), mendoapply(union, grlist, grlist2) )) stopifnot(identical( intersect(grlist, grlist2), mendoapply(intersect, grlist, grlist2) )) stopifnot(identical( setdiff(grlist, grlist2), mendoapply(setdiff, grlist, grlist2) )) ## --------------------------------------------------------------------- ## B. PARALLEL SET OPERATIONS ## --------------------------------------------------------------------- punion(x, shift(x, 6)) \dontrun{ punion(x, shift(x, 7)) # will fail } punion(x, shift(x, 7), fill.gap=TRUE) pintersect(x, shift(x, 6)) pintersect(x, shift(x, 7)) psetdiff(x, shift(x, 7)) ## --------------------------------------------------------------------- ## C. MORE EXAMPLES ## --------------------------------------------------------------------- ## GRanges object: gr <- GRanges(seqnames=c("chr2", "chr1", "chr1"), ranges=IRanges(1:3, width = 12), strand=Rle(strand(c("-", "*", "-")))) ## Parallel intersection of a GRanges and a GRangesList object pintersect(gr, grlist) pintersect(grlist, gr) ## For a fast 'mendoapply(intersect, grlist, as(gr, "GRangesList"))' ## call pintersect() with 'strict.strand=TRUE' and call reduce() on ## the result with 'drop.empty.ranges=TRUE': reduce(pintersect(grlist, gr, strict.strand=TRUE), drop.empty.ranges=TRUE) ## Parallel set difference of a GRanges and a GRangesList object psetdiff(gr, grlist) } \keyword{methods} \keyword{utilities} GenomicRanges/man/strand-utils.Rd0000644000175400017540000001217713001230267020016 0ustar00biocbuildbiocbuild\name{strand-utils} \alias{strand-utils} \alias{strand,missing-method} \alias{strand,NULL-method} \alias{strand,character-method} \alias{strand,factor-method} \alias{strand,integer-method} \alias{strand,logical-method} \alias{strand,Rle-method} \alias{strand,RleList-method} \alias{strand,DataTable-method} \alias{strand<-,DataTable,ANY-method} \alias{invertStrand,NULL-method} \alias{invertStrand,character-method} \alias{invertStrand,factor-method} \alias{invertStrand,integer-method} \alias{invertStrand,logical-method} \alias{invertStrand,Rle-method} \alias{invertStrand,RleList-method} \title{Strand utilities} \description{ A bunch of useful \code{strand} and \code{invertStrand} methods. } \usage{ \S4method{strand}{missing}(x) \S4method{strand}{character}(x) \S4method{strand}{factor}(x) \S4method{strand}{integer}(x) \S4method{strand}{logical}(x) \S4method{strand}{Rle}(x) \S4method{strand}{RleList}(x) \S4method{strand}{DataTable}(x) \S4method{strand}{DataTable,ANY}(x) <- value \S4method{invertStrand}{character}(x) \S4method{invertStrand}{factor}(x) \S4method{invertStrand}{integer}(x) \S4method{invertStrand}{logical}(x) \S4method{invertStrand}{Rle}(x) \S4method{invertStrand}{RleList}(x) } \arguments{ \item{x}{ The object from which to obtain a \emph{strand factor}, \emph{strand factor \link[S4Vectors]{Rle}}, or \emph{strand factor \link{RleList}} object. Can be missing. See Details and Value sections below for more information. } \item{value}{ Replacement value for the strand. } } \details{ All the \code{strand} and \code{invertStrand} methods documented here return either a \emph{strand factor}, \emph{strand factor \link[S4Vectors]{Rle}}, or \emph{strand factor \link{RleList}} object. These are factor, factor-\link[S4Vectors]{Rle}, or factor-\link{RleList} objects containing the "standard strand levels" (i.e. \code{+}, \code{-}, and \code{*}) and no NAs. } \value{ All the \code{strand} and \code{invertStrand} methods documented here return an object that is \emph{parallel} to input object \code{x} when \code{x} is a character, factor, integer, logical, \link[S4Vectors]{Rle}, or \link{RleList} object. For the \code{strand} methods: \itemize{ \item If \code{x} is missing, returns an empty factor with the "standard strand levels" i.e. \code{+}, \code{-}, and \code{*}. \item If \code{x} is a character vector or factor, it is coerced to a factor with the levels listed above. \code{NA} values in \code{x} are not accepted. \item If \code{x} is an integer vector, it is coerced to a factor with the levels listed above. \code{1}, \code{-1}, and \code{NA} values in \code{x} are mapped to the \code{+}, \code{-}, and \code{*} levels respectively. \item If \code{x} is a logical vector, it is coerced to a factor with the levels listed above. \code{FALSE}, \code{TRUE}, and \code{NA} values in \code{x} are mapped to the \code{+}, \code{-}, and \code{*} levels respectively. \item If \code{x} is a character-, factor-, integer-, or logical-\link[S4Vectors]{Rle}, it is transformed with \code{runValue(x) <- strand(runValue(x))} and returned. \item If \code{x} is an \link{RleList} object, each list element in \code{x} is transformed by calling \code{strand()} on it and the resulting \link{RleList} object is returned. More precisely the returned object is \code{endoapply(x, strand)}. Note that in addition to being \emph{parallel} to \code{x}, this object also has the same \emph{shape} as \code{x} (i.e. its list elements have the same lengths as in \code{x}). \item If \code{x} inherits from \code{DataTable}, the \code{"strand"} column is passed thru \code{strand()} and returned. If \code{x} has no \code{"strand"} column, this return value is populated with \code{*}s. } Each \code{invertStrand} method returns the same object as its corresponding \code{strand} method but with \code{"+"} and \code{"-"} switched. } \author{M. Lawrence and H. Pagès} \seealso{ \code{\link[BiocGenerics]{strand}} } \examples{ strand() x1 <- c("-", "*", "*", "+", "-", "*") x2 <- factor(c("-", "-", "+", "-")) x3 <- c(-1L, NA, NA, 1L, -1L, NA) x4 <- c(TRUE, NA, NA, FALSE, TRUE, NA) strand(x1) invertStrand(x1) strand(x2) invertStrand(x2) strand(x3) invertStrand(x3) strand(x4) invertStrand(x4) strand(Rle(x1)) invertStrand(Rle(x1)) strand(Rle(x2)) invertStrand(Rle(x2)) strand(Rle(x3)) invertStrand(Rle(x3)) strand(Rle(x4)) invertStrand(Rle(x4)) x5 <- RleList(x1, character(0), as.character(x2)) strand(x5) invertStrand(x5) strand(DataFrame(score=2:-3)) strand(DataFrame(score=2:-3, strand=x3)) strand(DataFrame(score=2:-3, strand=Rle(x3))) ## Sanity checks: target <- strand(x1) stopifnot(identical(target, strand(x3))) stopifnot(identical(target, strand(x4))) stopifnot(identical(Rle(strand(x1)), strand(Rle(x1)))) stopifnot(identical(Rle(strand(x2)), strand(Rle(x2)))) stopifnot(identical(Rle(strand(x3)), strand(Rle(x3)))) stopifnot(identical(Rle(strand(x4)), strand(Rle(x4)))) } \keyword{methods} GenomicRanges/man/tile-methods.Rd0000644000175400017540000000524713001230267017763 0ustar00biocbuildbiocbuild\name{tile-methods} \alias{tile-methods} \alias{tile} \alias{tile,GenomicRanges-method} \alias{slidingWindows} \alias{slidingWindows,GenomicRanges-method} \title{Generate windows for a GenomicRanges} \description{ \code{\link[IRanges]{tile}} and \code{\link[IRanges]{slidingWindows}} methods for \link{GenomicRanges}. \code{tile} partitions each range into a set of tiles, which are defined in terms of their number or width. \code{slidingWindows} generates sliding windows of a specified width and frequency. } \usage{ \S4method{tile}{GenomicRanges}(x, n, width) \S4method{slidingWindows}{GenomicRanges}(x, width, step=1L) } \arguments{ \item{x}{ A \link{GenomicRanges} object, like a \code{GRanges}. } \item{n}{ The number of tiles to generate. See \code{?\link[IRanges]{tile}} in the \pkg{IRanges} package for more information about this argument. } \item{width}{ The (maximum) width of each tile. See \code{?\link[IRanges]{tile}} in the \pkg{IRanges} package for more information about this argument. } \item{step}{ The distance between the start positions of the sliding windows. } } \details{ The \code{tile} function splits \code{x} into a \code{GRangesList}, each element of which corresponds to a tile, or partition, of \code{x}. Specify the tile geometry with either \code{n} or \code{width} (not both). Passing \code{n} creates \code{n} tiles of approximately equal width, truncated by sequence end, while passing \code{width} tiles the region with ranges of the given width, again truncated by sequence end. The \code{slidingWindows} function generates sliding windows within each range of \code{x}, according to \code{width} and \code{step}, returning a \code{GRangesList}. If the sliding windows do not exactly cover a range in \code{x}, the last window is partial. } \value{ A \code{GRangesList} object, each element of which corresponds to a window. } \author{M. Lawrence} \seealso{ \code{\link[IRanges]{tile}} in the \pkg{IRanges} package. } \examples{ gr <- GRanges( seqnames=Rle(c("chr1", "chr2", "chr1", "chr3"), c(1, 3, 2, 4)), ranges=IRanges(1:10, end=11), strand=Rle(strand(c("-", "+", "*", "+", "-")), c(1, 2, 2, 3, 2)), seqlengths=c(chr1=11, chr2=12, chr3=13)) # split every range in half tiles <- tile(gr, n = 2L) stopifnot(all(elementNROWS(tiles) == 2L)) # split ranges into subranges of width 2 # odd width ranges must contain one subrange of width 1 tiles <- tile(gr, width = 2L) stopifnot(all(all(width(tiles) \%in\% c(1L, 2L)))) windows <- slidingWindows(gr, width=3L, step=2L) width(windows[[1L]]) # last range is truncated } \keyword{methods} \keyword{utilities} GenomicRanges/man/tileGenome.Rd0000644000175400017540000001111213001230267017441 0ustar00biocbuildbiocbuild\name{tileGenome} \alias{tileGenome} \title{Put (virtual) tiles on a given genome} \description{ \code{tileGenome} returns a set of genomic regions that form a partitioning of the specified genome. Each region is called a "tile". } \usage{ tileGenome(seqlengths, ntile, tilewidth, cut.last.tile.in.chrom=FALSE) } \arguments{ \item{seqlengths}{ Either a named numeric vector of chromosome lengths or a \link{Seqinfo} object. More precisely, if a named numeric vector, it must have a length >= 1, cannot contain NAs or negative values, and cannot have duplicated names. If a \link{Seqinfo} object, then it's first replaced with the vector of sequence lengths stored in the object (extracted from the object with the \code{\link{seqlengths}} getter), then the restrictions described previously apply to this vector. } \item{ntile}{ The number of tiles to generate. } \item{tilewidth}{ The desired tile width. The effective tile width might be slightly different but is guaranteed to never be more than the desired width. } \item{cut.last.tile.in.chrom}{ Whether or not to cut the last tile in each chromosome. This is set to \code{FALSE} by default. Can be set to \code{TRUE} only when \code{tilewidth} is specified. In that case, a tile will never overlap with more than 1 chromosome and a \link{GRanges} object is returned with one element (i.e. one genomic range) per tile. } } \value{ If \code{cut.last.tile.in.chrom} is \code{FALSE} (the default), a \link{GRangesList} object with one list element per tile, each of them containing a number of genomic ranges equal to the number of chromosomes it overlaps with. Note that when the tiles are small (i.e. much smaller than the chromosomes), most of them only overlap with a single chromosome. If \code{cut.last.tile.in.chrom} is \code{TRUE}, a \link{GRanges} object with one element (i.e. one genomic range) per tile. } \author{ H. Pagès, based on a proposal by M. Morgan } \seealso{ \itemize{ \item \link{genomicvars} for an example of how to compute the binned average of a numerical variable defined along a genome. \item \link{GRangesList} and \link{GRanges} objects. \item \link{Seqinfo} objects and the \code{\link{seqlengths}} getter. \item \link[IRanges]{IntegerList} objects. \item \link[IRanges]{Views} objects. } } \examples{ ## --------------------------------------------------------------------- ## A. WITH A TOY GENOME ## --------------------------------------------------------------------- seqlengths <- c(chr1=60, chr2=20, chr3=25) ## Create 5 tiles: tiles <- tileGenome(seqlengths, ntile=5) tiles elementNROWS(tiles) # tiles 3 and 4 contain 2 ranges width(tiles) ## Use sum() on this IntegerList object to get the effective tile ## widths: sum(width(tiles)) # each tile covers exactly 21 genomic positions ## Create 9 tiles: tiles <- tileGenome(seqlengths, ntile=9) elementNROWS(tiles) # tiles 6 and 7 contain 2 ranges table(sum(width(tiles))) # some tiles cover 12 genomic positions, # others 11 ## Specify the tile width: tiles <- tileGenome(seqlengths, tilewidth=20) length(tiles) # 6 tiles table(sum(width(tiles))) # effective tile width is <= specified ## Specify the tile width and cut the last tile in each chromosome: tiles <- tileGenome(seqlengths, tilewidth=24, cut.last.tile.in.chrom=TRUE) tiles width(tiles) # each tile covers exactly 24 genomic positions, except # the last tile in each chromosome ## Partition a genome by chromosome ("natural partitioning"): tiles <- tileGenome(seqlengths, tilewidth=max(seqlengths), cut.last.tile.in.chrom=TRUE) tiles # one tile per chromosome ## sanity check stopifnot(all.equal(setNames(end(tiles), seqnames(tiles)), seqlengths)) ## --------------------------------------------------------------------- ## B. WITH A REAL GENOME ## --------------------------------------------------------------------- library(BSgenome.Scerevisiae.UCSC.sacCer2) tiles <- tileGenome(seqinfo(Scerevisiae), ntile=20) tiles tiles <- tileGenome(seqinfo(Scerevisiae), tilewidth=50000, cut.last.tile.in.chrom=TRUE) tiles ## --------------------------------------------------------------------- ## C. AN APPLICATION: COMPUTE THE BINNED AVERAGE OF A NUMERICAL VARIABLE ## DEFINED ALONG A GENOME ## --------------------------------------------------------------------- ## See '?genomicvars' for an example of how to compute the binned ## average of a numerical variable defined along a genome. } \keyword{manip} GenomicRanges/src/0000755000175400017540000000000013032312065015103 5ustar00biocbuildbiocbuildGenomicRanges/src/GenomicRanges.h0000644000175400017540000000040713032312066017777 0ustar00biocbuildbiocbuild#include /* transcript_utils.c */ SEXP transcript_widths( SEXP exonStarts, SEXP exonEnds ); SEXP tlocs2rlocs( SEXP tlocs, SEXP exonStarts, SEXP exonEnds, SEXP strand, SEXP decreasing_rank_on_minus_strand, SEXP error_if_out_of_bounds ); GenomicRanges/src/IRanges_stubs.c0000644000175400017540000000003413032312066020015 0ustar00biocbuildbiocbuild#include "_IRanges_stubs.c" GenomicRanges/src/R_init_GenomicRanges.c0000644000175400017540000000062613032312066021301 0ustar00biocbuildbiocbuild#include "GenomicRanges.h" #include #define CALLMETHOD_DEF(fun, numArgs) {#fun, (DL_FUNC) &fun, numArgs} static const R_CallMethodDef callMethods[] = { /* transcript_utils.c */ CALLMETHOD_DEF(transcript_widths, 2), CALLMETHOD_DEF(tlocs2rlocs, 6), {NULL, NULL, 0} }; void R_init_GenomicRanges(DllInfo *info) { R_registerRoutines(info, NULL, callMethods, NULL, NULL); return; } GenomicRanges/src/S4Vectors_stubs.c0000644000175400017540000000003613032312066020323 0ustar00biocbuildbiocbuild#include "_S4Vectors_stubs.c" GenomicRanges/src/transcript_utils.c0000644000175400017540000001320313032312066020660 0ustar00biocbuildbiocbuild#include "GenomicRanges.h" #include "IRanges_interface.h" #include "S4Vectors_interface.h" static char errmsg_buf[200]; static int get_nexons(SEXP starts, SEXP ends) { int nstarts, nends; if (starts == R_NilValue) { nstarts = 0; } else if (IS_INTEGER(starts)) { nstarts = LENGTH(starts); } else { snprintf(errmsg_buf, sizeof(errmsg_buf), "'exonStarts' has invalid elements"); return -1; } if (ends == R_NilValue) { nends = 0; } else if (IS_INTEGER(ends)) { nends = LENGTH(ends); } else { snprintf(errmsg_buf, sizeof(errmsg_buf), "'exonEnds' has invalid elements"); return -1; } if (nstarts != nends) { snprintf(errmsg_buf, sizeof(errmsg_buf), "'exonStarts' and 'exonEnds' have different shapes"); return -1; } return nstarts; } static int get_transcript_width(SEXP starts, SEXP ends, int ref_length) { int nexons, transcript_width, j, start, end, width; if ((nexons = get_nexons(starts, ends)) == -1) return -1; transcript_width = 0; for (j = 0; j < nexons; j++) { start = INTEGER(starts)[j]; end = INTEGER(ends)[j]; if (start == NA_INTEGER || end == NA_INTEGER) { snprintf(errmsg_buf, sizeof(errmsg_buf), "'exonStarts' and/or 'exonEnds' contain NAs'"); return -1; } if (ref_length != -1) { if (start < 1 || start > ref_length + 1) { snprintf(errmsg_buf, sizeof(errmsg_buf), "'exonStarts' contains \"out of limits\" " "values"); return -1; } if (end < 0 || end > ref_length) { snprintf(errmsg_buf, sizeof(errmsg_buf), "'exonEnds' contains \"out of limits\" " "values"); return -1; } } width = end - start + 1; if (width < 0) { snprintf(errmsg_buf, sizeof(errmsg_buf), "'exonStarts/exonEnds' define exons " "of negative length"); return -1; } transcript_width += width; } return transcript_width; } static SEXP mk_transcript_widths(SEXP exonStarts, SEXP exonEnds, int ref_length) { int ntranscripts, i, transcript_width; SEXP ans, starts, ends; ntranscripts = LENGTH(exonStarts); PROTECT(ans = NEW_INTEGER(ntranscripts)); for (i = 0; i < ntranscripts; i++) { starts = VECTOR_ELT(exonStarts, i); ends = VECTOR_ELT(exonEnds, i); transcript_width = get_transcript_width(starts, ends, ref_length); if (transcript_width == -1) { UNPROTECT(1); error("%s", errmsg_buf); } INTEGER(ans)[i] = transcript_width; } UNPROTECT(1); return ans; } static int strand_is_minus(SEXP strand, int i) { SEXP strand_elt; strand_elt = STRING_ELT(strand, i); if (strand_elt != NA_STRING && LENGTH(strand_elt) == 1) { switch (CHAR(strand_elt)[0]) { case '+': return 0; case '-': return 1; } } snprintf(errmsg_buf, sizeof(errmsg_buf), "'strand' elements must be \"+\" or \"-\""); return -1; } static int tloc2rloc(int tloc, SEXP starts, SEXP ends, int on_minus_strand, int decreasing_rank_on_minus_strand) { int nexons, j, start, end, width; nexons = LENGTH(starts); if (on_minus_strand && decreasing_rank_on_minus_strand) { for (j = nexons - 1; j >= 0; j--) { start = INTEGER(starts)[j]; end = INTEGER(ends)[j]; width = end - start + 1; if (tloc <= width) break; tloc -= width; } } else { for (j = 0; j < nexons; j++) { start = INTEGER(starts)[j]; end = INTEGER(ends)[j]; width = end - start + 1; if (tloc <= width) break; tloc -= width; } } tloc--; return on_minus_strand ? end - tloc : start + tloc; } /**************************************************************************** * --- .Call ENTRY POINTS --- * ****************************************************************************/ SEXP transcript_widths(SEXP exonStarts, SEXP exonEnds) { return mk_transcript_widths(exonStarts, exonEnds, -1); } SEXP tlocs2rlocs(SEXP tlocs, SEXP exonStarts, SEXP exonEnds, SEXP strand, SEXP decreasing_rank_on_minus_strand, SEXP error_if_out_of_bounds) { SEXP ans, starts, ends, ans_elt; int decreasing_rank_on_minus_strand0, oob_error, ans_length, i, transcript_width, on_minus_strand, nlocs, j, tloc; decreasing_rank_on_minus_strand0 = LOGICAL(decreasing_rank_on_minus_strand)[0]; oob_error = LOGICAL(error_if_out_of_bounds)[0]; ans_length = LENGTH(tlocs); PROTECT(ans = duplicate(tlocs)); for (i = 0; i < ans_length; i++) { starts = VECTOR_ELT(exonStarts, i); ends = VECTOR_ELT(exonEnds, i); transcript_width = get_transcript_width(starts, ends, -1); if (transcript_width == -1) { UNPROTECT(1); error("%s", errmsg_buf); } on_minus_strand = strand_is_minus(strand, i); if (on_minus_strand == -1) { UNPROTECT(1); error("%s", errmsg_buf); } ans_elt = VECTOR_ELT(ans, i); if (ans_elt == R_NilValue) { nlocs = 0; } else if (IS_INTEGER(ans_elt)) { nlocs = LENGTH(ans_elt); } else { UNPROTECT(1); error("'tlocs' has invalid elements"); } for (j = 0; j < nlocs; j++) { tloc = INTEGER(ans_elt)[j]; if (tloc == NA_INTEGER) continue; if (tloc < 1 || tloc > transcript_width) { if (oob_error) { UNPROTECT(1); error("'tlocs[[%d]]' contains " "\"out of limits\" transcript " "locations (length of " "transcript is %d)", j + 1, transcript_width); } else { INTEGER(ans_elt)[j] = NA_INTEGER; break; } } INTEGER(ans_elt)[j] = tloc2rloc(tloc, starts, ends, on_minus_strand, decreasing_rank_on_minus_strand0); } } UNPROTECT(1); return ans; } GenomicRanges/tests/0000755000175400017540000000000013001230267015455 5ustar00biocbuildbiocbuildGenomicRanges/tests/run_unitTests.R0000644000175400017540000000014113001230267020462 0ustar00biocbuildbiocbuildrequire("GenomicRanges") || stop("unable to load GenomicRanges package") GenomicRanges:::.test() GenomicRanges/vignettes/0000755000175400017540000000000013032312064016323 5ustar00biocbuildbiocbuildGenomicRanges/vignettes/ExtendingGenomicRanges.Rnw0000644000175400017540000001113713001230267023405 0ustar00biocbuildbiocbuild% \VignetteIndexEntry{Extending GenomicRanges} % \VignetteDepends{GenomicRanges, VariantAnnotation} % \VignetteKeywords{ranges} % \VignettePackage{GenomicRanges} \documentclass{article} \usepackage[authoryear,round]{natbib} <>= BiocStyle::latex(use.unsrturl=FALSE) @ \title{Extending \Biocpkg{GenomicRanges}} \author{Michael Lawrence, Bioconductor Team} \date{Edited: Oct 2014; Compiled: \today} \begin{document} \maketitle \tableofcontents <>= options(width=72) options(showHeadLines=3) options(showTailLines=3) @ %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \section{Introduction} The goal of \Biocpkg{GenomicRanges} is to provide general containers for genomic data. The central class, at least from the user perspective, is \Rclass{GRanges}, which formalizes the notion of ranges, while allowing for arbitrary ``metadata columns'' to be attached to it. These columns offer the same flexibility as the venerable \Rclass{data.frame} and permit users to adapt \Rclass{GRanges} to a wide variety of \textit{adhoc} use-cases. The more we encounter a particular problem, the better we understand it. We eventually develop a systematic approach for solving the most frequently encountered problems, and every systematic approach deserves a systematic implementation. For example, we might want to formally store genetic variants, with information on alleles and read depths. The metadata columns, which were so useful during prototyping, are inappropriate for extending the formal semantics of our data structure: for the sake of data integrity, we need to ensure that the columns are always present and that they meet certain constraints. We might also find that our prototype does not scale well to the increased data volume that often occurs when we advance past the prototype stage. \Rclass{GRanges} is meant mostly for prototyping and stores its data in memory as simple R data structures. We may require something more specialized when the data are large; for example, we might store the data as a Tabix-indexed file, or in a database. The \Biocpkg{GenomicRanges} package does not directly solve either of these problems, because there are no general solutions. However, it is adaptible to specialized use cases. \section{The \Rclass{GenomicRanges} abstraction} Unbeknownst to many, most of the \Rclass{GRanges} implementation is provided by methods on the \Rclass{GenomicRanges} class, the virtual parent class of \Rclass{GRanges}. \Rclass{GenomicRanges} methods provide everything except for the actual data storage and retrieval, which \Rclass{GRanges} implements directly using slots. For example, the ranges are retrieved like this: <>= library(GenomicRanges) selectMethod(ranges, "GRanges") @ An alternative implementation is \Rclass{DelegatingGenomicRanges}, which stores all of its data in a delegate \Rclass{GenomicRanges} object: <>= selectMethod(ranges, "DelegatingGenomicRanges") @ This abstraction enables us to pursue more efficient implementations for particular tasks. One example is \Rclass{GNCList}, which is indexed for fast range queries, we expose here: <>= getSlots("GNCList")["granges"] @ The \Biocpkg{MutableRanges} package in svn provides other, untested examples. \section{Formalizing \texttt{mcols}: Extra column slots} An orthogonal problem to data storage is adding semantics by the formalization of metadata columns, and we solve it using the ``extra column slot'' mechanism. Whenever \Rclass{GenomicRanges} needs to operate on its metadata columns, it also delegates to the internal \Rfunction{extraColumnSlotNames} generic, methods of which should return a character vector, naming the slots in the \Rclass{GenomicRanges} subclass that correspond to columns (i.e., they have one value per range). It extracts the slot values and manipulates them as it would a metadata column -- except they are now formal slots, with formal types. An example is the \Rclass{VRanges} class in \Biocpkg{VariantAnnotation}. It stores information on the variants by adding these column slots: <>= GenomicRanges:::extraColumnSlotNames(VariantAnnotation:::VRanges()) @ Mostly for historical reasons, \Rclass{VRanges} extends \Rclass{GRanges}. However, since the data storage mechanism and the set of extra column slots are orthogonal, it is probably best practice to take a composition approach by extending \Rclass{DelegatingGenomicRanges}. \end{document} GenomicRanges/vignettes/GRanges_and_GRangesList_slides.Rnw0000644000175400017540000006607313001230267025004 0ustar00biocbuildbiocbuild%\VignetteIndexEntry{A quick introduction to GRanges and GRangesList objects (slides)} %\VignetteDepends{IRanges, GenomicRanges, pasillaBamSubset, GenomicAlignments, TxDb.Dmelanogaster.UCSC.dm3.ensGene} \SweaveOpts{keep.source=TRUE, eps=FALSE, width=9, height=3} \documentclass[8pt]{beamer} \usepackage{slides} \renewcommand\Rclass[1]{{\texttt{#1}\index{#1 (class)}}} % A Beamer Quickstart: % http://www.math.umbc.edu/~rouben/beamer/ \newcommand\DefaultBackground{\setbeamertemplate{background canvas}[vertical shading][bottom=black!10,top=black!10]\setbeamertemplate{background canvas}[vertical shading][bottom=blue!40,top=blue!15]} \DefaultBackground \setbeamercolor{block body example}{bg=white} \definecolor{YESgreen}{RGB}{0, 160, 80} \newcommand\YES{\textcolor{YESgreen}{\textbf{YES}}} \newcommand\PartiallySupported{\textcolor{YESgreen}{\textbf{partially supported}}} \definecolor{NOgray}{RGB}{144, 144, 144} \newcommand\NO{\textcolor{NOgray}{\textbf{NO}}} %\AtBeginSection[] %{ % \setbeamertemplate{background canvas}[vertical shading][bottom=black!47,top=black!47] % \begin{frame}{} % \tableofcontents[currentsection,currentsubsection] % \end{frame} % \DefaultBackground %} %\AtBeginSubsection[] %{ % \setbeamertemplate{background canvas}[vertical shading][bottom=black!47,top=black!47] % \begin{frame}{} % \tableofcontents[currentsection,currentsubsection] % \end{frame} % \DefaultBackground %} \title{A quick introduction to GRanges and GRangesList objects} \author{Herv\'e Pag\`es\\ \href{mailto:hpages@fredhutch.org}{hpages@fredhutch.org}\\ \---\\ Michael Lawrence\\ \href{mailto:lawrence.michael@gene.com}{lawrence.michael@gene.com}} %\institute[FHCRC]{Fred Hutchinson Cancer Research Center\\ % Seattle, WA} \date{July 2015} \begin{document} <>= options(width=84) plotRanges <- function(x, xlim = x, main = deparse(substitute(x)), col = "black", sep = 0.5, ...) { height <- 1 if (is(xlim, "Ranges")) xlim <- c(min(start(xlim)), max(end(xlim))) bins <- disjointBins(IRanges(start(x), end(x) + 1)) plot.new() par(mai=c(0.5, 0.2, 1.2, 0.2)) plot.window(xlim, c(0, max(bins)*(height + sep))) ybottom <- bins * (sep + height) - height rect(start(x)-0.5, ybottom, end(x)+0.5, ybottom + height, col = col, ...) title(main, cex.main=2.8, font.main=1) axis(1) } @ \maketitle \frame{\tableofcontents} %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \section{\Rclass{GRanges} objects} %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \begin{frame}[fragile] \frametitle{The \Rclass{GRanges} class is a container for...} ... storing a set of {\em genomic ranges} (a.k.a. {\em genomic regions} or {\em genomic intervals}). \begin{block}{} \begin{itemize} \item Each genomic range is described by a chromosome name, a {\em start}, an {\em end}, and a strand. \item {\em start} and {\em end} are both {\bf 1-based} positions relative to the 5' end of the plus strand of the chromosome, even when the range is on the minus strand. \item {\em start} and {\em end} are both considered to be included in the interval (except when the range is empty). \item The {\em width} of the range is the number of genomic positions included in it. So {\em width} = {\em end} - {\em start} + 1. \item {\em end} is always >= {\em start}, except for empty ranges (a.k.a. zero-width ranges) where {\em end} = {\em start} - 1. \end{itemize} Note that the {\em start} is always the leftmost position and the {\em end} the rightmost, even when the range is on the minus strand. Gotcha: A TSS is at the {\em end} of the range associated with a transcript located on the minus strand. \end{block} \end{frame} %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \subsection{The \Rcode{GRanges()} constructor} \begin{frame}[fragile] \frametitle{The \Rcode{GRanges()} constructor} \begin{exampleblock}{} {\small <>= library(GenomicRanges) gr1 <- GRanges(seqnames=Rle(c("ch1", "chMT"), c(2, 4)), ranges=IRanges(16:21, 20), strand=rep(c("+", "-", "*"), 2)) gr1 @ } \end{exampleblock} \end{frame} %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \subsection{\Rclass{GRanges} accessors} \begin{frame}[fragile] \frametitle{\Rclass{GRanges} accessors: \Rcode{length()}, \Rcode{seqnames()}, \Rcode{ranges()}} \begin{exampleblock}{} {\small <>= length(gr1) seqnames(gr1) ranges(gr1) @ } \end{exampleblock} \end{frame} \begin{frame}[fragile] \frametitle{\Rclass{GRanges} accessors: \Rcode{start()}, \Rcode{end()}, \Rcode{width()}, \Rcode{strand()}} \begin{exampleblock}{} {\small <>= start(gr1) end(gr1) width(gr1) strand(gr1) strand(gr1) <- c("-", "-", "+") strand(gr1) @ } \end{exampleblock} \end{frame} \begin{frame}[fragile] \frametitle{\Rclass{GRanges} accessors: \Rcode{names()}} \begin{exampleblock}{} {\small <>= names(gr1) <- LETTERS[1:6] gr1 names(gr1) @ } \end{exampleblock} \end{frame} \begin{frame}[fragile] \frametitle{\Rclass{GRanges} accessors: \Rcode{mcols()}} Like with most \Bioconductor{} vector-like objects, {\em metadata columns} can be added to a \Rclass{GRanges} object: \begin{exampleblock}{} {\small <>= mcols(gr1) <- DataFrame(score=11:16, GC=seq(1, 0, length=6)) gr1 mcols(gr1) @ } \end{exampleblock} \end{frame} \begin{frame}[fragile] \frametitle{\Rclass{GRanges} accessors: \Rcode{seqinfo()}, \Rcode{seqlevels()}, \Rcode{seqlengths()}} \begin{exampleblock}{} <>= seqinfo(gr1) seqlevels(gr1) seqlengths(gr1) seqlengths(gr1) <- c(50000, 800) seqlengths(gr1) @ \end{exampleblock} \end{frame} %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \subsection{Vector operations on \Rclass{GRanges} objects} \setbeamertemplate{background canvas}[vertical shading][bottom=green!20,top=green!20] \begin{frame}[fragile] \frametitle{Vector operations on \Rclass{GRanges} objects} \begin{block}{} What we call {\em vector operations} are operations that work on any ordinary vector: \begin{itemize} \item \Rcode{length()}, \Rcode{names()} \item Single-bracket subsetting: \Rcode{[} \item Combining: \Rcode{c()} \item \Rcode{split()}, \Rcode{relist()} \item Comparing: \Rcode{==}, \Rcode{!=}, \Rcode{match()}, \Rcode{\%in\%}, \Rcode{duplicated()}, \Rcode{unique()} \item Ordering: \Rcode{<=}, \Rcode{>=}, \Rcode{<}, \Rcode{>}, \Rcode{order()}, \Rcode{sort()}, \Rcode{rank()} \end{itemize} \Rclass{GRanges} objects support all these {\em vector operations} $==>$ They're considered {\em vector-like} objects. \end{block} \end{frame} \DefaultBackground \begin{frame}[fragile] \frametitle{Vector operations on \Rclass{GRanges} objects: Single-bracket subsetting} \begin{exampleblock}{} {\small <>= gr1[c("F", "A")] gr1[strand(gr1) == "+"] @ } \end{exampleblock} \end{frame} \begin{frame}[fragile] \frametitle{Vector operations on \Rclass{GRanges} objects: Single-bracket subsetting} \begin{exampleblock}{} {\small <>= gr1 <- gr1[-5] gr1 @ } \end{exampleblock} \end{frame} \begin{frame}[fragile] \frametitle{Vector operations on \Rclass{GRanges} objects: Combining} \begin{exampleblock}{} {\small <>= gr2 <- GRanges(seqnames="ch2", ranges=IRanges(start=c(2:1,2), width=6), score=15:13, GC=seq(0, 0.4, length=3)) gr12 <- c(gr1, gr2) gr12 @ } \end{exampleblock} \end{frame} \begin{frame}[fragile] \frametitle{Vector operations on \Rclass{GRanges} objects: Comparing} \begin{exampleblock}{} {\small <>= gr12[length(gr12)] == gr12 duplicated(gr12) unique(gr12) @ } \end{exampleblock} \end{frame} \begin{frame}[fragile] \frametitle{Vector operations on \Rclass{GRanges} objects: Ordering} \begin{exampleblock}{} {\small <>= sort(gr12) @ } \end{exampleblock} \end{frame} \begin{frame}[fragile] \frametitle{Splitting a \Rclass{GRanges} object} \begin{exampleblock}{} {\small <>= split(gr12, seqnames(gr12)) @ } \end{exampleblock} \end{frame} \setbeamertemplate{background canvas}[vertical shading][bottom=orange!40,top=orange!20] \begin{frame}[fragile] \frametitle{Exercise 1} \setbeamertemplate{enumerate items}{\alph{enumi}.} \begin{enumerate} \item Load the \Rpackage{GenomicRanges} package. \item Open the man page for the \Rclass{GRanges} class and run the examples in it. \item Extract from \Rclass{GRanges} object \Rcode{gr} the elements (i.e. ranges) with a score between 4 and 8. \item Split \Rcode{gr} by strand. \end{enumerate} \end{frame} \DefaultBackground %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \subsection{Range-based operations on \Rclass{GRanges} objects} \setbeamertemplate{background canvas}[vertical shading][bottom=green!20,top=green!20] \begin{frame}[fragile] \frametitle{An overview of {\em range-based} operations} \begin{columns}[t] \begin{column}{0.44\textwidth} \begin{block}{} {\bf Intra range transformations} \Rcode{shift()}, \Rcode{narrow()}, \Rcode{resize()}, \Rcode{flank()} \end{block} \begin{block}{} {\bf Inter range transformations} \Rcode{range()}, \Rcode{reduce()}, \Rcode{gaps()}, \Rcode{disjoin()} \end{block} \begin{block}{} {\bf Range-based set operations} \Rcode{union()}, \Rcode{intersect()}, \Rcode{setdiff()}, \Rcode{punion()}, \Rcode{pintersect()}, \Rcode{psetdiff()}, \Rcode{pgap()} \end{block} \end{column} \begin{column}{0.44\textwidth} \begin{block}{} {\bf Coverage and slicing} \Rcode{coverage()}, \Rcode{slice()} \end{block} \begin{block}{} {\bf Finding/counting overlapping ranges} \Rcode{findOverlaps()}, \Rcode{countOverlaps()} \end{block} \begin{block}{} {\bf Finding the nearest range neighbor} \Rcode{nearest()}, \Rcode{precede()}, \Rcode{follow()} \end{block} \begin{block}{} and more... \end{block} \end{column} \end{columns} \end{frame} \begin{frame}[fragile] \frametitle{Examples of some common {\em range-based} operations} \begin{exampleblock}{} <>= library(IRanges) ir0 <- IRanges(start=c(7, 9, 12, 14, 22:24), end=c(15, 11, 12, 18, 26, 27, 28)) png("ranges-ir0-plot.png", width=800, height=170) plotRanges(ir0, xlim=c(5, 35), main="ir0", col="blue") dev.off() @ <>= png("ranges-shift-ir0-plot.png", width=800, height=170) plotRanges(shift(ir0, 5), xlim=c(5, 35), main="shift(ir0, 5)", col="blue") dev.off() @ <>= png("ranges-reduce-ir0-plot.png", width=800, height=170) plotRanges(reduce(ir0), xlim=c(5, 35), main="reduce(ir0)", col="blue") dev.off() @ <>= png("ranges-disjoin-ir0-plot.png", width=800, height=170) plotRanges(disjoin(ir0), xlim=c(5, 35), main="disjoin(ir0)", col="blue") dev.off() @ \begin{figure} \centering \includegraphics[width=0.8\textwidth,height=!]{ranges-ir0-plot}\\ \includegraphics[width=0.8\textwidth,height=!]{ranges-shift-ir0-plot}\\ \includegraphics[width=0.8\textwidth,height=!]{ranges-reduce-ir0-plot}\\ \includegraphics[width=0.8\textwidth,height=!]{ranges-disjoin-ir0-plot} %\caption{Range-based operations} \end{figure} \end{exampleblock} \end{frame} \DefaultBackground \begin{frame}[fragile] \frametitle{Range-based operations on \Rclass{GRanges} objects} \begin{exampleblock}{} {\small <>= gr2 shift(gr2, 50) @ } \end{exampleblock} \end{frame} \begin{frame}[fragile] \frametitle{Range-based operations on \Rclass{GRanges} objects (continued)} \begin{exampleblock}{} {\small <>= gr1 resize(gr1, 12) @ } \end{exampleblock} \end{frame} \begin{frame}[fragile] \frametitle{Range-based operations on \Rclass{GRanges} objects (continued)} \begin{exampleblock}{} {\small <>= gr1 flank(gr1, 3) @ } \end{exampleblock} \end{frame} \begin{frame}[fragile] \frametitle{Range-based operations on \Rclass{GRanges} objects (continued)} \begin{exampleblock}{} {\small <>= gr3 <- shift(gr1, c(35000, rep(0, 3), 100)) width(gr3)[c(3,5)] <- 117 gr3 range(gr3) @ } \end{exampleblock} \end{frame} \begin{frame}[fragile] \frametitle{Range-based operations on \Rclass{GRanges} objects (continued)} \begin{exampleblock}{} {\small <>= gr3 reduce(gr3) @ } \end{exampleblock} \end{frame} \begin{frame}[fragile] \frametitle{Range-based operations on \Rclass{GRanges} objects (continued)} \begin{exampleblock}{} {\scriptsize <>= gr3 gaps(gr3) @ } \end{exampleblock} \end{frame} \begin{frame}[fragile] \frametitle{Range-based operations on \Rclass{GRanges} objects (continued)} \begin{exampleblock}{} {\scriptsize <>= gr3 disjoin(gr3) @ } \end{exampleblock} \end{frame} \setbeamertemplate{background canvas}[vertical shading][bottom=orange!40,top=orange!20] \begin{frame}[fragile] \frametitle{Exercise 2} \setbeamertemplate{enumerate items}{\alph{enumi}.} Using \Rclass{GRanges} object \Rcode{gr} created at Exercise 1: \begin{enumerate} \item Shift the ranges in \Rcode{gr} by 1000 positions to the right. \item What method is called when doing \Rcode{shift()} on a \Rclass{GRanges} object? Find the man page for this method. \end{enumerate} \end{frame} \DefaultBackground \begin{frame}[fragile] \frametitle{Coverage} \begin{exampleblock}{} {\small <>= cvg12 <- coverage(gr12) cvg12 @ } \end{exampleblock} \end{frame} \begin{frame}[fragile] \frametitle{Coverage (continued)} \begin{exampleblock}{} {\small <>= mean(cvg12) max(cvg12) @ } \end{exampleblock} \end{frame} \begin{frame}[fragile] \frametitle{Slicing the coverage} \begin{exampleblock}{} {\small <>= sl12 <- slice(cvg12, lower=1) sl12 elementNROWS(sl12) sl12$chMT mean(sl12$chMT) max(sl12$chMT) @ } \end{exampleblock} \end{frame} \begin{frame}[fragile] \frametitle{findOverlaps()} \begin{block}{} Load aligned reads from a BAM file: \end{block} \begin{exampleblock}{} {\small <>= library(pasillaBamSubset) untreated1_chr4() library(GenomicAlignments) reads <- readGAlignments(untreated1_chr4()) @ } \end{exampleblock} \begin{block}{} and store them in a \Rclass{GRanges} object: \end{block} \begin{exampleblock}{} {\small <>= reads <- as(reads, "GRanges") reads[1:4] @ } \end{exampleblock} \end{frame} \begin{frame}[fragile] \frametitle{findOverlaps() (continued)} \begin{block}{} Load the gene ranges from a {\em TxDb} package: \end{block} \begin{exampleblock}{} {\small <>= library(TxDb.Dmelanogaster.UCSC.dm3.ensGene) txdb <- TxDb.Dmelanogaster.UCSC.dm3.ensGene dm3_genes <- genes(txdb) @ } \end{exampleblock} \begin{block}{} and find the overlaps between the reads and the genes: \end{block} \begin{exampleblock}{} {\small <>= hits <- findOverlaps(reads, dm3_genes) head(hits) @ } \end{exampleblock} \end{frame} \setbeamertemplate{background canvas}[vertical shading][bottom=orange!40,top=orange!20] \begin{frame}[fragile] \frametitle{Exercise 3} \setbeamertemplate{enumerate items}{\alph{enumi}.} \begin{enumerate} \item Recreate \Rclass{GRanges} objects \Rcode{reads} and \Rcode{dm3\_genes} from previous slides. \item What method is called when calling \Rcode{findOverlaps()} on them? Open the man page for this method. \item Find the overlaps between the 2 objects but this time the strand should be ignored. \end{enumerate} \end{frame} \DefaultBackground \setbeamertemplate{background canvas}[vertical shading][bottom=orange!40,top=orange!20] \begin{frame}[fragile] \frametitle{Exercise 4} \setbeamertemplate{enumerate items}{\alph{enumi}.} In this exercise we want to get the exon sequences for the dm3 genome. \begin{enumerate} \item Extract the exon ranges from \Rcode{txdb}. \item Load the \Rpackage{BSgenome.Dmelanogaster.UCSC.dm3} package. \item Use \Rcode{getSeq()} to extract the exon sequences from the \Rcode{BSgenome} object in \Rpackage{BSgenome.Dmelanogaster.UCSC.dm3}. \end{enumerate} \end{frame} \DefaultBackground %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \section{\Rclass{GRangesList} objects} %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \begin{frame}[fragile] \frametitle{The \Rclass{GRangesList} class is a container for...} storing a list of {\em compatible} \Rclass{GRanges} objects. \begin{block}{} {\em compatible} means: \begin{itemize} \item they are relative to the same genome, \item AND they have the same metadata columns (accessible with the \Rcode{mcols()} accessor). \end{itemize} \end{block} \end{frame} %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \subsection{The \Rcode{GRangesList()} constructor} \begin{frame}[fragile] \frametitle{The \Rcode{GRangesList()} constructor} \begin{exampleblock}{} {\small <>= grl <- GRangesList(gr3, gr2) grl @ } \end{exampleblock} \end{frame} %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \subsection{\Rclass{GRangesList} accessors} \begin{frame}[fragile] \frametitle{\Rclass{GRangesList} accessors} \begin{exampleblock}{} <>= length(grl) @ \end{exampleblock} \begin{columns}[t] \begin{column}{0.44\textwidth} \begin{exampleblock}{} {\small <>= seqnames(grl) @ } \end{exampleblock} \end{column} \begin{column}{0.44\textwidth} \begin{exampleblock}{} {\small <>= strand(grl) @ } \end{exampleblock} \end{column} \end{columns} \end{frame} \begin{frame}[fragile] \frametitle{\Rclass{GRangesList} accessors (continued)} \begin{columns}[t] \begin{column}{0.44\textwidth} \begin{exampleblock}{} {\small <>= ranges(grl) @ } \end{exampleblock} \end{column} \begin{column}{0.44\textwidth} \begin{exampleblock}{} {\small <>= start(grl) end(grl) width(grl) @ } \end{exampleblock} \end{column} \end{columns} \end{frame} \begin{frame}[fragile] \frametitle{\Rclass{GRangesList} accessors (continued)} \begin{exampleblock}{} {\small <>= names(grl) <- c("TX1", "TX2") grl @ } \end{exampleblock} \end{frame} \begin{frame}[fragile] \frametitle{\Rclass{GRangesList} accessors (continued)} \begin{exampleblock}{} {\scriptsize <>= mcols(grl)$geneid <- c("GENE1", "GENE2") mcols(grl) grl @ } \end{exampleblock} \end{frame} \begin{frame}[fragile] \frametitle{\Rclass{GRangesList} accessors (continued)} \begin{exampleblock}{} <>= seqinfo(grl) @ \end{exampleblock} \end{frame} %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \subsection{Vector operations on \Rclass{GRangesList} objects} \setbeamertemplate{background canvas}[vertical shading][bottom=green!20,top=green!20] \begin{frame}[fragile] \frametitle{Vector operations on \Rclass{GRangesList} objects} \begin{block}{} Only the following {\em vector operations} are supported on \Rclass{GRangesList} objects: \begin{itemize} \item \Rcode{length()}, \Rcode{names()} \item Single-bracket subsetting: \Rcode{[} \item Combining: \Rcode{c()} \end{itemize} \end{block} \end{frame} \DefaultBackground \begin{frame}[fragile] \frametitle{Vector operations on \Rclass{GRangesList} objects} \begin{exampleblock}{} {\small <>= grl[c("TX2", "TX1")] @ } \end{exampleblock} \end{frame} \begin{frame}[fragile] \frametitle{Vector operations on \Rclass{GRangesList} objects (continued)} \begin{exampleblock}{} {\scriptsize <>= c(grl, GRangesList(gr3)) @ } \end{exampleblock} \end{frame} %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \subsection{List operations on \Rclass{GRangesList} objects} \setbeamertemplate{background canvas}[vertical shading][bottom=green!20,top=green!20] \begin{frame}[fragile] \frametitle{List operations on \Rclass{GRangesList} objects} \begin{block}{} What we call {\em list operations} are operations that work on an ordinary list: \begin{itemize} \item Double-bracket subsetting: \Rcode{[[} \item \Rcode{elementNROWS()}, \Rcode{unlist()} \item \Rcode{lapply()}, \Rcode{sapply()}, \Rcode{endoapply()} \item \Rcode{mendoapply()} (not covered in this presentation) \end{itemize} \Rclass{GRangesList} objects support all these {\em list operations} $==>$ They're considered {\em list-like} objects. \end{block} \end{frame} \DefaultBackground \begin{frame}[fragile] \frametitle{elementNROWS() and unlist()} \begin{exampleblock}{} {\scriptsize <>= grl[[2]] elementNROWS(grl) unlisted <- unlist(grl, use.names=FALSE) # same as c(grl[[1]], grl[[2]]) unlisted @ } \end{exampleblock} \end{frame} \begin{frame}[fragile] \frametitle{relist()} \begin{exampleblock}{} {\small <>= grl100 <- relist(shift(unlisted, 100), grl) grl100 @ } \end{exampleblock} \end{frame} \begin{frame}[fragile] \frametitle{endoapply()} \begin{exampleblock}{} {\scriptsize <>= grl100b <- endoapply(grl, shift, 100) grl100b mcols(grl100) mcols(grl100b) @ } \end{exampleblock} \end{frame} %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \subsection{Range-based operations on \Rclass{GRangesList} objects} \begin{frame}[fragile] \frametitle{Range-based operations on \Rclass{GRangesList} objects} \begin{columns}[t] \begin{column}{0.49\textwidth} \begin{exampleblock}{} {\scriptsize <>= grl @ } \end{exampleblock} \end{column} \begin{column}{0.49\textwidth} \begin{exampleblock}{} {\scriptsize <>= shift(grl, 100) @ } \end{exampleblock} \end{column} \end{columns} \begin{block}{} \Rcode{shift(grl, 100)} is equivalent to \Rcode{endoapply(grl, shift, 100)} \end{block} \end{frame} \begin{frame}[fragile] \frametitle{Range-based operations on \Rclass{GRangesList} objects (continued)} \begin{columns}[t] \begin{column}{0.49\textwidth} \begin{exampleblock}{} {\scriptsize <>= grl @ } \end{exampleblock} \end{column} \begin{column}{0.49\textwidth} \begin{exampleblock}{} {\scriptsize <>= flank(grl, 10) @ } \end{exampleblock} \end{column} \end{columns} \begin{block}{} \Rcode{flank(grl, 10)} is equivalent to \Rcode{endoapply(grl, flank, 10)} \end{block} \end{frame} \begin{frame}[fragile] \frametitle{Range-based operations on \Rclass{GRangesList} objects (continued)} \begin{columns}[t] \begin{column}{0.49\textwidth} \begin{exampleblock}{} {\scriptsize <>= grl @ } \end{exampleblock} \end{column} \begin{column}{0.49\textwidth} \begin{exampleblock}{} {\scriptsize <>= range(grl) @ } \end{exampleblock} \end{column} \end{columns} \begin{block}{} \Rcode{range(grl)} is equivalent to \Rcode{endoapply(grl, range)} \end{block} \end{frame} \begin{frame}[fragile] \frametitle{Range-based operations on \Rclass{GRangesList} objects (continued)} \begin{columns}[t] \begin{column}{0.49\textwidth} \begin{exampleblock}{} {\scriptsize <>= grl @ } \end{exampleblock} \end{column} \begin{column}{0.49\textwidth} \begin{exampleblock}{} {\scriptsize <>= reduce(grl) @ } \end{exampleblock} \end{column} \end{columns} \begin{block}{} \Rcode{reduce(grl)} is equivalent to \Rcode{endoapply(grl, reduce)} \end{block} \end{frame} \begin{frame}[fragile] \frametitle{Range-based operations on \Rclass{GRangesList} objects (continued)} <>= grl2 <- grl grl2[[1]] <- grl2[[1]][3]; grl2[[2]] <- grl2[[2]][1] grl3 <- unname(grl2) grl3[[1]] <- narrow(unname(grl3[[1]]), start=5, end=-5) @ \begin{columns}[t] \begin{column}{0.49\textwidth} \begin{exampleblock}{} {\scriptsize <>= grl2 grl3 @ } \end{exampleblock} \end{column} \begin{column}{0.49\textwidth} \begin{exampleblock}{} {\scriptsize <>= setdiff(grl2, grl3) @ } \end{exampleblock} \end{column} \end{columns} \begin{block}{} \Rcode{setdiff(grl2, grl)} is equivalent to \Rcode{mendoapply(setdiff, grl2, grl)} \end{block} \end{frame} %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \section{Other resources} %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \begin{frame}[fragile] \frametitle{Other resources} \begin{itemize} \item Great slides from Michael on ranges sequences and alignments: \url{http://bioconductor.org/help/course-materials/2014/CSAMA2014/2_Tuesday/lectures/Ranges_Sequences_and_Alignments-Lawrence.pdf} \item Vignettes in the \Rpackage{GenomicRanges} package (\Rcode{browseVignettes("GenomicRanges")}). \item \Rclass{GRanges} and \Rclass{GRangesList} man pages in the \Rpackage{GenomicRanges} package. \item Vignettes and \Rclass{GAlignments} man page in the \Rpackage{GenomicAlignments} package. \item \Bioconductor{} support site: \url{http://support.bioconductor.org/} \item The {\em genomic ranges} paper: Michael Lawrence, Wolfgang Huber, Herv\'e Pag\`es, Patrick Aboyoun, Marc Carlson, Robert Gentleman, Martin T. Morgan, Vincent J. Carey. Software for Computing and Annotating Genomic Ranges. {\em PLOS Computational Biology}, 4(3), 2013. \end{itemize} \end{frame} %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \end{document} GenomicRanges/vignettes/GenomicRanges.bib0000644000175400017540000000055613001230267021530 0ustar00biocbuildbiocbuild@article{Lawrence2013ranges, author={Michael Lawrence and Wolfgang Huber and Herv\'e Pag\`es and Patrick Aboyoun and Marc Carlson and Robert Gentleman and Martin T. Morgan and Vincent J. Carey}, title={Software for Computing and Annotating Genomic Ranges}, journal={PLOS Computational Biology}, volume={4}, number={3}, year={2013} } GenomicRanges/vignettes/GenomicRangesHOWTOs.Rnw0000644000175400017540000011362413001230267022547 0ustar00biocbuildbiocbuild%\VignetteIndexEntry{GenomicRanges HOWTOs} %\VignetteDepends{GenomicRanges, Rsamtools, GenomicAlignments, pasillaBamSubset, TxDb.Dmelanogaster.UCSC.dm3.ensGene, TxDb.Athaliana.BioMart.plantsmart22, AnnotationHub, DESeq2, edgeR, TxDb.Hsapiens.UCSC.hg19.knownGene, GenomicFeatures, Biostrings, BSgenome.Hsapiens.UCSC.hg19, KEGG.db, KEGGgraph, BSgenome.Scerevisiae.UCSC.sacCer2} %\VignetteKeywords{sequence, sequencing, alignments} %\VignettePackage{GenomicRanges} \documentclass{article} \usepackage[authoryear,round]{natbib} <>= BiocStyle::latex(use.unsrturl=FALSE) @ \title{\Biocpkg{GenomicRanges} HOWTOs} \author{Bioconductor Team} \date{Edited: July 2014; Compiled: \today} \begin{document} \maketitle \tableofcontents <>= options(width=72) options(showHeadLines=3) options(showTailLines=3) .precomputed_results_path <- "precomputed_results" @ %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \section{Introduction} \subsection{Purpose of this document} This document is a collection of {\it HOWTOs}. Each {\it HOWTO} is a short section that demonstrates how to use the containers and operations implemented in the \Biocpkg{GenomicRanges} and related packages (\Biocpkg{IRanges}, \Biocpkg{Biostrings}, \Biocpkg{Rsamtools}, \Biocpkg{GenomicAlignments}, \Biocpkg{BSgenome}, and \Biocpkg{GenomicFeatures}) to perform a task typically found in the context of a high throughput sequence analysis. Unless stated otherwise, the {\it HOWTOs} are self contained, independent of each other, and can be studied and reproduced in any order. \subsection{Prerequisites and additional recommended reading} We assume the reader has some previous experience with \R{} and with basic manipulation of \Rcode{GRanges}, \Rcode{GRangesList}, \Rcode{Rle}, \Rcode{RleList}, and \Rcode{DataFrame} objects. See the ``An Introduction to Genomic Ranges Classes'' vignette located in the \Biocpkg{GenomicRanges} package (in the same folder as this document) for an introduction to these containers. Additional recommended readings after this document are the ``Software for Computing and Annotating Genomic Ranges'' paper[\citet{Lawrence2013ranges}] and the ``Counting reads with \Rfunction{summarizeOverlaps}'' vignette located in the \Biocpkg{GenomicAlignments} package. To display the list of vignettes available in the \Biocpkg{GenomicRanges} package, use \Rcode{browseVignettes("GenomicRanges")}. \subsection{Input data and terminology used across the HOWTOs} In order to avoid repetition, input data, concepts and terms used in more than one {\it HOWTO} are described here: \begin{itemize} \item {\bf The \Biocpkg{pasillaBamSubset} data package}: contains both a BAM file with single-end reads (untreated1\_chr4) and a BAM file with paired-end reads (untreated3\_chr4). Each file is a subset of chr4 from the "Pasilla" experiment. <>= library(pasillaBamSubset) untreated1_chr4() untreated3_chr4() @ See \Rcode{?pasillaBamSubset} for more information. <>= ?pasillaBamSubset @ \item {\bf Gene models and \Rclass{TxDb} objects}: A \textit{gene model} is essentially a set of annotations that describes the genomic locations of the known genes, transcripts, exons, and CDS, for a given organism. In \Bioconductor{} it is typically represented as a \Rclass{TxDb} object but also sometimes as a \Rclass{GRanges} or \Rclass{GRangesList} object. The \Biocpkg{GenomicFeatures} package contains tools for making and manipulating \Rclass{TxDb} objects. \end{itemize} %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \section{HOWTOs} %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \subsection{How to read single-end reads from a BAM file} As sample data we use the \Biocpkg{pasillaBamSubset} data package described in the introduction. <>= library(pasillaBamSubset) un1 <- untreated1_chr4() # single-end reads @ Several functions are available for reading BAM files into \R{}: \begin{verbatim} readGAlignments() readGAlignmentPairs() readGAlignmentsList() scanBam() \end{verbatim} \Rfunction{scanBam} is a low-level function that returns a list of lists and is not discussed further here. See \Rcode{?scanBam} in the \Biocpkg{Rsamtools} package for more information. Single-end reads can be loaded with the \Rfunction{readGAlignments} function from the \Biocpkg{GenomicAlignments} package. <>= library(GenomicAlignments) gal <- readGAlignments(un1) @ Data subsets can be specified by genomic position, field names, or flag criteria in the \Rcode{ScanBamParam}. Here we input records that overlap position 1 to 5000 on the negative strand with \Rcode{flag} and \Rcode{cigar} as metadata columns. <>= what <- c("flag", "cigar") which <- GRanges("chr4", IRanges(1, 5000)) flag <- scanBamFlag(isMinusStrand = TRUE) param <- ScanBamParam(which=which, what=what, flag=flag) neg <- readGAlignments(un1, param=param) neg @ Another approach to subsetting the data is to use \Rfunction{filterBam}. This function creates a new BAM file of records passing user-defined criteria. See \Rcode{?filterBam} in the \Biocpkg{Rsamtools} package for more information. %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \subsection{How to read paired-end reads from a BAM file} As sample data we use the \Biocpkg{pasillaBamSubset} data package described in the introduction. <>= library(pasillaBamSubset) un3 <- untreated3_chr4() # paired-end reads @ Paired-end reads can be loaded with the \Rfunction{readGAlignmentPairs} or \Rfunction{readGAlignmentsList} function from the \Biocpkg{GenomicAlignments} package. These functions use the same mate paring algorithm but output different objects. Let's start with \Rfunction{readGAlignmentPairs}: <>= un3 <- untreated3_chr4() gapairs <- readGAlignmentPairs(un3) @ The \Robject{GAlignmentPairs} class holds only pairs; reads with no mate or with ambiguous pairing are discarded. Each list element holds exactly 2 records (a mated pair). Records can be accessed as the \Rcode{first} and\Rcode{last} segments in a template or as \Rcode{left} and \Rcode{right} alignments. See \Rcode{?GAlignmentPairs} in the \Biocpkg{GenomicAlignments} package for more information. <>= gapairs @ For \Rcode{readGAlignmentsList}, mate pairing is performed when \Rcode{asMates} is set to \Rcode{TRUE} on the \Rcode{BamFile} object, otherwise records are treated as single-end. <>= galist <- readGAlignmentsList(BamFile(un3, asMates=TRUE)) @ \Robject{GAlignmentsList} is a more general `list-like' structure that holds mate pairs as well as non-mates (i.e., singletons, records with unmapped mates etc.) A \Rcode{mates\_status} metadata column (accessed with \Rfunction{mcols}) indicates which records were paired. <>= galist @ Non-mated reads are returned as groups by QNAME and contain any number of records. Here the non-mate groups range in size from 1 to 9. <>= non_mates <- galist[unlist(mcols(galist)$mate_status) == "unmated"] table(elementNROWS(non_mates)) @ %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \subsection{How to read and process a big BAM file by chunks in order to reduce memory usage} A large BAM file can be iterated through in chunks by setting a \Rcode{yieldSize} on the \Rclass{BamFile} object. As sample data we use the \Biocpkg{pasillaBamSubset} data package described in the introduction. <>= library(pasillaBamSubset) un1 <- untreated1_chr4() bf <- BamFile(un1, yieldSize=100000) @ Iteration through a BAM file requires that the file be opened, repeatedly queried inside a loop, then closed. Repeated calls to \Rfunction{readGAlignments} without opening the file first result in the same 100000 records returned each time. <>= open(bf) cvg <- NULL repeat { chunk <- readGAlignments(bf) if (length(chunk) == 0L) break chunk_cvg <- coverage(chunk) if (is.null(cvg)) { cvg <- chunk_cvg } else { cvg <- cvg + chunk_cvg } } close(bf) cvg @ %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \subsection{How to compute read coverage} The ``read coverage'' is the number of reads that cover a given genomic position. Computing the read coverage generally consists in computing the coverage at each position in the genome. This can be done with the \Rcode{coverage()} function. As sample data we use the \Biocpkg{pasillaBamSubset} data package described in the introduction. <>= library(pasillaBamSubset) un1 <- untreated1_chr4() # single-end reads library(GenomicAlignments) reads1 <- readGAlignments(un1) cvg1 <- coverage(reads1) cvg1 @ Coverage on chr4: <>= cvg1$chr4 @ Average and max coverage: <>= mean(cvg1$chr4) max(cvg1$chr4) @ Note that \Rcode{coverage()} is a generic function with methods for different types of objects. See \Rcode{?coverage} for more information. %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \subsection{How to find peaks in read coverage} ChIP-Seq analysis usually involves finding peaks in read coverage. This process is sometimes called ``peak calling'' or ``peak detection''. Here we're only showing a naive way to find peaks in the object returned by the \Rcode{coverage()} function. \Bioconductor{} packages \Biocpkg{BayesPeak}, \Biocpkg{bumphunter}, \Biocpkg{Starr}, \Biocpkg{CexoR}, \Biocpkg{exomePeak}, \Biocpkg{RIPSeeker}, and others, provide sophisticated peak calling tools for ChIP-Seq, RIP-Seq, and other kind of high throughput sequencing data. Let's assume \Rcode{cvg1} is the object returned by \Rcode{coverage()} (see previous {\it HOWTO} for how to compute it). We can use the \Rcode{slice()} function to find the genomic regions where the coverage is greater or equal to a given threshold. <>= chr4_peaks <- slice(cvg1$chr4, lower=500) chr4_peaks length(chr4_peaks) # nb of peaks @ The weight of a given peak can be defined as the number of aligned nucleotides that belong to the peak (a.k.a. the area under the peak in mathematics). It can be obtained with \Rcode{sum()}: <>= sum(chr4_peaks) @ %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \subsection{How to retrieve a gene model from the UCSC genome browser} See introduction for a quick description of what \textit{gene models} and \Rclass{TxDb} objects are. We can use the \Rcode{make\-Transcript\-Db\-From\-UCSC()} function from the \Biocpkg{GenomicFeatures} package to import a UCSC genome browser track as a \Rclass{TxDb} object. <>= library(GenomicFeatures) ### Internet connection required! Can take several minutes... txdb <- makeTxDbFromUCSC(genome="sacCer2", tablename="ensGene") @ See \Rcode{?makeTxDbFromUCSC} in the \Biocpkg{GenomicFeatures} package for more information. Note that some of the most frequently used gene models are available as TxDb packages. A TxDb package consists of a pre-made \Rclass{TxDb} object wrapped into an annotation data package. Go to \url{http://bioconductor.org/packages/release/BiocViews.html#\_\_\_TxDb} to browse the list of available TxDb packages. <>= library(TxDb.Hsapiens.UCSC.hg19.knownGene) txdb <- TxDb.Hsapiens.UCSC.hg19.knownGene txdb @ Extract the transcript coordinates from this gene model: <>= transcripts(txdb) @ %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \subsection{How to retrieve a gene model from Ensembl} See introduction for a quick description of what \textit{gene models} and \Rclass{TxDb} objects are. We can use the \Rcode{make\-Transcript\-Db\-From\-Biomart()} function from the \Biocpkg{GenomicFeatures} package to retrieve a gene model from the Ensembl Mart. <>= library(GenomicFeatures) ### Internet connection required! Can take several minutes... txdb <- makeTxDbFromBiomart(biomart="ensembl", dataset="hsapiens_gene_ensembl") @ See \Rcode{?makeTxDbFromBiomart} in the \Biocpkg{GenomicFeatures} package for more information. Note that some of the most frequently used gene models are available as TxDb packages. A TxDb package consists of a pre-made \Rclass{TxDb} object wrapped into an annotation data package. Go to \url{http://bioconductor.org/packages/release/BiocViews.html#\_\_\_TxDb} to browse the list of available TxDb packages. <>= library(TxDb.Athaliana.BioMart.plantsmart22) txdb <- TxDb.Athaliana.BioMart.plantsmart22 txdb @ Extract the exon coordinates from this gene model: <>= exons(txdb) @ %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \subsection{How to load a gene model from a GFF or GTF file} See introduction for a quick description of what \textit{gene models} and \Rclass{TxDb} objects are. We can use the \Rcode{make\-Transcript\-Db\-From\-GFF()} function from the \Biocpkg{GenomicFeatures} package to import a GFF or GTF file as a \Rclass{TxDb} object. <>= library(GenomicFeatures) gff_file <- system.file("extdata", "GFF3_files", "a.gff3", package="GenomicFeatures") txdb <- makeTxDbFromGFF(gff_file, format="gff3") txdb @ See \Rcode{?makeTxDbFromGFF} in the \Biocpkg{GenomicFeatures} package for more information. Extract the exon coordinates grouped by gene from this gene model: <>= exonsBy(txdb, by="gene") @ %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \subsection{How to retrieve a gene model from \Biocpkg{AnnotationHub}} When a gene model is not available as a \Rclass{GRanges} or \Rclass{GRangesList} object or as a \Bioconductor{} data package, it may be available on \Biocpkg{AnnotationHub}. In this {\it HOWTO}, will look for a gene model for Drosophila melanogaster on \Biocpkg{AnnotationHub}. Create a `hub' and then filter on Drosophila melanogaster: <>= library(AnnotationHub) ### Internet connection required! hub <- AnnotationHub() hub <- subset(hub, hub$species=='Drosophila melanogaster') @ There are 87 files that match Drosophila melanogaster. If you look at the metadata in hub, you can see that the 7th record representes a GRanges object from UCSC <>= length(hub) head(names(hub)) head(hub$title, n=10) ## then look at a specific slice of the hub object. hub[7] @ So you can retrieve that dm3 file as a \Rcode{GRanges} like this: <>= gr <- hub[[names(hub)[7]]] summary(gr) @ The metadata fields contain the details of file origin and content. <>= metadata(gr) @ Split the \Rclass{GRanges} object by gene name to get a \Rclass{GRangesList} object of transcript ranges grouped by gene. <>= txbygn <- split(gr, gr$name) @ You can now use \Rcode{txbygn} with the \Rcode{summarizeOverlaps} function to prepare a table of read counts for RNA-Seq differential gene expression. Note that before passing \Rcode{txbygn} to \Rcode{summarizeOverlaps}, you should confirm that the seqlevels (chromosome names) in it match those in the BAM file. See \Rcode{?renameSeqlevels}, \Rcode{?keepSeqlevels} and \Rcode{?seqlevels} for examples of renaming seqlevels. %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \subsection{How to annotate peaks in read coverage} [coming soon...] %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \subsection{How to prepare a table of read counts for RNA-Seq differential gene expression} Methods for RNA-Seq gene expression analysis generally require a table of counts that summarize the number of reads that overlap or `hit' a particular gene. In this {\it HOWTO} we count with the \Rcode{summarizeOverlaps} function from the \Biocpkg{GenomicAlignments} package and create a count table from the results. Other packages that provide read counting are \Biocpkg{Rsubread} and \Biocpkg{easyRNASeq}. The \Biocpkg{parathyroidSE} package vignette contains a workflow on counting and other common operations required for differential expression analysis. As sample data we use the \Biocpkg{pasillaBamSubset} data package described in the introduction. <>= library(pasillaBamSubset) reads <- c(untrt1=untreated1_chr4(), # single-end reads untrt3=untreated3_chr4()) # paired-end reads @ \Rcode{summarizeOverlaps} requires the name of a BAM file(s) and a {\textit gene model} to count against. See introduction for a quick description of what a \textit{gene models} is. The gene model must match the genome build the reads in the BAM file were aligned to. For the pasilla data this is dm3 Dmelanogaster which is available as a \Bioconductor{} package. Load the package and extract the exon ranges grouped by gene: <>= library(TxDb.Dmelanogaster.UCSC.dm3.ensGene) exbygene <- exonsBy(TxDb.Dmelanogaster.UCSC.dm3.ensGene, "gene") @ \Rcode{exbygene} is a \Rclass{GRangesList} object with one list element per gene in the gene model. \Rcode{summarizeOverlaps} automatically sets a \Rcode{yieldSize} on large BAM files and iterates over them in chunks. When reading paired-end data set the \Rcode{singleEnd} argument to FALSE. See ?\Rfunction{summarizeOverlaps} for details reguarding the count \Rcode{modes} and additional arguments. <>= library(GenomicAlignments) se <- summarizeOverlaps(exbygene, reads, mode="IntersectionNotEmpty") @ The return object is a \Rcode{SummarizedExperiment} with counts accessible with the \Rcode{assays} accessor: <>= class(se) head(table(assays(se)$counts)) @ The count vector is the same length as \Rcode{exbygene}: <>= identical(length(exbygene), length(assays(se)$counts)) @ A copy of \Rcode{exbygene} is stored in the \Rcode{se} object and accessible with \Rcode{rowRanges} accessor: <>= rowRanges(se) @ Two popular packages for RNA-Seq differential gene expression are \Biocpkg{DESeq2} and \Biocpkg{edgeR}. Tables of counts per gene are required for both and can be easily created with a vector of counts. Here we use the counts from our \Rclass{SummarizedExperiment} object: <>= colData(se)$trt <- factor(c("untrt", "untrt"), levels=c("trt", "untrt")) colData(se) library(DESeq2) deseq <- DESeqDataSet(se, design= ~ 1) library(edgeR) edger <- DGEList(assays(se)$counts, group=rownames(colData(se))) @ %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \subsection{How to summarize junctions from a BAM file containing RNA-Seq reads} As sample data we use the \Biocpkg{pasillaBamSubset} data package described in the introduction. <>= library(pasillaBamSubset) un1 <- untreated1_chr4() # single-end reads library(GenomicAlignments) reads1 <- readGAlignments(un1) reads1 @ For each alignment, the aligner generated a CIGAR string that describes its "geometry", that is, the locations of insertions, deletions and junctions in the alignment. See the SAM Spec available on the SAMtools website for the details (\url{http://samtools.sourceforge.net/}). The \Rcode{summarizeJunctions()} function from the \Biocpkg{GenomicAlignments} package can be used to summarize the junctions in \Rcode{reads1}. <>= junc_summary <- summarizeJunctions(reads1) junc_summary @ See \Rcode{?summarizeJunctions} in the \Biocpkg{GenomicAlignments} package for more information. %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \subsection{How to get the exon and intron sequences of a given gene} The exon and intron sequences of a gene are essentially the DNA sequences of the introns and exons of all known transcripts of the gene. The first task is to identify all transcripts associated with the gene of interest. Our sample gene is the human TRAK2 which is involved in regulation of endosome-to-lysosome trafficking of membrane cargo. The Entrez gene id is `66008'. <>= trak2 <- "66008" @ The \Biocpkg{TxDb.Hsapiens.UCSC.hg19.knownGene} data package contains the gene model corresponding to the UCSC `Known Genes' track. <>= library(TxDb.Hsapiens.UCSC.hg19.knownGene) txdb <- TxDb.Hsapiens.UCSC.hg19.knownGene @ The transcript ranges for all the genes in the gene model can be extracted with the \Rfunction{transcriptsBy} function from the \Biocpkg{GenomicFeatures} package. They will be returned in a named \Rclass{GRangesList} object containing all the transcripts grouped by gene. In order to keep only the transcripts of the TRAK2 gene we will subset the \Rclass{GRangesList} object using the \Rcode{[[} operator. <>= library(GenomicFeatures) trak2_txs <- transcriptsBy(txdb, by="gene")[[trak2]] trak2_txs @ \Rcode{trak2\_txs} is a \Rclass{GRanges} object with one range per transcript in the TRAK2 gene. The transcript names are stored in the \Rcode{tx\_name} metadata column. We will need them to subset the extracted intron and exon regions: <>= trak2_tx_names <- mcols(trak2_txs)$tx_name trak2_tx_names @ The exon and intron genomic ranges for all the transcripts in the gene model can be extracted with the \Rfunction{exonsBy} and \Rfunction{intronsByTranscript} functions, respectively. Both functions return a \Rclass{GRangesList} object. Then we keep only the exon and intron for the transcripts of the TRAK2 gene by subsetting each \Rclass{GRangesList} object by the TRAK2 transcript names. Extract the exon regions: <>= trak2_exbytx <- exonsBy(txdb, "tx", use.names=TRUE)[trak2_tx_names] elementNROWS(trak2_exbytx) @ ... and the intron regions: <>= trak2_inbytx <- intronsByTranscript(txdb, use.names=TRUE)[trak2_tx_names] elementNROWS(trak2_inbytx) @ Next we want the DNA sequences for these exons and introns. The \Rfunction{getSeq} function from the \Biocpkg{Biostrings} package can be used to query a \Biocpkg{BSgenome} object with a set of genomic ranges and retrieve the corresponding DNA sequences. <>= library(BSgenome.Hsapiens.UCSC.hg19) @ Extract the exon sequences: <>= trak2_ex_seqs <- getSeq(Hsapiens, trak2_exbytx) trak2_ex_seqs trak2_ex_seqs[["uc002uyb.4"]] trak2_ex_seqs[["uc002uyc.2"]] @ ... and the intron sequences: <>= trak2_in_seqs <- getSeq(Hsapiens, trak2_inbytx) trak2_in_seqs trak2_in_seqs[["uc002uyb.4"]] trak2_in_seqs[["uc002uyc.2"]] @ %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \subsection{How to get the CDS and UTR sequences of genes associated with colorectal cancer} In this {\it HOWTO} we extract the CDS and UTR sequences of genes involved in colorectal cancer. The workflow extends the ideas presented in the previous {\it HOWTO} and suggests an approach for identifying disease-related genes. \subsubsection{Build a gene list} We start with a list of gene or transcript ids. If you do not have pre-defined list one can be created with the \Biocpkg{KEGG.db} and \Biocpkg{KEGGgraph} packages. Updates to the data in the \Biocpkg{KEGG.db} package are no longer available, however, the resource is still useful for identifying pathway names and ids. Create a table of KEGG pathways and ids and search on the term `cancer'. <>= library(KEGG.db) pathways <- toTable(KEGGPATHNAME2ID) pathways[grepl("cancer", pathways$path_name, fixed=TRUE),] @ Use the "05210" id to query the KEGG web resource (accesses the currently maintained data). <>= library(KEGGgraph) dest <- tempfile() retrieveKGML("05200", "hsa", dest, "internal") @ The suffix of the KEGG id is the Entrez gene id. The \Rfunction{translateKEGGID2GeneID} simply removes the prefix leaving just the Entrez gene ids. <>= crids <- as.character(parseKGML2DataFrame(dest)[,1]) crgenes <- unique(translateKEGGID2GeneID(crids)) head(crgenes) @ \subsubsection{Identify genomic coordinates} The list of gene ids is used to extract genomic positions of the regions of interest. The Known Gene table from UCSC will be the annotation and is available as a \Bioconductor{} package. <>= library(TxDb.Hsapiens.UCSC.hg19.knownGene) txdb <- TxDb.Hsapiens.UCSC.hg19.knownGene @ If an annotation is not available as a \Bioconductor{} annotation package it may be available in \Biocpkg{AnnotationHub}. Additionally, there are functions in \Biocpkg{GenomicFeatures} which can retrieve data from UCSC and Ensembl to create a \Robject{TxDb}. See \Rcode{?makeTxDbFromUCSC} for more information. As in the previous {\it HOWTO} we need to identify the transcripts corresponding to each gene. The transcript id (or name) is used to isolate the UTR and coding regions of interest. This grouping of transcript by gene is also used to re-group the final sequence results. The \Rcode{transcriptsBy} function outputs both the gene and transcript identifiers which we use to create a map between the two. The \Rcode{map} is a \Robject{CharacterList} with gene ids as names and transcript ids as the list elements. <>= txbygene <- transcriptsBy(txdb, "gene")[crgenes] ## subset on colorectal genes map <- relist(unlist(txbygene, use.names=FALSE)$tx_id, txbygene) map @ Extract the UTR and coding regions. <>= cds <- cdsBy(txdb, "tx") threeUTR <- threeUTRsByTranscript(txdb) fiveUTR <- fiveUTRsByTranscript(txdb) @ Coding and UTR regions may not be present for all transcripts specified in \Rcode{map}. Consequently, the subset results will not be the same length. This length discrepancy must be taken into account when re-listing the final results by gene. <>= txid <- unlist(map, use.names=FALSE) cds <- cds[names(cds) %in% txid] threeUTR <- threeUTR[names(threeUTR) %in% txid] fiveUTR <- fiveUTR[names(fiveUTR) %in% txid] @ Note the different lengths of the subset regions. <>= length(txid) ## all possible transcripts length(cds) length(threeUTR) length(fiveUTR) @ These objects are \Robject{GRangesList}s with the transcript id as the outer list element. <>= cds @ \subsubsection{Extract sequences from BSgenome} The \Rcode{BSgenome} packages contain complete genome sequences for a given organism. Load the \Rcode{BSgenome} package for homo sapiens. <>= library(BSgenome.Hsapiens.UCSC.hg19) genome <- BSgenome.Hsapiens.UCSC.hg19 @ Use \Rfunction{extractTranscriptSeqs} to extract the UTR and coding regions from the \Rcode{BSgenome}. This function retrieves the sequences for an any \Robject{GRanges} or \Robject{GRangesList} (i.e., not just transcripts like the name implies). <>= threeUTR_seqs <- extractTranscriptSeqs(genome, threeUTR) fiveUTR_seqs <- extractTranscriptSeqs(genome, fiveUTR) cds_seqs <- extractTranscriptSeqs(genome, cds) @ The return values are \Robject{DNAStringSet} objects. <>= cds_seqs @ Our final step is to collect the coding and UTR regions (currently organzied by transcript) into groups by gene id. The \Rfunction{relist} function groups the sequences of a \Robject{DNAStringSet} object into a \Robject{DNAStringSetList} object, based on the specified \Rcode{skeleton} argument. The \Rcode{skeleton} must be a list-like object and only its shape (i.e. its element lengths) matters (its exact content is ignored). A simple form of \Rcode{skeleton} is to use a partitioning object that we make by specifying the size of each partition. The partitioning objects are different for each type of region because not all transcripts had a coding or 3' or 5' UTR region defined. <>= lst3 <- relist(threeUTR_seqs, PartitioningByWidth(sum(map %in% names(threeUTR)))) lst5 <- relist(fiveUTR_seqs, PartitioningByWidth(sum(map %in% names(fiveUTR)))) lstc <- relist(cds_seqs, PartitioningByWidth(sum(map %in% names(cds)))) @ There are 239 genes in \Rcode{map} each of which have 1 or more transcripts. The table of element lengths shows how many genes have each number of transcripts. For example, 47 genes have 1 transcript, 48 genes have 2 etc. <>= length(map) table(elementNROWS(map)) @ The lists of DNA sequences all have the same length as \Rcode{map} but one or more of the element lengths may be zero. This would indicate that data were not available for that gene. The tables below show that there was at least 1 coding region available for all genes (i.e., none of the element lengths are 0). However, both the 3' and 5' UTR results have element lengths of 0 which indicates no UTR data were available for that gene. <>= table(elementNROWS(lstc)) table(elementNROWS(lst3)) names(lst3)[elementNROWS(lst3) == 0L] ## genes with no 3' UTR data table(elementNROWS(lst5)) names(lst5)[elementNROWS(lst5) == 0L] ## genes with no 5' UTR data @ %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \subsection{How to create DNA consensus sequences for read group `families'} The motivation for this {\it HOWTO} comes from a study which explored the dynamics of point mutations. The mutations of interest exist with a range of frequencies in the control group (e.g., 0.1\% - 50\%). PCR and sequencing error rates make it difficult to identify low frequency events (e.g., < 20\%). When a library is prepared with Nextera, random fragments are generated followed by a few rounds of PCR. When the genome is large enough, reads aligning to the same start position are likely descendant from the same template fragment and should have identical sequences. The goal is to elimininate noise by grouping the reads by common start position and discarding those that do not exceed a certain threshold within each family. A new consensus sequence will be created for each read group family. \subsubsection{Sort reads into groups by start position} Load the BAM file into a GAlignments object. <>= library(Rsamtools) bamfile <- system.file("extdata", "ex1.bam", package="Rsamtools") param <- ScanBamParam(what=c("seq", "qual")) library(GenomicAlignments) gal <- readGAlignments(bamfile, use.names=TRUE, param=param) @ Use the \Rfunction{sequenceLayer} function to {\it lay} the query sequences and quality strings on the reference. <>= qseq <- setNames(mcols(gal)$seq, names(gal)) qual <- setNames(mcols(gal)$qual, names(gal)) qseq_on_ref <- sequenceLayer(qseq, cigar(gal), from="query", to="reference") qual_on_ref <- sequenceLayer(qual, cigar(gal), from="query", to="reference") @ Split by chromosome. <>= qseq_on_ref_by_chrom <- splitAsList(qseq_on_ref, seqnames(gal)) qual_on_ref_by_chrom <- splitAsList(qual_on_ref, seqnames(gal)) pos_by_chrom <- splitAsList(start(gal), seqnames(gal)) @ For each chromosome generate one GRanges object that contains unique alignment start positions and attach 3 metadata columns to it: the number of reads, the query sequences, and the quality strings. <>= gr_by_chrom <- lapply(seqlevels(gal), function(seqname) { qseq_on_ref2 <- qseq_on_ref_by_chrom[[seqname]] qual_on_ref2 <- qual_on_ref_by_chrom[[seqname]] pos2 <- pos_by_chrom[[seqname]] qseq_on_ref_per_pos <- split(qseq_on_ref2, pos2) qual_on_ref_per_pos <- split(qual_on_ref2, pos2) nread <- elementNROWS(qseq_on_ref_per_pos) gr_mcols <- DataFrame(nread=unname(nread), qseq_on_ref=unname(qseq_on_ref_per_pos), qual_on_ref=unname(qual_on_ref_per_pos)) gr <- GRanges(Rle(seqname, nrow(gr_mcols)), IRanges(as.integer(names(nread)), width=1)) mcols(gr) <- gr_mcols seqlevels(gr) <- seqlevels(gal) gr }) @ Combine all the GRanges objects obtained in (4) in 1 big GRanges object: <>= gr <- do.call(c, gr_by_chrom) seqinfo(gr) <- seqinfo(gal) @ `gr' is a GRanges object that contains unique alignment start positions: <>= gr[1:6] @ Look at qseq\_on\_ref and qual\_on\_ref. <>= qseq_on_ref qual_on_ref @ 2 reads align to start position 13. Let's have a close look at their sequences: <>= mcols(gr)$qseq_on_ref[[6]] @ and their qualities: <>= mcols(gr)$qual_on_ref[[6]] @ Note that the sequence and quality strings are those projected to the reference so the first letter in those strings are on top of start position 13, the 2nd letter on top of position 14, etc... \subsubsection{Remove low frequency reads} For each start position, remove reads with and under-represented sequence (e.g. threshold = 20\% for the data used here which is low coverage). A unique number is assigned to each unique sequence. This will make future calculations easier and a little bit faster. <>= qseq_on_ref <- mcols(gr)$qseq_on_ref tmp <- unlist(qseq_on_ref, use.names=FALSE) qseq_on_ref_id <- relist(match(tmp, tmp), qseq_on_ref) @ Quick look at `qseq\_on\_ref\_id': It's an IntegerList object with the same length and "shape" as `qseq\_on\_ref'. <>= qseq_on_ref_id @ Remove the under represented ids from each list element of `qseq\_on\_ref\_id': <>= qseq_on_ref_id2 <- endoapply(qseq_on_ref_id, function(ids) ids[countMatches(ids, ids) >= 0.2 * length(ids)]) @ Remove corresponding sequences from `qseq\_on\_ref': <>= tmp <- unlist(qseq_on_ref_id2, use.names=FALSE) qseq_on_ref2 <- relist(unlist(qseq_on_ref, use.names=FALSE)[tmp], qseq_on_ref_id2) @ \subsubsection{Create a consensus sequence for each read group family} Compute 1 consensus matrix per chromosome: <>= split_factor <- rep.int(seqnames(gr), elementNROWS(qseq_on_ref2)) qseq_on_ref2 <- unlist(qseq_on_ref2, use.names=FALSE) qseq_on_ref2_by_chrom <- splitAsList(qseq_on_ref2, split_factor) qseq_pos_by_chrom <- splitAsList(start(gr), split_factor) cm_by_chrom <- lapply(names(qseq_pos_by_chrom), function(seqname) consensusMatrix(qseq_on_ref2_by_chrom[[seqname]], as.prob=TRUE, shift=qseq_pos_by_chrom[[seqname]]-1, width=seqlengths(gr)[[seqname]])) names(cm_by_chrom) <- names(qseq_pos_by_chrom) @ 'cm\_by\_chrom' is a list of consensus matrices. Each matrix has 17 rows (1 per letter in the DNA alphabet) and 1 column per chromosome position. <>= lapply(cm_by_chrom, dim) @ Compute the consensus string from each consensus matrix. We'll put "+" in the strings wherever there is no coverage for that position, and "N" where there is coverage but no consensus. <>= cs_by_chrom <- lapply(cm_by_chrom, function(cm) { ## need to "fix" 'cm' because consensusString() ## doesn't like consensus matrices with columns ## that contain only zeroes (e.g., chromosome ## positions with no coverage) idx <- colSums(cm) == 0L cm["+", idx] <- 1 DNAString(consensusString(cm, ambiguityMap="N")) }) @ The new consensus strings. <>= cs_by_chrom @ %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \subsection{How to compute binned averages along a genome} In some applications (e.g. visualization), there is the need to compute the average of a variable defined along a genome (a.k.a. genomic variable) for a set of predefined fixed-width regions (sometimes called "bins"). The genomic variable is typically represented as a named \Robject{RleList} object with one list element per chromosome. One such example is coverage. Here we create an artificial genomic variable: <>= library(BSgenome.Scerevisiae.UCSC.sacCer2) set.seed(55) my_var <- RleList( lapply(seqlengths(Scerevisiae), function(seqlen) { tmp <- sample(50L, seqlen, replace=TRUE) %/% 50L Rle(cumsum(tmp - rev(tmp))) } ), compress=FALSE) my_var @ Use the \Rfunction{tileGenome} function to create a set of bins along the genome. <>= bins <- tileGenome(seqinfo(Scerevisiae), tilewidth=100, cut.last.tile.in.chrom=TRUE) @ Compute the binned average for \Rcode{my\_var}: <>= binnedAverage(bins, my_var, "binned_var") @ The bin size can be modified with the \Rcode{tilewidth} argument to \Rfunction{tileGenome}. See \Rcode{?binnedAverage} for additional examples. %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \section{Session Information} <>= sessionInfo() @ %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \bibliography{GenomicRanges} \bibliographystyle{plainnat} \end{document} GenomicRanges/vignettes/GenomicRangesIntroduction.Rnw0000644000175400017540000004365213001230267024150 0ustar00biocbuildbiocbuild%\VignetteIndexEntry{An Introduction to the GenomicRanges Package} %\VignetteKeywords{sequence, sequencing} %\VignettePackage{GenomicRanges} \documentclass[10pt]{article} \usepackage{times} \usepackage{hyperref} \textwidth=6.5in \textheight=8.5in %\parskip=.3cm \oddsidemargin=-.1in \evensidemargin=-.1in \headheight=-.3in \newcommand{\Rfunction}[1]{{\texttt{#1}}} \newcommand{\Robject}[1]{{\texttt{#1}}} \newcommand{\Rpackage}[1]{{\textit{#1}}} \newcommand{\Rmethod}[1]{{\texttt{#1}}} \newcommand{\Rfunarg}[1]{{\texttt{#1}}} \newcommand{\Rclass}[1]{{\textit{#1}}} \newcommand{\Rcode}[1]{{\texttt{#1}}} \newcommand{\software}[1]{\textsf{#1}} \newcommand{\R}{\software{R}} \newcommand{\Bioconductor}{\software{Bioconductor}} \newcommand{\GenomicRanges}{\Rpackage{GenomicRanges}} \title{An Introduction to the GenomicRanges Package} \author{Marc Carlson \and Patrick Aboyoun \and Herv\'{e} Pag\`{e}s} \date{\today} \begin{document} \maketitle <>= options(width=72) @ \tableofcontents %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \section{Introduction} The \Rpackage{GenomicRanges} package serves as the foundation for representing genomic locations within the \software{Bioconductor} project. In the \software{Bioconductor} package hierarchy, it builds upon the \Rpackage{IRanges} (infrastructure) package and provides support for the \Rpackage{BSgenome} (infrastructure), \Rpackage{Rsamtools} (I/O), \Rpackage{ShortRead} (I/O \& QA), \Rpackage{rtracklayer} (I/O), and \Rpackage{GenomicFeatures} (infrastructure) packages, and many other \software{Bioconductor} packages. This package lays a foundation for genomic analysis by introducing three classes (\Rclass{GRanges}, \Rclass{GPos}, and \Rclass{GRangesList}), which are used to represent genomic ranges, genomic positions, and groups of genomic ranges. This vignette focuses on the \Rclass{GRanges} and \Rclass{GRangesList} classes and their associated methods. The \Rpackage{GenomicRanges} package is available at bioconductor.org and can be downloaded via \Rfunction{biocLite}: <>= source("https://bioconductor.org/biocLite.R") biocLite("GenomicRanges") @ <>= library(GenomicRanges) @ %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \section{\Rclass{GRanges}: Genomic Ranges} The \Rclass{GRanges} class represents a collection of genomic ranges that each have a single start and end location on the genome. It can be used to store the location of genomic features such as contiguous binding sites, transcripts, and exons. These objects can be created by using the \Rfunction{GRanges} constructor function. For example, <>= gr <- GRanges(seqnames = Rle(c("chr1", "chr2", "chr1", "chr3"), c(1, 3, 2, 4)), ranges = IRanges(1:10, end = 7:16, names = head(letters, 10)), strand = Rle(strand(c("-", "+", "*", "+", "-")), c(1, 2, 2, 3, 2)), score = 1:10, GC = seq(1, 0, length=10)) gr @ \noindent creates a \Rclass{GRanges} object with 10 genomic ranges. The output of the \Rclass{GRanges} \Rmethod{show} method separates the information into a left and right hand region that are separated by \Rcode{|} symbols. The genomic coordinates (seqnames, ranges, and strand) are located on the left-hand side and the metadata columns (annotation) are located on the right. For this example, the metadata is comprised of \Rcode{score} and \Rcode{GC} information, but almost anything can be stored in the metadata portion of a \Rclass{GRanges} object. The components of the genomic coordinates within a \Rclass{GRanges} object can be extracted using the \Rmethod{seqnames}, \Rmethod{ranges}, and \Rmethod{strand} accessor functions. <>= seqnames(gr) ranges(gr) strand(gr) @ Stored annotations for these coordinates can be extracted as a \Rclass{DataFrame} object using the \Rmethod{mcols} accessor. <>= mcols(gr) mcols(gr)$score @ Finally, the total lengths of the various sequences that the ranges are aligned to can also be stored in the \Rclass{GRanges} object. So if this is data from \textit{Homo sapiens}, we can set the values as: <>= seqlengths(gr) <- c(249250621,243199373,198022430) @ And then retrieves as: <>= seqlengths(gr) @ Methods for accessing the \Rmethod{length} and \Rmethod{names} have also been defined. <>= names(gr) length(gr) @ \subsection{Splitting and combining \Rclass{GRanges} objects} \Rclass{GRanges} objects can be devided into groups using the \Rmethod{split} method. This produces a \Rclass{GRangesList} object, a class that will be discussed in detail in the next section. <>= sp <- split(gr, rep(1:2, each=5)) sp @ If you then grab the components of this list, they can also be merged by using the \Rmethod{c} and \Rmethod{append} methods. <>= c(sp[[1]], sp[[2]]) @ \subsection{Subsetting \Rclass{GRanges} objects} The expected subsetting operations are also available for \Rclass{GRanges} objects. <>= gr[2:3] @ A second argument to the \Rmethod{[} subset operator can be used to specify which metadata columns to extract from the \Rclass{GRanges} object. For example, <>= gr[2:3, "GC"] @ You can also assign into elements of the \Rclass{GRanges} object. Here is an example where the 2nd row of a \Rclass{GRanges} object is replaced with the 1st row of \Robject{gr}. <>= singles <- split(gr, names(gr)) grMod <- gr grMod[2] <- singles[[1]] head(grMod, n=3) @ Here is a second example where the metadata for score from the 3rd element is replaced with the score from the 2nd row etc. <>= grMod[2,1] <- singles[[3]][,1] head(grMod, n=3) @ There are also methods to repeat, reverse, or select specific portions of \Rclass{GRanges} objects. <>= rep(singles[[2]], times = 3) rev(gr) head(gr,n=2) tail(gr,n=2) window(gr, start=2,end=4) gr[IRanges(start=c(2,7), end=c(3,9))] @ \subsection{Basic interval operations for \Rclass{GRanges} objects} Basic interval characteristics of \Rclass{GRanges} objects can be extracted using the \Rmethod{start}, \Rmethod{end}, \Rmethod{width}, and \Rmethod{range} methods. <>= g <- gr[1:3] g <- append(g, singles[[10]]) start(g) end(g) width(g) range(g) @ The \Rclass{GRanges} class also has many methods for manipulating the intervals. For example, the \Rmethod{flank} method can be used to recover regions flanking the set of ranges represented by the \Rclass{GRanges} object. So to get a \Rclass{GRanges} object containing the ranges that include the 10 bases upstream of the ranges: <>= flank(g, 10) @ And to include the downstream bases: <>= flank(g, 10, start=FALSE) @ Similar to \Rmethod{flank}, there are also operations to \Rmethod{resize} and \Rmethod{shift} our \Rclass{GRanges} object. The \Rmethod{shift} method will move the ranges by a specific number of base pairs, and the \Rmethod{resize} method will extend the ranges by a specified width. <>= shift(g, 5) resize(g, 30) @ The \Rmethod{reduce} will align the ranges and merge overlapping ranges to produce a simplified set. <>= reduce(g) @ Sometimes you may be interested in the spaces or the qualities of the spaces between the ranges represented by your \Rclass{GRanges} object. The \Rmethod{gaps} method will help you calculate the spaces between a reduced version of your ranges: <>= gaps(g) @ And sometimes you also may want to know how many quantitatively unique fragments your ranges could possibly represent. For this task there is the \Rmethod{disjoin} method. <>= disjoin(g) @ One of the most powerful methods for looking at \Rclass{GRanges} objects is the \Rmethod{coverage} method. The \Rmethod{coverage} method quantifies the degree of overlap for all the ranges in a \Rclass{GRanges} object. <>= coverage(g) @ \subsection{Interval set operations for \Rclass{GRanges} objects} There are also operations for calculating relationships between different \Rclass{GRanges} objects. Here are some examples for how you can calculate the \Rmethod{union}, the \Rmethod{intersect} and the asymmetric difference (using \Rmethod{setdiff}). <>= g2 <- head(gr, n=2) union(g, g2) intersect(g, g2) setdiff(g, g2) @ In addition, there is similar set of operations that act at the level of the individual ranges within each \Rclass{GRanges}. These operations all begin with a ``p", which is short for parallel. A requirement for this set of operations is that the number of elements in each \Rclass{GRanges} object has to be the same, and that both of the objects have to have the same seqnames and strand assignments throughout. <>= g3 <- g[1:2] ranges(g3[1]) <- IRanges(start=5, end=12) punion(g2, g3) pintersect(g2, g3) psetdiff(g2, g3) @ For even more information on the \Rcode{GRanges} classes be sure to consult the manual page. <>= ?GRanges @ %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \section{\Rclass{GRangesList}: Groups of Genomic Ranges} Some important genomic features, such as spliced transcripts that are are comprised of exons, are inherently compound structures. Such a feature makes much more sense when expressed as a compound object such as a \Rclass{GRangesList}. Whenever genomic features consist of multiple ranges that are grouped by a parent feature, they can be represented as a \Rclass{GRangesList} object. Consider the simple example of the two transcript \Rfunction{GRangesList} below created using the \Rfunction{GRangesList} constructor. <>= gr1 <- GRanges(seqnames = "chr2", ranges = IRanges(3, 6), strand = "+", score = 5L, GC = 0.45) gr2 <- GRanges(seqnames = c("chr1", "chr1"), ranges = IRanges(c(7,13), width = 3), strand = c("+", "-"), score = 3:4, GC = c(0.3, 0.5)) grl <- GRangesList("txA" = gr1, "txB" = gr2) grl @ The \Rmethod{show} method for a \Rclass{GRangesList} object displays it as a named list of \Rclass{GRanges} objects, where the names of this list are considered to be the names of the grouping feature. In the example above, the groups of individual exon ranges are represented as separate \Rclass{GRanges} objects which are further organized into a list structure where each element name is a transcript name. Many other combinations of grouped and labeled \Rclass{GRanges} objects are possible of course, but this example is expected to be a common arrangement. \subsection{Basic \Rclass{GRangesList} accessors} Just as with \Rclass{GRanges} object, the components of the genomic coordinates within a \Rclass{GRangesList} object can be extracted using simple accessor methods. Not surprisingly, the \Rclass{GRangesList} objects have many of the same accessors as \Rclass{GRanges} objects. The difference is that many of these methods return a list since the input is now essentially a list of \Rclass{GRanges} objects. Here are a few examples: <>= seqnames(grl) ranges(grl) strand(grl) @ The \Rmethod{length} and \Rmethod{names} methods will return the length or names of the list and the \Rmethod{seqlengths} method will return the set of sequence lengths. <>= length(grl) names(grl) seqlengths(grl) @ The \Rmethod{elementNROWS} method returns a list of integers corresponding to the result of calling \Rmethod{NROW} on each individual \Rclass{GRanges} object contained by the \Rclass{GRangesList}. This is a faster alternative to calling \Rmethod{lapply} on the \Rclass{GRangesList}. <>= elementNROWS(grl) @ You can also use \Rmethod{isEmpty} to test if a \Rclass{GRangesList} object contains anything. <>= isEmpty(grl) @ Finally, in the context of a \Rclass{GRangesList} object, the \Rmethod{mcols} method performs a similar operation to what it does on a \Rclass{GRanges} object. However, this metadata now refers to information at the list level instead of the level of the individual \Rclass{GRanges} objects. <>= mcols(grl) <- c("Transcript A","Transcript B") mcols(grl) @ \subsection{Combining \Rclass{GRangesList} objects} \Rclass{GRangesList} objects can be unlisted to combine the separate \Rclass{GRanges} objects that they contain as an expanded \Rclass{GRanges}. <>= ul <- unlist(grl) @ You can also append values together useing \Rmethod{append} or \Rmethod{c}. % <>= % grl2 <- shift(grl,10) % names(grl2) <- c("shiftTxA","shiftTxB") % append(grl, grl2) % c(grl, grl2) % @ \subsection{Basic interval operations for \Rclass{GRangesList} objects} For interval operations, many of the same methods exist for \Rclass{GRangesList} objects that exist for \Rclass{GRanges} objects. <>= start(grl) end(grl) width(grl) @ And as with \Rclass{GRanges} objects, you can also shift all the \Rclass{GRanges} objects in a \Rclass{GRangesList} object, or calculate the coverage. Both of these operations are also carried out across each \Rclass{GRanges} list member. <>= shift(grl, 20) coverage(grl) @ \subsection{Subsetting \Rclass{GRangesList} objects} As you might guess, the subsetting of a \Rclass{GRangesList} object is quite different from subsetting on a \Rclass{GRanges} object in that it acts as if you are subseting a list. If you try out the following you will notice that the standard conventions have been followed. <>= grl[1] grl[[1]] grl["txA"] grl$txB @ But in addition to this, when subsetting a \Rclass{GRangesList}, you can also pass in a second parameter (as with a \Rclass{GRanges} object) to again specify which of the metadata columns you wish to select. <>= grl[1, "score"] grl["txB", "GC"] @ The \Rmethod{head}, \Rmethod{tail}, \Rmethod{rep}, \Rmethod{rev}, and \Rmethod{window} methods all behave as you would expect them to for a list object. For example, the elements referred to by \Rmethod{window} are now list elements instead of \Rclass{GRanges} elements. <>= rep(grl[[1]], times = 3) rev(grl) head(grl, n=1) tail(grl, n=1) window(grl, start=1, end=1) grl[IRanges(start=2, end=2)] @ \subsection{Looping over \Rclass{GRangesList} objects} For \Rclass{GRangesList} objects there is also a family of \Rmethod{apply} methods. These include \Rmethod{lapply}, \Rmethod{sapply}, \Rmethod{mapply}, \Rmethod{endoapply}, \Rmethod{mendoapply}, \Rmethod{Map}, and \Rmethod{Reduce}. The different looping methods defined for \Rclass{GRangesList} objects are useful for returning different kinds of results. The standard \Rmethod{lapply} and \Rmethod{sapply} behave according to convention, with the \Rmethod{lapply} method returning a list and \Rmethod{sapply} returning a more simplified output. <>= lapply(grl, length) sapply(grl, length) @ As with \Rclass{IRanges} objects, there is also a multivariate version of \Rmethod{sapply}, called \Rmethod{mapply}, defined for \Rclass{GRangesList} objects. And, if you don't want the results simplified, you can call the \Rmethod{Map} method, which does the same things as \Rmethod{mapply} but without simplifying the output. <>= grl2 <- shift(grl, 10) names(grl2) <- c("shiftTxA", "shiftTxB") mapply(c, grl, grl2) Map(c, grl, grl2) @ Sometimes, you may not want to get back a simplified output or a list. Sometimes you will want to get back a modified version of the \Rclass{GRangesList} that you originally passed in. This is conceptually similar to the mathematical notion of an endomorphism. This is achieved using the \Rmethod{endoapply} method, which will return the results as a \Rclass{GRangesList} object. <>= endoapply(grl,rev) @ And, there is also a multivariate version of the \Rmethod{endoapply} method in the form of the \Rmethod{mendoapply} method. <>= mendoapply(c,grl,grl2) @ Finally, the \Rmethod{Reduce} method will allow the \Rclass{GRanges} objects to be collapsed across the whole of the \Rclass{GRangesList} object. % Again, this seems like a sub-optimal example to me. <>= Reduce(c,grl) @ For even more information on the \Rcode{GRangesList} classes be sure to consult the manual page. <>= ?GRangesList @ %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \section{Interval overlaps involving \Rclass{GRanges} and \Rclass{GRangesList} objects} Interval overlapping is the process of comparing the ranges in two objects to determine if and when they overlap. As such, it is perhaps the most common operation performed on \Rclass{GRanges} and \Rclass{GRangesList} objects. To this end, the \Rpackage{GenomicRanges} package provides a family of interval overlap functions. The most general of these functions is \Rfunction{findOverlaps}, which takes a query and a subject as inputs and returns a \Rclass{Hits} object containing the index pairings for the overlapping elements. <>= mtch <- findOverlaps(gr, grl) as.matrix(mtch) @ \noindent As suggested in the sections discussing the nature of the \Rclass{GRanges} and \Rclass{GRangesList} classes, the index in the above matrix of hits for a \Rclass{GRanges} object is a single range while for a \Rclass{GRangesList} object it is the set of ranges that define a "feature". Another function in the overlaps family is \Rfunction{countOverlaps}, which tabulates the number of overlaps for each element in the query. <>= countOverlaps(gr, grl) @ A third function in this family is \Rfunction{subsetByOverlaps}, which extracts the elements in the query that overlap at least one element in the subject. <>= subsetByOverlaps(gr,grl) @ Finally, you can use the \Rcode{select} argument to get the index of the first overlapping element in the subject for each element in the query. <>= findOverlaps(gr, grl, select="first") findOverlaps(grl, gr, select="first") @ %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \section{Session Information} All of the output in this vignette was produced under the following conditions: \begin{small} <>= sessionInfo() @ \end{small} \end{document} GenomicRanges/vignettes/Ten_things_slides.Rnw0000644000175400017540000002152613001230267022466 0ustar00biocbuildbiocbuild%\VignetteIndexEntry{10 Things You Didn't Know (slides from BioC 2016)} %\VignetteDepends{GenomicRanges, Biostrings, Rsamtools, BSgenome, hgu95av2probe} \SweaveOpts{keep.source=TRUE, eps=FALSE, width=9, height=3} \documentclass[9pt]{beamer} \usepackage{slides} \renewcommand\Rclass[1]{{\texttt{#1}\index{#1 (class)}}} \title{10 things (maybe) you didn't know about GenomicRanges, Biostrings, and Rsamtools} \author{Herv\'e Pag\`es\\ \href{mailto:hpages@fredhutch.org}{hpages@fredhutch.org}} \date{June 2016} \begin{document} <>= options(width=80) library(GenomicRanges) library(Biostrings) library(Rsamtools) library(BSgenome) library(hgu95av2probe) example(GRangesList) gr <- GRanges(Rle(c("chr2", "chr2", "chr1", "chr3"), c(1, 3, 2, 4)), IRanges(1:10, width=10:1, names=head(letters, 10)), Rle(strand(c("-", "+", "*", "+", "-")), c(1, 2, 2, 3, 2)), score=1:10, GC=seq(1, 0, length=10)) ir <- IRanges(c(11:13, 2, 7:6), width=3) mcols(ir) <- DataFrame(id=letters[1:6], score=3:-2) x <- GRanges(c("chr1:1-1000", "chr2:2000-3000"), score=c(0.45, 0.1), a1=c(5L, 7L), a2=c(6, 8)) mcols(x)$score[2] <- NA y <- GRanges(c("chr2:150-151", "chr1:1-10", "chr2:2000-3000"), score=c(0.7, 0.82, 0.1), b1=c(0L, 5L, 1L), b2=c(1, -2, 1)) @ \maketitle %\frame{\tableofcontents} %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \begin{frame}[fragile] \frametitle{1. {\it Inner} vs {\it outer} metadata columns} \begin{exampleblock}{} {\small <>= mcols(grl)$id <- paste0("ID", seq_along(grl)) grl @ } \end{exampleblock} \end{frame} \begin{frame}[fragile] \frametitle{1. {\it Inner} vs {\it outer} metadata columns} \begin{exampleblock}{} {\small <>= mcols(grl) # outer mcols mcols(unlist(grl, use.names=FALSE)) # inner mcols @ } \end{exampleblock} \end{frame} %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \begin{frame}[fragile] \frametitle{2. invertStrand()} Works out-of-the-box on any object that has a strand() getter and setter ==> no need to implement specific methods. \begin{exampleblock}{} {\small <<>>= gr @ } \end{exampleblock} \end{frame} \begin{frame}[fragile] \frametitle{2. invertStrand()} \begin{exampleblock}{} {\small <<>>= invertStrand(gr) @ } \end{exampleblock} \end{frame} \begin{frame}[fragile] \frametitle{2. invertStrand()} \begin{exampleblock}{} {\small <<>>= grl @ } \end{exampleblock} \end{frame} \begin{frame}[fragile] \frametitle{2. invertStrand()} \begin{exampleblock}{} {\small <<>>= invertStrand(grl) @ } \end{exampleblock} \end{frame} %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \begin{frame}[fragile] \frametitle{3. extractList()} Extract groups of elements from a vector-like object and return them in a list-like object. \begin{exampleblock}{} <<>>= cvg <- Rle(c(0L, 2L, 5L, 1L, 0L), c(10, 6, 3, 4, 15)) cvg i <- IRanges(c(16, 19, 9), width=5, names=letters[1:3]) i @ \end{exampleblock} \end{frame} \begin{frame}[fragile] \frametitle{3. extractList()} \begin{exampleblock}{} <<>>= extractList(cvg, i) @ \end{exampleblock} \end{frame} \begin{frame}[fragile] \frametitle{3. extractList()} \begin{exampleblock}{} \Rcode{i} can be an IntegerList object: {\small <<>>= i <- IntegerList(c(25:20), NULL, seq(from=2, to=length(cvg), by=2)) i extractList(cvg, i) @ } \end{exampleblock} \end{frame} %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \begin{frame}[fragile] \frametitle{4. 'with.revmap' arg for reduce() and (now) disjoin()} \begin{exampleblock}{} <<>>= ir ir2 <- reduce(ir, with.revmap=TRUE) ir2 @ \end{exampleblock} \end{frame} \begin{frame}[fragile] \frametitle{4. 'with.revmap' arg for reduce() and disjoin()} \begin{exampleblock}{} {\small <<>>= revmap <- mcols(ir2)$revmap extractList(mcols(ir)$id, revmap) extractList(mcols(ir)$score, revmap) mcols(ir2) <- DataFrame(id=extractList(mcols(ir)$id, revmap), score=extractList(mcols(ir)$score, revmap)) ir2 @ } \end{exampleblock} \end{frame} %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \begin{frame}[fragile] \frametitle{5. Zero-width ranges} \Rcode{findOverlaps}/\Rcode{countOverlaps} support zero-width ranges. \begin{exampleblock}{} {\small <<>>= sliding_query <- IRanges(1:6, width=0) sliding_query countOverlaps(sliding_query, IRanges(3, 4)) @ } \end{exampleblock} But you have to specify \Rcode{minoverlap=0} for this to work (default is 1). \begin{exampleblock}{} {\small <<>>= countOverlaps(sliding_query, IRanges(3, 4), minoverlap=0) @ } \end{exampleblock} \end{frame} %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \begin{frame}[fragile] \frametitle{6. Biostrings::replaceAt()} Perform multiple substitutions at arbitrary positions in a set of sequences. \begin{exampleblock}{} <<>>= library(Biostrings) library(hgu95av2probe) probes <- DNAStringSet(hgu95av2probe) probes @ \end{exampleblock} \end{frame} \begin{frame}[fragile] \frametitle{6. Biostrings::replaceAt()} Replace 3rd and 4th nucleotides by pattern \Rcode{-++-}. \begin{exampleblock}{} <<>>= replaceAt(probes, at=IRanges(3, 4), value="-++-") @ \end{exampleblock} \end{frame} \begin{frame}[fragile] \frametitle{6. Biostrings::replaceAt()} If supplied pattern is empty, then performs deletions. \begin{exampleblock}{} <<>>= replaceAt(probes, at=IRanges(3, 4), value="") @ \end{exampleblock} \end{frame} \begin{frame}[fragile] \frametitle{6. Biostrings::replaceAt()} If \Rcode{at} is a zero-with range, then performs insertions. \begin{exampleblock}{} <<>>= replaceAt(probes, at=IRanges(4, 3), value="-++-") @ \end{exampleblock} \end{frame} \begin{frame}[fragile] \frametitle{6. Biostrings::replaceAt()} Use it in combination with \Rcode{vmatchPattern} to replace all the occurences of a given pattern with another pattern: \begin{exampleblock}{} <<>>= midx <- vmatchPattern("VCGTT", probes, fixed=FALSE) replaceAt(probes, at=midx, value="-++-") @ \end{exampleblock} \end{frame} %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \begin{frame}[fragile] \frametitle{7. GRanges as a subscript} \begin{exampleblock}{} {\small <>= cvg <- RleList(chr1=101:120, chr2=2:-8, chr3=31:40) gr @ } \end{exampleblock} \end{frame} \begin{frame}[fragile] \frametitle{7. GRanges as a subscript} \begin{exampleblock}{} {\scriptsize <>= cvg[gr] @ } \end{exampleblock} \end{frame} %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \begin{frame}[fragile] \frametitle{8. BSgenomeViews objects} \begin{exampleblock}{} <<>>= library(BSgenome.Mmusculus.UCSC.mm10) genome <- BSgenome.Mmusculus.UCSC.mm10 library(TxDb.Mmusculus.UCSC.mm10.knownGene) txdb <- TxDb.Mmusculus.UCSC.mm10.knownGene ex <- exons(txdb, columns=c("exon_id", "tx_name", "gene_id")) v <- Views(genome, ex) @ \end{exampleblock} \end{frame} \begin{frame}[fragile] \frametitle{8. BSgenomeViews objects} \begin{exampleblock}{} {\scriptsize <<>>= v @ } \end{exampleblock} \end{frame} \begin{frame}[fragile] \frametitle{8. BSgenomeViews objects} \begin{exampleblock}{} <<>>= af <- alphabetFrequency(v, baseOnly=TRUE) head(af) @ \end{exampleblock} \end{frame} %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \begin{frame}[fragile] \frametitle{9. Pile-up statistics on a BAM file with Rsamtools::pileup()} \begin{exampleblock}{} <<>>= library(Rsamtools) library(RNAseqData.HNRNPC.bam.chr14) fl <- RNAseqData.HNRNPC.bam.chr14_BAMFILES[1] sbp <- ScanBamParam(which=GRanges("chr14", IRanges(1, 53674770))) pp <- PileupParam(distinguish_nucleotides=FALSE, distinguish_strands=FALSE, min_mapq=13, min_base_quality=10, min_nucleotide_depth=4) res <- pileup(fl, scanBamParam=sbp, pileupParam=pp) @ \end{exampleblock} \end{frame} \begin{frame}[fragile] \frametitle{9. Pile-up statistics on a BAM file with Rsamtools::pileup()} \begin{exampleblock}{} <<>>= dim(res) head(res) @ \end{exampleblock} \end{frame} %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \begin{frame}[fragile] \frametitle{10. Merging 2 GRanges objects (added this week)} \begin{exampleblock}{} {\small <<>>= x y @ } \end{exampleblock} \end{frame} \begin{frame}[fragile] \frametitle{10. Merging 2 GRanges objects} \begin{exampleblock}{} {\small <<>>= merge(x, y) @ } \end{exampleblock} \end{frame} \begin{frame}[fragile] \frametitle{10. Merging 2 GRanges objects} \begin{exampleblock}{} {\small <<>>= merge(x, y, all=TRUE) @ } \end{exampleblock} \end{frame} %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \end{document} GenomicRanges/vignettes/slides.sty0000644000175400017540000000211313001230267020344 0ustar00biocbuildbiocbuild\usepackage{Sweave} \usepackage{color, graphics} \usepackage{latexsym, amsmath, amssymb} %% simple macros \newcommand{\software}[1]{\textsl{#1}} \newcommand\R{\textsl{R}} \newcommand\Bioconductor{\textsl{Bioconductor}} \newcommand\Rpackage[1]{{\textsl{#1}\index{#1 (package)}}} \newcommand\Biocpkg[1]{% {\href{http://bioconductor.org/packages/release/bioc/html/#1.html}% {\textsl{#1}}}% \index{#1 (package)}} \newcommand\Rpkg[1]{% {\href{http://cran.fhcrc.org/web/packages/#1/index.html}% {\textsl{#1}}}% \index{#1 (package)}} \newcommand\Biocdatapkg[1]{% {\href{http://bioconductor.org/packages/release/data/experiment/html/#1.html}% {\textsl{#1}}}% \index{#1 (package)}} \newcommand\Robject[1]{{\small\texttt{#1}}} \newcommand\Rclass[1]{{\textit{#1}\index{#1 (class)}}} \newcommand\Rfunction[1]{{{\small\texttt{#1}}\index{#1 (function)}}} \newcommand\Rmethod[1]{{\texttt{#1}}} \newcommand\Rfunarg[1]{{\small\texttt{#1}}} \newcommand\Rcode[1]{{\small\texttt{#1}}} %% \AtBeginSection[] %% { %% \begin{frame}{Outline} %% \tableofcontents %% \end{frame} %% }