SummarizedExperiment/DESCRIPTION0000644000175400017540000000255213001262612017464 0ustar00biocbuildbiocbuildPackage: SummarizedExperiment Title: SummarizedExperiment container Description: The SummarizedExperiment container contains one or more assays, each represented by a matrix-like object of numeric or other mode. The rows typically represent genomic ranges of interest and the columns represent samples. Version: 1.4.0 Encoding: UTF-8 Author: Martin Morgan, Valerie Obenchain, Jim Hester, Hervé Pagès Maintainer: Bioconductor Package Maintainer biocViews: Genetics, Infrastructure, Sequencing, Annotation, Coverage, GenomeAnnotation Depends: R (>= 3.2), methods, GenomicRanges (>= 1.23.15), Biobase Imports: utils, stats, Matrix, BiocGenerics (>= 0.15.3), S4Vectors (>= 0.11.7), IRanges (>= 2.7.2), GenomeInfoDb Suggests: annotate, AnnotationDbi, hgu95av2.db, GenomicFeatures, TxDb.Hsapiens.UCSC.hg19.knownGene, BiocStyle, knitr, rmarkdown, digest, jsonlite, rhdf5, airway, RUnit VignetteBuilder: knitr License: Artistic-2.0 Collate: Assays-class.R SummarizedExperiment-class.R RangedSummarizedExperiment-class.R intra-range-methods.R inter-range-methods.R coverage-methods.R findOverlaps-methods.R nearest-methods.R makeSummarizedExperimentFromExpressionSet.R makeSummarizedExperimentFromDataFrame.R readKallisto.R zzz.R NeedsCompilation: no Packaged: 2016-10-17 23:58:02 UTC; biocbuild SummarizedExperiment/NAMESPACE0000644000175400017540000000534513001230160017171 0ustar00biocbuildbiocbuildimport(methods) importFrom(utils, read.delim, .DollarNames) importFrom(stats, setNames) importClassFrom(Matrix, Matrix) # for the "acbind" and "arbind" methods import(BiocGenerics) import(S4Vectors) import(IRanges) import(GenomeInfoDb) import(GenomicRanges) import(Biobase) ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ### Export S4 classes ### exportClasses( Assays, ShallowData, ShallowSimpleListAssays, SummarizedExperiment, SummarizedExperiment0, RangedSummarizedExperiment ) ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ### Export S4 methods for generics not defined in SummarizedExperiment ### exportMethods( length, names, "names<-", dim, dimnames, "dimnames<-", "[", "[<-", "[[", "[[<-", "$", "$<-", as.data.frame, coerce, show, c, merge, Compare, ## Generics defined in BiocGenerics: cbind, rbind, duplicated, match, is.unsorted, order, rank, sort, start, "start<-", end, "end<-", width, "width<-", strand, "strand<-", updateObject, ## Generics defined in S4Vectors: "metadata<-", mcols, "mcols<-", elementMetadata, "elementMetadata<-", values, "values<-", extractROWS, replaceROWS, subset, pcompare, ## Generics defined in IRanges: ranges, "ranges<-", shift, narrow, resize, flank, promoters, restrict, trim, isDisjoint, disjointBins, coverage, findOverlaps, precede, follow, nearest, distance, distanceToNearest, acbind, arbind, ## Generics defined in GenomeInfoDb: seqinfo, "seqinfo<-", seqnames, "seqnames<-" ) ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ### Export non-generic functions ### export( Assays, .DollarNames.SummarizedExperiment, makeSummarizedExperimentFromExpressionSet, makeSummarizedExperimentFromDataFrame, naiveRangeMapper, probeRangeMapper, geneRangeMapper, readKallisto, readKallistoBootstrap, KALLISTO_ASSAYS ) ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ### Export S4 generics defined in SummarizedExperiment + export corresponding ### methods ### export( SummarizedExperiment, rowRanges, "rowRanges<-", rowData, "rowData<-", colData, "colData<-", assayNames, "assayNames<-", assays, "assays<-", assay, "assay<-" ) ### Exactly the same list as above. exportMethods( SummarizedExperiment, rowRanges, "rowRanges<-", rowData, "rowData<-", colData, "colData<-", assayNames, "assayNames<-", assays, "assays<-", assay, "assay<-" ) ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ### Register S3 methods ### S3method(.DollarNames, SummarizedExperiment) SummarizedExperiment/NEWS0000644000175400017540000000622113001230160016443 0ustar00biocbuildbiocbuildCHANGES IN VERSION 1.4.0 ------------------------ NEW FEATURES o Add makeSummarizedExperimentFromDataFrame() function. o Add "acbind" and "arbind" methods for Matrix objects. SIGNIFICANT USER-VISIBLE CHANGES o Speed up "cbind" method for SummarizedExperiment objects based on a suggestion by Peter Hickey. DEPRECATED AND DEFUNCT o Remove exptData() getter and setter (were defunct in BioC 3.3). BUG FIXES CHANGES IN VERSION 1.2.0 ------------------------ NEW FEATURES o Add 'rowData' argument to SummarizedExperiment() constructor. This allows the user to supply the row data at construction time. o The SummarizedExperiment() constructor function and the assay() setter now both take any matrix-like object as long as the resulting SummarizedExperiment object is valid. o Support r/cbind'ing of SummarizedExperiment objects with assays of arbitrary dimensions (based on a patch by Pete Hickey). o Add "is.unsorted" method for RangedSummarizedExperiment objects. o NULL colnames() supported during SummarizedExperiment construction. o readKallisto() warns early when files need names. 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 RangedSummarizedExperiment objects. SIGNIFICANT USER-VISIBLE CHANGES o Re-introduce the rowData() accessor (was defunt in BioC 3.2) as an alias for mcols() and make it the preferred way to access the row data. There is now a pleasant symmetry between rowData and colData. o Rename SummarizedExperiment0 class -> SummarizedExperiment. o Improved vignette. o Remove updateObject() method for "old" SummarizedExperiment objects. DEPRECATED AND DEFUNCT o exptData() is now defunct, metadata() should be used instead. BUG FIXES o Fix bug in "sort" method for RangedSummarizedExperiment objects when 'ignore.strand=TRUE' (the argument was ignored). o Fix 2 bugs when r/cbind'ing SummarizedExperiment objects: - r/cbind'ing assays without names would return only the first element. See https://stat.ethz.ch/pipermail/bioc-devel/2015-November/008318.html - r/cbind'ing assays with names in different order would stop() with 'Assays must have the same names()" o Fix validity method for SummarizedExperiment objects reporting incorrect numbers when the nb of cols in assay(x) doesn't match the nb of rows in colData(x). o assay colnames() must agree with colData rownames() o Fix bug where assays(se, withDimnames=TRUE) was dropping the dimnames of the 3rd and higher-order dimensions of the assays. Thanks to Pete Hickey for catching this and providing a patch. o A couple of minor tweaks to the rowData() setter to make it behave consistently with mcols()/elementMetadata() setters for Vector objects in general. CHANGES IN VERSION 0.3.* ------------------------ NEW FEATURES o readKallisto() and readKallistoBootstrap() input kallisto transcript quantification output into SummarizedExperiment (and other) instances. SummarizedExperiment/R/0000755000175400017540000000000013001230160016144 5ustar00biocbuildbiocbuildSummarizedExperiment/R/Assays-class.R0000644000175400017540000002675713001230160020656 0ustar00biocbuildbiocbuild### ========================================================================= ### Assays objects ### ------------------------------------------------------------------------- ### ### The Assays API consists of: ### (a) The Assays() constructor function. ### (b) Lossless back and forth coercion from/to SimpleList. The coercion ### method from SimpleList doesn't need (and should not) validate the ### returned object. ### (c) length, names, names<-, [[, [[<-, dim, [, [<-, rbind, cbind ### ### An Assays concrete subclass needs to implement (b) (required) plus ### optionally any of the methods in (c). ### ### IMPORTANT: Methods that return a modified Assays object (a.k.a. ### endomorphisms), that is, [ as well as replacement methods names<-, [[<-, ### and [<-, must respect the copy-on-change contract. With objects that ### don't make use of references internally, the developer doesn't need to ### take any special action for that because it's automatically taken care of ### by R itself. However, for objects that do make use of references internally ### (e.g. environments, external pointers, pointer to a file on disk, etc...), ### the developer needs to be careful to implement endomorphisms with ### copy-on-change semantics. This can easily be achieved (and is what the ### default methods for Assays objects do) by performaing a full (deep) copy ### of the object before modifying it instead of trying to modify it in-place. ### Note that the full (deep) copy is not always necessary in order to achieve ### copy-on-change semantics: it's enough (and often preferrable for ### performance reasons) to copy only the parts of the objects that need to ### be modified. ### ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ### Assays class ### setClass("Assays") ### Validity .valid.Assays <- function(x) { assays <- as(x, "SimpleList", strict=FALSE) if (!is(assays, "SimpleList")) return("'assays' must be a SimpleList object") if (length(assays) == 0L) return(NULL) ## Check dims. all_dims <- sapply(assays, function(assay) dim(assay)[1:2]) if (any(is.na(all_dims))) return(wmsg("all assays must be matrix-like objects ", "with 2 (or more?) dimensions")) if (!all(all_dims == all_dims[ , 1L])) stop("all assays must have the same nrow and ncol") NULL } setValidity2("Assays", .valid.Assays) ### Constructor .normarg_assays <- function(assays) { if (!is(assays, "SimpleList")) { if (is.list(assays) || is.array(assays)) { assays <- SimpleList(assays) } else { stop("'assays' must be a SimpleList, list or array") } } assays } Assays <- function(assays=SimpleList()) { assays <- .normarg_assays(assays) #ans <- as(assays, "SimpleListAssays") ans <- as(assays, "ShallowSimpleListAssays") #ans <- as(assays, "AssaysInEnv") # a *broken* alternative validObject(ans) ans } ### Accessors .SL_get_length <- selectMethod("length", "SimpleList") setMethod("length", "Assays", function(x) { assays <- as(x, "SimpleList", strict=FALSE) .SL_get_length(assays) } ) .SL_get_names <- selectMethod("names", "SimpleList") setMethod("names", "Assays", function(x) { assays <- as(x, "SimpleList", strict=FALSE) .SL_get_names(assays) } ) .SL_set_names <- selectMethod("names<-", "SimpleList") setReplaceMethod("names", "Assays", function(x, value) { assays <- as(x, "SimpleList", strict=FALSE) assays <- .SL_set_names(assays, value) as(assays, class(x)) } ) setMethod("[[", "Assays", function(x, i, j, ...) { assays <- as(x, "SimpleList", strict=FALSE) getListElement(assays, i) } ) setReplaceMethod("[[", "Assays", function(x, i, j, ..., value) { assays <- as(x, "SimpleList", strict=FALSE) assays <- setListElement(assays, i, value) ans <- as(assays, class(x)) validObject(ans) ans } ) setMethod("dim", "Assays", function(x) { if (length(x) == 0L) return(c(0L, 0L)) dim(x[[1L]]) } ) ### 2D-Subsetting .extract_Assays_subset <- function(x, i, j) { ## need to expand Rle's for subsetting standard matrix if (!missing(i) && !missing(j)) { fun <- function(x) { switch(length(dim(x)), stop("'[' on assays() with 1 dimension not supported"), x[i, j, drop=FALSE], x[i, j, , drop=FALSE], x[i, j, , , drop=FALSE], stop("'[' on assays() with >4 dimensions not supported")) } } else if (!missing(i)) { fun <- function(x) { switch(length(dim(x)), stop("'[' on assays() with 1 dimension not supported"), x[i, , drop=FALSE], x[i, , , drop=FALSE], x[i, , , , drop=FALSE], stop("'[' on assays() with >4 dimensions not supported")) } } else if (!missing(j)) { fun <- function(x) { switch(length(dim(x)), stop("'[' on assays() with 1 dimension not supported"), x[, j, drop=FALSE], x[, j, , drop=FALSE], x[, j, , , drop=FALSE], stop("'[' on assays() with >4 dimensions not supported")) } } assays <- as(x, "SimpleList", strict=FALSE) as(endoapply(assays, fun), class(x)) } setMethod("[", "Assays", function(x, i, j, ..., drop=TRUE) .extract_Assays_subset(x, i, j) ) .replace_Assays_subset <- function(x, i, j, value) { ## need to expand Rle's for subsetting standard matrix if (!missing(i) && !missing(j)) { fun <- function(x, value) { switch(length(dim(x)), stop("'[<-' on assays() with 1 dimension not supported"), x[i, j] <- value, x[i, j, ] <- value, x[i, j, , ] <- value, stop("'[<-' on assays() with >4 dimensions not supported")) x } } else if (!missing(i)) { fun <- function(x, value) { switch(length(dim(x)), stop("'[<-' on assays() with 1 dimension not supported"), x[i, ] <- value, x[i, , ] <- value, x[i, , , ] <- value, stop("'[<-' on assays() with >4 dimensions not supported")) x } } else if (!missing(j)) { fun <- function(x, value) { switch(length(dim(x)), stop("'[<-' on assays() with 1 dimension not supported"), x[, j] <- value, x[, j, ] <- value, x[, j, , ] <- value, stop("'[<-' on assays() with >4 dimensions not supported")) x } } a <- as(x, "SimpleList", strict=FALSE) v <- as(value, "SimpleList", strict=FALSE) as(mendoapply(fun, x=a, value=v), class(x)) } setReplaceMethod("[", "Assays", function(x, i, j, ..., value) .replace_Assays_subset(x, i, j, value) ) ### rbind/cbind .bind_Assays <- function(lst, bind) { if (length(lst) == 0L) return(Assays()) lens <- sapply(lst, length) if (length(unique(lens)) != 1) stop("assays must have the same length") len1 <- lens[1L] if (len1 == 0L) return(Assays()) var <- lapply(lst, names) uvar <- unique(unlist(var)) if (is.null(uvar)) { ## no names, match by position res <- lapply(seq_len(len1), function(index) { e1 <- lapply(lst, "[[", index) do.call(bind, e1) }) } else { ## match by name ok <- all(vapply(var, function(x, y) identical(sort(x), y), logical(1), sort(uvar))) if (!ok) stop("assays must have the same names()") res <- lapply(uvar, function(index) { e1 <- lapply(lst, "[[", index) do.call(bind, e1) }) names(res) <- uvar } as(SimpleList(res), class(lst[[1L]])) } setMethod("rbind", "Assays", function(..., deparse.level=1) .bind_Assays(unname(list(...)), arbind) ) setMethod("cbind", "Assays", function(..., deparse.level=1) .bind_Assays(unname(list(...)), acbind) ) ### Having "arbind" and "acbind" methods for Matrix objects will make rbind() ### and cbind() work on Assays objects with Matrix list elements. ### Maybe these methods should be defined next to the arbind() and acbind() ### generics (which are defined in the IRanges package) but that would require ### to make IRanges depend on the Matrix package. setMethod("arbind", "Matrix", function(...) rbind(...)) setMethod("acbind", "Matrix", function(...) cbind(...)) ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ### SimpleListAssays class ### ### The order of inheritance is important: first Assays, then SimpleList! setClass("SimpleListAssays", contains=c("Assays", "SimpleList")) ### Lossless back and forth coercion from/to SimpleList are automatically ### taken care of by automatic methods defined by the methods package. ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ### ShallowSimpleListAssays class ### ### We implement the REQUIRED coercions only. ### .ShallowData <- setRefClass("ShallowData", fields = list( data = "ANY" )) .ShallowSimpleListAssays0 <- setRefClass("ShallowSimpleListAssays", fields = list( data = "SimpleList" ), contains = c("ShallowData", "Assays")) setAs("SimpleList", "ShallowSimpleListAssays", function(from) .ShallowSimpleListAssays0(data=from) ) setAs("ShallowSimpleListAssays", "SimpleList", function(from) from$data) ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ### AssaysInEnv class ### ### A *broken* alternative to ShallowSimpleListAssays that does NOT respect ### the copy-on-change contract (only provided for illustration purposes). ### ### We implement the REQUIRED coercions plus OPTIONAL methods: length, names, ### names<-, [[, and [[<-. ### setClass("AssaysInEnv", contains="Assays", representation(envir="environment") ) .NAMES_SYMBOL <- ".names" # must begin with a . so is ommitted by ls() setMethod("length", "AssaysInEnv", function(x) length(x@envir) - 1L) setMethod("names", "AssaysInEnv", function(x) x@envir[[.NAMES_SYMBOL]]) ### Does NOT respect the copy-on-change contract! setReplaceMethod("names", "AssaysInEnv", function(x, value) { value <- S4Vectors:::normalize_names_replacement_value(value, x) x@envir[[.NAMES_SYMBOL]] <- value x } ) setMethod("[[", "AssaysInEnv", function(x, i, j, ...) { key <- setNames(ls(x@envir, sorted=TRUE), names(x))[[i]] get(key, envir=x@envir) } ) ### Does NOT respect the copy-on-change contract! setReplaceMethod("[[", "AssaysInEnv", function(x, i, j, ..., value) { key <- setNames(ls(x@envir, sorted=TRUE), names(x))[[i]] assign(key, value, envir=x@envir) x } ) setAs("SimpleList", "AssaysInEnv", function(from) { from <- as.list(from) from_names <- names(from) keys <- paste(sprintf("%09d", seq_along(from)), from_names, sep=":") names(from) <- keys envir <- list2env(from, parent=emptyenv()) envir[[.NAMES_SYMBOL]] <- from_names new("AssaysInEnv", envir=envir) } ) setAs("AssaysInEnv", "SimpleList", function(from) SimpleList(setNames(as.list(from@envir, sorted=TRUE), names(from))) ) SummarizedExperiment/R/RangedSummarizedExperiment-class.R0000644000175400017540000004241413001230160024701 0ustar00biocbuildbiocbuild### ========================================================================= ### RangedSummarizedExperiment objects ### ------------------------------------------------------------------------- ### ### The 'elementMetadata' slot must contain a zero-column DataFrame at all time ### (this is checked by the validity method). The top-level mcols are stored on ### the rowRanges component. setClass("RangedSummarizedExperiment", contains="SummarizedExperiment", representation( rowRanges="GenomicRangesORGRangesList" ), prototype( rowRanges=GRanges() ) ) ### Combine the new parallel slots with those of the parent class. Make sure ### to put the new parallel slots *first*. setMethod("parallelSlotNames", "RangedSummarizedExperiment", function(x) c("rowRanges", callNextMethod()) ) ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ### Validity. ### ### The names and mcols of a RangedSummarizedExperiment must be set on its ### rowRanges slot, not in its NAMES and elementMetadata slots! .valid.RangedSummarizedExperiment <- function(x) { if (!is.null(x@NAMES)) return("'NAMES' slot must be set to NULL at all time") if (ncol(x@elementMetadata) != 0L) return(wmsg("'elementMetadata' slot must contain a zero-column ", "DataFrame at all time")) rowRanges_len <- length(x@rowRanges) x_nrow <- length(x) if (rowRanges_len != x_nrow) { txt <- sprintf( "\n length of 'rowRanges' (%d) must equal nb of rows in 'x' (%d)", rowRanges_len, x_nrow) return(txt) } NULL } setValidity2("RangedSummarizedExperiment", .valid.RangedSummarizedExperiment) ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ### Constructor. ### .new_RangedSummarizedExperiment <- function(assays, rowRanges, colData, metadata) { elementMetadata <- S4Vectors:::make_zero_col_DataFrame(length(rowRanges)) if (!is(assays, "Assays")) assays <- Assays(assays) new("RangedSummarizedExperiment", rowRanges=rowRanges, colData=colData, assays=assays, elementMetadata=elementMetadata, metadata=as.list(metadata)) } .get_colnames_from_assays <- function(assays) { if (length(assays) == 0L) return(NULL) colnames(assays[[1L]]) } .get_rownames_from_assays <- function(assays) { if (length(assays) == 0L) return(NULL) rownames(assays[[1L]]) } setGeneric("SummarizedExperiment", function(assays, ...) standardGeneric("SummarizedExperiment")) setMethod("SummarizedExperiment", "SimpleList", function(assays, rowData=NULL, rowRanges=GRangesList(), colData=DataFrame(), metadata=list()) { if (missing(colData) && 0L != length(assays)) { assay <- assays[[1]] nms <- colnames(assay) colData <- DataFrame(x=seq_len(ncol(assay)), row.names=nms)[, FALSE] } else if (!missing(colData)) { colData <- as(colData, "DataFrame") if (is.null(rownames(colData))) rownames(colData) <- .get_colnames_from_assays(assays) } ans_colnames <- rownames(colData) if (is.null(rowData)) { if (missing(rowRanges)) { ans_rownames <- .get_rownames_from_assays(assays) } else { if (is.null(names(rowRanges))) names(rowRanges) <- .get_rownames_from_assays(assays) ans_rownames <- names(rowRanges) } } else { if (!missing(rowRanges)) stop("only one of 'rowData' and 'rowRanges' can be specified") if (is(rowData, "GenomicRangesORGRangesList")) { rowRanges <- rowData if (is.null(names(rowRanges))) names(rowRanges) <- .get_rownames_from_assays(assays) ans_rownames <- names(rowRanges) } else { rowData <- as(rowData, "DataFrame") ans_rownames <- rownames(rowData) if (is.null(ans_rownames)) ans_rownames <- .get_rownames_from_assays(assays) } } ## validate ok <- vapply(assays, function(x) { colnames <- colnames(x) test <- is.null(colnames) || identical(colnames, ans_colnames) if (!test) stop("assay colnames() must be NULL or equal colData rownames()") rownames <- rownames(x) test <- test && is.null(rownames) || identical(rownames, ans_rownames) if (!test) { txt <- "assay rownames() must be NULL or equal rowData rownames() / rowRanges names()" stop(paste(strwrap(txt, exdent=2), collapse="\n")) } test }, logical(1)) assays <- Assays(assays) if (missing(rowRanges) && !is(rowData, "GenomicRangesORGRangesList")) { new_SummarizedExperiment(assays, ans_rownames, rowData, colData, metadata) } else { .new_RangedSummarizedExperiment(assays, rowRanges, colData, metadata) } }) setMethod("SummarizedExperiment", "ANY", function(assays, ...) { if (is.matrix(assays) && is.list(assays)) ## special case -- matrix of lists assays <- list(assays) SummarizedExperiment(SimpleList(assays), ...) }) setMethod("SummarizedExperiment", "list", function(assays, ...) { SummarizedExperiment(do.call(SimpleList, assays), ...) }) setMethod("SummarizedExperiment", "missing", function(assays, ...) { SummarizedExperiment(SimpleList(), ...) }) ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ### Coercion. ### ### See makeSummarizedExperimentFromExpressionSet.R for coercion back and ### forth between SummarizedExperiment and ExpressionSet. ### .from_RangedSummarizedExperiment_to_SummarizedExperiment <- function(from) { new_SummarizedExperiment(from@assays, names(from@rowRanges), mcols(from@rowRanges), from@colData, from@metadata) } setAs("RangedSummarizedExperiment", "SummarizedExperiment", .from_RangedSummarizedExperiment_to_SummarizedExperiment ) .from_SummarizedExperiment_to_RangedSummarizedExperiment <- function(from) { partitioning <- PartitioningByEnd(integer(length(from)), names=names(from)) rowRanges <- relist(GRanges(), partitioning) .new_RangedSummarizedExperiment(from@assays, rowRanges, from@colData, from@metadata) } setAs("SummarizedExperiment", "RangedSummarizedExperiment", .from_SummarizedExperiment_to_RangedSummarizedExperiment ) ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ### Getters and setters. ### setGeneric("rowRanges", function(x, ...) standardGeneric("rowRanges")) setMethod("rowRanges", "RangedSummarizedExperiment", function(x, ...) x@rowRanges ) setGeneric("rowRanges<-", function(x, ..., value) standardGeneric("rowRanges<-")) .SummarizedExperiment.rowRanges.replace <- function(x, ..., value) { if (!is(x, "RangedSummarizedExperiment")) x <- as(x, "RangedSummarizedExperiment") x <- BiocGenerics:::replaceSlots(x, ..., rowRanges=value, elementMetadata=S4Vectors:::make_zero_col_DataFrame(length(value)), check=FALSE) msg <- .valid.SummarizedExperiment.assays_nrow(x) if (!is.null(msg)) stop(msg) x } setReplaceMethod("rowRanges", c("SummarizedExperiment", "GenomicRanges"), .SummarizedExperiment.rowRanges.replace) setReplaceMethod("rowRanges", c("SummarizedExperiment", "GRangesList"), .SummarizedExperiment.rowRanges.replace) setMethod("names", "RangedSummarizedExperiment", function(x) names(rowRanges(x)) ) setReplaceMethod("names", "RangedSummarizedExperiment", function(x, value) { rowRanges <- rowRanges(x) names(rowRanges) <- value BiocGenerics:::replaceSlots(x, rowRanges=rowRanges, check=FALSE) }) setMethod("dimnames", "RangedSummarizedExperiment", function(x) { list(names(x), rownames(colData(x))) }) setReplaceMethod("dimnames", c("RangedSummarizedExperiment", "list"), function(x, value) { rowRanges <- rowRanges(x) names(rowRanges) <- value[[1]] colData <- colData(x) rownames(colData) <- value[[2]] BiocGenerics:::replaceSlots(x, rowRanges=rowRanges, colData=colData, check=FALSE) }) ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ### Subsetting. ### setMethod("subset", "RangedSummarizedExperiment", function(x, subset, select, ...) { i <- S4Vectors:::evalqForSubset(subset, rowRanges(x), ...) j <- S4Vectors:::evalqForSubset(select, colData(x), ...) x[i, j] }) ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ## colData-as-GRanges compatibility: allow direct access to GRanges / ## GRangesList colData for select functions ## Not supported: ## ## Not consistent SummarizedExperiment structure: length, names, ## as.data.frame, c. ## Length-changing endomorphisms: disjoin, gaps, reduce, unique. ## 'legacy' data types / functions: as "RangedData", as "RangesList", ## renameSeqlevels, keepSeqlevels. ## Possile to implement, but not yet: Ops, map, window, window<- ## mcols setMethod("mcols", "RangedSummarizedExperiment", function(x, use.names=FALSE, ...) { mcols(rowRanges(x), use.names=use.names, ...) }) setReplaceMethod("mcols", "RangedSummarizedExperiment", function(x, ..., value) { BiocGenerics:::replaceSlots(x, rowRanges=local({ r <- rowRanges(x) mcols(r) <- value r }), check=FALSE) }) ### mcols() is the recommended way for accessing the metadata columns. ### Use of values() or elementMetadata() is discouraged. setMethod("elementMetadata", "RangedSummarizedExperiment", function(x, use.names=FALSE, ...) { elementMetadata(rowRanges(x), use.names=use.names, ...) }) setReplaceMethod("elementMetadata", "RangedSummarizedExperiment", function(x, ..., value) { elementMetadata(rowRanges(x), ...) <- value x }) ## Single dispatch, generic signature fun(x, ...) local({ .funs <- c("duplicated", "end", "end<-", "ranges", "seqinfo", "seqnames", "start", "start<-", "strand", "width", "width<-") endomorphisms <- .funs[grepl("<-$", .funs)] tmpl <- function() {} environment(tmpl) <- parent.frame(2) for (.fun in .funs) { generic <- getGeneric(.fun) formals(tmpl) <- formals(generic) fmls <- as.list(formals(tmpl)) fmls[] <- sapply(names(fmls), as.symbol) fmls[[generic@signature]] <- quote(rowRanges(x)) if (.fun %in% endomorphisms) body(tmpl) <- substitute({ rowRanges(x) <- do.call(FUN, ARGS) x }, list(FUN=.fun, ARGS=fmls)) else body(tmpl) <- substitute(do.call(FUN, ARGS), list(FUN=as.symbol(.fun), ARGS=fmls)) setMethod(.fun, "RangedSummarizedExperiment", tmpl) } }) setMethod("granges", "RangedSummarizedExperiment", function(x, use.mcols=FALSE, ...) { if (!identical(use.mcols, FALSE)) stop("\"granges\" method for RangedSummarizedExperiment objects ", "does not support the 'use.mcols' argument") rowRanges(x) }) ## 2-argument dispatch: ## pcompare / Compare ## .RangedSummarizedExperiment.pcompare <- function(x, y) { if (is(x, "RangedSummarizedExperiment")) x <- rowRanges(x) if (is(y, "RangedSummarizedExperiment")) y <- rowRanges(y) pcompare(x, y) } .RangedSummarizedExperiment.Compare <- function(e1, e2) { if (is(e1, "RangedSummarizedExperiment")) e1 <- rowRanges(e1) if (is(e2, "RangedSummarizedExperiment")) e2 <- rowRanges(e2) callGeneric(e1=e1, e2=e2) } local({ .signatures <- list( c("RangedSummarizedExperiment", "ANY"), c("ANY", "RangedSummarizedExperiment"), c("RangedSummarizedExperiment", "RangedSummarizedExperiment")) for (.sig in .signatures) { setMethod("pcompare", .sig, .RangedSummarizedExperiment.pcompare) setMethod("Compare", .sig, .RangedSummarizedExperiment.Compare) } }) ## additional getters / setters setReplaceMethod("strand", "RangedSummarizedExperiment", function(x, ..., value) { strand(rowRanges(x)) <- value x }) setReplaceMethod("ranges", "RangedSummarizedExperiment", function(x, ..., value) { ranges(rowRanges(x)) <- value x }) ## order, rank, sort setMethod("is.unsorted", "RangedSummarizedExperiment", function(x, na.rm = FALSE, strictly = FALSE, ignore.strand = FALSE) { x <- rowRanges(x) if (!is(x, "GenomicRanges")) stop("is.unsorted() is not yet supported when 'rowRanges(x)' is a ", class(x), " object") callGeneric() }) setMethod("order", "RangedSummarizedExperiment", function(..., na.last=TRUE, decreasing=FALSE, method=c("shell", "radix")) { args <- lapply(list(...), rowRanges) do.call("order", c(args, list(na.last=na.last, decreasing=decreasing, method=method))) }) setMethod("rank", "RangedSummarizedExperiment", function(x, na.last = TRUE, ties.method = c("average", "first", "last", "random", "max", "min")) { ties.method <- match.arg(ties.method) rank(rowRanges(x), na.last=na.last, ties.method=ties.method) }) setMethod("sort", "RangedSummarizedExperiment", function(x, decreasing = FALSE, ignore.strand = FALSE) { x_rowRanges <- rowRanges(x) if (!is(x_rowRanges, "GenomicRanges")) stop("sort() is not yet supported when 'rowRanges(x)' is a ", class(x_rowRanges), " object") oo <- GenomicRanges:::.order_GenomicRanges(x_rowRanges, decreasing = decreasing, ignore.strand = ignore.strand) x[oo] }) ## seqinfo (also seqlevels, genome, seqlevels<-, genome<-), seqinfo<- setMethod("seqinfo", "RangedSummarizedExperiment", function(x) { seqinfo(x@rowRanges) }) setReplaceMethod("seqinfo", "RangedSummarizedExperiment", 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@rowRanges, new2old = new2old, force = force, seqlevels(value)) if (length(dangling_seqlevels) != 0L) x <- x[!(seqnames(x) %in% dangling_seqlevels)] x@rowRanges <- update(x@rowRanges, seqnames = GenomeInfoDb:::makeNewSeqnames(x, new2old, seqlevels(value)), seqinfo = value) if (is.character(msg <- .valid.RangedSummarizedExperiment(x))) stop(msg) x }) setMethod("split", "RangedSummarizedExperiment", function(x, f, drop=FALSE, ...) { splitAsList(x, f, drop=drop) }) ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ### updateObject() ### .old_SummarizedExperiment_slots <- c( "assays", "rowData", "colData", "exptData" ) .has_old_SummarizedExperiment_internal_structure <- function(object) all(sapply(.old_SummarizedExperiment_slots, .hasSlot, object=object)) .from_old_SummarizedExperiment_to_RangedSummarizedExperiment <- function(from) .new_RangedSummarizedExperiment(from@assays, from@rowData, from@colData, from@exptData) .update_old_SummarizedExperiment <- function(object, ..., verbose=FALSE) { if (.has_old_SummarizedExperiment_internal_structure(object)) { rse <- .from_old_SummarizedExperiment_to_RangedSummarizedExperiment(object) } else if (!(.hasSlot(object, "NAMES") && .hasSlot(object, "elementMetadata"))) { rse <- .new_RangedSummarizedExperiment(object@assays, object@rowRanges, object@colData, object@metadata) } else { return(object) } if (!(extends(class(object), "RangedSummarizedExperiment") && class(object) != "RangedSummarizedExperiment")) return(rse) xslotnames <- setdiff(slotNames(class(object)), slotNames(class(rse))) xslots <- attributes(object)[xslotnames] #do.call("new", c(list(Class=class(object), rse), xslots)) ## The line above doesn't work because of a bug in R (see ## https://stat.ethz.ch/pipermail/r-devel/2015-May/071130.html), ## so we use the workaround below. rse_slots <- attributes(rse)[slotNames(class(rse))] ## Because of another bug in R, rse_slots$NAMES is broken when the NAMES ## slot is NULL so we repair it (see ## https://bugs.r-project.org/bugzilla/show_bug.cgi?id=16428). if (is.name(rse_slots$NAMES)) rse_slots$NAMES <- NULL do.call("new", c(list(Class=class(object)), rse_slots, xslots)) } setMethod("updateObject", "SummarizedExperiment", .update_old_SummarizedExperiment ) SummarizedExperiment/R/SummarizedExperiment-class.R0000644000175400017540000006172113001230160023562 0ustar00biocbuildbiocbuild### ========================================================================= ### SummarizedExperiment objects ### ------------------------------------------------------------------------- ### setClass("SummarizedExperiment", contains="Vector", representation( colData="DataFrame", # columns and their annotations assays="Assays", # Data -- e.g., list of matricies NAMES="characterORNULL", elementMetadata="DataFrame" ), prototype( assays=Assays() ) ) ### Temporary, for backward compatibility only. ### TODO: Remove in BioC 3.4. setClass("SummarizedExperiment0", contains="SummarizedExperiment") ### Combine the new parallel slots with those of the parent class. Make sure ### to put the new parallel slots *first*. setMethod("parallelSlotNames", "SummarizedExperiment", function(x) c("NAMES", callNextMethod()) ) ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ### Validity. ### .valid.SummarizedExperiment.assays_nrow <- function(x) { if (length(x@assays) == 0L) return(NULL) assays_nrow <- nrow(x@assays) rowData_nrow <- length(x) if (assays_nrow != rowData_nrow) { txt <- sprintf( "\n nb of rows in 'assay' (%d) must equal nb of rows in 'rowData' (%d)", assays_nrow, rowData_nrow) return(txt) } NULL } .valid.SummarizedExperiment.assays_ncol <- function(x) { if (length(x@assays) == 0L) return(NULL) assays_ncol <- ncol(x@assays) colData_nrow <- nrow(colData(x)) if (assays_ncol != colData_nrow) { txt <- sprintf( "\n nb of cols in 'assay' (%d) must equal nb of rows in 'colData' (%d)", assays_ncol, colData_nrow) return(txt) } NULL } .valid.SummarizedExperiment.assays_dim <- function(x) { c(.valid.SummarizedExperiment.assays_nrow(x), .valid.SummarizedExperiment.assays_ncol(x)) } .valid.SummarizedExperiment <- function(x) { .valid.SummarizedExperiment.assays_dim(x) } setValidity2("SummarizedExperiment", .valid.SummarizedExperiment) ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ### Low-level constructor (not exported). ### new_SummarizedExperiment <- function(assays, names, rowData, colData, metadata) { if (!is(assays, "Assays")) assays <- Assays(assays) if (is.null(rowData)) { if (is.null(names)) nrow <- nrow(assays) else nrow <- length(names) rowData <- S4Vectors:::make_zero_col_DataFrame(nrow) } else { rownames(rowData) <- NULL } new("SummarizedExperiment", NAMES=names, elementMetadata=rowData, colData=colData, assays=assays, metadata=as.list(metadata)) } ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ### Getters and setters. ### setMethod("length", "SummarizedExperiment", function(x) nrow(x@elementMetadata) ) setMethod("names", "SummarizedExperiment", function(x) x@NAMES) setReplaceMethod("names", "SummarizedExperiment", function(x, value) { NAMES <- S4Vectors:::normalize_names_replacement_value(value, x) BiocGenerics:::replaceSlots(x, NAMES=NAMES, check=FALSE) } ) ## rowData, colData seem too vague, but from eSet derived classes wanted to ## call the rows / cols something different from 'features' or 'samples', so ## might as well avoid the issue setGeneric("rowData", function(x, ...) standardGeneric("rowData")) setMethod("rowData", "SummarizedExperiment", function(x, ...) mcols(x, ...) ) setGeneric("rowData<-", function(x, ..., value) standardGeneric("rowData<-")) setReplaceMethod("rowData", "SummarizedExperiment", function(x, ..., value) `mcols<-`(x, ..., value=value) ) setGeneric("colData", function(x, ...) standardGeneric("colData")) setMethod("colData", "SummarizedExperiment", function(x, ...) x@colData) setGeneric("colData<-", function(x, ..., value) standardGeneric("colData<-")) setReplaceMethod("colData", c("SummarizedExperiment", "DataFrame"), function(x, ..., value) { if (nrow(value) != ncol(x)) stop("nrow of supplied 'colData' must equal ncol of object") BiocGenerics:::replaceSlots(x, colData=value, check=FALSE) }) setGeneric("assays", function(x, ..., withDimnames=TRUE) standardGeneric("assays"), signature="x") setMethod("assays", "SummarizedExperiment", function(x, ..., withDimnames=TRUE) { assays <- as(x@assays, "SimpleList") if (withDimnames) { assays <- endoapply(assays, function(assay) { dimnames(assay)[1:2] <- dimnames(x) assay } ) } assays }) setGeneric("assays<-", function(x, ..., withDimnames=TRUE, value) standardGeneric("assays<-"), signature=c("x", "value")) .SummarizedExperiment.assays.replace <- function(x, ..., withDimnames=TRUE, value) { ## withDimnames arg allows names(assays(se, withDimnames=FALSE)) <- value ok <- vapply(value, function(elt, xdimnames) { e <- dimnames(elt) (is.null(e[[1]]) || identical(e[[1]], xdimnames[[1]])) && (is.null(e[[2]]) || identical(e[[2]], xdimnames[[2]])) }, logical(1), xdimnames=dimnames(x)) if (!all(ok)) stop("current and replacement dimnames() differ") x <- BiocGenerics:::replaceSlots(x, assays=Assays(value), check=FALSE) ## validObject(x) should be called below because it would then fully ## re-validate objects that derive from SummarizedExperiment (e.g. ## DESeqDataSet objects) after the user sets the assays slot with ## assays(x) <- value. For example the assays slot of a DESeqDataSet ## object must contain a matrix named 'counts' and calling validObject(x) ## would check that but .valid.SummarizedExperiment(x) doesn't. ## The FourC() constructor function defined in the FourCSeq package ## actually takes advantage of the incomplete validation below to ## purposedly return invalid FourC objects! msg <- .valid.SummarizedExperiment(x) if (!is.null(msg)) stop(msg) x } setReplaceMethod("assays", c("SummarizedExperiment", "SimpleList"), .SummarizedExperiment.assays.replace) setReplaceMethod("assays", c("SummarizedExperiment", "list"), .SummarizedExperiment.assays.replace) setGeneric("assay", function(x, i, ...) standardGeneric("assay")) ## convenience for common use case setMethod("assay", c("SummarizedExperiment", "missing"), function(x, i, ...) { assays <- assays(x, ...) if (0L == length(assays)) stop("'assay(<", class(x), ">, i=\"missing\", ...) ", "length(assays(<", class(x), ">)) is 0'") assays[[1]] }) setMethod("assay", c("SummarizedExperiment", "numeric"), function(x, i, ...) { tryCatch({ assays(x, ...)[[i]] }, error=function(err) { stop("'assay(<", class(x), ">, i=\"numeric\", ...)' ", "invalid subscript 'i'\n", conditionMessage(err)) }) }) setMethod("assay", c("SummarizedExperiment", "character"), function(x, i, ...) { msg <- paste0("'assay(<", class(x), ">, i=\"character\", ...)' ", "invalid subscript 'i'") res <- tryCatch({ assays(x, ...)[[i]] }, error=function(err) { stop(msg, "\n", conditionMessage(err)) }) if (is.null(res)) stop(msg, "\n'i' not in names(assays(<", class(x), ">))") res }) setGeneric("assay<-", signature=c("x", "i"), function(x, i, ..., value) standardGeneric("assay<-")) setReplaceMethod("assay", c("SummarizedExperiment", "missing"), function(x, i, ..., value) { if (0L == length(assays(x))) stop("'assay(<", class(x), ">) <- value' ", "length(assays(<", class(x), ">)) is 0") assays(x)[[1]] <- value x }) setReplaceMethod("assay", c("SummarizedExperiment", "numeric"), function(x, i = 1, ..., value) { assays(x, ...)[[i]] <- value x }) setReplaceMethod("assay", c("SummarizedExperiment", "character"), function(x, i, ..., value) { assays(x, ...)[[i]] <- value x }) setGeneric("assayNames", function(x, ...) standardGeneric("assayNames")) setMethod("assayNames", "SummarizedExperiment", function(x, ...) { names(assays(x, withDimnames=FALSE)) }) setGeneric("assayNames<-", function(x, ..., value) standardGeneric("assayNames<-")) setReplaceMethod("assayNames", c("SummarizedExperiment", "character"), function(x, ..., value) { names(assays(x, withDimnames=FALSE)) <- value x }) ## cannonical location for dim, dimnames; dimnames should be checked ## for consistency (if non-null) and stripped from assays on ## construction, or added from assays if row/col names are NULL in ## but not assays. dimnames need to be added on ## to assays when assays() invoked setMethod("dim", "SummarizedExperiment", function(x) { c(length(x), nrow(colData(x))) }) setMethod("dimnames", "SummarizedExperiment", function(x) { list(names(x), rownames(colData(x))) }) setReplaceMethod("dimnames", c("SummarizedExperiment", "list"), function(x, value) { NAMES <- S4Vectors:::normalize_names_replacement_value(value[[1]], x) colData <- colData(x) rownames(colData) <- value[[2]] BiocGenerics:::replaceSlots(x, NAMES=NAMES, colData=colData, check=FALSE) }) setReplaceMethod("dimnames", c("SummarizedExperiment", "NULL"), function(x, value) { dimnames(x) <- list(NULL, NULL) x }) ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ### Subsetting. ### .SummarizedExperiment.charbound <- function(idx, txt, fmt) { orig <- idx idx <- match(idx, txt) if (any(bad <- is.na(idx))) { msg <- paste(S4Vectors:::selectSome(orig[bad]), collapse=" ") stop(sprintf(fmt, msg)) } idx } setMethod("[", c("SummarizedExperiment", "ANY", "ANY"), function(x, i, j, ..., drop=TRUE) { if (1L != length(drop) || (!missing(drop) && drop)) warning("'drop' ignored '[,", class(x), ",ANY,ANY-method'") if (missing(i) && missing(j)) return(x) if (!missing(i)) { if (is.character(i)) { fmt <- paste0("<", class(x), ">[i,] index out of bounds: %s") i <- .SummarizedExperiment.charbound(i, rownames(x), fmt) } ii <- as.vector(i) ans_elementMetadata <- x@elementMetadata[i, , drop=FALSE] if (is(x, "RangedSummarizedExperiment")) { ans_rowRanges <- x@rowRanges[i] } else { ans_NAMES <- x@NAMES[ii] } } if (!missing(j)) { if (is.character(j)) { fmt <- paste0("<", class(x), ">[,j] index out of bounds: %s") j <- .SummarizedExperiment.charbound(j, colnames(x), fmt) } ans_colData <- x@colData[j, , drop=FALSE] jj <- as.vector(j) } if (missing(i)) { ans_assays <- x@assays[ , jj] ans <- BiocGenerics:::replaceSlots(x, ..., colData=ans_colData, assays=ans_assays, check=FALSE) } else if (missing(j)) { ans_assays <- x@assays[ii, ] if (is(x, "RangedSummarizedExperiment")) { ans <- BiocGenerics:::replaceSlots(x, ..., elementMetadata=ans_elementMetadata, rowRanges=ans_rowRanges, assays=ans_assays, check=FALSE) } else { ans <- BiocGenerics:::replaceSlots(x, ..., elementMetadata=ans_elementMetadata, NAMES=ans_NAMES, assays=ans_assays, check=FALSE) } } else { ans_assays <- x@assays[ii, jj] if (is(x, "RangedSummarizedExperiment")) { ans <- BiocGenerics:::replaceSlots(x, ..., elementMetadata=ans_elementMetadata, rowRanges=ans_rowRanges, colData=ans_colData, assays=ans_assays, check=FALSE) } else { ans <- BiocGenerics:::replaceSlots(x, ..., elementMetadata=ans_elementMetadata, NAMES=ans_NAMES, colData=ans_colData, assays=ans_assays, check=FALSE) } } ans }) setReplaceMethod("[", c("SummarizedExperiment", "ANY", "ANY", "SummarizedExperiment"), function(x, i, j, ..., value) { if (missing(i) && missing(j)) return(value) ans_metadata <- c(metadata(x), metadata(value)) if (!missing(i)) { if (is.character(i)) { fmt <- paste0("<", class(x), ">[i,] index out of bounds: %s") i <- .SummarizedExperiment.charbound(i, rownames(x), fmt) } ii <- as.vector(i) ans_elementMetadata <- local({ emd <- x@elementMetadata emd[i,] <- value@elementMetadata emd }) if (is(x, "RangedSummarizedExperiment")) { ans_rowRanges <- local({ r <- x@rowRanges r[i] <- value@rowRanges names(r)[ii] <- names(value@rowRanges) r }) } else { ans_NAMES <- local({ nms <- x@NAMES nms[ii] <- value@NAMES nms }) } } if (!missing(j)) { if (is.character(j)) { fmt <- paste0("<", class(x), ">[,j] index out of bounds: %s") j <- .SummarizedExperiment.charbound(j, colnames(x), fmt) } jj <- as.vector(j) ans_colData <- local({ c <- x@colData c[j,] <- value@colData rownames(c)[jj] <- rownames(value@colData) c }) } if (missing(i)) { ans_assays <- local({ a <- x@assays a[ , jj] <- value@assays a }) ans <- BiocGenerics:::replaceSlots(x, ..., metadata=ans_metadata, colData=ans_colData, assays=ans_assays, check=FALSE) msg <- .valid.SummarizedExperiment.assays_ncol(ans) } else if (missing(j)) { ans_assays <- local({ a <- x@assays a[ii, ] <- value@assays a }) if (is(x, "RangedSummarizedExperiment")) { ans <- BiocGenerics:::replaceSlots(x, ..., metadata=ans_metadata, elementMetadata=ans_elementMetadata, rowRanges=ans_rowRanges, assays=ans_assays, check=FALSE) } else { ans <- BiocGenerics:::replaceSlots(x, ..., metadata=ans_metadata, elementMetadata=ans_elementMetadata, NAMES=ans_NAMES, assays=ans_assays, check=FALSE) } msg <- .valid.SummarizedExperiment.assays_nrow(ans) } else { ans_assays <- local({ a <- x@assays a[ii, jj] <- value@assays a }) if (is(x, "RangedSummarizedExperiment")) { ans <- BiocGenerics:::replaceSlots(x, ..., metadata=ans_metadata, elementMetadata=ans_elementMetadata, rowRanges=ans_rowRanges, colData=ans_colData, assays=ans_assays, check=FALSE) } else { ans <- BiocGenerics:::replaceSlots(x, ..., metadata=ans_metadata, elementMetadata=ans_elementMetadata, NAMES=ans_NAMES, colData=ans_colData, assays=ans_assays, check=FALSE) } msg <- .valid.SummarizedExperiment.assays_dim(ans) } if (!is.null(msg)) stop(msg) ans }) setMethod("extractROWS", "SummarizedExperiment", function(x, i) { i <- normalizeSingleBracketSubscript(i, x) x[i, ] } ) setMethod("replaceROWS", "SummarizedExperiment", function(x, i, value) { i <- normalizeSingleBracketSubscript(i, x) x[i, ] <- value x } ) ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ### Quick colData access. ### setMethod("[[", c("SummarizedExperiment", "ANY", "missing"), function(x, i, j, ...) { colData(x)[[i, ...]] }) setReplaceMethod("[[", c("SummarizedExperiment", "ANY", "missing"), function(x, i, j, ..., value) { colData(x)[[i, ...]] <- value x }) .DollarNames.SummarizedExperiment <- function(x, pattern) grep(pattern, names(colData(x)), value=TRUE) setMethod("$", "SummarizedExperiment", function(x, name) { colData(x)[[name]] }) setReplaceMethod("$", "SummarizedExperiment", function(x, name, value) { colData(x)[[name]] <- value x }) ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ### Display. ### setMethod("show", "SummarizedExperiment", function(object) { selectSome <- S4Vectors:::selectSome scat <- function(fmt, vals=character(), exdent=2, ...) { vals <- ifelse(nzchar(vals), vals, "''") lbls <- paste(S4Vectors:::selectSome(vals), collapse=" ") txt <- sprintf(fmt, length(vals), lbls) cat(strwrap(txt, exdent=exdent, ...), sep="\n") } cat("class:", class(object), "\n") cat("dim:", dim(object), "\n") ## metadata() expt <- names(metadata(object)) if (is.null(expt)) expt <- character(length(metadata(object))) scat("metadata(%d): %s\n", expt) ## assays() nms <- assayNames(object) if (is.null(nms)) nms <- character(length(assays(object, withDimnames=FALSE))) scat("assays(%d): %s\n", nms) ## rownames() dimnames <- dimnames(object) dlen <- sapply(dimnames, length) if (dlen[[1]]) scat("rownames(%d): %s\n", dimnames[[1]]) else scat("rownames: NULL\n") ## rowData() scat("rowData names(%d): %s\n", names(rowData(object))) ## colnames() if (dlen[[2]]) scat("colnames(%d): %s\n", dimnames[[2]]) else cat("colnames: NULL\n") ## colData() scat("colData names(%d): %s\n", names(colData(object))) }) ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ### Combine. ### ### Appropriate for objects with different ranges and same samples. setMethod("rbind", "SummarizedExperiment", function(..., deparse.level=1) { args <- unname(list(...)) .rbind.SummarizedExperiment(args) }) .rbind.SummarizedExperiment <- function(args) { if (!.compare(lapply(args, colnames))) stop("'...' objects must have the same colnames") if (!.compare(lapply(args, ncol))) stop("'...' objects must have the same number of samples") if (is(args[[1L]], "RangedSummarizedExperiment")) { rowRanges <- do.call(c, lapply(args, rowRanges)) } else { ## Code below taken from combine_GAlignments_objects() from the ## GenomicAlignments package. ## Combine "NAMES" slots. NAMES_slots <- lapply(args, function(x) x@NAMES) ## TODO: Use elementIsNull() here when it becomes available. has_no_names <- sapply(NAMES_slots, is.null, USE.NAMES=FALSE) if (all(has_no_names)) { NAMES <- NULL } else { noname_idx <- which(has_no_names) if (length(noname_idx) != 0L) NAMES_slots[noname_idx] <- lapply(elementNROWS(args[noname_idx]), character) NAMES <- unlist(NAMES_slots, use.names=FALSE) } } colData <- .cbind.DataFrame(args, colData, "colData") assays <- do.call(rbind, lapply(args, slot, "assays")) elementMetadata <- do.call(rbind, lapply(args, slot, "elementMetadata")) metadata <- do.call(c, lapply(args, metadata)) if (is(args[[1L]], "RangedSummarizedExperiment")) { BiocGenerics:::replaceSlots(args[[1L]], rowRanges=rowRanges, colData=colData, assays=assays, elementMetadata=elementMetadata, metadata=metadata) } else { BiocGenerics:::replaceSlots(args[[1L]], NAMES=NAMES, colData=colData, assays=assays, elementMetadata=elementMetadata, metadata=metadata) } } ### Appropriate for objects with same ranges and different samples. setMethod("cbind", "SummarizedExperiment", function(..., deparse.level=1) { args <- unname(list(...)) .cbind.SummarizedExperiment(args) }) .cbind.SummarizedExperiment <- function(args) { if (is(args[[1L]], "RangedSummarizedExperiment")) { if (!.compare(lapply(args, rowRanges), TRUE)) stop("'...' object ranges (rows) are not compatible") rowRanges <- rowRanges(args[[1L]]) mcols(rowRanges) <- .cbind.DataFrame(args, mcols, "mcols") } else { elementMetadata <- .cbind.DataFrame(args, mcols, "mcols") } colData <- do.call(rbind, lapply(args, colData)) assays <- do.call(cbind, lapply(args, slot, "assays")) metadata <- do.call(c, lapply(args, metadata)) if (is(args[[1L]], "RangedSummarizedExperiment")) { BiocGenerics:::replaceSlots(args[[1L]], rowRanges=rowRanges, colData=colData, assays=assays, metadata=metadata) } else { BiocGenerics:::replaceSlots(args[[1L]], elementMetadata=elementMetadata, colData=colData, assays=assays, metadata=metadata) } } .compare <- function(x, GenomicRanges=FALSE) { x1 <- x[[1]] if (GenomicRanges) { if (is(x1, "GRangesList")) { x <- lapply(x, unlist) x1 <- x[[1]] } for (i in seq_along(x)[-1]) { if (!identicalVals(x1, x[[i]])) return(FALSE) } return(TRUE) } else { all(sapply(x[-1], function(xelt) all(identical(xelt, x[[1]])))) } } .cbind.DataFrame <- function(args, accessor, accessorName) { lst <- lapply(args, accessor) if (!.compare(lst)) { nms <- lapply(lst, names) nmsv <- unlist(nms, use.names=FALSE) names(nmsv) <- rep(seq_along(nms), elementNROWS(nms)) dups <- duplicated(nmsv) ## no duplicates if (!any(dups)) return(do.call(cbind, lst)) ## confirm duplicates are the same lapply(nmsv[duplicated(nmsv)], function(d) { if (!.compare(lapply(lst, "[", d))) stop("column(s) '", unname(d), "' in ", sQuote(accessorName), " are duplicated and the data do not match")}) ## remove duplicates do.call(cbind, lst)[,!dups] } else { lst[[1]] } } ### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ### identicalVals() ### ### Internal generic and methods (i.e. not exported). ### Provides a fast implementation of 'length(x) == length(y) && all(x == y)' ### for various kinds of vector-like objects. ### TODO: Move this to S4Vectors (for the generic and methods for factor and ### Rle objects) and IRanges (for the method for Ranges objects). ### setGeneric("identicalVals", function(x, y) standardGeneric("identicalVals")) ### Semantically equivalent to identical(as.character(x), as.character(y)) ### but avoids turning the 2 factor objects into character vectors so is more ### efficient. setMethod("identicalVals", c("factor", "factor"), function(x, y) { m <- match(levels(y), levels(x), nomatch=0L) identical(as.integer(x), m[y]) } ) ### Only support factor-Rle objects at the moment! ### Semantically equivalent to identical(as.character(x), as.character(y)) ### but avoids turning the 2 factor-Rle objects into character vectors so is ### more efficient. setMethod("identicalVals", c("Rle", "Rle"), function(x, y) identical(runLength(x), runLength(y)) && identicalVals(runValue(x), runValue(y)) ) setMethod("identicalVals", c("Ranges", "Ranges"), function(x, y) identical(start(x), start(y)) && identical(width(x), width(y)) ) ### Like 'x == y' this method ignores circularity of the underlying sequences ### e.g. ranges [1, 10] and [101, 110] represent the same position on a ### circular sequence of length 100 so should be considered equal. However ### for 'x == y' and the method below, they are not. ### TODO: Take circularity of the underlying sequences into account. setMethod("identicalVals", c("GenomicRanges", "GenomicRanges"), function(x, y) { ## Trying to merge 'seqinfo(x)' and 'seqinfo(y)' will raise an error ## if 'x' and 'y' are not based on the same reference genome. This is ## the standard way to check that 'x' and 'y' are based on the same ## reference genome. merge(seqinfo(x), seqinfo(y)) # we ignore the returned value identicalVals(seqnames(x), seqnames(y)) && identicalVals(ranges(x), ranges(y)) && identicalVals(strand(x), strand(y)) } ) SummarizedExperiment/R/coverage-methods.R0000644000175400017540000000061613001230160021526 0ustar00biocbuildbiocbuild### ========================================================================= ### "coverage" method ### ------------------------------------------------------------------------- ### setMethod("coverage", "RangedSummarizedExperiment", function(x, shift=0L, width=NULL, weight=1L, method=c("auto", "sort", "hash")) { x <- rowRanges(x) callGeneric() } ) SummarizedExperiment/R/findOverlaps-methods.R0000644000175400017540000000250313001230160022364 0ustar00biocbuildbiocbuild### ========================================================================= ### findOverlaps methods ### ------------------------------------------------------------------------- ### findOverlaps setMethod("findOverlaps", c("RangedSummarizedExperiment", "Vector"), function(query, subject, maxgap=0L, minoverlap=1L, type=c("any", "start", "end", "within", "equal"), select=c("all", "first", "last", "arbitrary"), ignore.strand=FALSE) { query <- rowRanges(query) callGeneric() } ) setMethod("findOverlaps", c("Vector", "RangedSummarizedExperiment"), function(query, subject, maxgap=0L, minoverlap=1L, type=c("any", "start", "end", "within", "equal"), select=c("all", "first", "last", "arbitrary"), ignore.strand=FALSE) { subject <- rowRanges(subject) callGeneric() } ) setMethod("findOverlaps", c("RangedSummarizedExperiment", "RangedSummarizedExperiment"), function(query, subject, maxgap=0L, minoverlap=1L, type=c("any", "start", "end", "within", "equal"), select=c("all", "first", "last", "arbitrary"), ignore.strand=FALSE) { query <- rowRanges(query) subject <- rowRanges(subject) callGeneric() } ) SummarizedExperiment/R/inter-range-methods.R0000644000175400017540000000076213001230160022150 0ustar00biocbuildbiocbuild### ========================================================================= ### Inter-range methods ### ------------------------------------------------------------------------- ### setMethod("isDisjoint", "RangedSummarizedExperiment", function(x, ignore.strand=FALSE) { x <- rowRanges(x) callGeneric() } ) setMethod("disjointBins", "RangedSummarizedExperiment", function(x, ignore.strand = FALSE) { x <- rowRanges(x) callGeneric() } ) SummarizedExperiment/R/intra-range-methods.R0000644000175400017540000000342013001230160022136 0ustar00biocbuildbiocbuild### ========================================================================= ### Intra-range methods ### ------------------------------------------------------------------------- ### setMethod("shift", "RangedSummarizedExperiment", function(x, shift=0L, use.names=TRUE) { x0 <- x x <- rowRanges(x) rowRanges(x0) <- callGeneric() x0 } ) setMethod("narrow", "RangedSummarizedExperiment", function(x, start=NA, end=NA, width=NA, use.names=TRUE) { x0 <- x x <- rowRanges(x) rowRanges(x0) <- callGeneric() x0 } ) setMethod("resize", "RangedSummarizedExperiment", function(x, width, fix="start", use.names=TRUE, ignore.strand=FALSE) { x0 <- x x <- rowRanges(x) rowRanges(x0) <- callGeneric() x0 } ) setMethod("flank", "RangedSummarizedExperiment", function(x, width, start=TRUE, both=FALSE, use.names=TRUE, ignore.strand=FALSE) { x0 <- x x <- rowRanges(x) rowRanges(x0) <- callGeneric() x0 } ) setMethod("promoters", "RangedSummarizedExperiment", function(x, upstream=2000, downstream=200) { x0 <- x x <- rowRanges(x) rowRanges(x0) <- callGeneric() x0 } ) ### Because 'keep.all.ranges' is FALSE by default, it will break if some ### ranges are dropped. setMethod("restrict", "RangedSummarizedExperiment", function(x, start=NA, end=NA, keep.all.ranges=FALSE, use.names=TRUE) { x0 <- x x <- rowRanges(x) rowRanges(x0) <- callGeneric() x0 } ) setMethod("trim", "RangedSummarizedExperiment", function(x, use.names=TRUE) { x0 <- x x <- rowRanges(x) rowRanges(x0) <- callGeneric() x0 } ) SummarizedExperiment/R/makeSummarizedExperimentFromDataFrame.R0000644000175400017540000000214213001230160025676 0ustar00biocbuildbiocbuild### ========================================================================= ### makeSummarizedExperimentFromDataFrame() ### ------------------------------------------------------------------------- ### 'df' must be a data.frame or DataFrame object. makeSummarizedExperimentFromDataFrame <- function(df, ..., seqinfo = NULL, starts.in.df.are.0based = FALSE) { rowRanges <- makeGRangesFromDataFrame( df, ..., keep.extra.columns = FALSE, seqinfo = seqinfo, starts.in.df.are.0based = starts.in.df.are.0based) # Find column names for rowRanges granges_cols <- GenomicRanges:::.find_GRanges_cols(names(df), ...) rangedNames <- names(df)[na.omit(granges_cols)] idx <- match(rangedNames, names(df)) counts <- as.matrix(df[, -idx, drop = FALSE]) if (!is(as.vector(counts), "numeric")) stop("failed to coerce non-range columns to 'numeric'") SummarizedExperiment( assays=SimpleList(counts), rowRanges=rowRanges) } SummarizedExperiment/R/makeSummarizedExperimentFromExpressionSet.R0000644000175400017540000002114213001230160026666 0ustar00biocbuildbiocbuild## ## makeSummarizedExperimentFromExpressionSet ## coercion .from_rowRanges_to_FeatureData <- function(from) { if (is(from, "GRanges")) { fd <- .from_GRanges_to_FeatureData(from) } else if (is(from, "GRangesList")) { fd <- .from_GRangesList_to_FeatureData(from) } else { stop("class ", sQuote(class(from)), " is not a supported type for rowRanges coercion") } featureNames(fd) <- names(from) fd } .from_GRanges_to_FeatureData <- function(from) { data <- as.data.frame(from) ## the first mcols are automatically included in the data.frame from ## as.data.frame, the secondary mcols holds the metadata for the first ## metadata columns. metaData <- mcols(mcols(from)) if (is.null(metaData)) { metaData <- as.data.frame(matrix(ncol=0, nrow=NCOL(data))) } else { metaData <- as.data.frame(metaData) } AnnotatedDataFrame(data, metaData) } .from_GRangesList_to_FeatureData <- function(from) { data <- as.data.frame(mcols(from)) ## the first mcols are automatically included in the data.frame from ## as.data.frame, the secondary mcols holds the metadata for the first ## metadata columns. metaData <- mcols(mcols(from)) if (is.null(metaData)) { metaData <- as.data.frame(matrix(ncol=0, nrow=NCOL(data))) } else { metaData <- as.data.frame(metaData) } AnnotatedDataFrame(data, metaData) } .from_AnnotatedDataFrame_to_DataFrame <- function(from) { df <- DataFrame(pData(from), row.names=rownames(from)) mcols(df) <- DataFrame(varMetadata(from)) df } .from_DataFrame_to_AnnotatedDataFrame <- function(df) { data <- as(df, "data.frame") metaData <- mcols(df) if (is.null(metaData)) { metaData <- as.data.frame(matrix(ncol=0, nrow=NCOL(data))) } else { metaData <- as(metaData, "data.frame") } AnnotatedDataFrame(data, metaData) } ## If the ExpressionSet has featureData with range information make ## GRanges out of that, otherwise make an empty GRangesList with names ## from the featureNames naiveRangeMapper <- function(from) { nms <- featureNames(from) res <- tryCatch({ makeGRangesFromDataFrame(pData(featureData(from)), keep.extra.columns = TRUE) }, error = function(e) { res <- relist(GRanges(), vector("list", length=length(nms))) mcols(res) <- .from_AnnotatedDataFrame_to_DataFrame(featureData(from)) res }) names(res) <- nms res } # Simple ProbeId to Range mapper # Probes with multiple ranges are dropped # The sign of the chromosome location is assumed to contain the strand # information probeRangeMapper <- function(from) { annotation <- annotation(from) if (identical(annotation, character(0))) { return(naiveRangeMapper(from)) } if (requireNamespace("annotate", quietly = TRUE)) { annotationPackage <- annotate::annPkgName(annotation) test <- require(annotationPackage, character.only = TRUE, quietly = TRUE) if (test) { db <- get(annotationPackage, envir = asNamespace(annotationPackage)) pid <- featureNames(from) locs <- AnnotationDbi::select( db, pid, columns = c("CHR", "CHRLOC", "CHRLOCEND")) locs <- na.omit(locs) dups <- duplicated(locs$PROBEID) if (any(dups)) { locs <- locs[!dups, , drop = FALSE] } strand <- ifelse(locs$CHRLOC > 0, "+", "-") res <- GRanges(seqnames = locs$CHR, ranges = IRanges(abs(locs$CHRLOC), abs(locs$CHRLOCEND)), strand = strand) names(res) <- locs$PROBEID if (NROW(res) < length(pid)) { warning(length(pid) - NROW(res), " probes could not be mapped.", call. = FALSE) } res } else { stop("Failed to load ", sQuote(annotationPackage), " package", call. = FALSE) } } else { stop("Failed to load annotate package", call. = FALSE) } } # Simple ProbeId to Gene mapper # Is there a way to get the txDb given the annotation package? geneRangeMapper <- function(txDbPackage, key = "ENTREZID") { function(from) { annotation <- annotation(from) if (identical(annotation, character(0))) { return(naiveRangeMapper(from)) } if (requireNamespace("annotate", quietly = TRUE)) { annotationPackage <- annotate::annPkgName(annotation) test <- require(annotationPackage, character.only = TRUE, quietly = TRUE) if (test) { db <- get(annotationPackage, envir = asNamespace(annotationPackage)) pid <- featureNames(from) probeIdToGeneId <- AnnotationDbi::mapIds(db, pid, key, "PROBEID") geneIdToProbeId <- setNames(names(probeIdToGeneId), probeIdToGeneId) if (requireNamespace(txDbPackage, quietly = TRUE)) { txDb <- get(txDbPackage, envir = asNamespace(txDbPackage)) genes <- GenomicFeatures::genes(txDb) probesWithAMatch <- probeIdToGeneId[probeIdToGeneId %in% names(genes)] res <- genes[probesWithAMatch] names(res) <- geneIdToProbeId[names(res)] if (NROW(res) < length(pid)) { warning(length(pid) - NROW(res), " probes could not be mapped.", call. = FALSE) } res } else { stop("Failed to load ", sQuote(txDbPackage), " package", call. = FALSE) } } else { stop("Failed to load ", sQuote(annotationPackage), " package", call. = FALSE) } } else { stop("Failed to load annotate package", call. = FALSE) } } } makeSummarizedExperimentFromExpressionSet <- function(from, mapFun = naiveRangeMapper, ...) { mapFun <- match.fun(mapFun) rowRanges <- mapFun(from, ...) matches <- match(names(rowRanges), featureNames(from), nomatch = 0) from <- from[matches, drop = FALSE] assays <- as.list(assayData(from)) colData <- .from_AnnotatedDataFrame_to_DataFrame(phenoData(from)) metadata <- SimpleList( experimentData = experimentData(from), annotation = annotation(from), protocolData = protocolData(from) ) SummarizedExperiment( assays = assays, rowRanges = rowRanges, colData = colData, metadata = metadata ) } setAs("ExpressionSet", "RangedSummarizedExperiment", function(from) { makeSummarizedExperimentFromExpressionSet(from) }) setAs("RangedSummarizedExperiment", "ExpressionSet", function(from) { assayData <- list2env(as.list(assays(from))) numAssays <- length(assayData) if (numAssays == 0) { assayData$exprs <- new("matrix") } else if (!"exprs" %in% ls(assayData)) { ## if there isn't an exprs assay we need to pick one as exprs, ## so rename the first element exprs and issue a warning. exprs <- ls(assayData)[[1]] warning("No assay named ", sQuote("exprs"), " found, renaming ", exprs, " to ", sQuote("exprs"), ".") assayData[["exprs"]] <- assayData[[exprs]] rm(list=exprs, envir=assayData) } featureData <- .from_rowRanges_to_FeatureData(rowRanges(from)) phenoData <- .from_DataFrame_to_AnnotatedDataFrame(colData(from)) metadata <- metadata(from) experimentData <- if (!is.null(metadata$experimentData)) { metadata$experimentData } else { MIAME() } annotation <- if (!is.null(metadata$annotation)) { metadata$annotation } else { character() } protocolData <- if (!is.null(metadata$protocolData)) { metadata$protocolData } else { annotatedDataFrameFrom(assayData, byrow=FALSE) } ExpressionSet(assayData, phenoData = phenoData, featureData = featureData, experimentData = experimentData, annotation = annotation, protocolData = protocolData ) }) setAs("SummarizedExperiment", "ExpressionSet", function(from) as(as(from, "RangedSummarizedExperiment"), "ExpressionSet") ) SummarizedExperiment/R/nearest-methods.R0000644000175400017540000000564513001230160021403 0ustar00biocbuildbiocbuild### ========================================================================= ### nearest (and related) methods ### ------------------------------------------------------------------------- ### ### precede & follow for (f in c("precede", "follow")) { setMethod(f, c("RangedSummarizedExperiment", "ANY"), function(x, subject, select=c("arbitrary", "all"), ignore.strand=FALSE) { x <- rowRanges(x) callGeneric() } ) setMethod(f, c("ANY", "RangedSummarizedExperiment"), function(x, subject, select=c("arbitrary", "all"), ignore.strand=FALSE) { subject <- rowRanges(subject) callGeneric() } ) setMethod(f, c("RangedSummarizedExperiment", "RangedSummarizedExperiment"), function(x, subject, select=c("arbitrary", "all"), ignore.strand=FALSE) { x <- rowRanges(x) subject <- rowRanges(subject) callGeneric() } ) } ### nearest setMethod("nearest", c("RangedSummarizedExperiment", "ANY"), function(x, subject, select=c("arbitrary", "all"), ignore.strand=FALSE) { x <- rowRanges(x) callGeneric() } ) setMethod("nearest", c("ANY", "RangedSummarizedExperiment"), function(x, subject, select=c("arbitrary", "all"), ignore.strand=FALSE) { subject <- rowRanges(subject) callGeneric() } ) setMethod("nearest", c("RangedSummarizedExperiment", "RangedSummarizedExperiment"), function(x, subject, select=c("arbitrary", "all"), ignore.strand=FALSE) { x <- rowRanges(x) subject <- rowRanges(subject) callGeneric() } ) ### distance setMethod("distance", c("RangedSummarizedExperiment", "ANY"), function(x, y, ignore.strand=FALSE, ...) { x <- rowRanges(x) callGeneric() } ) setMethod("distance", c("ANY", "RangedSummarizedExperiment"), function(x, y, ignore.strand=FALSE, ...) { y <- rowRanges(y) callGeneric() } ) setMethod("distance", c("RangedSummarizedExperiment", "RangedSummarizedExperiment"), function(x, y, ignore.strand=FALSE, ...) { x <- rowRanges(x) y <- rowRanges(y) callGeneric() } ) ### distanceToNearest setMethod("distanceToNearest", c("RangedSummarizedExperiment", "ANY"), function(x, subject, ignore.strand=FALSE, ...) { x <- rowRanges(x) callGeneric() } ) setMethod("distanceToNearest", c("ANY", "RangedSummarizedExperiment"), function(x, subject, ignore.strand=FALSE, ...) { subject <- rowRanges(subject) callGeneric() } ) setMethod("distanceToNearest", c("RangedSummarizedExperiment", "RangedSummarizedExperiment"), function(x, subject, ignore.strand=FALSE, ...) { x <- rowRanges(x) subject <- rowRanges(subject) callGeneric() } ) SummarizedExperiment/R/readKallisto.R0000644000175400017540000001363013001230160020710 0ustar00biocbuildbiocbuild.require <- function(pkg) { withCallingHandlers({ requireNamespace(pkg) }, warning=function(w) { invokeRestart("muffleWarning") }) || { msg <- sprintf('install %s with source("http://bioconductor.org/biocLite.R"); biocLite("%s")', pkg, pkg) stop(paste(strwrap(msg, exdent=2), collapse="\n")) } } .open <- function(files, h5) { lapply(files, function(file) { if (h5 && rhdf5::H5Fis_hdf5(file)) rhdf5::H5Fopen(file) else file(file, open="rt") }) } .close <- function(cons) UseMethod(".close") .close.list <- function(cons) for (con in cons) .close(con) .close.connection <- function(cons) close(cons) .close.H5IdComponent <- function(cons) rhdf5::H5Fclose(cons) .colData <- function(jsonfile) { json <- jsonlite::fromJSON(jsonfile) do.call("data.frame", c(json, stringsAsFactors=FALSE)) } .KALLISTO_COLCLASSES <- c("character", "integer" , "numeric", "numeric", "numeric") .KALLISTO_ROWDATA <- "length" KALLISTO_ASSAYS <- c("est_counts", "tpm", "eff_length") .read <- function(con) UseMethod(".read") .read.connection <- function(con) read.delim(con, header=TRUE, colClasses=.KALLISTO_COLCLASSES, row.names=1) .read.H5IdComponent <- function(con) { eff_length <- rhdf5::h5read(con, "/aux/eff_lengths") est_counts <- rhdf5::h5read(con, "/est_counts") tpm0 <- est_counts / eff_length data.frame(row.names=rhdf5::h5read(con, "/aux/ids"), length=rhdf5::h5read(con, "/aux/lengths"), eff_length=eff_length, est_counts=est_counts, tpm=tpm0 / (sum(tpm0) / 1e6)) } readKallisto <- function(files, json=file.path(dirname(files), "run_info.json"), h5=any(grepl("\\.h5$", files)), what=KALLISTO_ASSAYS, as=c("SummarizedExperiment", "list", "matrix")) { as <- match.arg(as) if (missing(what)) what <- what[1] else { whats <- eval(formals()[["what"]]) if (!all(what %in% KALLISTO_ASSAYS)) stop("'what' must be in ", paste(sQuote(KALLISTO_ASSAYS), collapse=", "), call.=FALSE) } stopifnot(is.character(files)) test <- file.exists(files) if (!all(test)) stop("file(s) do not exist:\n ", paste(files[!test], collapse="\n ")) if (is.null(names(files))) names(files) <- basename(dirname(files)) if (anyDuplicated(names(files))) stop("'names()' of 'files' must be unique") if (as != "matrix") { .require("jsonlite") stopifnot(length(files) == length(json)) if (!is.null(names(json))) stopifnot(identical(names(json), names(files))) else names(json) <- names(files) test <- file.exists(json) if (!all(test)) stop("json file(s) do not exist:\n ", paste(json[!test], collapse="\n ")) } if (h5) .require("rhdf5") cons <- .open(files, h5) value <- .read(cons[[1]]) rowData <- value[, .KALLISTO_ROWDATA, drop=FALSE] assay <- matrix(0, nrow(rowData), length(cons), dimnames=list(rownames(rowData), names(cons))) assays <- setNames(replicate(length(what), assay, FALSE), what) for (w in what) assays[[w]][,1] <- value[[w]] for (i in seq_along(cons)[-1]) { value <- .read(cons[[i]]) if (!identical(rowData, value[, .KALLISTO_ROWDATA, drop=FALSE])) stop("rowData differs between files:\n ", paste(files[c(1, i)], collapse="\n ")) for (w in what) assays[[w]][,i] <- value[[w]] } .close(cons) if (as != "matrix") colData <- do.call("rbind", lapply(json, .colData)) switch(as, matrix={ if (length(assays) == 1L) assays[[1]] else assays }, list={ c(setNames(list(colData, rowData), c("colData", "rowData")), assays) }, SummarizedExperiment={ SummarizedExperiment(assays=assays, rowData=as(rowData, "DataFrame"), colData=as(colData, "DataFrame")) }) } .readIds <- function(con, i) { if (!missing(i)) rhdf5::h5read(con, "/aux/ids", list(i)) else rhdf5::h5read(con, "/aux/ids") } readKallistoBootstrap <- function(file, i, j) { .require("rhdf5") stopifnot(length(file) == 1L, is.character(file)) stopifnot(file.exists(file)) stopifnot(rhdf5::H5Fis_hdf5(file)) con <- rhdf5::H5Fopen(file) on.exit(rhdf5::H5Fclose(con)) nboot <- as.integer(rhdf5::h5read(con, "/aux/num_bootstrap")) if (nboot == 0L) stop("file contains no bootstraps:\n ", file) if (!missing(i) && is.character(i)) { idx <- match(i, .readIds(con)) if (anyNA(i)) stop("unknown target id(s)", i[is.na(idx)]) i <- idx } if (!missing(j) && is.numeric(j)) { if (any((j < 1L) || any(j > nboot))) stop("'j' must be >0 and <=", nboot) j <- paste0("bs", as.integer(j) - 1L) } m <- if (missing(i) && missing(j)) { simplify2array(rhdf5::h5read(con, "/bootstrap")) } else if (missing(i)) { query <- setNames(sprintf("/bootstrap/%s", j), j) simplify2array(lapply(query, rhdf5::h5read, file=con)) } else if (missing(j)) { group <- rhdf5::H5Gopen(con, "/bootstrap") name <- rhdf5::h5ls(group)$name rhdf5::H5Gclose(group) query <- setNames(sprintf("/bootstrap/%s", name), name) simplify2array(lapply(query, rhdf5::h5read, file=con, index=list(i))) } else { query <- setNames(sprintf("/bootstrap/%s", j), j) simplify2array(lapply(query, rhdf5::h5read, file=con, index=list(i))) } rownames(m) <- .readIds(con, i) if (missing(j)) { o <- order(as.integer(sub("bs", "", colnames(m), fixed=TRUE))) m[,o] } else m } SummarizedExperiment/R/zzz.R0000644000175400017540000000011013001230160017114 0ustar00biocbuildbiocbuild.test <- function() BiocGenerics:::testPackage("SummarizedExperiment") SummarizedExperiment/TODO0000644000175400017540000001666013001230160016444 0ustar00biocbuildbiocbuildTentative roadmap for the SummarizedExperiment to RangedSummarizedExperiment transition ============================================================================ Where we are: (1) We have a new class, RangedSummarizedExperiment (defined in the SummarizedExperiment package), that is *functionally* equivalent to the old SummarizedExperiment class (defined in the GenomicRanges package). In particular, it supports the rowRanges() setter and getter, for annotating the rows with a range-based object (only GRanges or GRangesList at the moment). Note that, just like the old SummarizedExperiment class, it also supports the mcols() setter and getter for additional annotations on the rows. More precisely, mcols(), like for any vector-like object defined in our core infrastructure, takes or returns a DataFrame. In the case of a RangedSummarizedExperiment object, this DataFrame must have one row per row in the object. (2) We have a new class, SummarizedExperiment0, that is the direct parent of RangedSummarizedExperiment. The only difference with the latter is that the former doesn't support the rowRanges() getter. In other words, the rows can only be annotated via the mcols() accessor, which behaves exactly as in the case of a RangedSummarizedExperiment object. Note however that the rowRanges() setter DOES work on a SummarizedExperiment0 object but turns it into a RangedSummarizedExperiment object (thus violating the somewhat general expectation that a setter should behave as an endomorphism). (3) SummarizedExperiment0 extends Vector: Annotated (defined in S4Vectors) ^ | Vector (defined in S4Vectors) ^ | SummarizedExperiment0 ^ | RangedSummarizedExperiment And therefore RangedSummarizedExperiment also extends Vector! Note that the old SummarizedExperiment class didn't. As a consequence, a noticeable difference between a RangedSummarizedExperiment object and an old SummarizedExperiment object is that the former has a length whereas the length was always 1 for the latter. More precisely, the length of a SummarizedExperiment0 (or RangedSummarizedExperiment) object is the same as its number of rows, and thus the same as the number of rows in its mcols(). (4) The old SummarizedExperiment class is deprecated in favor of the new RangedSummarizedExperiment class. (5) Because SummarizedExperiment0 and RangedSummarizedExperiment now extend the Annotated class (via Vector), they inherit the 'metadata' slot and accessor. Therefore the 'exptData' slot and accessor of the old SummarizedExperiment class are not needed anymore. Note that exptData() still works on SummarizedExperiment0 but is deprecated in favor of metadata(). A noticeable difference though is that metadata() returns an ordinary list whereas exptData() was returning a SimpleList. (6) There are coercion methods to switch back and forth between RangedSummarizedExperiment and SummarizedExperiment0. Doing SummarizedExperiment0 -> RangedSummarizedExperiment -> SummarizedExperiment0 is a no-op, but not the other way around. (7) rowData() is defunct on old SummarizedExperiment objects. It could (and should) be brought back on SummarizedExperiment0 objects as an alias for mcols(). This will restore the rowData/colData combo which is more intuitive than the mcols/colData combo. That cannot be done until the old defunct rowData() has completely disappeared from the GenomicRanges package though (see (e) below). What should happen before the BioC 3.2 release: (a) Remove test_SummarizedExperiment-rowData-methods.R from the GenomicRanges package (but first make sure that no testing is lost by checking that the unit tests in SummarizedExperiment cover everything that was in test_SummarizedExperiment-rowData-methods.R). (b) Write the vignette in new SummarizedExperiment package. What should happen in BioC 3.3 (preferably at the beginning of the devel cycle i.e. soon after the BioC 3.2 release): (c) Remove the old SummarizedExperiment class entirely from the GenomicRanges package. Right now it's deprecated only so that means we skip the 6-month defunct cycle which I think is OK. (d) In the SummarizedExperiment package, rename SummarizedExperiment0 -> SummarizedExperiment. This renaming should NOT break any serialized RangedSummarizedExperiment (or derived) objects! It would break serialized SummarizedExperiment0 instances but AFAIK we don't have any in the software or data-experiment repos (because the class is so new). However, this renaming will possibly impact some packages that depend on the SummarizedExperiment package and contain occurrences of the SummarizedExperiment0 string in their source. All these occurrences need to be replaced with SummarizedExperiment. If this blunt renaming turns out to be more damaging than anticipated we can always temporarily re-introduce the SummarizedExperiment0 class as an alias for SummarizedExperiment but hopefully that won't be necessary. (e) Remove rowData() entirely from the GenomicRanges package and re-introduce it in the SummarizedExperiment package with a method for SummarizedExperiment0 that is an alias for mcols(). (f) Note that the user should also be able to specify the rowData component at construction-time with something like: # return a SummarizedExperiment0 SummarizedExperiment(SimpleList(counts=counts), rowData=DF) Something Sonali was expecting to work in a recent email on devteam-bioc. Makes a lot of sense given that the user can already specify the rowRanges and/or colData components with something like: # return a RangedSummarizedExperiment SummarizedExperiment(SimpleList(counts=counts), rowRanges=GR) or # return a SummarizedExperiment0 SummarizedExperiment(SimpleList(counts=counts), colData=DF) Question: should we support the case where the user specifies both rowData and rowRanges? # return a RangedSummarizedExperiment SummarizedExperiment(..., rowData=DF, rowRanges=GR) Maybe at least when GR has no mcols on it? (g) Defunct exptData(). (h) Move any remaining generic from GenomicRanges/R/SummarizedExperiment-class.R to the appropriate place in SummarizedExperiment (either SummarizedExperiment0-class.R or RangedSummarizedExperiment-class.R) Except the value() generic and methods, which can go away. (i) The "clone" methods defined in GenomicRanges/R/SummarizedExperiment-class.R can also go away. (j) Move helper classes Assays, ShallowData, and ShallowSimpleListAssays from GenomicRanges/R/SummarizedExperiment-class.R to SummarizedExperiment/R/Assays-class.R (k) Remove the SummarizedExperiment-class.R and SummarizedExperiment-rowData-methods.R files from GenomicRanges. (l) Update the SummarizedExperiment vignette (including completing section on extending SummarizedExperiment). I'm probably missing a few things that we'll discover along the road. I'll take care of (a) in September and can take care of (c)-(l) after the BioC 3.2 release. I'm happy to leave (b) (SummarizedExperiment vignette) to someone else... H.P. - August 2015 SummarizedExperiment/build/0000755000175400017540000000000013001262612017051 5ustar00biocbuildbiocbuildSummarizedExperiment/build/vignette.rds0000644000175400017540000000032213001262612021405 0ustar00biocbuildbiocbuildb```b`ffd`b2 1# '.M,ʬJMq(H-M+ MAS'MIfe)n)е`U@„5/17(vԂԼ?iN,/AQU▙ 7$apq2݀a>9`~MMI,F(WJbI^ZP?qSummarizedExperiment/inst/0000755000175400017540000000000013001262611016726 5ustar00biocbuildbiocbuildSummarizedExperiment/inst/doc/0000755000175400017540000000000013001262611017473 5ustar00biocbuildbiocbuildSummarizedExperiment/inst/doc/SummarizedExperiment.R0000644000175400017540000000723313001262611024004 0ustar00biocbuildbiocbuild## ----style, echo=FALSE, results='asis'----------------------------------- BiocStyle::markdown() ## ----include = FALSE----------------------------------------------------- # download current version of SE diagram #download.file("https://docs.google.com/feeds/download/drawings/Export?id=18OcDb80FpvSGRYnFl-8vUqwNNLaNHrG1I9SWKHCselo&exportFormat=svg", "SE.svg") download.file("https://docs.google.com/feeds/download/drawings/Export?id=1kiC8Qlo1mhSnLDqkGiRNPSo6GWn3C2duBszCFbJCB-g&exportFormat=svg", "SE.svg") ## ---- echo=FALSE--------------------------------------------------------- suppressPackageStartupMessages(library(SummarizedExperiment)) suppressPackageStartupMessages(data(airway, package="airway")) ## ------------------------------------------------------------------------ library(SummarizedExperiment) data(airway, package="airway") se <- airway se ## ----assays, eval = FALSE------------------------------------------------ # assays(se)$counts ## ----assays_table, echo = FALSE------------------------------------------ knitr::kable(assays(se)$counts[1:10,]) ## ----rowRanges----------------------------------------------------------- rowRanges(se) ## ----colData------------------------------------------------------------- colData(se) ## ----columnSubset-------------------------------------------------------- # subset for only those samples treated with dexamethasone se[, se$dex == "trt"] ## ----metadata------------------------------------------------------------ metadata(se) ## ----metadata-formula---------------------------------------------------- metadata(se)$formula <- counts ~ dex + albut metadata(se) ## ----constructRSE-------------------------------------------------------- nrows <- 200 ncols <- 6 counts <- matrix(runif(nrows * ncols, 1, 1e4), nrows) rowRanges <- GRanges(rep(c("chr1", "chr2"), c(50, 150)), IRanges(floor(runif(200, 1e5, 1e6)), width=100), strand=sample(c("+", "-"), 200, TRUE), feature_id=sprintf("ID%03d", 1:200)) colData <- DataFrame(Treatment=rep(c("ChIP", "Input"), 3), row.names=LETTERS[1:6]) SummarizedExperiment(assays=list(counts=counts), rowRanges=rowRanges, colData=colData) ## ----constructSE--------------------------------------------------------- SummarizedExperiment(assays=list(counts=counts), colData=colData) ## ----2d------------------------------------------------------------------ # subset the first five transcripts and first three samples se[1:5, 1:3] ## ----colDataExtraction--------------------------------------------------- se[, se$cell == "N61311"] ## ----getSet-------------------------------------------------------------- counts <- matrix(1:15, 5, 3, dimnames=list(LETTERS[1:5], LETTERS[1:3])) dates <- SummarizedExperiment(assays=list(counts=counts), rowData=DataFrame(month=month.name[1:5], day=1:5)) # Subset all January assays dates[rowData(dates)$month == "January", ] ## ----assay_assays-------------------------------------------------------- assays(se) assays(se)[[1]][1:5, 1:5] # assay defaults to the first assay if no i is given assay(se)[1:5, 1:5] assay(se, 1)[1:5, 1:5] ## ----overlap------------------------------------------------------------- # Subset for only rows which are in the interval 100,000 to 110,000 of # chromosome 1 roi <- GRanges(seqnames="1", ranges=100000:1100000) subsetByOverlaps(se, roi) ## ----rseSubclass--------------------------------------------------------- setClass("MyRSESubclass", contains="RangedSummarizedExperiment", representation=representation( slot1="integer", slot2="function" ## ... maybe more ... ) ) SummarizedExperiment/inst/doc/SummarizedExperiment.Rmd0000644000175400017540000003112513001262611024322 0ustar00biocbuildbiocbuild--- title: "_SummarizedExperiment_ for Coordinating Experimental Assays, Samples, and Regions of Interest" author: "Martin Morgan, Valerie Obenchain, Jim Hester, Hervé Pagès" date: "Revised: 22 June, 2016" output: BiocStyle::html_document: toc: true vignette: > %\VignetteIndexEntry{SummarizedExperiment} %\VignetteEngine{knitr::rmarkdown} \usepackage[utf8]{inputenc} --- ```{r style, echo=FALSE, results='asis'} BiocStyle::markdown() ``` # Introduction The `SummarizedExperiment` class is used to store rectangular matrices of experimental results, which are commonly produced by sequencing and microarray experiments. Each object stores observations of one or more samples, along with additional meta-data describing both the observations (features) and samples (phenotypes). A key aspect of the `SummarizedExperiment` class is the coordination of the meta-data and assays when subsetting. For example, if you want to exclude a given sample you can do for both the meta-data and assay in one operation, which ensures the meta-data and observed data will remain in sync. Improperly accounting for meta and observational data has resulted in a number of incorrect results and retractions so this is a very desirable property. `SummarizedExperiment` is in many ways similar to the historical `ExpressionSet`, the main distinction being that `SummarizedExperiment` is more flexible in it's row information, allowing both `GRanges` based as well as those described by arbitrary `DataFrame`s. This makes it ideally suited to a variety of experiments, particularly sequencing based experiments such as RNA-Seq and ChIp-Seq. # Anatomy of a `SummarizedExperiment` The _SummarizedExperiment_ package contains two classes: `SummarizedExperiment` and `RangedSummarizedExperiment`. `SummarizedExperiment` is a matrix-like container where rows represent features of interest (e.g. genes, transcripts, exons, etc.) and columns represent samples. The objects contain one or more assays, each represented by a matrix-like object of numeric or other mode. The rows of a `SummarizedExperiment` object represent features of interest. Information about these features is stored in a `DataFrame` object, accessible using the function `rowData()`. Each row of the `DataFrame` provides information on the feature in the corresponding row of the `SummarizedExperiment` object. Columns of the DataFrame represent different attributes of the features of interest, e.g., gene or transcript IDs, etc. `RangedSummarizedExperiment` is the child of the `SummarizedExperiment` class which means that all the methods on `SummarizedExperiment` also work on a `RangedSummarizedExperiment`. The fundamental difference between the two classes is that the rows of a `RangedSummarizedExperiment` object represent genomic ranges of interest instead of a `DataFrame` of features. The `RangedSummarizedExperiment` ranges are described by a `GRanges` or a `GRangesList` object, accessible using the `rowRanges()` function. The following graphic displays the class geometry and highlights the vertical (column) and horizontal (row) relationships. ```{r include = FALSE} # download current version of SE diagram #download.file("https://docs.google.com/feeds/download/drawings/Export?id=18OcDb80FpvSGRYnFl-8vUqwNNLaNHrG1I9SWKHCselo&exportFormat=svg", "SE.svg") download.file("https://docs.google.com/feeds/download/drawings/Export?id=1kiC8Qlo1mhSnLDqkGiRNPSo6GWn3C2duBszCFbJCB-g&exportFormat=svg", "SE.svg") ``` ![Summarized Experiment](SE.svg) ## Assays The `airway` package contains an example dataset from an RNA-Seq experiment of read counts per gene for airway smooth muscles. These data are stored in a `RangedSummarizedExperiment` object which contains 8 different experimental and assays 64,102 gene transcripts. ```{r, echo=FALSE} suppressPackageStartupMessages(library(SummarizedExperiment)) suppressPackageStartupMessages(data(airway, package="airway")) ``` ```{r} library(SummarizedExperiment) data(airway, package="airway") se <- airway se ``` To retrieve the experiment data from a `SummarizedExperiment` object one can use the `assays()` accessor. An object can have multiple assay datasets each of which can be accessed using the `$` operator. The `airway` dataset contains only one assay (`counts`). Here each row represents a gene transcript and each column one of the samples. ```{r assays, eval = FALSE} assays(se)$counts ``` ```{r assays_table, echo = FALSE} knitr::kable(assays(se)$counts[1:10,]) ``` ## 'Row' (regions-of-interest) data The `rowRanges()` accessor is used to view the range information for a `RangedSummarizedExperiment`. (Note if this were the parent `SummarizedExperiment` class we'd use `rowData()`). The data are stored in a `GRangesList` object, where each list element corresponds to one gene transcript and the ranges in each `GRanges` correspond to the exons in the transcript. ```{r rowRanges} rowRanges(se) ``` ## 'Column' (sample) data Sample meta-data describing the samples can be accessed using `colData()`, and is a `DataFrame` that can store any number of descriptive columns for each sample row. ```{r colData} colData(se) ``` This sample metadata can be accessed using the `$` accessor which makes it easy to subset the entire object by a given phenotype. ```{r columnSubset} # subset for only those samples treated with dexamethasone se[, se$dex == "trt"] ``` ## Experiment-wide metadata Meta-data describing the experimental methods and publication references can be accessed using `metadata()`. ```{r metadata} metadata(se) ``` Note that `metadata()` is just a simple list, so it is appropriate for _any_ experiment wide metadata the user wishes to save, such as storing model formulas. ```{r metadata-formula} metadata(se)$formula <- counts ~ dex + albut metadata(se) ``` # Constructing a `SummarizedExperiment` Often, `SummarizedExperiment` or `RangedSummarizedExperiment` objects are returned by functions written by other packages. However it is possible to create them by hand with a call to the `SummarizedExperiment()` constructor. Constructing a `RangedSummarizedExperiment` with a `GRanges` as the _rowRanges_ argument: ```{r constructRSE} nrows <- 200 ncols <- 6 counts <- matrix(runif(nrows * ncols, 1, 1e4), nrows) rowRanges <- GRanges(rep(c("chr1", "chr2"), c(50, 150)), IRanges(floor(runif(200, 1e5, 1e6)), width=100), strand=sample(c("+", "-"), 200, TRUE), feature_id=sprintf("ID%03d", 1:200)) colData <- DataFrame(Treatment=rep(c("ChIP", "Input"), 3), row.names=LETTERS[1:6]) SummarizedExperiment(assays=list(counts=counts), rowRanges=rowRanges, colData=colData) ``` A `SummarizedExperiment` can be constructed with or without supplying a `DataFrame` for the _rowData_ argument: ```{r constructSE} SummarizedExperiment(assays=list(counts=counts), colData=colData) ``` # Common operations on `SummarizedExperiment` ## Subsetting - `[` Performs two dimensional subsetting, just like subsetting a matrix or data frame. ```{r 2d} # subset the first five transcripts and first three samples se[1:5, 1:3] ``` - `$` operates on `colData()` columns, for easy sample extraction. ```{r colDataExtraction} se[, se$cell == "N61311"] ``` ## Getters and setters - `rowRanges()` / (`rowData()`), `colData()`, `metadata()` ```{r getSet} counts <- matrix(1:15, 5, 3, dimnames=list(LETTERS[1:5], LETTERS[1:3])) dates <- SummarizedExperiment(assays=list(counts=counts), rowData=DataFrame(month=month.name[1:5], day=1:5)) # Subset all January assays dates[rowData(dates)$month == "January", ] ``` - `assay()` versus `assays()` There are two accessor functions for extracting the assay data from a `SummarizedExperiment` object. `assays()` operates on the entire list of assay data as a whole, while `assay()` operates on only one assay at a time. `assay(x, i)` is simply a convenience function which is equivalent to `assays(x)[[i]]`. ```{r assay_assays} assays(se) assays(se)[[1]][1:5, 1:5] # assay defaults to the first assay if no i is given assay(se)[1:5, 1:5] assay(se, 1)[1:5, 1:5] ``` ## Range-based operations - `subsetByOverlaps()` `SummarizedExperiment` objects support all of the `findOverlaps()` methods and associated functions. This includes `subsetByOverlaps()`, which makes it easy to subset a `SummarizedExperiment` object by an interval. ```{r overlap} # Subset for only rows which are in the interval 100,000 to 110,000 of # chromosome 1 roi <- GRanges(seqnames="1", ranges=100000:1100000) subsetByOverlaps(se, roi) ``` # Advanced: Extending `RangedSummarizedExperiment` For representing and manipulating data in their own package, Bioconductor developers are encouraged to re-use existing classes defined in other packages like the `RangedSummarizedExperiment` or `GRanges` containers defined in the `SummarizedExperiment` or `GenomicRanges` infrastructure packages, respectively. Many Bioconductor packages are designed around these basic containers, that is, they define functions that take and/or return a `RangedSummarizedExperiment` or `GRanges` object. For example the `csaw` package defines various functions that operate on `RangedSummarizedExperiment` objects, which are used to represent the number of ChIP-seq reads from each BAM file overlapping pre-specified regions. However, sometimes re-using the `RangedSummarizedExperiment` class as-is does not satisfy the needs of the package and the developer makes the choice to extend the class in order to accomodate the special needs of the package and/or the specificities of the data that it deals with. For example the `DESeq2` package defines the `DESeqDataSet` class which extends `RangedSummarizedExperiment` to add the `design` and `dispersionFunction` slots to it. The following subsections describe in a nutshell how the developer would typically proceed for extending `RangedSummarizedExperiment` in his/her own package. Some familiarity with the S4 class system is required. Readers not familiar with the S4 class system are encouraged to consult the vignette `A quick overview of the S4 class system` located in the `S4Vectors` package for the basics of implementing and extending S4 classes. The approach described below allows the developer to extend `RangedSummarizedExperiment` it in a way that remains agnostic of its internals. Keeping this separation between the responsibilities of the owners of the parent and child classes facilitate maintenance in the long run. In particular, the implementation of the child class won't be affected by changes in the internals of the parent class. ## Depend on, and import, the `SummarizedExperiment` package Add `SummarizedExperiment` to the Depends field of the DESCRIPTION file of the package and the `import(SummarizedExperiment)` directive to its NAMESPACE file. ## Define and export the `RangedSummarizedExperiment` subclass Define the subclass with something like: ```{r rseSubclass} setClass("MyRSESubclass", contains="RangedSummarizedExperiment", representation=representation( slot1="integer", slot2="function" ## ... maybe more ... ) ) ``` Export it by adding the `exportClasses(MyRSESubclass)` directive to the NAMESPACE file. ## Construct `MyRSESubclass` instances When calling `new()` for constructing a `MyRSESubclass` instance, specify only the MyRSESubclass-specific slots: `new("MyRSESubclass", rse, slot1=value1, slot2=value2)`, where `rse` is a `RangedSummarizedExperiment` object. Providing a `MyRSESubclass` constructor function (named as the class itself) is recommended. ## Define a validity method The validity method for `MyRSESubclass` only needs to take care of what's new in `MyRSESubclass` with respect to `RangedSummarizedExperiment`, that is, of the aspects of `MyRSESubclass` objects that are not already covered by the validity method for `RangedSummarizedExperiment` objects. This is because calling `validObject()` on a `MyRSESubclass` object automatically validates it as a `RangedSummarizedExperiment` object first and then calls the validity method for `MyRSESubclass` objects. In other words, validation works incrementally starting from the root of the class hierarchy and going in the parent-to-child direction. ## Use the `RangedSummarizedExperiment` accessors on `MyRSESubclass` objects Like any user of `RangedSummarizedExperiment` objects, the developer of `MyRSESubclass` should always use the `RangedSummarizedExperiment` accessors to access the `RangedSummarizedExperiment`-specific parts of his/her `MyRSESubclass` objects. ## Use `callNextMethod` In case some of the methods defined for `RangedSummarizedExperiment` objects need to be overwritten, the new methods should call `callNextMethod` internally. SummarizedExperiment/inst/doc/SummarizedExperiment.html0000644000175400017540000110767213001262611024560 0ustar00biocbuildbiocbuild SummarizedExperiment for Coordinating Experimental Assays, Samples, and Regions of Interest

Contents

1 Introduction

The SummarizedExperiment class is used to store rectangular matrices of experimental results, which are commonly produced by sequencing and microarray experiments. Each object stores observations of one or more samples, along with additional meta-data describing both the observations (features) and samples (phenotypes).

A key aspect of the SummarizedExperiment class is the coordination of the meta-data and assays when subsetting. For example, if you want to exclude a given sample you can do for both the meta-data and assay in one operation, which ensures the meta-data and observed data will remain in sync. Improperly accounting for meta and observational data has resulted in a number of incorrect results and retractions so this is a very desirable property.

SummarizedExperiment is in many ways similar to the historical ExpressionSet, the main distinction being that SummarizedExperiment is more flexible in it’s row information, allowing both GRanges based as well as those described by arbitrary DataFrames. This makes it ideally suited to a variety of experiments, particularly sequencing based experiments such as RNA-Seq and ChIp-Seq.

2 Anatomy of a SummarizedExperiment

The SummarizedExperiment package contains two classes: SummarizedExperiment and RangedSummarizedExperiment.

SummarizedExperiment is a matrix-like container where rows represent features of interest (e.g. genes, transcripts, exons, etc.) and columns represent samples. The objects contain one or more assays, each represented by a matrix-like object of numeric or other mode. The rows of a SummarizedExperiment object represent features of interest. Information about these features is stored in a DataFrame object, accessible using the function rowData(). Each row of the DataFrame provides information on the feature in the corresponding row of the SummarizedExperiment object. Columns of the DataFrame represent different attributes of the features of interest, e.g., gene or transcript IDs, etc.

RangedSummarizedExperiment is the child of the SummarizedExperiment class which means that all the methods on SummarizedExperiment also work on a RangedSummarizedExperiment.

The fundamental difference between the two classes is that the rows of a RangedSummarizedExperiment object represent genomic ranges of interest instead of a DataFrame of features. The RangedSummarizedExperiment ranges are described by a GRanges or a GRangesList object, accessible using the rowRanges() function.

The following graphic displays the class geometry and highlights the vertical (column) and horizontal (row) relationships.

Summarized Experiment

2.1 Assays

The airway package contains an example dataset from an RNA-Seq experiment of read counts per gene for airway smooth muscles. These data are stored in a RangedSummarizedExperiment object which contains 8 different experimental and assays 64,102 gene transcripts.

library(SummarizedExperiment)
data(airway, package="airway")
se <- airway
se
## class: RangedSummarizedExperiment 
## dim: 64102 8 
## metadata(1): ''
## assays(1): counts
## rownames(64102): ENSG00000000003 ENSG00000000005 ... LRG_98 LRG_99
## rowData names(0):
## colnames(8): SRR1039508 SRR1039509 ... SRR1039520 SRR1039521
## colData names(9): SampleName cell ... Sample BioSample

To retrieve the experiment data from a SummarizedExperiment object one can use the assays() accessor. An object can have multiple assay datasets each of which can be accessed using the $ operator. The airway dataset contains only one assay (counts). Here each row represents a gene transcript and each column one of the samples.

assays(se)$counts
SRR1039508 SRR1039509 SRR1039512 SRR1039513 SRR1039516 SRR1039517 SRR1039520 SRR1039521
ENSG00000000003 679 448 873 408 1138 1047 770 572
ENSG00000000005 0 0 0 0 0 0 0 0
ENSG00000000419 467 515 621 365 587 799 417 508
ENSG00000000457 260 211 263 164 245 331 233 229
ENSG00000000460 60 55 40 35 78 63 76 60
ENSG00000000938 0 0 2 0 1 0 0 0
ENSG00000000971 3251 3679 6177 4252 6721 11027 5176 7995
ENSG00000001036 1433 1062 1733 881 1424 1439 1359 1109
ENSG00000001084 519 380 595 493 820 714 696 704
ENSG00000001167 394 236 464 175 658 584 360 269

2.2 ‘Row’ (regions-of-interest) data

The rowRanges() accessor is used to view the range information for a RangedSummarizedExperiment. (Note if this were the parent SummarizedExperiment class we’d use rowData()). The data are stored in a GRangesList object, where each list element corresponds to one gene transcript and the ranges in each GRanges correspond to the exons in the transcript.

rowRanges(se)
## GRangesList object of length 64102:
## $ENSG00000000003 
## GRanges object with 17 ranges and 2 metadata columns:
##        seqnames               ranges strand |   exon_id       exon_name
##           <Rle>            <IRanges>  <Rle> | <integer>     <character>
##    [1]        X [99883667, 99884983]      - |    667145 ENSE00001459322
##    [2]        X [99885756, 99885863]      - |    667146 ENSE00000868868
##    [3]        X [99887482, 99887565]      - |    667147 ENSE00000401072
##    [4]        X [99887538, 99887565]      - |    667148 ENSE00001849132
##    [5]        X [99888402, 99888536]      - |    667149 ENSE00003554016
##    ...      ...                  ...    ... .       ...             ...
##   [13]        X [99890555, 99890743]      - |    667156 ENSE00003512331
##   [14]        X [99891188, 99891686]      - |    667158 ENSE00001886883
##   [15]        X [99891605, 99891803]      - |    667159 ENSE00001855382
##   [16]        X [99891790, 99892101]      - |    667160 ENSE00001863395
##   [17]        X [99894942, 99894988]      - |    667161 ENSE00001828996
## 
## ...
## <64101 more elements>
## -------
## seqinfo: 722 sequences (1 circular) from an unspecified genome

2.3 ‘Column’ (sample) data

Sample meta-data describing the samples can be accessed using colData(), and is a DataFrame that can store any number of descriptive columns for each sample row.

colData(se)
## DataFrame with 8 rows and 9 columns
##            SampleName     cell      dex    albut        Run avgLength
##              <factor> <factor> <factor> <factor>   <factor> <integer>
## SRR1039508 GSM1275862   N61311    untrt    untrt SRR1039508       126
## SRR1039509 GSM1275863   N61311      trt    untrt SRR1039509       126
## SRR1039512 GSM1275866  N052611    untrt    untrt SRR1039512       126
## SRR1039513 GSM1275867  N052611      trt    untrt SRR1039513        87
## SRR1039516 GSM1275870  N080611    untrt    untrt SRR1039516       120
## SRR1039517 GSM1275871  N080611      trt    untrt SRR1039517       126
## SRR1039520 GSM1275874  N061011    untrt    untrt SRR1039520       101
## SRR1039521 GSM1275875  N061011      trt    untrt SRR1039521        98
##            Experiment    Sample    BioSample
##              <factor>  <factor>     <factor>
## SRR1039508  SRX384345 SRS508568 SAMN02422669
## SRR1039509  SRX384346 SRS508567 SAMN02422675
## SRR1039512  SRX384349 SRS508571 SAMN02422678
## SRR1039513  SRX384350 SRS508572 SAMN02422670
## SRR1039516  SRX384353 SRS508575 SAMN02422682
## SRR1039517  SRX384354 SRS508576 SAMN02422673
## SRR1039520  SRX384357 SRS508579 SAMN02422683
## SRR1039521  SRX384358 SRS508580 SAMN02422677

This sample metadata can be accessed using the $ accessor which makes it easy to subset the entire object by a given phenotype.

# subset for only those samples treated with dexamethasone
se[, se$dex == "trt"]
## class: RangedSummarizedExperiment 
## dim: 64102 4 
## metadata(1): ''
## assays(1): counts
## rownames(64102): ENSG00000000003 ENSG00000000005 ... LRG_98 LRG_99
## rowData names(0):
## colnames(4): SRR1039509 SRR1039513 SRR1039517 SRR1039521
## colData names(9): SampleName cell ... Sample BioSample

2.4 Experiment-wide metadata

Meta-data describing the experimental methods and publication references can be accessed using metadata().

metadata(se)
## [[1]]
## Experiment data
##   Experimenter name: Himes BE 
##   Laboratory: NA 
##   Contact information:  
##   Title: RNA-Seq transcriptome profiling identifies CRISPLD2 as a glucocorticoid responsive gene that modulates cytokine function in airway smooth muscle cells. 
##   URL: http://www.ncbi.nlm.nih.gov/pubmed/24926665 
##   PMIDs: 24926665 
## 
##   Abstract: A 226 word abstract is available. Use 'abstract' method.

Note that metadata() is just a simple list, so it is appropriate for any experiment wide metadata the user wishes to save, such as storing model formulas.

metadata(se)$formula <- counts ~ dex + albut

metadata(se)
## [[1]]
## Experiment data
##   Experimenter name: Himes BE 
##   Laboratory: NA 
##   Contact information:  
##   Title: RNA-Seq transcriptome profiling identifies CRISPLD2 as a glucocorticoid responsive gene that modulates cytokine function in airway smooth muscle cells. 
##   URL: http://www.ncbi.nlm.nih.gov/pubmed/24926665 
##   PMIDs: 24926665 
## 
##   Abstract: A 226 word abstract is available. Use 'abstract' method.
## 
## $formula
## counts ~ dex + albut

3 Constructing a SummarizedExperiment

Often, SummarizedExperiment or RangedSummarizedExperiment objects are returned by functions written by other packages. However it is possible to create them by hand with a call to the SummarizedExperiment() constructor.

Constructing a RangedSummarizedExperiment with a GRanges as the rowRanges argument:

nrows <- 200
ncols <- 6
counts <- matrix(runif(nrows * ncols, 1, 1e4), nrows)
rowRanges <- GRanges(rep(c("chr1", "chr2"), c(50, 150)),
                     IRanges(floor(runif(200, 1e5, 1e6)), width=100),
                     strand=sample(c("+", "-"), 200, TRUE),
                     feature_id=sprintf("ID%03d", 1:200))
colData <- DataFrame(Treatment=rep(c("ChIP", "Input"), 3),
                     row.names=LETTERS[1:6])

SummarizedExperiment(assays=list(counts=counts),
                     rowRanges=rowRanges, colData=colData)
## class: RangedSummarizedExperiment 
## dim: 200 6 
## metadata(0):
## assays(1): counts
## rownames: NULL
## rowData names(1): feature_id
## colnames(6): A B ... E F
## colData names(1): Treatment

A SummarizedExperiment can be constructed with or without supplying a DataFrame for the rowData argument:

SummarizedExperiment(assays=list(counts=counts), colData=colData)
## class: SummarizedExperiment 
## dim: 200 6 
## metadata(0):
## assays(1): counts
## rownames: NULL
## rowData names(0):
## colnames(6): A B ... E F
## colData names(1): Treatment

4 Common operations on SummarizedExperiment

4.1 Subsetting

  • [ Performs two dimensional subsetting, just like subsetting a matrix or data frame.
# subset the first five transcripts and first three samples
se[1:5, 1:3]
## class: RangedSummarizedExperiment 
## dim: 5 3 
## metadata(2): '' formula
## assays(1): counts
## rownames(5): ENSG00000000003 ENSG00000000005 ENSG00000000419
##   ENSG00000000457 ENSG00000000460
## rowData names(0):
## colnames(3): SRR1039508 SRR1039509 SRR1039512
## colData names(9): SampleName cell ... Sample BioSample
  • $ operates on colData() columns, for easy sample extraction.
se[, se$cell == "N61311"]
## class: RangedSummarizedExperiment 
## dim: 64102 2 
## metadata(2): '' formula
## assays(1): counts
## rownames(64102): ENSG00000000003 ENSG00000000005 ... LRG_98 LRG_99
## rowData names(0):
## colnames(2): SRR1039508 SRR1039509
## colData names(9): SampleName cell ... Sample BioSample

4.2 Getters and setters

  • rowRanges() / (rowData()), colData(), metadata()
counts <- matrix(1:15, 5, 3, dimnames=list(LETTERS[1:5], LETTERS[1:3]))

dates <- SummarizedExperiment(assays=list(counts=counts),
                              rowData=DataFrame(month=month.name[1:5], day=1:5))

# Subset all January assays
dates[rowData(dates)$month == "January", ]
## class: SummarizedExperiment 
## dim: 1 3 
## metadata(0):
## assays(1): counts
## rownames(1): A
## rowData names(2): month day
## colnames(3): A B C
## colData names(0):
  • assay() versus assays() There are two accessor functions for extracting the assay data from a SummarizedExperiment object. assays() operates on the entire list of assay data as a whole, while assay() operates on only one assay at a time. assay(x, i) is simply a convenience function which is equivalent to assays(x)[[i]].
assays(se)
## List of length 1
## names(1): counts
assays(se)[[1]][1:5, 1:5]
##                 SRR1039508 SRR1039509 SRR1039512 SRR1039513 SRR1039516
## ENSG00000000003        679        448        873        408       1138
## ENSG00000000005          0          0          0          0          0
## ENSG00000000419        467        515        621        365        587
## ENSG00000000457        260        211        263        164        245
## ENSG00000000460         60         55         40         35         78
# assay defaults to the first assay if no i is given
assay(se)[1:5, 1:5]
##                 SRR1039508 SRR1039509 SRR1039512 SRR1039513 SRR1039516
## ENSG00000000003        679        448        873        408       1138
## ENSG00000000005          0          0          0          0          0
## ENSG00000000419        467        515        621        365        587
## ENSG00000000457        260        211        263        164        245
## ENSG00000000460         60         55         40         35         78
assay(se, 1)[1:5, 1:5]
##                 SRR1039508 SRR1039509 SRR1039512 SRR1039513 SRR1039516
## ENSG00000000003        679        448        873        408       1138
## ENSG00000000005          0          0          0          0          0
## ENSG00000000419        467        515        621        365        587
## ENSG00000000457        260        211        263        164        245
## ENSG00000000460         60         55         40         35         78

4.3 Range-based operations

  • subsetByOverlaps() SummarizedExperiment objects support all of the findOverlaps() methods and associated functions. This includes subsetByOverlaps(), which makes it easy to subset a SummarizedExperiment object by an interval.
# Subset for only rows which are in the interval 100,000 to 110,000 of
# chromosome 1
roi <- GRanges(seqnames="1", ranges=100000:1100000)
subsetByOverlaps(se, roi)
## class: RangedSummarizedExperiment 
## dim: 74 8 
## metadata(2): '' formula
## assays(1): counts
## rownames(74): ENSG00000131591 ENSG00000177757 ... ENSG00000272512
##   ENSG00000273443
## rowData names(0):
## colnames(8): SRR1039508 SRR1039509 ... SRR1039520 SRR1039521
## colData names(9): SampleName cell ... Sample BioSample

5 Advanced: Extending RangedSummarizedExperiment

For representing and manipulating data in their own package, Bioconductor developers are encouraged to re-use existing classes defined in other packages like the RangedSummarizedExperiment or GRanges containers defined in the SummarizedExperiment or GenomicRanges infrastructure packages, respectively. Many Bioconductor packages are designed around these basic containers, that is, they define functions that take and/or return a RangedSummarizedExperiment or GRanges object. For example the csaw package defines various functions that operate on RangedSummarizedExperiment objects, which are used to represent the number of ChIP-seq reads from each BAM file overlapping pre-specified regions.

However, sometimes re-using the RangedSummarizedExperiment class as-is does not satisfy the needs of the package and the developer makes the choice to extend the class in order to accomodate the special needs of the package and/or the specificities of the data that it deals with. For example the DESeq2 package defines the DESeqDataSet class which extends RangedSummarizedExperiment to add the design and dispersionFunction slots to it.

The following subsections describe in a nutshell how the developer would typically proceed for extending RangedSummarizedExperiment in his/her own package. Some familiarity with the S4 class system is required. Readers not familiar with the S4 class system are encouraged to consult the vignette A quick overview of the S4 class system located in the S4Vectors package for the basics of implementing and extending S4 classes.

The approach described below allows the developer to extend RangedSummarizedExperiment it in a way that remains agnostic of its internals. Keeping this separation between the responsibilities of the owners of the parent and child classes facilitate maintenance in the long run. In particular, the implementation of the child class won’t be affected by changes in the internals of the parent class.

5.1 Depend on, and import, the SummarizedExperiment package

Add SummarizedExperiment to the Depends field of the DESCRIPTION file of the package and the import(SummarizedExperiment) directive to its NAMESPACE file.

5.2 Define and export the RangedSummarizedExperiment subclass

Define the subclass with something like:

setClass("MyRSESubclass",
    contains="RangedSummarizedExperiment",
    representation=representation(
        slot1="integer",
        slot2="function"
        ## ... maybe more ...
    )
)

Export it by adding the exportClasses(MyRSESubclass) directive to the NAMESPACE file.

5.3 Construct MyRSESubclass instances

When calling new() for constructing a MyRSESubclass instance, specify only the MyRSESubclass-specific slots: new("MyRSESubclass", rse, slot1=value1, slot2=value2), where rse is a RangedSummarizedExperiment object.

Providing a MyRSESubclass constructor function (named as the class itself) is recommended.

5.4 Define a validity method

The validity method for MyRSESubclass only needs to take care of what’s new in MyRSESubclass with respect to RangedSummarizedExperiment, that is, of the aspects of MyRSESubclass objects that are not already covered by the validity method for RangedSummarizedExperiment objects. This is because calling validObject() on a MyRSESubclass object automatically validates it as a RangedSummarizedExperiment object first and then calls the validity method for MyRSESubclass objects. In other words, validation works incrementally starting from the root of the class hierarchy and going in the parent-to-child direction.

5.5 Use the RangedSummarizedExperiment accessors on MyRSESubclass objects

Like any user of RangedSummarizedExperiment objects, the developer of MyRSESubclass should always use the RangedSummarizedExperiment accessors to access the RangedSummarizedExperiment-specific parts of his/her MyRSESubclass objects.

5.6 Use callNextMethod

In case some of the methods defined for RangedSummarizedExperiment objects need to be overwritten, the new methods should call callNextMethod internally.

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uc021rna.1 28 28 0 0 uc021rnb.1 37 37 0 0 uc021rnd.1 37 37 0 0 uc021rne.1 41 41 0 0 uc021rnf.1 30 30 0 0 uc010tkq.2 32 32 0 0 uc021rng.1 28 28 0 0 uc021rnh.1 28 28 0 0 uc001vvf.2 40 40 0 0 uc001vvh.3 32 32 0 0 uc001vvi.3 34158 34009 0 0 uc001vvk.1 34259 34110 0 0 uc001vvl.4 42885 42736 2206.9 476.273 uc021rnj.1 69004 68855 444.552 59.5463 uc001vvn.3 1387 1238 0 0 uc001vvt.3 10204 10055 0 0 uc001vwd.3 4044 3895 0 0 uc010tkt.2 942 793 0 0 uc010tku.2 942 793 0 0 uc010tkv.2 924 775 0 0 uc001vwh.1 945 796 0 0 uc010tkw.2 972 823 0 0 uc001vwj.2 1076 927 0 0 uc010tkx.2 1047 898 0 0 uc001vwn.1 939 790 0 0 uc001vwo.1 1032 883 0 0 uc010tla.2 927 778 0 0 uc010tlb.2 1038 889 0 0 uc010tlc.2 993 844 0 0 uc010tld.2 975 826 0 0 uc001vxb.1 14290 14141 247.606 161.491 uc001vxc.3 14291 14142 231.649 151.073 uc001vxd.3 14291 14142 231.649 151.073 uc010tle.2 3310 3161 268.096 782.228 uc001vxg.3 2642 2493 1428 5282.92 uc001vxh.3 2642 2493 1428 5282.92 uc001vxi.3 2642 2493 1428 5282.92 uc021rnr.1 2642 2493 1428 5282.92 uc010ahm.2 3822 3673 0 0 uc010ahn.3 5785 5636 1559.52 2552.04 uc001vxo.4 8628 8479 0 0 uc021rns.1 3246 3097 3840.48 11437 uc001vxp.2 5619 5470 0 0 uc010tlj.2 651 502 0 0 uc001vxu.1 16736 16587 0 0 uc021rnw.1 82 82 0 0 uc021rnz.1 82 82 0 0 uc021rob.1 72 72 0 0 uc021roh.1 73 73 0 0 uc021roi.1 89 89 0 0 uc021roj.1 72 72 0 0 uc001vxw.4 10010 9861 28.5268 26.6809 uc001vxx.4 16423 16274 17.2901 9.79877 uc001vxy.4 16423 16274 17.2901 9.79877 uc001vxz.3 5414 5265 0 0 uc001vya.3 11827 11678 0 0 uc021rok.1 444 295 50.5198 1579.45 uc021rol.1 444 295 14.3732 449.364 uc001vyc.3 2441 2292 0 0 uc021rom.1 444 295 0 0 uc001vyd.3 2522 2373 0 0 uc021ron.1 444 295 0 0 uc001vye.4 1417 1268 0 0 uc021roo.1 453 304 0 0 uc001vyj.3 946 797 0 0 uc021roq.1 483 334 0 0 uc021ror.1 836 687 0 0 uc010aif.2 37095 36946 1 0.249632 uc001vyl.1 965 816 0 0 uc021ros.1 486 337 0 0 uc010tlk.2 3311 3162 0 0 uc001vym.3 7225 7076 1092.63 1424.14 uc001vyn.3 7230 7081 561.687 731.589 uc001vyo.3 7230 7081 561.687 731.589 uc001vyr.3 2621 2472 1 3.73095 uc001vys.4 2621 2472 1 3.73095 uc001vzh.3 1988 1839 0 0 uc001vzk.4 2008 1859 0 0 uc001vzl.2 2009 1860 0 0 uc021rou.1 471 322 0 0 uc010tlm.2 465 316 0 0 uc001vzn.1 5186 5037 0 0 uc001vzo.1 16539 16390 0 0 uc001vzp.3 19510 19361 111 52.8765 uc010aij.3 19510 19361 111 52.8765 uc010tln.2 19510 19361 111 52.8765 uc010tlo.2 4788 4639 61.4477 122.166 uc001wag.3 63325 63176 3.51495 0.513139 uc001wah.3 33748 33599 0 0 uc001wai.3 33748 33599 0 0 uc001waj.1 3812 3663 0 0 uc001wak.3 30043 29894 0 0 uc010aim.3 29943 29794 0 0 uc001wal.3 29448 29299 0 0 uc001wam.3 27378 27229 0.485047 0.164293 uc001way.3 19037 18888 318.807 155.671 uc001waz.3 21985 21836 5.57481e-06 2.35464e-06 uc010tlu.2 21985 21836 5.57481e-06 2.35464e-06 uc010tlv.2 11575 11426 1450.81 1171.07 uc001wbi.2 711 562 0 0 uc001wbk.3 698 549 0 0 uc010tmd.2 942 793 0 0 uc021roz.1 520 371 0 0 uc001wbn.3 561 412 0 0 uc021rpa.1 255349 255200 1.00344 0.0362641 uc021rpb.1 159 10 0 0 uc001wbp.2 810 661 0 0 uc010aip.1 13535 13386 0 0 uc021rpc.1 244 95 0 0 uc010aiq.1 592 443 0 0 uc001wbt.1 585 436 0 0 uc021rpd.1 309 160 0 0 uc010air.1 594 445 0 0 uc001wbv.3 117 117 0 0 uc010ais.1 588 439 0 0 uc001wbw.2 725946 725797 0 0 uc001wbx.2 635 486 0 0 uc001wbz.1 681 532 0 0 uc010ait.1 681 532 0 0 uc021rpe.1 219473 219324 0 0 uc001wcb.2 647 498 0 0 uc001wcc.3 3761 3612 0 0 uc021rpf.1 276 127 0 0 uc021rpg.1 396747 396598 0 0 uc021rph.1 648 499 0 0 uc010aiv.1 389875 389726 0 0 uc021rpi.1 245 96 0 0 uc021rpj.2 172012 171863 0 0 uc010aiy.2 577 428 0 0 uc001wch.2 578 429 0 0 uc010aiz.2 588 439 0 0 uc021rpk.1 248 99 0 0 uc001wck.3 527 378 0 0 uc021rpl.1 538598 538449 0 0 uc021rpm.1 594 445 0 0 uc010tmm.2 87855 87706 0 0 uc010tmo.2 508219 508070 0 0 uc001wcq.3 8442 8293 0 0 uc001wco.3 558067 557918 0 0 uc010ajd.1 5004 4855 0 0 uc010aje.1 550895 550746 0 0 uc001wcp.2 550824 550675 0 0 uc001wcr.1 550597 550448 0 0 uc001wcs.1 550597 550448 0 0 uc010ajf.1 550597 550448 0 0 uc001wct.4 5069 4920 0 0 uc001wcu.4 476295 476146 0 0 uc021rpn.1 476295 476146 0 0 uc021rpo.1 554 405 0 0 uc021rpp.1 277 128 0 0 uc001wcx.4 500362 500213 0 0 uc001wcy.3 562 413 0 0 uc021rpq.1 256 107 0 0 uc001wcz.2 570 421 0 0 uc021rpr.1 461793 461644 0 0 uc010tmp.1 595 446 0 0 uc001wdb.2 1375 1226 0 0 uc010ajh.1 686 537 0 0 uc010aji.1 915 766 0 0 uc001wdd.2 360721 360572 0 0 uc010ajj.1 398119 397970 0 0 uc001wde.1 424764 424615 0 0 uc010tmq.2 24 24 0 0 uc010ajk.2 400760 400611 0 0 uc001wdg.1 400414 400265 0 0 uc021rpt.1 400414 400265 0 0 uc001wdh.2 582 433 0 0 uc010ajl.1 368898 368749 0 0 uc001wdi.2 587 438 0 0 uc021rpu.1 124 124 0 0 uc010ajm.2 776 627 0 0 uc021rpv.1 311404 311255 0 0 uc001wdk.2 612 463 0 0 uc010ajn.1 605 456 0 0 uc010ajo.1 326632 326483 0 0 uc010ajp.1 322108 321959 0 0 uc021rpw.1 305227 305078 0 0 uc001wdn.3 622 473 0 0 uc021rpx.1 170 21 0 0 uc010tmr.2 213989 213840 4.31181 0.185968 uc010ajq.1 563 414 0.991652 22.0916 uc001wdq.2 459 310 0 0 uc001wdr.2 555 406 0 0 uc001wdu.2 562 413 0 0 uc001wdv.4 129626 129477 0 0 uc001wdw.2 7594 7445 0 0 uc001wdx.4 15832 15683 0 0 uc010ajs.1 7958 7809 0 0 uc001wdz.4 7266 7117 0 0 uc010tms.2 8153 8004 0 0 uc010ajt.3 4741 4592 0 0 uc010aju.1 5548 5399 0 0 uc001wea.4 6824 6675 0 0 uc001wec.3 78798 78649 0 0 uc001wed.1 1569 1420 0 0 uc001wee.4 73237 73088 0 0 uc021rpy.1 58 58 0 0 uc010tmt.1 68202 68053 0 0 uc010ajv.1 65443 65294 0 0 uc001weg.2 68325 68176 0 0 uc001weh.1 33370 33221 0 0 uc001wei.3 65884 65735 0 0 uc001wej.3 63516 63367 0 0 uc001wek.3 62821 62672 0 0 uc001wel.3 62624 62475 0 0 uc001wem.4 61619 61470 0 0 uc001wen.2 54882 54733 0 0 uc001weo.2 56700 56551 0 0 uc010ajw.2 65 65 0 0 uc001wep.3 57287 57138 0 0 uc001weq.3 56202 56053 0 0 uc021rpz.1 66 66 0 0 uc021rqa.1 50847 50698 0 0 uc021rqb.1 55214 55065 0 0 uc001wet.3 52964 52815 0 0 uc021rqc.1 64 64 0 0 uc001weu.3 52422 52273 0 0 uc021rqd.1 112 112 0 0 uc001wev.3 50509 50360 0 0 uc021rqe.1 59 59 0 0 uc021rqf.1 62 62 0 0 uc010tmv.2 43983 43834 0 0 uc021rqg.1 55 55 0 0 uc001wez.2 42616 42467 0 0 uc001wfa.2 276 127 0 0 uc010ajx.1 43975 43826 0 0 uc001wfb.2 40068 39919 0 0 uc021rqh.1 39133 38984 0 0 uc001wfd.1 42015 41866 0 0 uc001wfe.3 42875 42726 0 0 uc001wfg.2 38390 38241 0 0 uc021rqi.1 56 56 0 0 uc001wfh.1 39653 39504 0 0 uc001wfi.2 34879 34730 0 0 uc001wfj.1 2388 2239 0 0 uc001wfk.3 38176 38027 0 0 uc001wfl.3 30236 30087 0 0 uc010ajy.2 36496 36347 0 0 uc001wfn.3 35888 35739 0 0 uc021rqj.1 61 61 0 0 uc001wfo.2 2030 1881 0 0 uc001wfp.3 33671 33522 0 0 uc021rqk.1 63 63 0 0 uc001wfq.2 30661 30512 0 0 uc001wfr.1 27327 27178 0 0 uc010ajz.1 27312 27163 0 0 uc021rql.1 65 65 0 0 uc001wfs.2 25699 25550 0 0 uc001wft.2 27035 26886 0 0 uc001wfu.3 21541 21392 0 0 uc001wfv.2 26312 26163 0 0 uc001wfw.1 24990 24841 0 0 uc021rqm.1 62 62 0 0 uc001wfx.3 25282 25133 0 0 uc001wfy.2 19228 19079 0 0 uc001wfz.1 18143 17994 0 0 uc021rqn.1 137 137 0 0 uc001wgb.3 1618 1469 0 0 uc001wgc.2 15829 15680 0 0 uc021rqo.1 15270 15121 0 0 uc001wge.4 18649 18500 0 0 uc021rqp.1 18640 18491 0 0 uc021rqq.1 59 59 0 0 uc010tmw.2 15103 14954 0 0 uc010tmx.2 16006 15857 0 0 uc010tmy.2 10150 10001 0 0 uc010akb.2 131 131 0 0 uc021rqr.1 6820 6671 0 0 uc001wgh.3 13080 12931 0 0 uc001wgi.2 7530 7381 0 0 uc001wgj.1 8463 8314 0 0 uc001wgk.3 8978 8829 4 4.17846 uc010tmz.1 6015 5866 0 0 uc010tna.1 11670 11521 0 0 uc001wgm.3 14119 13970 458 302.369 uc010tnb.2 14119 13970 458 302.369 uc010tnc.2 2101 1952 0 0 uc001wgn.2 5268 5119 2092.11 3769.36 uc001wgp.2 5038 4889 4605.89 8688.82 uc001wgw.4 5155 5006 63 116.069 uc001wgx.4 5155 5006 63 116.069 uc001wgy.4 5155 5006 63 116.069 uc001wgz.4 5155 5006 63 116.069 uc001wha.4 5155 5006 63 116.069 uc001whb.4 5155 5006 63 116.069 uc001whc.3 11011 10862 12 10.1892 uc001whd.3 6332 6183 1144.79 1707.63 uc001whe.3 5323 5174 4258.21 7590.47 uc010tnd.1 3621 3472 5.7037 15.1511 uc001whf.1 4458 4309 10.2963 22.038 uc021rqs.1 73 73 0 0 uc021rqt.1 73 73 0 0 uc010ake.1 1894 1745 0 0 uc001wiu.3 4983 4834 523.402 998.611 uc021rqv.1 423 274 420.333 14148.5 uc001wjg.4 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3241.49 uc001ybm.4 22161 22012 330.858 138.628 uc001ybn.4 22161 22012 330.858 138.628 uc010auq.3 19400 19251 460.285 220.516 uc001ybs.1 374125 373976 27.0093 0.666098 uc001ybt.1 1164 1015 0 0 uc001ybu.1 63274 63125 0 0 uc001ybv.1 229786 229637 0 0 uc001ybz.2 10715 10566 0 0 uc021say.1 10715 10566 0 0 uc021saz.1 2267 2118 0 0 uc010aus.2 2266 2117 0 0 uc001yca.2 2224 2075 0 0 uc031qqc.1 879 730 0 0 uc001ycf.3 10283 10134 0 0 uc001ycg.1 11568 11419 0 0 uc010twp.2 14400 14251 0 0 uc001ych.4 13034 12885 0 0 uc001yci.3 22553 22404 92 37.873 uc001yck.3 21422 21273 256.5 111.206 uc001ycl.3 21422 21273 256.5 111.206 uc001ycm.1 4148 3999 0 0 uc001ycn.1 5201 5052 0 0 uc021sba.1 5955 5806 19 30.1817 uc001ycr.3 53534 53385 0 0 uc001ycs.1 105424 105275 0 0 uc010avd.3 8465 8316 0 0 uc001ydk.3 8461 8312 0 0 uc001ydl.3 8402 8253 0 0 uc010ave.3 9399 9250 0 0 uc001ydm.3 11752 11603 0 0 uc001ydn.1 6497 6348 0 0 uc001ydo.4 31996 31847 0 0 uc010avf.1 2788 2639 0 0 uc001ydp.3 11757 11608 0 0 uc001ydr.3 11757 11608 0 0 uc021sbb.2 11757 11608 0 0 uc001yds.3 11663 11514 0 0 uc010avg.3 11663 11514 0 0 uc001ydt.3 6270 6121 0 0 uc010twt.1 22246 22097 37.7096 15.7393 uc001yee.1 2451 2302 4.07152 16.3124 uc001yem.1 9733 9584 571 549.487 uc001yen.1 5077 4928 0 0 uc001yep.1 21063 20914 25.4757 11.2346 uc021sbf.1 20670 20521 0 0 uc021sbg.1 22275 22126 0 0 uc021sbh.1 8592 8443 0 0 uc001yet.1 10197 10048 0 0 uc001yeu.2 16577 16428 1.7411 0.977478 uc001yev.2 16577 16428 1.7411 0.977478 uc001yew.3 29388 29239 0 0 uc001yex.3 29388 29239 0 0 uc010avj.3 29388 29239 0 0 uc021sbi.1 29388 29239 0 0 uc001yfa.3 6212 6063 0 0 uc001yez.3 6227 6078 0 0 uc001yfd.1 41297 41148 0 0 uc001yfe.3 48800 48651 0 0 uc001yff.4 54566 54417 2 0.338971 uc010avl.2 39369 39220 1151.55 270.797 uc001yfg.2 39532 39383 23.1498 5.42133 uc010avm.1 39532 39383 23.1498 5.42133 uc010twu.1 39532 39383 23.1498 5.42133 uc001yfh.3 8554 8405 6.73118 7.3862 uc021sbj.1 1062 913 1.26882 12.8173 uc001yfj.5 23839 23690 79.8356 31.0813 uc031qqe.1 23839 23690 79.8356 31.0813 uc031qqf.1 23839 23690 79.8356 31.0813 uc001yfl.4 22555 22406 17.4933 7.20071 uc001yfm.1 7181 7032 0 0 uc001yfn.3 97317 97168 4.0372 0.383199 uc001yfo.3 33223 33074 0 0 uc001yfp.3 33223 33074 0 0 uc001yfq.3 64741 64592 479.25 68.4307 uc001yfr.3 64741 64592 479.25 68.4307 uc010avp.3 64741 64592 479.25 68.4307 uc010twv.2 64741 64592 479.25 68.4307 uc001yfs.3 3010 2861 0 0 uc001yft.3 84268 84119 308 33.7695 uc001yfu.3 5290 5141 0 0 uc031qqg.1 3892 3743 0 0 uc001yfz.2 6154 6005 0 0 uc021sbm.1 91 91 0 0 uc001ygi.4 30114 29965 1678.67 516.678 uc021sbo.1 102 102 0 0 uc001ygl.1 24132 23983 0 0 uc010avs.3 31532 31383 0 0 uc031qqk.1 23 23 0 0 uc001ygn.1 29020 28871 0 0 uc001ygo.3 42884 42735 0 0 uc031qql.1 42598 42449 0 0 uc001ygp.3 21305 21156 0 0 uc010avt.1 171748 171599 0 0 uc010tww.2 168460 168311 0 0 uc001ygq.3 118177 118028 0 0 uc001ygr.3 148651 148502 2 0.124213 uc001ygs.3 148651 148502 2 0.124213 uc001ygt.3 172423 172274 29.5475 1.58186 uc001ygv.2 120736 120587 38.9759 2.98101 uc001ygu.3 78823 78674 1220.48 143.076 uc021sbp.1 99 99 0 0 uc001ygw.1 2317 2168 346.996 1476.16 uc001ygy.2 40270 40121 1994.75 458.548 uc001ygz.2 2376 2227 1613 6680.08 uc010twy.1 98 98 0 0 uc001yhc.3 7037 6888 1 1.33898 uc001yhd.3 7037 6888 1 1.33898 uc010avx.3 153886 153737 45.2619 2.71533 uc021sbq.1 5313 5164 26.7655 47.8031 uc001yhn.3 153808 153659 3.14912e-07 1.89016e-08 uc010avy.3 149394 149245 282.973 17.4869 uc001yho.3 148304 148155 0 0 uc021sbr.1 244 95 0 0 uc001yhr.1 15477 15328 0 0 uc001yhu.4 8263 8114 0 0 uc001yhs.4 8266 8117 0 0 uc021sbs.1 330 181 0 0 uc021sbt.1 84 84 0 0 uc021sbu.1 204 55 0 0 uc021sbw.2 28198 28049 0 0 uc001yhw.3 34916 34767 0 0 uc001yhy.3 34916 34767 0 0 uc001yhz.3 34916 34767 0 0 uc001yib.3 34916 34767 0 0 uc001yic.3 34916 34767 0 0 uc001yie.3 34916 34767 0 0 uc010avz.2 34916 34767 0 0 uc010txb.2 34916 34767 0 0 uc010txc.2 34916 34767 0 0 uc010txd.2 34916 34767 0 0 uc010txe.2 34916 34767 0 0 uc010txf.2 34916 34767 0 0 uc010txg.2 34916 34767 0 0 uc010txh.2 34916 34767 0 0 uc021sbv.1 87 87 0 0 uc021sbx.1 30419 30270 0 0 uc001yid.2 29603 29454 0 0 uc021sbz.1 98 98 0 0 uc001yif.2 2963 2814 0 0 uc021sca.1 89 89 0 0 uc010txi.2 93 93 0 0 uc021scb.1 72 72 0 0 uc021scc.1 114 114 0 0 uc021scd.1 93 93 0 0 uc001yig.3 97 97 0 0 uc021sce.1 94 94 0 0 uc010txk.1 82 82 0 0 uc010txl.1 12199 12050 0 0 uc021scf.1 77 77 0 0 uc021scg.1 79 79 0 0 uc021sch.1 58 58 0 0 uc001yii.1 70 70 0 0 uc001yij.3 71 71 0 0 uc021sci.1 72 72 0 0 uc001yil.3 74 74 0 0 uc001yim.1 77 77 0 0 uc001yin.3 74 74 0 0 uc001yio.3 76 76 0 0 uc001yiq.3 71 71 0 0 uc001yir.3 71 71 0 0 uc001yis.3 77 77 0 0 uc001yit.3 74 74 0 0 uc001yiu.3 104 104 0 0 uc001yiv.3 69 69 0 0 uc021scj.1 73 73 0 0 uc001yiw.3 71 71 0 0 uc001yix.1 76 76 0 0 uc001yiy.1 71 71 0 0 uc001yiz.3 71 71 0 0 uc001yja.3 71 71 0 0 uc001yjb.3 74 74 0 0 uc001yjc.3 74 74 0 0 uc001yjd.3 73 73 0 0 uc001yje.3 74 74 0 0 uc001yjf.1 71 71 0 0 uc001yjg.3 69 69 0 0 uc001yjh.3 74 74 0 0 uc001yji.3 71 71 0 0 uc001yjj.3 74 74 0 0 uc021sck.1 78 78 0 0 uc001yjk.1 71 71 0 0 uc001yjl.3 71 71 0 0 uc001yjm.3 71 71 0 0 uc001yjn.3 71 71 0 0 uc001yjo.1 71 71 0 0 uc001yjp.1 71 71 0 0 uc001yjq.3 71 71 0 0 uc001yjr.3 69 69 0 0 uc001yjs.3 71 71 0 0 uc001yjt.1 69 69 0 0 uc001yju.3 71 71 0 0 uc001yjv.3 74 74 0 0 uc021scl.1 75 75 0 0 uc010awa.1 67 67 0 0 uc021scm.1 96 96 0 0 uc001yjx.1 63 63 0 0 uc021scn.1 80 80 0 0 uc010awb.1 61 61 0 0 uc021sco.1 88 88 0 0 uc001yjy.1 86 86 0 0 uc021scp.1 88 88 0 0 uc021scq.1 80 80 0 0 uc021scr.1 84 84 0 0 uc010txm.2 81 81 0 0 uc021scs.1 78 78 0 0 uc021sct.1 78 78 0 0 uc021scu.1 82 82 0 0 uc021scv.1 81 81 0 0 uc021scw.1 77 77 0 0 uc021scx.1 80 80 0 0 uc010txn.1 66 66 0 0 uc021scy.1 109 109 0 0 uc021scz.1 80 80 0 0 uc021sda.1 81 81 0 0 uc021sdb.1 100 100 0 0 uc010awc.1 68 68 0 0 uc021sdc.1 82 82 0 0 uc021sdd.1 85 85 0 0 uc021sde.1 85 85 0 0 uc010awd.3 75 75 0 0 uc021sdf.1 84 84 0 0 uc021sdg.1 78 78 0 0 uc021sdh.1 79 79 0 0 uc021sdi.1 91 91 0 0 uc021sdj.1 97 97 0 0 uc021sdk.1 80 80 0 0 uc010awe.3 76 76 0 0 uc010txo.1 73 73 0 0 uc021sdl.1 66 66 0 0 uc021sdm.1 73 73 0 0 uc021sdn.1 82 82 0 0 uc010awg.3 84 84 0 0 uc021sdo.1 81 81 0 0 uc021sdp.1 102 102 0 0 uc010awh.1 69 69 0 0 uc021sdq.1 84 84 0 0 uc021sdr.1 79 79 0 0 uc021sds.1 91 91 0 0 uc010txp.1 70 70 0 0 uc021sdt.1 80 80 0 0 uc021sdu.1 79 79 0 0 uc021sdv.1 78 78 0 0 uc001yjz.2 3026 2877 0 0 uc010awi.1 557 408 0 0 uc021sdx.1 2102 1953 20 94.4486 uc010awj.3 2089 1940 0 0 uc001ykj.4 74775 74626 26.0803 3.22322 uc001ykk.3 166194 166045 168.544 9.36173 uc010txr.2 166194 166045 168.544 9.36173 uc001ykl.1 1005 856 9.96308 107.346 uc010txs.1 73214 73065 0 0 uc010txt.2 96845 96696 0 0 uc001ykn.3 102823 102674 0 0 uc001yko.3 118189 118040 348.017 27.1919 uc001ykp.3 118189 118040 348.017 27.1919 uc001ykq.3 30465 30316 0 0 uc001ykr.1 2194 2045 0 0 uc001yks.2 86271 86122 59445.6 6366.1 uc001ykt.1 8699 8550 4346.37 4688.44 uc001ykz.3 70679 70530 0 0 uc001yla.3 70679 70530 0 0 uc001ylb.3 70679 70530 0 0 uc001ylf.3 70679 70530 0 0 uc010txu.2 70679 70530 0 0 uc001ylc.3 75153 75004 0 0 uc001yld.3 83822 83673 330.57 36.4372 uc001yle.3 83822 83673 330.57 36.4372 uc021sdy.1 2766 2617 8.32187 29.3282 uc021sdz.1 74 74 0 0 uc001ylo.3 25798 25649 0 0 uc031qqo.1 23877 23728 1.60428e-06 6.23571e-07 uc001ylp.3 23424 23275 123.362 48.883 uc010awk.2 23424 23275 123.362 48.883 uc001ylq.1 16383 16234 15.6066 8.86645 uc001ylr.3 17190 17041 0 0 uc001yls.3 11553 11404 0 0 uc001ylt.3 8242 8093 17.8563 20.3493 uc010txw.2 62310 62161 0 0 uc010awl.3 102709 102560 0 0 uc001ylw.2 139519 139370 822.558 54.4333 uc010txx.2 139519 139370 822.558 54.4333 uc021sed.1 83 83 0 0 uc001ymb.4 137918 137769 1344.52 90.0088 uc001ymc.2 134022 133873 543.654 37.4539 uc001ymd.2 134022 133873 543.654 37.4539 uc001yme.2 134022 133873 543.654 37.4539 uc010txy.2 134022 133873 543.654 37.4539 uc001ymf.1 2717 2568 27.2857 97.996 uc001ymg.4 8187 8038 0 0 uc001ymh.4 7921 7772 12 14.2402 uc001ymk.3 10414 10265 40 35.9392 uc001yml.1 3096 2947 71.9949 225.315 uc001ymm.1 11113 10964 849.023 714.198 uc010awo.1 11113 10964 849.023 714.198 uc010txz.1 10435 10286 928.591 832.618 uc010tya.1 3525 3376 1317.37 3598.92 uc021see.1 23 23 0 0 uc001ymq.4 11023 10874 1062.17 900.89 uc001ymr.4 11023 10874 1062.17 900.89 uc001ymt.4 10164 10015 0.547871 0.50454 uc001ymu.3 10076 9927 2235.12 2076.59 uc001ymv.1 126 126 0 0 uc001ymw.4 118466 118317 84.0853 6.55451 uc001ymx.4 118466 118317 84.0853 6.55451 uc001ymy.4 118466 118317 84.0853 6.55451 uc001ymz.4 118466 118317 84.0853 6.55451 uc001yna.4 118466 118317 84.0853 6.55451 uc010awp.3 118466 118317 84.0853 6.55451 uc021sef.1 542 393 138.428 3248.61 uc010tyb.2 38273 38124 857.891 207.54 uc010awq.3 28837 28688 0 0 uc001ynd.3 3962 3813 705.938 1707.53 uc001yng.3 7902 7753 688 818.439 uc010aws.3 7902 7753 688 818.439 uc010tyc.2 27938 27789 170.924 56.728 uc010tyd.1 123270 123121 93.3797 6.995 uc010tye.1 56129 55980 0 0 uc001ynm.1 57044 56895 63.3319 10.2664 uc001yno.3 72364 72215 714.772 91.2868 uc010tyf.2 72364 72215 714.772 91.2868 uc021seh.1 10543 10394 0.455076 0.403803 uc001ynp.1 18149 18000 0 0 uc001ynq.1 18149 18000 0 0 uc001ynr.1 18149 18000 0 0 uc010awu.1 18149 18000 0 0 uc001yns.3 25863 25714 1638.07 587.532 uc001ynw.1 3277 3128 0 0 uc001yoc.3 17925 17776 664.603 344.823 uc001yod.3 17925 17776 664.603 344.823 uc001yoe.3 2238 2089 1033.79 4564.19 uc001yoh.1 10329 10180 2 1.81197 uc001yom.4 124188 124039 990.873 73.6762 uc001yon.4 82127 81978 366.83 41.2701 uc031qqq.1 3250 3101 158.352 470.965 uc001yoo.2 21660 21511 0 0 uc001yoq.2 27024 26875 24 8.23627 uc001yor.2 27024 26875 24 8.23627 uc010tyg.2 110 110 0 0 uc001yos.4 42176 42027 3069 673.498 uc010tyh.1 9063 8914 0 0 uc001yow.1 10128 9979 0 0 uc010awy.1 2367 2218 0 0 uc001yoy.4 15203 15054 121.003 74.1334 uc001ypb.2 30005 29856 2158.79 666.878 uc001ypc.2 30005 29856 2158.79 666.878 uc010awz.1 12358 12209 1203.41 909.082 uc001ypd.3 23114 22965 2452 984.74 uc001ype.3 17420 17271 0 0 uc001ypf.3 17420 17271 0 0 uc010tyj.1 4353 4204 0.266608 0.584896 uc001ypg.1 6521 6372 272.217 394.01 uc001yph.3 6527 6378 1151.6 1665.27 uc001ypi.3 6527 6378 1151.6 1665.27 uc021sel.1 2232 2083 150.311 665.531 uc001ypp.3 4116 3967 411.052 955.658 uc021sem.1 1269 1120 21.948 180.736 uc001ypq.4 2518 2369 22 85.6496 uc001yps.3 31458 31309 2134.92 628.899 uc010axb.4 31458 31309 2134.92 628.899 uc001ypt.3 8727 8578 442.153 475.394 uc001ypu.1 8387 8238 0 0 uc010tyl.1 8387 8238 0 0 uc001ypy.1 9240 9091 95 96.3784 uc001ypz.1 9240 9091 95 96.3784 uc010tym.1 9240 9091 95 96.3784 uc010tyq.2 2552 2403 1008.62 3871.18 uc001yqs.2 97315 97166 269.656 25.5954 uc001yqt.3 83733 83584 891.028 98.3186 uc001yqu.3 83733 83584 891.028 98.3186 uc001yqw.3 15277 15128 101 61.5755 uc001yqx.3 50872 50723 59.0234 10.7322 uc001yqy.3 50872 50723 59.0234 10.7322 uc021seq.1 50872 50723 59.0234 10.7322 uc001yqz.1 22282 22133 633.359 263.923 uc001yra.1 22282 22133 633.359 263.923 uc001yrb.3 12451 12302 6080.21 4558.38 uc010tyr.2 7233 7084 1.28543 1.67354 uc031qqr.1 7233 7084 1.28543 1.67354 uc031qqs.1 7233 7084 1.28543 1.67354 uc001yrc.3 5987 5838 0 0 uc001yrd.2 5396 5247 142.087 249.753 uc031qqt.1 5396 5247 142.087 249.753 uc031qqu.1 5396 5247 142.087 249.753 uc031qqv.1 5396 5247 142.087 249.753 uc001yrf.2 4312 4163 0 0 uc001yrg.3 2925 2776 2851.8 9474.73 uc031qqw.1 22 22 0 0 uc001yri.4 1868 1719 2830 15183.7 uc001yrj.3 9394 9245 0 0 uc001yrk.3 9066 8917 184.028 190.341 uc001yrm.3 8000 7851 281.324 330.483 uc001yrn.3 8000 7851 281.324 330.483 uc001yro.3 8000 7851 281.324 330.483 uc010tys.2 7142 6993 0 0 uc001yrp.1 3587 3438 0 0 uc021ses.1 960 811 0.831088 9.45135 uc001yrr.3 2463 2314 0 0 uc001ysq.3 32551 32402 0 0 uc001ysr.3 32551 32402 0 0 uc001yss.3 13815 13666 0 0 uc001yst.3 14665 14516 0 0 uc021seu.1 698 549 0 0 uc001ysy.2 13075 12926 763.369 544.676 uc031qnr.1 3097 2948 0 0 uc001vvb.3 4041 3892 0 0 uc021rnc.1 40 40 0 0 uc001vvg.2 40 40 0 0 uc021rni.1 40 40 0 0 uc001vvo.1 10225 10076 0 0 uc001vvq.1 68973 68824 204.029 27.3413 uc001vvr.1 45125 44976 524.761 107.609 uc010ahe.1 45125 44976 524.761 107.609 uc001vvs.1 34237 34088 0 0 uc001vvu.3 1388 1239 0 0 uc001vvv.1 5842 5693 0 0 uc001vvw.2 34158 34009 0 0 uc001vvx.2 32 32 0 0 uc021rnk.1 40 40 0 0 uc001vvz.2 40 40 0 0 uc010tkr.2 32 32 0 0 uc021rnl.1 41 41 0 0 uc021rnm.1 28 28 0 0 uc010tks.2 32 32 0 0 uc021rnn.1 41 41 0 0 uc001vwb.3 36319 36170 64.5 16.4467 uc001vwc.3 36319 36170 64.5 16.4467 uc021rno.1 1429 1280 0 0 uc010tky.2 933 784 0 0 uc010tkz.2 915 766 0 0 uc001vwt.3 16853 16704 249.5 137.758 uc001vwu.3 16853 16704 249.5 137.758 uc001vwv.4 18006 17857 1094.33 565.208 uc001vwx.4 21643 21494 0 0 uc001vwy.4 21945 21796 36.8892 15.6096 uc001vwz.4 21945 21796 36.8892 15.6096 uc021rnp.2 21945 21796 36.8892 15.6096 uc010ahh.2 17828 17679 0 0 uc021rnq.1 148 148 0 0 uc010ahi.1 99 99 0 0 uc001vxa.1 341 192 24 1152.86 uc010ahj.1 7481 7332 31.9164 40.1475 uc010ahk.3 26101 25952 191.944 68.2136 uc001vxe.3 47754 47605 0.000314474 6.09257e-05 uc010tlf.1 45513 45364 90.6021 18.4202 uc010tlg.1 45513 45364 90.6021 18.4202 uc010tlh.1 10868 10719 112.935 97.1724 uc010tli.2 6832 6683 0 0 uc001vxf.3 8061 7912 1341 1563.18 uc001vxk.2 3626 3477 890 2360.77 uc001vxl.2 3626 3477 890 2360.77 uc001vxq.4 4839 4690 0 0 uc010aho.3 4839 4690 0 0 uc010ahp.3 4839 4690 0 0 uc010ahq.3 4839 4690 0 0 uc010ahr.3 4839 4690 0 0 uc010ahs.3 4839 4690 0 0 uc010aht.3 4839 4690 0 0 uc010ahu.3 4839 4690 0 0 uc021rnt.1 618 469 0 0 uc010ahv.3 4833 4684 0 0 uc010ahx.3 5096 4947 0 0 uc010ahw.3 5101 4952 0 0 uc001vxs.3 7366 7217 0 0 uc021rnu.1 600 451 0 0 uc001vxt.3 743 594 0 0 uc021rnv.1 72 72 0 0 uc021rnx.1 72 72 0 0 uc021rny.1 73 73 0 0 uc021roa.1 73 73 0 0 uc001vxv.1 996 847 0 0 uc021roc.1 94 94 0 0 uc021rod.1 94 94 0 0 uc021roe.1 94 94 0 0 uc021rof.1 94 94 0 0 uc021rog.1 82 82 0 0 uc001vyf.3 1044 895 0 0 uc001vyg.3 1522 1373 0 0 uc001vyh.3 1522 1373 0 0 uc001vyi.3 1522 1373 0 0 uc021rop.1 471 322 0 0 uc001vyt.3 6058 5909 177.465 276.992 uc001vyu.3 6559 6410 104.73 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uc001yat.3 29022 28873 1177.47 376.119 uc001yau.3 29022 28873 1177.47 376.119 uc001yav.3 44896 44747 124.031 25.5643 uc001yaw.3 44896 44747 124.031 25.5643 uc001ybe.2 179005 178856 250.632 12.9241 uc001ybf.3 175583 175434 1515.01 79.6472 uc001ybg.3 176196 176047 704.677 36.9172 uc001ybh.3 176196 176047 704.677 36.9172 uc001ybj.3 2709 2560 431 1552.76 uc021saw.1 1056 907 0 0 uc001ybl.1 4223 4074 220 498.046 uc010aur.3 57388 57239 546.013 87.979 uc010two.2 57388 57239 546.013 87.979 uc001ybo.3 95490 95341 8.98455 0.869129 uc001ybp.3 95490 95341 8.98455 0.869129 uc001ybq.4 39418 39269 0 0 uc001ybr.3 45412 45263 19.0045 3.87241 uc001ybw.1 70123 69974 0 0 uc001ybx.1 70123 69974 0 0 uc001yby.2 21643 21494 0 0 uc001ycb.2 6343 6194 0 0 uc001ycc.2 23269 23120 0 0 uc001ycd.3 42578 42429 0 0 uc001yce.1 17882 17733 0 0 uc001ycj.3 30291 30142 1906.33 583.303 uc010twq.2 30291 30142 1906.33 583.303 uc010twr.2 30291 30142 1906.33 583.303 uc001ycq.3 1840 1691 10 54.5411 uc001yct.3 9712 9563 0 0 uc001ycu.5 9959 9810 0 0 uc001ycv.3 19104 18955 0 0 uc010auv.3 19104 18955 0 0 uc001ycw.4 6495 6346 0 0 uc001ycx.4 12070 11921 0 0 uc001ycy.4 13946 13797 0 0 uc001ycz.4 13946 13797 0 0 uc001ydb.4 13946 13797 0 0 uc001ydc.4 13946 13797 0 0 uc010auw.3 13946 13797 0 0 uc010aux.3 13946 13797 0 0 uc010auy.3 13946 13797 0 0 uc010auz.3 13946 13797 0 0 uc010ava.3 13946 13797 0 0 uc010avb.3 13946 13797 0 0 uc001yda.1 9548 9399 0 0 uc001ydd.1 10322 10173 0 0 uc001yde.3 13613 13464 0 0 uc001ydf.3 13613 13464 0 0 uc001ydg.3 13613 13464 0 0 uc010avc.3 13613 13464 0 0 uc001ydh.1 11708 11559 0 0 uc001ydi.1 11708 11559 0 0 uc001ydj.3 30562 30413 0 0 uc001ydu.3 1940 1791 0 0 uc010avh.1 19996 19847 263.865 122.618 uc021sbc.1 47276 47127 986.316 193.025 uc001ydv.3 50583 50434 0 0 uc001ydw.2 55521 55372 0 0 uc001ydx.2 71195 71046 0 0 uc001ydy.1 8702 8553 0 0 uc021sbd.1 11671 11522 73.4508 58.7945 uc001ydz.1 24908 24759 0.0278167 0.0103619 uc001yea.1 24908 24759 0.0278167 0.0103619 uc001yeb.1 24908 24759 0.0278167 0.0103619 uc001yec.1 24696 24547 13.5041 5.0738 uc021sbe.1 68 68 0 0 uc001yef.2 137970 137821 66.0216 4.41813 uc001yeh.4 2824 2675 56 193.078 uc001yei.4 58343 58194 138.62 21.9692 uc010avi.3 58343 58194 138.62 21.9692 uc001yej.1 31685 31536 53.761 15.7227 uc001yek.3 1961 1812 98.5 501.355 uc001yel.3 1961 1812 98.5 501.355 uc001yfb.4 4230 4081 0 0 uc001yfc.4 4230 4081 0 0 uc031qqd.1 4230 4081 0 0 uc001yfi.3 82084 81935 571 64.2739 uc001yfv.2 54012 53863 1.00161 0.171505 uc001yfw.2 52515 52366 0 0 uc001yfx.2 45063 44914 0 0 uc001yfy.2 20195 20046 0 0 uc010avq.2 20195 20046 0 0 uc021sbl.1 41 41 0 0 uc001yga.3 102198 102049 0 0 uc001ygb.3 102198 102049 0 0 uc031qqh.1 102198 102049 0 0 uc031qqi.1 102198 102049 0 0 uc031qqj.1 82499 82350 590.547 66.1391 uc001ygc.3 83144 82995 387.453 43.0561 uc001ygd.3 71086 70937 0 0 uc021sbn.1 70794 70645 0 0 uc001ygf.3 69611 69462 0 0 uc010avr.3 93125 92976 4125.51 409.237 uc001ygx.2 13260 13111 13 9.14482 uc010avv.3 4619 4470 2595.1 5354.43 uc001yha.3 15408 15259 528.952 319.711 uc010twx.2 15408 15259 528.952 319.711 uc010avw.3 10866 10717 5.84164e-06 5.02724e-06 uc001yhg.2 41803 41654 1238.21 274.161 uc001yhh.1 41803 41654 1238.21 274.161 uc001yhi.1 41803 41654 1238.21 274.161 uc001yhk.1 42556 42407 1154.51 251.089 uc001yhl.1 42556 42407 1154.51 251.089 uc010twz.1 21398 21249 0 0 uc001yhp.3 9633 9484 1191.89 1159.08 uc010txa.2 30924 30775 1880.75 563.638 uc001yhq.3 32648 32499 207.36 58.8468 uc010txj.1 4193 4044 0 0 uc031qqm.1 21 21 0 0 uc001ykb.1 3454 3305 6.27637 17.5148 uc001ykd.1 8181 8032 9.04937 10.3911 uc001yke.3 3105 2956 1.57537 4.91525 uc031qqn.1 3105 2956 1.57537 4.91525 uc001ykf.3 3100 2951 7.76176 24.2582 uc001ykg.3 3100 2951 7.76176 24.2582 uc001ykh.3 3096 2947 0 0 uc021sdw.1 136 136 0 0 uc001yku.4 6438 6289 24671.2 36180.6 uc001ykv.4 59012 58863 35.8363 5.61498 uc001ykw.1 3714 3565 0 0 uc001ykx.1 3387 3238 0 0 uc001ylh.4 16125 15976 51.7262 29.8614 uc001ylj.4 11801 11652 37.4979 29.6807 uc001ylk.4 11801 11652 37.4979 29.6807 uc001yll.4 11801 11652 37.4979 29.6807 uc001ylm.4 76380 76231 12.6053 1.52507 uc001yln.4 76380 76231 12.6053 1.52507 uc010txv.3 76380 76231 12.6053 1.52507 uc001ylu.1 16977 16828 511.38 280.271 uc021sea.1 14436 14287 0 0 uc001ylv.1 14638 14489 18.1323 11.542 uc021seb.1 14638 14489 18.1323 11.542 uc001ylx.1 1957 1808 23.2597 118.652 uc001ylz.1 2931 2782 148.526 492.393 uc001yma.1 2931 2782 148.526 492.393 uc021sec.1 954 805 407.689 4670.9 uc001ymi.1 125027 124878 6070.82 448.362 uc001ymj.1 20760 20611 1504.02 673.01 uc001ymn.3 1808 1659 0 0 uc001ymp.1 694 545 2 33.8455 uc001ynf.2 3202 3053 31419 94914.7 uc001ynh.2 5775 5626 622.317 1020.19 uc001yni.2 5775 5626 622.317 1020.19 uc001ynj.2 6271 6122 49.371 74.3783 uc021seg.1 1344 1195 291.994 2253.59 uc001ynu.1 1647 1498 0 0 uc001ynv.1 1660 1511 0 0 uc001ynx.4 17870 17721 398.153 207.219 uc001yny.4 17870 17721 398.153 207.219 uc001ynz.4 17870 17721 398.153 207.219 uc001yoa.4 17870 17721 398.153 207.219 uc021sei.1 20 20 0 0 uc010awv.1 12803 12654 633.353 461.621 uc001yof.1 113840 113691 86.6785 7.03158 uc001yog.1 107062 106913 0 0 uc001yoi.4 9279 9130 1393.61 1407.8 uc001yoj.4 9279 9130 1393.61 1407.8 uc001yok.3 9057 8908 0 0 uc010aww.2 9057 8908 0 0 uc031qqp.1 1883 1734 0 0 uc021sej.1 86 86 0 0 uc001you.2 2237 2088 0 0 uc001yov.1 1201 1052 0 0 uc001yox.1 10678 10529 0 0 uc021sek.1 56 56 1 164.695 uc001ypj.3 2372 2223 592.883 2459.79 uc010tyk.2 8329 8180 4174.05 4706.22 uc001ypk.3 24252 24103 33.3262 12.7521 uc001ypl.3 24252 24103 33.3262 12.7521 uc010axa.3 24842 24693 463.371 173.07 uc001ypm.3 26394 26245 271.02 95.2405 uc001ypn.3 26394 26245 271.02 95.2405 uc021sen.1 4440 4291 30.1699 64.846 uc021seo.1 16652 16503 8304.66 4641.16 uc001ypx.2 33230 33081 951.349 265.234 uc010axc.1 41104 40955 98.8246 22.2549 uc001yqa.2 11516 11367 422.97 343.188 uc001yqb.2 11516 11367 422.97 343.188 uc021sep.1 726 577 1148.06 18350.9 uc001yqc.3 6767 6618 2 2.78722 uc001yqd.3 16018 15869 0 0 uc001yqe.3 16018 15869 0 0 uc010axe.1 10031 9882 0 0 uc001yqf.4 10423 10274 5162.34 4634.2 uc001yqg.4 27844 27695 3313.83 1103.56 uc001yqh.4 27844 27695 3313.83 1103.56 uc001yqi.4 8387 8238 214 239.585 uc010tyn.3 8387 8238 214 239.585 uc001yqk.2 13107 12958 493.271 351.087 uc001yql.2 38600 38451 0.696621 0.167092 uc001yqo.2 40119 39970 161.141 37.1825 uc001yqp.2 91707 91558 248.197 25.0016 uc010axg.1 91707 91558 248.197 25.0016 uc010tyo.1 106292 106143 6.41266e-05 5.57204e-06 uc010typ.1 106292 106143 6.41266e-05 5.57204e-06 uc001yqn.2 38457 38308 7.53597e-08 1.81434e-08 uc010axh.1 38457 38308 7.53597e-08 1.81434e-08 uc010axi.1 10793 10644 0 0 uc010axj.1 21252 21103 0 0 uc001yqr.3 44807 44658 0 0 uc031qqx.1 1288830 1.28868e+06 1857.4 13.2932 uc001yrs.3 277245 277096 0 0 uc001yrt.3 277622 277473 0 0 uc010axp.1 290 141 0 0 uc001yru.2 2323 2174 0 0 uc001yrw.1 263185 263036 0 0 uc001yrx.2 264192 264043 0 0 uc001yry.1 180 31 0 0 uc001yrz.2 224573 224424 0 0 uc001ysb.2 4706 4557 0 0 uc031qqy.1 3257 3108 0 0 uc001ysc.3 639 490 0 0 uc001ysd.1 290 141 0 0 uc001yse.3 127790 127641 0 0 uc001ysg.1 5201 5052 0 0 uc001ysh.1 8273 8124 0 0 uc001ysi.2 8273 8124 0 0 uc001ysj.3 26364 26215 0 0 uc001ysk.1 24815 24666 0 0 uc001ysl.1 24815 24666 0 0 uc001ysm.2 25822 25673 0 0 uc001ysn.1 26820 26671 0 0 uc021set.1 2474 2325 0 0 uc001ysu.1 2540 2391 0 0 uc001ysv.3 21198 21049 0 0 uc001ysw.1 741 592 0 0 uc001ysx.1 4114 3965 0 0 uc001ysz.3 1023 874 0 0 uc001yta.1 1088 939 0 0 SummarizedExperiment/inst/extdata/kallisto/abundance.txt0000644000175400017540000000111513001230160024653 0ustar00biocbuildbiocbuildtarget_id length eff_length est_counts tpm NM_001168316 2283 2105.9 164.133 12856.9 NM_174914 2385 2207.9 1495.6 111741 NR_031764 1853 1675.9 104.27 10263.4 NM_004503 1681 1503.9 332.001 36416.5 NM_006897 1541 1363.9 664 80308.9 NM_014212 2037 1859.9 55 4878.11 NM_014620 2300 2122.9 592.584 46046.7 NM_017409 1959 1781.9 47 4351.04 NM_017410 2396 2218.9 42 3122.41 NM_018953 1612 1434.9 227.995 26210.8 NM_022658 2288 2110.9 4881 381434 NM_153633 1666 1488.9 359.898 39874.2 NM_153693 2072 1894.9 72.5147 6312.74 NM_173860 849 671.903 962 236182 NR_003084 1640 1462.9 0.00787013 0.887453 SummarizedExperiment/inst/extdata/kallisto/run_info.json0000644000175400017540000000040613001230160024706 0ustar00biocbuildbiocbuild{ "n_targets": 2858, "n_bootstraps": 100, "kallisto_version": "0.42.2", "index_version": 10, "start_time": "Wed Jun 10 15:03:26 2015", "call": "../kallisto/build/src/kallisto quant -i hg19chr14transcripts.idx -o output -b 100 --single -l 150 /dev/fd/63" } SummarizedExperiment/inst/scripts/0000755000175400017540000000000013001230160020407 5ustar00biocbuildbiocbuildSummarizedExperiment/inst/scripts/Find_and_update_objects/0000755000175400017540000000000013001230160025164 5ustar00biocbuildbiocbuildSummarizedExperiment/inst/scripts/Find_and_update_objects/README0000644000175400017540000000536713001230160026057 0ustar00biocbuildbiocbuildFind and update objects ======================= This document describes the procedure for finding and updating serialized objects in the rda files located in a directory hierarchy. This procedure consists of 4 STEPS, as described below. Note that this procedure was developped and used to update all "old" serialized SummarizedExperiment objects located in the data-experiment svn repository. It should be easy to adapt to update other types of objects in other locations. Also note that the purpose of STEPS 1 & 2 & 3 below is to collect the list of rda objects to update. Since this can take a long time, the 2 lists obtained for the "old" serialized SummarizedExperiment objects located in https://hedgehog.fhcrc.org/bioc-data/trunk/experiment/pkgs and https://hedgehog.fhcrc.org/bioc-data/trunk/experiment/data_store were saved to the pkgs_RDA_OBJECTS_TO_UPDATE and data_store_RDA_OBJECTS_TO_UPDATE files, respectively, and these 2 files placed in this folder. So in case these objects need to be updated again, these 2 files can be re-used to run STEP 4 directly without the need to re-run STEPS 1 & 2 & 3. STEP 1: Prepare list of rda files to scan. cd

find . -type d -name '.svn' -prune -o -type f -print | \ grep -Ei '\.(rda|RData)$' >RDA_FILES STEP 2: Scan the rda files. cd R="$HOME/bin/R-3.2" R_SCRIPT="scriptfile <- system.file('scripts', 'Find_and_update_objects', " R_SCRIPT="$R_SCRIPT 'scan_rda_files.R', " R_SCRIPT="$R_SCRIPT package='SummarizedExperiment', mustWork=TRUE)" R_SCRIPT="$R_SCRIPT; source(scriptfile)" echo "$R_SCRIPT" | $R --vanilla >scan_rda_files.log 2>&1 & STEP 3: Collect rda objects to update. cd R="$HOME/bin/R-3.2" R_SCRIPT="scriptfile <- system.file('scripts', 'Find_and_update_objects', " R_SCRIPT="$R_SCRIPT 'collect_rda_objects_to_update.R', " R_SCRIPT="$R_SCRIPT package='SummarizedExperiment', mustWork=TRUE)" R_SCRIPT="$R_SCRIPT; source(scriptfile)" echo "$R_SCRIPT" | $R --vanilla >collect_rda_objects_to_update.log 2>&1 & STEP 4: Update rda objects. cd #To update the "old" serialized SummarizedExperiment objects located in # https://hedgehog.fhcrc.org/bioc-data/trunk/experiment/pkgs #replace the above command with # svn co https://hedgehog.fhcrc.org/bioc-data/trunk/experiment/pkgs # cp path/to/pkgs_RDA_OBJECTS_TO_UPDATE pkgs/RDA_OBJECTS_TO_UPDATE # cd pkgs R="$HOME/bin/R-3.2" R_SCRIPT="scriptfile <- system.file('scripts', 'Find_and_update_objects', " R_SCRIPT="$R_SCRIPT 'update_rda_objects.R', " R_SCRIPT="$R_SCRIPT package='SummarizedExperiment', mustWork=TRUE)" R_SCRIPT="$R_SCRIPT; source(scriptfile)" echo "$R_SCRIPT" | $R --vanilla >update_rda_objects.log 2>&1 & SummarizedExperiment/inst/scripts/Find_and_update_objects/collect_rda_objects_to_update.R0000644000175400017540000001172113001230160033341 0ustar00biocbuildbiocbuild### ========================================================================= ### collect_rda_objects_to_update.R ### ------------------------------------------------------------------------- ### ### This script performs STEP 3 of the "Find and update objects" procedure ### described in the README file located in the same folder. ### ### Before you run this script, make sure you performed STEPS 1 & 2. ### See README file for more information. ### ### Then to run STEP 3 in "batch" mode: ### ### cd # RDA_OBJECTS file should be here ### R CMD BATCH collect_rda_objects_to_update.R \ ### >collect_rda_objects_to_update.log 2>&1 & ### ### The output of STEP 3 is a file created in the current directory and named ### RDA_OBJECTS_TO_UPDATE. It is a subset of input file RDA_OBJECTS. ### INFILE <- "RDA_OBJECTS" OUTFILE <- "RDA_OBJECTS_TO_UPDATE" library(BiocInstaller) library(SummarizedExperiment) if (FALSE) { ### Unfortunately, loading all the required packages in the main process will ### sometimes hit the maximal number of DLLs that can be loaded ("maximal ### number of DLLs reached..." infamous error). .check_classes <- function(classes, package) { suppressWarnings(suppressPackageStartupMessages( library(package, character.only=TRUE, quietly=TRUE) )) sapply(classes, function(class) { extends(class, "SummarizedExperiment") || extends(class, "RangedSummarizedExperiment") }) } } ### We check the classes in a subprocess to work around the "maximal number ### of DLLs reached..." infamous error. .check_classes <- function(classes, package) { classes_in1string <- paste0("\"", classes, "\"") classes_in1string <- paste0("c(", paste(classes_in1string, collapse=", "), ")") outfile <- file.path(tempdir(), paste0(package, "_class_summary")) input <- c("suppressWarnings(suppressPackageStartupMessages(", sprintf(" library(%s)", "SummarizedExperiment"), "))", "suppressWarnings(suppressPackageStartupMessages(", sprintf(" library(%s)", package), "))", sprintf("classes <- %s", classes_in1string), "ok <- sapply(classes, function(class) {", " extends(class, \"SummarizedExperiment\") ||", " extends(class, \"RangedSummarizedExperiment\")", "})", "class_summary <- data.frame(class=classes, ok=unname(ok))", sprintf("write.table(class_summary, file=\"%s\", sep=\"\t\")", outfile) ) command <- file.path(R.home("bin"), "R") args <- c("--vanilla", "--slave") system2(command, args=args, input=input) class_summary <- read.table(outfile, stringsAsFactors=FALSE) file.remove(outfile) stopifnot(identical(class_summary[ , "class"], classes)) # sanity check setNames(class_summary[ , "ok"], classes) } collectRdaObjectsToUpdate <- function(rda_objects, outfile="") { rda_objects2 <- unique(rda_objects[ , c("objclass", "objclass_pkg")]) objclass2 <- rda_objects2[ , "objclass"] objclass_pkg2 <- rda_objects2[ , "objclass_pkg"] idx <- which(duplicated(objclass2)) if (length(idx) != 0L) { msg <- c("the following classes are defined in more than 1 package: ", paste0(unique(objclass2[idx]), collapse=", ")) warning(msg) } pkg2class <- split(objclass2, objclass_pkg2) pkg2class[c(".", ".GlobalEnv")] <- NULL pkgs <- names(pkg2class) ## Install missing packages. installed_pkgs <- rownames(installed.packages()) missing_pkgs <- setdiff(pkgs, installed_pkgs) if (length(missing_pkgs) != 0L) { biocLite(missing_pkgs) installed_pkgs <- rownames(installed.packages()) missing_pkgs <- setdiff(pkgs, installed_pkgs) if (length(missing_pkgs) != 0L) { ## Some packages could not be installed. pkgs <- intersect(pkgs, installed_pkgs) pkg2class <- pkg2class[pkgs] } } ## Check classes, one package at a time. class2ok <- unlist( lapply(seq_along(pkgs), function(i) { pkg <- pkgs[[i]] cat("[", i , "/", length(pkgs), "] Check classes defined ", "in package ", pkg, " ... ", sep="") ans <- .check_classes(pkg2class[[pkg]], pkg) cat("OK\n") ans } ) ) ## Write output to file. objclass <- rda_objects[ , "objclass"] ok <- class2ok[objclass] ok[is.na(ok)] <- FALSE rda_objects_to_update <- rda_objects[ok, , drop=FALSE] rda_objects_to_update <- do.call( paste, c(as.list(rda_objects_to_update), list(sep="\t")) ) writeLines(rda_objects_to_update, con=outfile) } rda_objects <- read.table(INFILE, stringsAsFactors=FALSE) colnames(rda_objects) <- c("rda_file", "objname", "objclass", "objclass_pkg") collectRdaObjectsToUpdate(rda_objects, outfile=OUTFILE) SummarizedExperiment/inst/scripts/Find_and_update_objects/data_store_RDA_OBJECTS_TO_UPDATE0000644000175400017540000000422613001230160032543 0ustar00biocbuildbiocbuild# This file contains the list of rda objects to update located in # https://hedgehog.fhcrc.org/bioc-data/trunk/experiment/data_store # It was obtained by running STEPS 1 & 2 & 3 of the "Find and update objects" # procedure on May 26, 2015. See README file for more information. # To update the objects listed in this file: # 1. Get a working copy of the above URL. # 2. Copy this file to the working copy and rename it RDA_OBJECTS_TO_UPDATE. # 3. Run STEP 4 (see README file for more information). ./geuvPack/data/geuFPKM.rda geuFPKM SummarizedExperiment GenomicRanges ./bsseqData/data/BS.cancer.ex.fit.rda BS.cancer.ex.fit BSseq bsseq ./bsseqData/data/BS.cancer.ex.rda BS.cancer.ex BSseq bsseq ./fission/data/fission.RData fission SummarizedExperiment GenomicRanges ./DREAM4/data/dream4_010_04.RData dream4_010_04 SummarizedExperiment GenomicRanges ./DREAM4/data/dream4_100_01.RData dream4_100_01 SummarizedExperiment GenomicRanges ./DREAM4/data/dream4_010_01.RData dream4_010_01 SummarizedExperiment GenomicRanges ./DREAM4/data/dream4_010_05.RData dream4_010_05 SummarizedExperiment GenomicRanges ./DREAM4/data/dream4_010_02.RData dream4_010_02 SummarizedExperiment GenomicRanges ./DREAM4/data/dream4_100_04.RData dream4_100_04 SummarizedExperiment GenomicRanges ./DREAM4/data/dream4_010_03.RData dream4_010_03 SummarizedExperiment GenomicRanges ./DREAM4/data/dream4_100_05.RData dream4_100_05 SummarizedExperiment GenomicRanges ./DREAM4/data/dream4_100_02.RData dream4_100_02 SummarizedExperiment GenomicRanges ./DREAM4/data/dream4_100_03.RData dream4_100_03 SummarizedExperiment GenomicRanges ./COSMIC.67/data/cosmic_67.rda cosmic_67 CollapsedVCF VariantAnnotation ./parathyroidSE/data/parathyroidGenesSE.RData parathyroidGenesSE SummarizedExperiment GenomicRanges ./parathyroidSE/data/parathyroidExonsSE.RData parathyroidExonsSE SummarizedExperiment GenomicRanges ./RRBSdata/data/rrbs.RData rrbs BSraw BiSeq ./airway/data/airway.RData airway SummarizedExperiment GenomicRanges ./dsQTL/data/DSQ_2.rda DSQ_2 SummarizedExperiment GenomicRanges ./dsQTL/data/DSQ_17.rda DSQ_17 SummarizedExperiment GenomicRanges ./dsQTL/data/DHStop5_hg19.rda DHStop5_hg19 SummarizedExperiment GenomicRanges SummarizedExperiment/inst/scripts/Find_and_update_objects/pkgs_RDA_OBJECTS_TO_UPDATE0000644000175400017540000000106713001230160031362 0ustar00biocbuildbiocbuild# This file contains the list of rda objects to update located in # https://hedgehog.fhcrc.org/bioc-data/trunk/experiment/pkgs # It was obtained by running STEPS 1 & 2 & 3 of the "Find and update objects" # procedure on May 26, 2015. See README file for more information. # To update the objects listed in this file: # 1. Get a working copy of the above URL. # 2. Copy this file to the working copy and rename it RDA_OBJECTS_TO_UPDATE. # 3. Run STEP 4 (see README file for more information). ./pasilla/data/pasillaDEXSeqDataSet.RData dxd DEXSeqDataSet DEXSeq SummarizedExperiment/inst/scripts/Find_and_update_objects/scan_rda_files.R0000644000175400017540000000464713001230160030256 0ustar00biocbuildbiocbuild### ========================================================================= ### scan_rda_files.R ### ------------------------------------------------------------------------- ### ### This script performs STEP 2 of the "Find and update objects" procedure ### described in the README file located in the same folder. ### ### Before you run this script, make sure you performed STEP 1, that is, you ### need to generate input file RDA_FILES. This can be achieved with ### something like: ### ### cd ### find . -type d -name '.svn' -prune -o -type f -print | \ ### grep -Ei '\.(rda|RData)$' >RDA_FILES ### ### See README file for more information. ### ### Then to run STEP 2 in "batch" mode: ### ### cd # RDA_FILES file should be here ### R CMD BATCH scan_rda_files.R >scan_rda_files.log 2>&1 & ### ### This can take a couple of hours to complete... ### ### The output of STEP 2 is a file created in the current directory and named ### RDA_OBJECTS. It has 1 line per serialized object and the 4 following ### fields (separated by tabs): ### 1. Path to rda file (as found in input file RDA_FILES). ### 2. Name of object in rda file. ### 3. Class of object in rda file. ### 4. Package where class of object is defined. ### INFILE <- "RDA_FILES" OUTFILE <- "RDA_OBJECTS" scanRdaFiles <- function(rda_files, outfile="") { cat("", file=outfile) # create (or overwrite) empty output file for (i in seq_along(rda_files)) { rda_file <- rda_files[[i]] cat("[", i , "/", length(rda_files), "] Loading ", rda_file, " ... ", sep="") envir <- new.env(parent=emptyenv()) load(rda_file, envir=envir) cat("OK\n") for (objname in names(envir)) { obj <- get(objname, envir=envir) objclass <- class(obj) objclass_pkg <- attr(objclass, "package") ## Fix 'objclass' and 'objclass_pkg' (they both need to be ## character vectors of length 1 before we pass them to paste()). objclass <- objclass[[1L]] if (is.null(objclass_pkg)) objclass_pkg <- "." outline <- paste(rda_file, objname, objclass, objclass_pkg, sep="\t") cat(outline, "\n", sep="", file=OUTFILE, append=TRUE) } } } rda_files <- read.table(INFILE, stringsAsFactors=FALSE)[[1L]] scanRdaFiles(rda_files, outfile=OUTFILE) SummarizedExperiment/inst/scripts/Find_and_update_objects/update_rda_objects.R0000644000175400017540000000443313001230160031134 0ustar00biocbuildbiocbuild### ========================================================================= ### update_rda_objects.R ### ------------------------------------------------------------------------- ### ### This script performs STEP 4 of the "Find and update objects" procedure ### described in the README file located in the same folder. ### ### Before you run this script, make sure you performed STEPS 1 & 2 & 3. ### See README file for more information. ### ### Then to run STEP 4 in "batch" mode: ### ### cd # RDA_OBJECTS_TO_UPDATE file should be here ### R CMD BATCH update_rda_objects.R >update_rda_objects.log 2>&1 & ### INFILE <- "RDA_OBJECTS_TO_UPDATE" library(SummarizedExperiment) .update_objects <- function(envir) { updated <- FALSE for (objname in names(envir)) { obj <- get(objname, envir=envir, inherits=FALSE) objclass <- class(obj) objclass_pkg <- attr(objclass, "package") if (!is.null(objclass_pkg)) { suppressWarnings(suppressPackageStartupMessages( library(objclass_pkg, character.only=TRUE, quietly=TRUE) )) } if (!SummarizedExperiment:::.has_SummarizedExperiment_internal_structure(obj)) next() cat(" Updating ", objname, " ... ", sep="") obj <- updateObject(obj) validObject(obj, complete=TRUE) assign(objname, obj, envir=envir, inherits=FALSE) cat("OK\n") updated <- TRUE } updated } updateRdaObjects <- function(rda_objects) { rda_files <- unique(rda_objects[ , "rda_file"]) for (i in seq_along(rda_files)) { rda_file <- rda_files[[i]] cat("[", i , "/", length(rda_files), "] Loading ", rda_file, " ... ", sep="") envir <- new.env(parent=emptyenv()) load(rda_file, envir=envir) cat("OK\n") if (.update_objects(envir)) { cat(" Saving updated objects to ", rda_file, " ... ", sep="") save(list=names(envir), file=rda_file, envir=envir, compress="xz") cat("OK\n") } } } rda_objects_to_update <- read.table(INFILE, stringsAsFactors=FALSE) colnames(rda_objects_to_update) <- c("rda_file", "objname", "objclass", "objclass_pkg") updateRdaObjects(rda_objects_to_update) SummarizedExperiment/inst/unitTests/0000755000175400017540000000000013001230160020722 5ustar00biocbuildbiocbuildSummarizedExperiment/inst/unitTests/test_Assays-class.R0000644000175400017540000000605213001230160024455 0ustar00biocbuildbiocbuildtest_bind_Assays <- function() { ## unnamed -- map by position l1 <- list(matrix(1, 3, 4), matrix(2, 3, 4)) l2 <- list(matrix(3, 3, 4), matrix(4, 3, 4)) a1 <- Assays(l1) a2 <- Assays(l2) target <- Map(rbind, l1, l2) current <- rbind(a1, a2) checkTrue(is(current, "ShallowSimpleListAssays")) checkIdentical(as(target, "SimpleList"), as(current, "SimpleList")) target <- Map(cbind, l1, l2) current <- cbind(a1, a2) checkTrue(is(current, "ShallowSimpleListAssays")) checkIdentical(as(target, "SimpleList"), as(current, "SimpleList")) ## named -- map by name l1 <- list(x=matrix(1, 3, 4), y=matrix(2, 3, 4)) l2 <- list(y=matrix(4, 3, 4), x=matrix(3, 3, 4)) a1 <- Assays(l1) a2 <- Assays(l2) target <- Map(rbind, l1, l2[match(names(l2), names(l1))]) current <- rbind(a1, a2) checkTrue(is(current, "ShallowSimpleListAssays")) checkIdentical(as(target, "SimpleList"), as(current, "SimpleList")) target <- Map(cbind, l1, l2[match(names(l2), names(l1))]) current <- cbind(a1, a2) checkTrue(is(current, "ShallowSimpleListAssays")) checkIdentical(as(target, "SimpleList"), as(current, "SimpleList")) } test_bind_higher_order_Assays <- function() { ## unnamed -- map by position l1 <- list(array(1, dim = c(3, 4, 5, 6)), array(2, dim = c(3, 4, 5, 6))) l2 <- list(array(4, dim = c(3, 4, 5, 6)), array(3, dim = c(3, 4, 5, 6))) a1 <- Assays(l1) a2 <- Assays(l2) target <- Map(arbind, l1, l2) current <- rbind(a1, a2) checkTrue(is(current, "ShallowSimpleListAssays")) checkIdentical(as(target, "SimpleList"), as(current, "SimpleList")) target <- Map(acbind, l1, l2) current <- cbind(a1, a2) checkTrue(is(current, "ShallowSimpleListAssays")) checkIdentical(as(target, "SimpleList"), as(current, "SimpleList")) ## named -- map by name l1 <- list(x = array(1, dim = c(3, 4, 5, 6)), y = array(2, dim = c(3, 4, 5, 6))) l2 <- list(y = array(4, dim = c(3, 4, 5, 6)), x = array(3, dim = c(3, 4, 5, 6))) a1 <- Assays(l1) a2 <- Assays(l2) target <- Map(arbind, l1, l2[match(names(l2), names(l1))]) current <- rbind(a1, a2) checkTrue(is(current, "ShallowSimpleListAssays")) checkIdentical(as(target, "SimpleList"), as(current, "SimpleList")) target <- Map(acbind, l1, l2[match(names(l2), names(l1))]) current <- cbind(a1, a2) checkTrue(is(current, "ShallowSimpleListAssays")) checkIdentical(as(target, "SimpleList"), as(current, "SimpleList")) } test_bind_error_on_incompatible_dimension_Assays <- function() { l1 <- list(x = array(1, dim = c(3, 4, 5, 6)), y = array(2, dim = c(3, 4, 5, 6))) l2 <- list(y = matrix(4, 3, 4), x = matrix(3, 3, 4)) a1 <- Assays(l1) a2 <- Assays(l2) ## arbind checkException(rbind(a1, a2), silent = TRUE) checkException(arbind(l1[[1]], l2[[1]]), silent = TRUE) ## acbind checkException(cbind(a1, a2), silent = TRUE) checkException(acbind(l1[[1]], l2[[1]]), silent = TRUE) } SummarizedExperiment/inst/unitTests/test_RangedSummarizedExperiment-class.R0000644000175400017540000003157213001230160030521 0ustar00biocbuildbiocbuildlibrary(digest) .singleDispatch <- c("duplicated", "end", "end<-", "granges", "ranges", "seqinfo", "seqinfo<-", "seqnames", "start", "start<-", "strand", "width", "width<-") .twoDispatch <- c("pcompare", "Compare") .otherFuns <- c("is.unsorted", "order", "rank", "sort") M1 <- matrix(1, 5, 3, dimnames=list(NULL, NULL)) M2 <- matrix(1, 3, 3, dimnames=list(NULL, NULL)) mList <- list(M1, M2) assaysList <- list(gr=SimpleList(m=M1), grl=SimpleList(m=M2)) rowRangesList <- list(gr=GRanges("chr1", IRanges(1:5, 10)), grl=split(GRanges("chr1", IRanges(1:5, 10)), c(1,1,2,2,3))) names(rowRangesList[["grl"]]) <- NULL colData <- DataFrame(x=letters[1:3]) ## a list of one SE with GRanges and one with GRangesList rseList <- list(SummarizedExperiment( assays=assaysList[["gr"]], rowRanges=rowRangesList[["gr"]], colData=colData), SummarizedExperiment( assays=assaysList[["grl"]], rowRanges=rowRangesList[["grl"]], colData=colData)) test_RangedSummarizedExperiment_construction <- function() { ## empty-ish m1 <- matrix(0, 0, 0) checkTrue(validObject(new("RangedSummarizedExperiment"))) ## substance for (i in seq_along(rseList)) { rse <- rseList[[i]] checkTrue(validObject(rse)) checkIdentical(SimpleList(m=mList[[i]]), assays(rse)) checkIdentical(rowRangesList[[i]], rowRanges(rse)) checkIdentical(DataFrame(x=letters[1:3]), colData(rse)) } ## array in assays slot ss <- rseList[[1]] assays(ss) <- SimpleList(array(1:5, c(5,3,2))) checkTrue(validObject(ss)) checkTrue(all(dim(assays(ss[1:3,1:2])[[1]]) == c(3, 2, 2))) } test_RangedSummarizedExperiment_getters <- function() { for (i in seq_along(rseList)) { rse <- rseList[[i]] rowRanges <- rowRangesList[[i]] ## dim, dimnames checkIdentical(c(length(rowRanges), nrow(colData)), dim(rse)) checkIdentical(list(NULL, NULL), dimnames(rse)) ## row / col / metadata checkIdentical(rowRanges, rowRanges(rse)) checkIdentical(colData, colData(rse)) checkIdentical(list(), metadata(rse)) } } test_RangedSummarizedExperiment_setters <- function() { for (i in seq_along(rseList)) { rse <- rseList[[i]] rowRanges <- rowRangesList[[i]] ## row / col / metadata<- ss1 <- rse revData <- rev(rowRanges) rowRanges(ss1) <- revData checkIdentical(revData, rowRanges(ss1)) checkException(rowRanges(ss1) <- rowRanges(rse)[1:2,,drop=FALSE], "incorrect row dimensions", TRUE) revData <- colData[rev(seq_len(nrow(colData))),,drop=FALSE] colData(ss1) <- revData checkIdentical(revData, colData(ss1)) checkException(colData(ss1) <- colData(rse)[1:2,,drop=FALSE], "incorrect col dimensions", TRUE) lst <- list("foo", "bar") metadata(ss1) <- lst checkIdentical(lst, metadata(ss1)) ## assay / assays ss1 <- rse assay(ss1) <- assay(ss1)+1 checkIdentical(assay(rse)+1, assay(ss1)) ss1 <- rse assay(ss1, 1) <- assay(ss1, 1) + 1 checkIdentical(assay(rse, "m") + 1, assay(ss1, "m")) ss1 <- rse assay(ss1, "m") <- assay(ss1, "m") + 1 checkIdentical(assay(rse, "m")+1, assay(ss1, "m")) ## dimnames<- ss1 <- rse dimnames <- list(letters[seq_len(nrow(ss1))], LETTERS[seq_len(ncol(ss1))]) rownames(ss1) <- dimnames[[1]] colnames(ss1) <- dimnames[[2]] checkIdentical(dimnames, dimnames(ss1)) rowRanges1 <- rowRanges names(rowRanges1) <- dimnames[[1]] checkIdentical(rowRanges1, rowRanges(ss1)) colData1 <- colData row.names(colData1) <- dimnames[[2]] checkIdentical(colData1, colData(ss1)) ss1 <- rse dimnames(ss1) <- dimnames checkIdentical(dimnames, dimnames(ss1)) dimnames(ss1) <- NULL checkIdentical(list(NULL, NULL), dimnames(ss1)) } } test_RangedSummarizedExperiment_subset <- function() { for (i in seq_along(rseList)) { rse <- rseList[[i]] rowRanges <- rowRangesList[[i]] ## numeric ss1 <- rse[2:3,] checkIdentical(c(2L, ncol(rse)), dim(ss1)) checkIdentical(rowRanges(ss1), rowRanges(rse)[2:3,]) checkIdentical(colData(ss1), colData(rse)) ss1 <- rse[,2:3] checkIdentical(c(nrow(rse), 2L), dim(ss1)) checkIdentical(rowRanges(ss1), rowRanges(rse)) checkIdentical(colData(ss1), colData(rse)[2:3,,drop=FALSE]) ss1 <- rse[2:3, 2:3] checkIdentical(c(2L, 2L), dim(ss1)) checkIdentical(rowRanges(ss1), rowRanges(rse)[2:3,,drop=FALSE]) checkIdentical(colData(ss1), colData(rse)[2:3,,drop=FALSE]) ## character ss1 <- rse dimnames(ss1) <- list(LETTERS[seq_len(nrow(ss1))], letters[seq_len(ncol(ss1))]) ridx <- c("B", "C") checkIdentical(rowRanges(ss1[ridx,]), rowRanges(ss1)[ridx,]) checkIdentical(rowRanges(ss1["C",]), rowRanges(ss1)["C",,drop=FALSE]) checkException(ss1[LETTERS,], "i-index out of bounds", TRUE) cidx <- c("b", "c") checkIdentical(colData(ss1[,cidx]), colData(ss1)[cidx,,drop=FALSE]) checkIdentical(colData(ss1[,"a"]), colData(ss1)["a",,drop=FALSE]) checkException(ss1[,letters], "j-index out of bounds", TRUE) ## logical ss1 <- rse dimnames(ss1) <- list(LETTERS[seq_len(nrow(ss1))], letters[seq_len(ncol(ss1))]) checkEquals(ss1, ss1[TRUE,]) checkIdentical(c(0L, ncol(ss1)), dim(ss1[FALSE,])) checkEquals(ss1, ss1[,TRUE]) checkIdentical(c(nrow(ss1), 0L), dim(ss1[,FALSE])) idx <- c(TRUE, FALSE) # recycling ss2 <- ss1[idx,] checkIdentical(rowRanges(ss1)[idx,,drop=FALSE], rowRanges(ss2)) ss2 <- ss1[,idx] checkIdentical(colData(ss1)[idx,,drop=FALSE], colData(ss2)) ## Rle ss1 <- rse rle <- rep(c(TRUE, FALSE), each=3, length.out=nrow(ss1)) checkIdentical(rowRanges(ss1[rle]), rowRanges(ss1[Rle(rle)])) checkIdentical(assays(ss1[rle]), assays(ss1[Rle(rle)])) } ## 0 columns se <- SummarizedExperiment(rowRanges=GRanges("chr1", IRanges(1:10, width=1))) checkIdentical(dim(se[1:5, ]), c(5L, 0L)) ## 0 rows se <- SummarizedExperiment(colData=DataFrame(samples=1:10)) checkIdentical(dim(se[ ,1:5]), c(0L, 5L)) } test_RangedSummarizedExperiment_subsetassign <- function() { for (i in seq_along(rseList)) { rse <- rseList[[i]] dimnames(rse) <- list(LETTERS[seq_len(nrow(rse))], letters[seq_len(ncol(rse))]) ## rows ss1 <- rse ss1[1:2,] <- ss1[2:1,] checkIdentical(rowRanges(rse)[2:1,], rowRanges(ss1)[1:2,]) checkIdentical(rowRanges(rse[-(1:2),]), rowRanges(ss1)[-(1:2),]) checkIdentical(colData(rse), colData(ss1)) checkIdentical(c(metadata(rse), metadata(rse)), metadata(ss1)) ## Rle ss1rle <- ss1Rle <- rse rle <- rep(c(TRUE, FALSE), each=3, length.out=nrow(ss1)) ss1rle[rle,] <- ss1rle[rle,] ss1Rle[Rle(rle),] <- ss1Rle[Rle(rle),] checkIdentical(rowRanges(ss1rle), rowRanges(ss1Rle)) checkIdentical(assays(ss1rle), assays(ss1Rle)) ## cols ss1 <- rse ss1[,1:2] <- ss1[,2:1,drop=FALSE] checkIdentical(colData(rse)[2:1,,drop=FALSE], colData(ss1)[1:2,,drop=FALSE]) checkIdentical(colData(rse)[-(1:2),,drop=FALSE], colData(ss1)[-(1:2),,drop=FALSE]) checkIdentical(rowRanges(rse), rowRanges(ss1)) checkIdentical(c(metadata(rse), metadata(rse)), metadata(ss1)) } ## full replacement ss1 <- ss2 <- rseList[[1]] rowRanges(ss2) <- rev(rowRanges(ss2)) ss1[,] <- ss2 checkIdentical(ss1, ss2) } quiet <- suppressWarnings test_RangedSummarizedExperiment_cbind <- function() ## requires matching ranges { ## empty se <- SummarizedExperiment() empty <- cbind(se, se) checkTrue(all.equal(se, empty)) ## different ranges se1 <- rseList[[1]] se2 <- se1[2:4] rownames(se2) <- month.name[seq_len(nrow(se2))] checkException(quiet(cbind(se1, se2)), silent=TRUE) ## same ranges se1 <- rseList[[1]] se2 <- se1[,1:2] colnames(se2) <- month.name[seq_len(ncol(se2))] res <- cbind(se1, se2) checkTrue(nrow(res) == 5) checkTrue(ncol(res) == 5) ## rowRanges rowData(se1) <- DataFrame("one"=1:5) rowData(se2) <- DataFrame("two"=6:10) res <- quiet(cbind(se1, se2)) checkIdentical(names(mcols(rowRanges(res))), c("one", "two")) rowData(se2) <- DataFrame("one"=6:10, "two"=6:10) checkException(cbind(se1, se2), silent=TRUE) ## colData checkTrue(nrow(colData(res)) == 5) ## assays se1 <- rseList[[1]] se2 <- se1[,1:2] assays(se1) <- SimpleList("m"=matrix(rep("m", 15), nrow=5), "a"=array(rep("a", 30), c(5,3,2))) assays(se2) <- SimpleList("m"=matrix(LETTERS[1:10], nrow=5), "a"=array(LETTERS[1:20], c(5,2,2))) res <- cbind(se1, se2) ## same variables checkTrue(nrow(res) == 5) checkTrue(ncol(res) == 5) checkTrue(all.equal(dim(assays(res)$m), c(5L, 5L))) checkTrue(all.equal(dim(assays(res)$a), c(5L, 5L, 2L))) names(assays(se1)) <- c("mm", "aa") checkException(cbind(se1, se2), silent=TRUE) ## different variables } test_RangedSummarizedExperiment_rbind <- function() ## requires matching samples { ## empty se <- SummarizedExperiment() empty <- rbind(se, se) checkTrue(all.equal(se, empty)) ## different samples se1 <- rseList[[1]] se2 <- se1[,1] checkException(quiet(rbind(se1, se2)), silent=TRUE) ## same samples se1 <- rseList[[1]] se2 <- se1 rownames(se2) <- LETTERS[seq_len(nrow(se2))] res <- rbind(se1, se2) checkTrue(nrow(res) == 10) checkTrue(ncol(res) == 3) ## rowRanges rowData(se1) <- DataFrame("one"=1:5) rowData(se2) <- DataFrame("two"=6:10) checkException(rbind(se1, se2), silent=TRUE) ## colDat se1 <- rseList[[1]] se2 <- se1 colData(se2) <- DataFrame("one"=1:3, "two"=4:6) res <- quiet(rbind(se1, se2)) checkTrue(ncol(colData(res)) == 3) ## assays se1 <- rseList[[1]] se2 <- se1 assays(se1) <- SimpleList("m"=matrix(rep("m", 15), nrow=5), "a"=array(rep("a", 30), c(5,3,2))) assays(se2) <- SimpleList("m"=matrix(LETTERS[1:15], nrow=5), "a"=array(LETTERS[1:30], c(5,3,2))) res <- rbind(se1, se2) ## same variables checkTrue(nrow(res) == 10) checkTrue(ncol(res) == 3) checkTrue(all.equal(dim(assays(res)$m), c(10L, 3L))) checkTrue(all.equal(dim(assays(res)$a), c(10L, 3L, 2L))) names(assays(se1)) <- c("mm", "aa") checkException(rbind(se1, se2), silent=TRUE) ## different variables } test_RangedSummarizedExperiment_GRanges_API <- function() { ## are we targetting the correct API? signature for ## RangedSummarizedExperiment method should match signature for ## GenomicRanges or similar, as in each test below for (.fun in .singleDispatch) { generic <- getGeneric(.fun) method <- getMethod(.fun, "RangedSummarizedExperiment") checkIdentical("x", generic@signature) checkIdentical(formals(generic@.Data), formals(method@.Data)) } ## FIXME: pcompare, Compare .sig <- "RangedSummarizedExperiment" for (.fun in .otherFuns) { generic <- getGeneric(.fun) method <- getMethod(.fun, "RangedSummarizedExperiment") checkIdentical(formals(generic@.Data), formals(method@.Data)) } } test_RangedSummarizedExperiment_GRanges_values <- function() { x <- rseList[[1]] isAssign <- grep("<-$", .singleDispatch, value=TRUE) .funs <- setdiff(.singleDispatch, isAssign) ## 'exp' created after manual inspection of results exp <- setNames(c("02dde", "80339", "49a3f", "86757", "77198", "ec53a", "35e2c", "625d9", "3c90a"), .funs) obs <- sapply(.funs, function(.fun) { substr(digest(getGeneric(.fun)(x)), 1, 5) }) checkIdentical(exp, obs) .funs <- isAssign .gets <- sub("<-$", "", isAssign) for (i in seq_along(isAssign)) { ## self-assignment isomorphism value <- getGeneric(.gets[[i]])(x) x1 <- do.call(isAssign[[i]], list(x, value=value)) checkIdentical(x, x1) } } test_RangedSummarizedExperiment_split <- function() { gr <- GRanges(Rle(c("A", "B"), c(2, 3)), IRanges(1:5, 10)) se <- SummarizedExperiment(M1, rowRanges=gr, colData=colData) ## FIXME: unname should not be necessary obs <- split(se, seqnames(se)) exp <- SimpleList(A=se[1:2], B=se[3:5]) checkEquals(obs, exp) } SummarizedExperiment/inst/unitTests/test_SummarizedExperiment-class.R0000644000175400017540000003030313001230160027367 0ustar00biocbuildbiocbuildM1 <- matrix(1, 5, 3, dimnames=list(NULL, NULL)) M2 <- matrix(1, 3, 3, dimnames=list(NULL, NULL)) mList <- list(M1, M2) assaysList <- list(M1=SimpleList(m=M1), M2=SimpleList(m=M2)) rowData1 <- DataFrame(id1=LETTERS[1:5]) rowData2 <- S4Vectors:::make_zero_col_DataFrame(3L) rowDataList <- list(rowData1, rowData2) colData0 <- DataFrame(x=letters[1:3]) se0List <- list(SummarizedExperiment( assays=assaysList[["M1"]], rowData=rowData1, colData=colData0), SummarizedExperiment( assays=assaysList[["M2"]], colData=colData0)) test_SummarizedExperiment_construction <- function() { ## empty-ish m1 <- matrix(0, 0, 0) checkTrue(validObject(new("SummarizedExperiment"))) checkTrue(validObject(SummarizedExperiment()), "empty constructor") checkTrue(validObject(SummarizedExperiment(SimpleList()))) checkTrue(validObject(SummarizedExperiment(assays=SimpleList(m1))), "0x0 constructor") checkException(SummarizedExperiment(assays=SimpleList(m1, matrix())), "assays dim mismatch", TRUE) ## substance for (i in seq_along(se0List)) { se0 <- se0List[[i]] checkTrue(validObject(se0)) checkIdentical(SimpleList(m=mList[[i]]), assays(se0)) checkIdentical(rowDataList[[i]], rowData(se0)) checkIdentical(colData0, colData(se0)) } ## array in assays slot ss <- se0List[[1]] assays(ss) <- SimpleList(array(1:5, c(5,3,2))) checkTrue(validObject(ss)) checkTrue(all(dim(assays(ss[1:3,1:2])[[1]]) == c(3, 2, 2))) ## matrix-of-list in assay slot m <- matrix(list(), 2, 3, dimnames=list(LETTERS[1:2], letters[1:3])) checkTrue(validObject(se <- SummarizedExperiment(m))) checkIdentical(m, assay(se)) checkIdentical(m[,1:2], assay(se[,1:2])) ## DataFrame in assay slot df <- DataFrame(a=1:3, b=1:3, row.names=LETTERS[1:3]) checkTrue(validObject(SummarizedExperiment(list(df)))) } test_SummarizedExperiment_construction_colnames <- function() { colnames <- LETTERS[1:3] checkException(SummarizedExperiment( assays=matrix(0, 2, 3, dimnames=list(NULL, colnames)), colData=DataFrame(row.names=letters[1:3])), "assay colnames() differ from colData rownames()", TRUE) checkTrue(validObject(SummarizedExperiment(matrix(0, 2, 3))), "NULL dimnames on assays-only construction") se <- SummarizedExperiment(matrix(0, 2, 3)) checkTrue(is.null(colnames(se))) checkTrue(validObject(SummarizedExperiment( matrix(0, 2, 3), colData=DataFrame(x=1:3)[,FALSE])), "NULL dimnames on assays and colData") se <- SummarizedExperiment(matrix(0, 2, 3), colData=DataFrame(x=1:3)[,FALSE]) checkTrue(is.null(colnames(se))) ## dimnames from colData rownames se <- SummarizedExperiment(matrix(0, 2, 3), colData=DataFrame(row.names=colnames)) checkIdentical(colnames(se), colnames) checkTrue(is.null(colnames(assay(se, withDimnames=FALSE))), "don't replace NULL colnames") ## when colData rownames == NULL, take dimnames from assay colnames colnames <- LETTERS[1:3] se <- SummarizedExperiment(matrix(0, 2, 3, dimnames=list(NULL, colnames)), colData=DataFrame(x=colnames)[,FALSE]) checkIdentical(colnames(se), colnames) checkIdentical(colnames(assay(se, withDimnames=FALSE)), colnames, "don't remove non-NULL colnames") ## matching colData rownames and assay colnames se <- SummarizedExperiment(matrix(0, 2, 3, dimnames=list(NULL, colnames)), colData=DataFrame(row.names=colnames)) checkIdentical(colnames(se), colnames) checkIdentical(colnames(assay(se, withDimnames=FALSE)), colnames) } test_SummarizedExperiment_getters <- function() { for (i in seq_along(se0List)) { se0 <- se0List[[i]] ## dim, dimnames checkIdentical(c(nrow(mList[[i]]), nrow(colData0)), dim(se0)) checkIdentical(list(NULL, NULL), dimnames(se0)) ## col / metadata checkIdentical(rowDataList[[i]], rowData(se0)) checkIdentical(colData0, colData(se0)) checkIdentical(list(), metadata(se0)) } ## assays m0 <- matrix(0L, 0, 0, dimnames=list(NULL, NULL)) m1 <- matrix(0, 0, 0, dimnames=list(NULL, NULL)) a <- SimpleList(a=m0, b=m1) checkIdentical(a, assays(SummarizedExperiment(assays=a))) ## assay checkException( assay(SummarizedExperiment()), "0-length assay", TRUE) checkIdentical(m0, assay(SummarizedExperiment(assays=a)), "default assay") checkIdentical(m1, assay(SummarizedExperiment(assays=a), 2), "assay, numeric index") checkException( assay(SummarizedExperiment(assays=a), 3), "invalid assay index", TRUE) checkIdentical(m1, assay(SummarizedExperiment(assays=a), "b"), "assay, character index") checkException( assay(SummarizedExperiment(assays=a), "c"), "invalid assay name", TRUE) } test_SummarizedExperiment_setters <- function() { for (i in seq_along(se0List)) { se0 <- se0List[[i]] ## row / col / metadata<- se1 <- se0 rowData <- rowDataList[[i]] rowData <- rowData[rev(seq_len(nrow(rowData))),,drop=FALSE] rowData(se1) <- rowData checkIdentical(rowData, rowData(se1)) colData <- colData0[rev(seq_len(nrow(colData0))),,drop=FALSE] colData(se1) <- colData checkIdentical(colData, colData(se1)) ## The rowData (alias for mcols) setter recycles the supplied ## DataFrame. This is consistent with what the mcols/elementMetadata ## setter does on Vector objects in general. rowData(se1) <- rowData(se0)[1:2,,drop=FALSE] idx <- rep(1:2, length.out=length(se1)) target_se1_rowData <- rowData(se0)[idx,,drop=FALSE] checkIdentical(target_se1_rowData, rowData(se1)) ## The colData setter does NOT recycle the supplied DataFrame. checkException(colData(se1) <- colData(se0)[1:2,,drop=FALSE], "incorrect col dimensions", TRUE) lst <- list("foo", "bar") metadata(se1) <- lst checkIdentical(lst, metadata(se1)) ## assay / assays se1 <- se0 assay(se1) <- assay(se1)+1 checkIdentical(assay(se0)+1, assay(se1)) se1 <- se0 assay(se1, 1) <- assay(se1, 1) + 1 checkIdentical(assay(se0, "m") + 1, assay(se1, "m")) se1 <- se0 assay(se1, "m") <- assay(se1, "m") + 1 checkIdentical(assay(se0, "m")+1, assay(se1, "m")) ## dimnames<- se1 <- se0 dimnames <- list(letters[seq_len(nrow(se1))], LETTERS[seq_len(ncol(se1))]) rownames(se1) <- dimnames[[1]] colnames(se1) <- dimnames[[2]] checkIdentical(dimnames, dimnames(se1)) colData1 <- colData0 row.names(colData1) <- dimnames[[2]] checkIdentical(colData1, colData(se1)) se1 <- se0 dimnames(se1) <- dimnames checkIdentical(dimnames, dimnames(se1)) dimnames(se1) <- NULL checkIdentical(list(NULL, NULL), dimnames(se1)) } } test_SummarizedExperiment_subset <- function() { for (i in seq_along(se0List)) { se0 <- se0List[[i]] ## numeric se1 <- se0[2:3,] checkIdentical(c(2L, ncol(se0)), dim(se1)) checkIdentical(rowData(se1), rowData(se0)[2:3,,drop=FALSE]) checkIdentical(colData(se1), colData(se0)) se1 <- se0[,2:3] checkIdentical(c(nrow(se0), 2L), dim(se1)) checkIdentical(rowData(se1), rowData(se0)) checkIdentical(colData(se1), colData(se0)[2:3,,drop=FALSE]) se1 <- se0[2:3, 2:3] checkIdentical(c(2L, 2L), dim(se1)) checkIdentical(colData(se1), colData(se0)[2:3,,drop=FALSE]) ## character se1 <- se0 dimnames(se1) <- list(LETTERS[seq_len(nrow(se1))], letters[seq_len(ncol(se1))]) ridx <- c("B", "C") checkException(se1[LETTERS,], "i-index out of bounds", TRUE) cidx <- c("b", "c") checkIdentical(colData(se1[,cidx]), colData(se1)[cidx,,drop=FALSE]) checkIdentical(colData(se1[,"a"]), colData(se1)["a",,drop=FALSE]) checkException(se1[,letters], "j-index out of bounds", TRUE) ## logical se1 <- se0 dimnames(se1) <- list(LETTERS[seq_len(nrow(se1))], letters[seq_len(ncol(se1))]) checkEquals(se1, se1[TRUE,]) checkIdentical(c(0L, ncol(se1)), dim(se1[FALSE,])) checkEquals(se1, se1[,TRUE]) checkIdentical(c(nrow(se1), 0L), dim(se1[,FALSE])) idx <- c(TRUE, FALSE) # recycling se2 <- se1[idx,] se2 <- se1[,idx] checkIdentical(colData(se1)[idx,,drop=FALSE], colData(se2)) ## Rle se1 <- se0 rle <- rep(c(TRUE, FALSE), each=3, length.out=nrow(se1)) checkIdentical(assays(se1[rle]), assays(se1[Rle(rle)])) } ## 0 columns se <- SummarizedExperiment(matrix(integer(0), nrow=5)) checkIdentical(dim(se[1:5, ]), c(5L, 0L)) ## 0 rows se <- SummarizedExperiment(colData=DataFrame(samples=1:10)) checkIdentical(dim(se[ ,1:5]), c(0L, 5L)) } test_SummarizedExperiment_subsetassign <- function() { for (i in seq_along(se0List)) { se0 <- se0List[[i]] dimnames(se0) <- list(LETTERS[seq_len(nrow(se0))], letters[seq_len(ncol(se0))]) ## rows se1 <- se0 se1[1:2,] <- se1[2:1,] checkIdentical(colData(se0), colData(se1)) checkIdentical(c(metadata(se0), metadata(se0)), metadata(se1)) ## Rle se1rle <- se1Rle <- se0 rle <- rep(c(TRUE, FALSE), each=3, length.out=nrow(se1)) se1rle[rle,] <- se1rle[rle,] se1Rle[Rle(rle),] <- se1Rle[Rle(rle),] checkIdentical(assays(se1rle), assays(se1Rle)) ## cols se1 <- se0 se1[,1:2] <- se1[,2:1,drop=FALSE] checkIdentical(colData(se0)[2:1,,drop=FALSE], colData(se1)[1:2,,drop=FALSE]) checkIdentical(colData(se0)[-(1:2),,drop=FALSE], colData(se1)[-(1:2),,drop=FALSE]) checkIdentical(c(metadata(se0), metadata(se0)), metadata(se1)) } ## full replacement se1 <- se2 <- se0List[[1]] se1[,] <- se2 checkIdentical(se1, se2) } test_SummarizedExperiment_assays_4d <- function() { ## construction/validation A <- array(0, c(3, 2, 5, 4), list(c("a1", "a2", "a3"), c("b1", "b2"), NULL, c("d1", "d2", "d3", "d4"))) B <- array(0, c(3, 2, 6), list(c("a1", "a2", "a3"), c("b1", "oops"), NULL)) assays0 <- SimpleList(A=A, B=B) checkException(SummarizedExperiment(assays0)) dimnames(B)[1:2] <- dimnames(A)[1:2] C <- array(0, c(3, 2, 4), list(NULL, c("b1", "b2"), c("z1", "z2", "z3", "z4"))) assays0 <- SimpleList(A=A, B=B, C=C) se <- SummarizedExperiment(assays0) checkTrue(validObject(se, complete=TRUE)) ## dimnames checkIdentical(dimnames(A)[1:2], dimnames(se)) checkIdentical(dimnames(B)[1:2], dimnames(se)) for (i in seq_along(assays(se))) { checkIdentical(assays0[[i]], assay(se, i, withDimnames=FALSE)) checkIdentical(dimnames(se), dimnames(assay(se, i))[1:2]) } ## [ se2 <- se[3:2, ] checkIdentical(A[3:2, , , , drop=FALSE], assay(se2, 1, withDimnames=FALSE)) checkIdentical(B[3:2, , , drop=FALSE], assay(se2, 2, withDimnames=FALSE)) checkIdentical(C[3:2, , , drop=FALSE], assay(se2, 3, withDimnames=FALSE)) ## [<- A1 <- A; A1[1, , , ] <- A[1, , , , drop=FALSE] + 1 assays(se[1, ])[[1]] <- 1 + assays(se[1, ])[[1]] checkIdentical(assays(se)[[1]], A1) ## [, [<- don't support more than 4 dimensions a <- array(0, c(3, 3, 3, 3, 3), list(LETTERS[1:3], letters[1:3], NULL, NULL, NULL)) assays <- SimpleList(a=a) se <- SummarizedExperiment(assays) checkException(se[1,], silent=TRUE) } SummarizedExperiment/inst/unitTests/test_coverage-methods.R0000644000175400017540000000355113001230160025344 0ustar00biocbuildbiocbuild### M1 <- matrix(1, 5, 3, dimnames=list(NULL, NULL)) M2 <- matrix(1, 3, 3, dimnames=list(NULL, NULL)) assaysList <- list(gr=SimpleList(m=M1), grl=SimpleList(m=M2)) rowRangesList <- list(gr=GRanges("chr1", IRanges(1:5, 10)), grl=split(GRanges("chr1", IRanges(1:5, 10)), c(1,1,2,2,3))) names(rowRangesList[["grl"]]) <- NULL colData <- DataFrame(x=letters[1:3]) ## a list of one SE with GRanges and one with GRangesList rseList <- list(SummarizedExperiment( assays=assaysList[["gr"]], rowRanges=rowRangesList[["gr"]], colData=colData), SummarizedExperiment( assays=assaysList[["grl"]], rowRanges=rowRangesList[["grl"]], colData=colData)) test_interfaces <- function() { generic_functions <- "coverage" for (fun in generic_functions) { generic <- getGeneric(fun) method <- getMethod(fun, "RangedSummarizedExperiment") checkIdentical("x", generic@signature) checkIdentical(formals(generic@.Data), formals(method@.Data)) } } test_coverage_RangedSummarizedExperiment <- function() { for (i in 1:2) { x <- rseList[[i]] target <- coverage(rowRanges(x)) current <- coverage(x) checkIdentical(target, current) weight <- runif(length(x)) ## Issues a warning (in BioC 3.3) when rowRanges(x) is a GRangesList ## object, which reveals a problem with how the "coverage" method for ## GRangesList objects handles the 'weight' argument. The warning is ## expected and healthy, don't try to suppress it here. It will go ## away when we fix the "coverage" method for GRangesList objects ## (defined in the GenomicRanges package). target <- coverage(rowRanges(x), weight=weight) current <- coverage(x, weight=weight) checkIdentical(target, current) } } SummarizedExperiment/inst/unitTests/test_findOverlaps-methods.R0000644000175400017540000000621113001230160026201 0ustar00biocbuildbiocbuild### M1 <- matrix(1, 5, 3, dimnames=list(NULL, NULL)) M2 <- matrix(1, 3, 3, dimnames=list(NULL, NULL)) assaysList <- list(gr=SimpleList(m=M1), grl=SimpleList(m=M2)) rowRangesList <- list(gr=GRanges("chr1", IRanges(1:5, 10)), grl=split(GRanges("chr1", IRanges(1:5, 10)), c(1,1,2,2,3))) names(rowRangesList[["grl"]]) <- NULL colData <- DataFrame(x=letters[1:3]) ## a list of one SE with GRanges and one with GRangesList rseList <- list(SummarizedExperiment( assays=assaysList[["gr"]], rowRanges=rowRangesList[["gr"]], colData=colData), SummarizedExperiment( assays=assaysList[["grl"]], rowRanges=rowRangesList[["grl"]], colData=colData)) test_interfaces <- function() { fun <- "findOverlaps" signatures <- list( c("RangedSummarizedExperiment", "Vector"), c("Vector", "RangedSummarizedExperiment"), c("RangedSummarizedExperiment", "RangedSummarizedExperiment") ) generic <- getGeneric(fun) for (sig in signatures) { method <- getMethod(fun, sig) checkIdentical(c("query", "subject"), generic@signature) checkIdentical(formals(generic@.Data), formals(method@.Data)) } } test_findOverlaps_methods <- function() { identical_SummarizedExperiment <- function(x, y) { x@assays <- as(assays(x), "SimpleListAssays") y@assays <- as(assays(y), "SimpleListAssays") identical(x, y) } for (i in 1:2) { x <- rseList[[i]] for (j in 1:2) { y <- rseList[[j]] ## findOverlaps target <- findOverlaps(rowRanges(x), rowRanges(y)) current <- findOverlaps(x, rowRanges(y)) checkIdentical(target, current) current <- findOverlaps(rowRanges(x), y) checkIdentical(target, current) current <- findOverlaps(x, y) checkIdentical(target, current) ## countOverlaps target <- countOverlaps(rowRanges(x), rowRanges(y)) current <- countOverlaps(x, rowRanges(y)) checkIdentical(target, current) current <- countOverlaps(rowRanges(x), y) checkIdentical(target, current) current <- countOverlaps(x, y) checkIdentical(target, current) ## overlapsAny target <- overlapsAny(rowRanges(x), rowRanges(y)) current <- overlapsAny(x, rowRanges(y)) checkIdentical(target, current) current <- overlapsAny(rowRanges(x), y) checkIdentical(target, current) current <- overlapsAny(x, y) checkIdentical(target, current) ## subsetByOverlaps target <- subsetByOverlaps(x, rowRanges(y)) current <- subsetByOverlaps(x, rowRanges(y)) checkTrue(identical_SummarizedExperiment(target, current)) current <- subsetByOverlaps(x, y) checkTrue(identical_SummarizedExperiment(target, current)) target <- subsetByOverlaps(rowRanges(x), rowRanges(y)) current <- subsetByOverlaps(rowRanges(x), y) checkIdentical(target, current) } } } SummarizedExperiment/inst/unitTests/test_inter-range-methods.R0000644000175400017540000000274213001230160025765 0ustar00biocbuildbiocbuild### M1 <- matrix(1, 5, 3, dimnames=list(NULL, NULL)) M2 <- matrix(1, 3, 3, dimnames=list(NULL, NULL)) assaysList <- list(gr=SimpleList(m=M1), grl=SimpleList(m=M2)) rowRangesList <- list(gr=GRanges("chr1", IRanges(1:5, 10), Rle(c("+", "-"), 3:2)), grl=split(GRanges("chr1", IRanges(1:5, 10)), c(1,1,2,2,3))) names(rowRangesList[["grl"]]) <- NULL colData <- DataFrame(x=letters[1:3]) ## a list of one SE with GRanges and one with GRangesList rseList <- list(SummarizedExperiment( assays=assaysList[["gr"]], rowRanges=rowRangesList[["gr"]], colData=colData), SummarizedExperiment( assays=assaysList[["grl"]], rowRanges=rowRangesList[["grl"]], colData=colData)) test_interfaces <- function() { generic_functions <- c("isDisjoint", "disjointBins") for (fun in generic_functions) { generic <- getGeneric(fun) method <- getMethod(fun, "RangedSummarizedExperiment") checkIdentical("x", generic@signature) checkIdentical(formals(generic@.Data), formals(method@.Data)) } } test_inter_range_methods <- function() { #for (i in 1:2) { for (i in 1L) { x <- rseList[[i]] ## isDisjoint target <- isDisjoint(rowRanges(x)) current <- isDisjoint(x) checkIdentical(target, current) ## disjointBins target <- disjointBins(rowRanges(x)) current <- disjointBins(x) checkIdentical(target, current) } } SummarizedExperiment/inst/unitTests/test_intra-range-methods.R0000644000175400017540000000643413001230160025763 0ustar00biocbuildbiocbuild### M1 <- matrix(1, 5, 3, dimnames=list(NULL, NULL)) M2 <- matrix(1, 3, 3, dimnames=list(NULL, NULL)) assaysList <- list(gr=SimpleList(m=M1), grl=SimpleList(m=M2)) rowRangesList <- list(gr=GRanges("chr1", IRanges(1:5, 10), Rle(c("+", "-"), 3:2)), grl=split(GRanges("chr1", IRanges(1:5, 10)), c(1,1,2,2,3))) names(rowRangesList[["grl"]]) <- NULL colData <- DataFrame(x=letters[1:3]) ## a list of one SE with GRanges and one with GRangesList rseList <- list(SummarizedExperiment( assays=assaysList[["gr"]], rowRanges=rowRangesList[["gr"]], colData=colData), SummarizedExperiment( assays=assaysList[["grl"]], rowRanges=rowRangesList[["grl"]], colData=colData)) test_interfaces <- function() { generic_functions <- c("shift", "narrow", "resize", "flank", "promoters", "restrict", "trim") for (fun in generic_functions) { generic <- getGeneric(fun) method <- getMethod(fun, "RangedSummarizedExperiment") checkIdentical("x", generic@signature) checkIdentical(formals(generic@.Data), formals(method@.Data)) } } test_intra_range_methods <- function() { identical_SummarizedExperiment <- function(x, y) { x@assays <- as(assays(x), "SimpleListAssays") y@assays <- as(assays(y), "SimpleListAssays") identical(x, y) } #for (i in 1:2) { for (i in 1L) { ## shift target <- rseList[[i]] rowRanges(target) <- shift(rowRanges(target), 50) current <- shift(rseList[[i]], 50) checkTrue(identical_SummarizedExperiment(target, current)) ## narrow target <- rseList[[i]] rowRanges(target) <- narrow(rowRanges(target), 2, -2) current <- narrow(rseList[[i]], 2, -2) checkTrue(identical_SummarizedExperiment(target, current)) ## resize target <- rseList[[i]] rowRanges(target) <- resize(rowRanges(target), 8) current <- resize(rseList[[i]], 8) checkTrue(identical_SummarizedExperiment(target, current)) ## flank target <- rseList[[i]] rowRanges(target) <- flank(rowRanges(target), 5, both=TRUE) current <- flank(rseList[[i]], 5, both=TRUE) checkTrue(identical_SummarizedExperiment(target, current)) ## promoters target <- rseList[[i]] rowRanges(target) <- promoters(rowRanges(target), upstream=20, downstream=5) current <- promoters(rseList[[i]], upstream=20, downstream=5) checkTrue(identical_SummarizedExperiment(target, current)) ## restrict target <- rseList[[i]] rowRanges(target) <- restrict(rowRanges(target), start=2, end=3, keep.all.ranges=TRUE) current <- restrict(rseList[[i]], start=2, end=3, keep.all.ranges=TRUE) checkTrue(identical_SummarizedExperiment(target, current)) ## trim suppressWarnings(seqlengths(rseList[[i]]) <- 8) target <- rseList[[i]] rowRanges(target) <- trim(rowRanges(target)) current <- trim(rseList[[i]]) checkTrue(identical_SummarizedExperiment(target, current)) seqlengths(rseList[[i]]) <- NA } } SummarizedExperiment/inst/unitTests/test_makeSummarizedExperimentFromDataFrame.R0000644000175400017540000000421213001230160031513 0ustar00biocbuildbiocbuild## rowNames <- paste0("GENE", letters[5:1]) range_info <- list(chr="chr2", start = 11:15, end = 12:16, strand = c("+", "-", "+", "*", ".")) expr_info <- list(expr0 = 3:7, expr1 = 8:12, expr2 = 12:16) df <- as.data.frame(c(range_info, expr_info), row.names = rowNames) DF <- DataFrame(c(range_info, expr_info), row.names = rowNames) test_makeSummarizedExperimentFromDataFrame <- function() { validObject(makeSummarizedExperimentFromDataFrame(df)) validObject(makeSummarizedExperimentFromDataFrame(DF)) rangesA <- GRanges(as.data.frame(range_info, row.names = rowNames)) rangesB <- rowRanges(makeSummarizedExperimentFromDataFrame(df)) # Check rowRanges to be identical checkIdentical(rangesA, rangesB) # Check assay matrix and expr_info matrix are identical checkIdentical(assay(makeSummarizedExperimentFromDataFrame(df)), as.matrix(as.data.frame(expr_info, row.names = rowNames))) checkIdentical(assay(makeSummarizedExperimentFromDataFrame(DF)), as.matrix(as.data.frame(expr_info, row.names = rowNames))) checkEquals(makeSummarizedExperimentFromDataFrame(df), makeSummarizedExperimentFromDataFrame(DF)) checkException( makeSummarizedExperimentFromDataFrame( cbind(df, expr3 = letters[seq_len(nrow(df))]))) checkException( makeSummarizedExperimentFromDataFrame( cbind(DF, DataFrame(expr3 = letters[seq_len(nrow(df))])))) checkIdentical(nrow(df), length(rowRanges( makeSummarizedExperimentFromDataFrame(df)))) checkIdentical(nrow(DF), length(rowRanges( makeSummarizedExperimentFromDataFrame(DF)))) checkIdentical(colnames(makeSummarizedExperimentFromDataFrame(df)), names(expr_info)) checkIdentical(rownames(makeSummarizedExperimentFromDataFrame(df)), rowNames) checkIdentical(colnames(makeSummarizedExperimentFromDataFrame(DF)), names(expr_info)) checkIdentical(rownames(makeSummarizedExperimentFromDataFrame(DF)), rowNames) } SummarizedExperiment/inst/unitTests/test_makeSummarizedExperimentFromExpressionSet.R0000644000175400017540000001433613001230160032512 0ustar00biocbuildbiocbuildM1 <- matrix(1, 5, 3, dimnames=list(NULL, NULL)) M2 <- matrix(1, 3, 3, dimnames=list(NULL, NULL)) assaysList <- list(gr=SimpleList(m=M1), grl=SimpleList(m=M2)) rowRangesList <- list(gr=GRanges("chr1", IRanges(1:5, 10)), grl=split(GRanges("chr1", IRanges(1:5, 10)), c(1,1,2,2,3))) names(rowRangesList[["grl"]]) <- NULL colData <- DataFrame(x=letters[1:3]) ## a list of one SE with GRanges and one with GRangesList rseList <- list(SummarizedExperiment( assays=assaysList[["gr"]], rowRanges=rowRangesList[["gr"]], colData=colData), SummarizedExperiment( assays=assaysList[["grl"]], rowRanges=rowRangesList[["grl"]], colData=colData)) test_SummarizedExperiment_GenomicRanges_coercion <- function() { eset1 <- ExpressionSet() checkTrue(validObject(eset1)) se1 <- as(eset1, "RangedSummarizedExperiment") checkTrue(validObject(se1)) data("sample.ExpressionSet", package = "Biobase") eset2 <- sample.ExpressionSet checkTrue(validObject(eset2)) se2 <- as(eset2, "RangedSummarizedExperiment") checkTrue(validObject(se2)) checkIdentical(experimentData(eset2), metadata(se2)$experimentData) checkIdentical(annotation(eset2), metadata(se2)$annotation) checkIdentical(protocolData(eset2), metadata(se2)$protocolData) eset2Assays <- SimpleList(as.list(assayData(eset2))) se2Assays <- assays(se2) checkIdentical(eset2Assays$exprs, se2Assays$exprs) checkIdentical(eset2Assays$se.exprs, se2Assays$se.exprs) checkIdentical(featureNames(eset2), rownames(se2)) checkIdentical(sampleNames(eset2), colnames(se2)) } test_GenomicRanges_SummarizedExperiment_coercion <- function() { ## empty SE simpleSE <- SummarizedExperiment() eset1 <- as(simpleSE, "ExpressionSet") checkTrue(validObject(eset1)) ## Back and forth empty ES simpleES <- ExpressionSet() simpleES2 <- as(as(simpleES, "RangedSummarizedExperiment"), "ExpressionSet") checkTrue(validObject(simpleES2)) checkEquals(as.list(assayData(simpleES)), as.list(assayData(simpleES2))) ## Simple SE simpleSE <- rseList[[1]] assayNames(simpleSE) <- "exprs" # No warning 'No assay named exprs..." eset2 <- as(simpleSE, "ExpressionSet") checkTrue(validObject(eset2)) ## The ExpressionSet features should have the data from the ## SummarizedExperiment rows if they are from GRanges. checkIdentical(pData(featureData(eset2)), as.data.frame(rowRanges(rseList[[1]]))) # the rowRanges are retained if the object has them to begin with. se2_2 <- as(eset2, "RangedSummarizedExperiment") rr_se2_2 <- unname(rowRanges(se2_2)) rr_eset2 <- rowRanges(rseList[[1]]) checkEquals(rr_se2_2, rr_eset2) simpleSE <- rseList[[2]] assayNames(simpleSE) <- "exprs" # No warning 'No assay named exprs..." eset3 <- as(simpleSE, "ExpressionSet") checkTrue(validObject(eset3)) ## The ExpressionSet features should not have the data from the ## SummarizedExperiment rows if they are from GRangesList, but they ## should be empty and the same length as the number of ranges. checkEquals(unname(NROW(featureData(eset3))), unname(length(rowRanges(rseList[[2]])))) data("sample.ExpressionSet", package = "Biobase") eset4 <- sample.ExpressionSet eset5 <- as(as(eset4, "RangedSummarizedExperiment"), "ExpressionSet") checkTrue(validObject(eset5)) ## this is necessary because the order in environments is undefined. compareLists <- function(x, y) { nmsX <- names(x) nmsY <- names(y) reorderY <- match(nmsY, nmsX) checkIdentical(x, y[reorderY]) } compareLists(as.list(assayData(eset4)), as.list(assayData(eset5))) checkIdentical(experimentData(eset4), experimentData(eset5)) checkIdentical(annotation(eset4), annotation(eset5)) checkIdentical(protocolData(eset4), protocolData(eset5)) checkIdentical(featureNames(eset4), featureNames(eset5)) checkIdentical(sampleNames(eset4), sampleNames(eset5)) } test_GenomicRanges_SummarizedExperiment_coercion_mappingFunctions <- function() { ## naiveRangeMapper ## valid object from empty object checkTrue(validObject(makeSummarizedExperimentFromExpressionSet(ExpressionSet()))) ## valid object from sample ExpressionSet data("sample.ExpressionSet", package = "Biobase") eset1 <- sample.ExpressionSet checkTrue(validObject(makeSummarizedExperimentFromExpressionSet(eset1))) ## makeSummarizedExperimentFromExpressionSet should be the same as `as` ## with default args checkEquals(makeSummarizedExperimentFromExpressionSet(eset1), as(eset1, "RangedSummarizedExperiment")) ## probeRangeMapper ## valid object from empty object checkTrue(validObject( makeSummarizedExperimentFromExpressionSet(ExpressionSet(), probeRangeMapper))) ## valid object from sample ExpressionSet se1 <- makeSummarizedExperimentFromExpressionSet(eset1, probeRangeMapper) checkTrue(validObject(se1)) ## Granges returned have rownames that were from the featureNames checkTrue(all(rownames(rowRanges(se1)) %in% featureNames(eset1))) ## geneRangeMapper ## valid object from empty object checkTrue(validObject( makeSummarizedExperimentFromExpressionSet(ExpressionSet(), geneRangeMapper(NULL)))) ## valid object from sample ExpressionSet se2 <- makeSummarizedExperimentFromExpressionSet(eset1, geneRangeMapper("TxDb.Hsapiens.UCSC.hg19.knownGene")) checkTrue(validObject(se2)) ## Granges returned have rownames that were from the featureNames checkTrue(all(rownames(rowRanges(se2)) %in% featureNames(eset1))) } SummarizedExperiment/inst/unitTests/test_nearest-methods.R0000644000175400017540000000411213001230160025204 0ustar00biocbuildbiocbuild### M1 <- matrix(1, 5, 3, dimnames=list(NULL, NULL)) M2 <- matrix(1, 3, 3, dimnames=list(NULL, NULL)) assaysList <- list(gr=SimpleList(m=M1), grl=SimpleList(m=M2)) rowRangesList <- list(gr=GRanges("chr1", IRanges(1:5, 10)), grl=split(GRanges("chr1", IRanges(1:5, 10)), c(1,1,2,2,3))) names(rowRangesList[["grl"]]) <- NULL colData <- DataFrame(x=letters[1:3]) ## a list of one SE with GRanges and one with GRangesList rseList <- list(SummarizedExperiment( assays=assaysList[["gr"]], rowRanges=rowRangesList[["gr"]], colData=colData), SummarizedExperiment( assays=assaysList[["grl"]], rowRanges=rowRangesList[["grl"]], colData=colData)) .GENERIC_SIGNATURES <- list( precede=c("x", "subject"), follow=c("x", "subject"), nearest=c("x", "subject"), distance=c("x", "y"), distanceToNearest=c("x", "subject") ) test_interfaces <- function() { method_signatures <- list( c("RangedSummarizedExperiment", "ANY"), c("ANY", "RangedSummarizedExperiment"), c("RangedSummarizedExperiment", "RangedSummarizedExperiment") ) for (fun in names(.GENERIC_SIGNATURES)) { generic <- getGeneric(fun) checkIdentical(.GENERIC_SIGNATURES[[fun]], generic@signature) for (sig in method_signatures) { method <- getMethod(fun, sig) checkIdentical(formals(generic@.Data), formals(method@.Data)) } } } test_nearest_methods <- function() { #for (i in 1:2) { for (i in 1L) { x <- rseList[[i]] #for (j in 1:2) { for (j in 1L) { y <- rseList[[j]] for (fun in names(.GENERIC_SIGNATURES)) { fun <- get(fun) target <- fun(rowRanges(x), rowRanges(y)) current <- fun(x, rowRanges(y)) checkIdentical(target, current) current <- fun(rowRanges(x), y) checkIdentical(target, current) current <- fun(x, y) checkIdentical(target, current) } } } } SummarizedExperiment/man/0000755000175400017540000000000013001230160016516 5ustar00biocbuildbiocbuildSummarizedExperiment/man/Assays-class.Rd0000644000175400017540000001343213001230160021356 0ustar00biocbuildbiocbuild\name{Assays-class} \docType{class} \alias{class:Assays} \alias{Assays-class} \alias{Assays} \alias{length,Assays-method} \alias{names,Assays-method} \alias{names<-,Assays-method} \alias{[[,Assays,ANY,ANY-method} \alias{[[<-,Assays,ANY,ANY-method} \alias{dim,Assays-method} \alias{[,Assays,ANY-method} \alias{[,Assays,ANY,ANY,ANY-method} \alias{[<-,Assays,ANY,ANY,ANY-method} \alias{rbind,Assays-method} \alias{cbind,Assays-method} \alias{arbind,Matrix-method} \alias{acbind,Matrix-method} \alias{class:SimpleListAssays} \alias{SimpleListAssays-class} \alias{SimpleListAssays} \alias{class:ShallowData} \alias{ShallowData-class} \alias{ShallowData} \alias{class:ShallowSimpleListAssays} \alias{ShallowSimpleListAssays-class} \alias{ShallowSimpleListAssays} \alias{coerce,SimpleList,ShallowSimpleListAssays-method} \alias{coerce,ShallowSimpleListAssays,SimpleList-method} \alias{class:AssaysInEnv} \alias{AssaysInEnv-class} \alias{AssaysInEnv} \alias{length,AssaysInEnv-method} \alias{names,AssaysInEnv-method} \alias{names<-,AssaysInEnv-method} \alias{[[,AssaysInEnv,ANY,ANY-method} \alias{[[<-,AssaysInEnv,ANY,ANY-method} \alias{coerce,SimpleList,AssaysInEnv-method} \alias{coerce,AssaysInEnv,SimpleList-method} \title{Assays objects} \description{ The Assays virtual class and its methods provide a formal abstraction of the assays slot of \link{SummarizedExperiment} objects. SimpleListAssays and ShallowSimpleListAssays are concrete subclasses of Assays with the latter being currently the default implementation of Assays objects. Other implementations (e.g. disk-based) could easily be added. Note that these classes are not meant to be used directly by the end-user and the material in this man page is aimed at package developers. } \details{ Assays objects have a list-like semantics with elements having matrix- or array-like semantics (e.g., \code{dim}, \code{dimnames}). The Assays API consists of: \itemize{ \item (a) The \code{Assays()} constructor function. \item (b) Lossless back and forth coercion from/to \link{SimpleList}. The coercion method from \link{SimpleList} doesn't need (and should not) validate the returned object. \item (c) \code{length}, \code{names}, \code{`names<-`}, \code{[[}, \code{`[[<-`}, \code{dim}, \code{[}, \code{`[<-`}, \code{rbind}, \code{cbind}. } An Assays concrete subclass needs to implement (b) (required) plus, optionally any of the methods in (c). IMPORTANT: Methods that return a modified Assays object (a.k.a. endomorphisms), that is, \code{[} as well as replacement methods \code{names<-}, \code{[[<-}, and \code{[<-}, must respect the \emph{copy-on-change contract}. With objects that don't make use of references internally, the developer doesn't need to take any special action for that because it's automatically taken care of by R itself. However, for objects that do make use of references internally (e.g. environments, external pointers, pointer to a file on disk, etc...), the developer needs to be careful to implement endomorphisms with copy-on-change semantics. This can easily be achieved (and is what the default methods for Assays objects do) by performaing a full (deep) copy of the object before modifying it instead of trying to modify it in-place. Note that the full (deep) copy is not always necessary in order to achieve copy-on-change semantics: it's enough (and often preferrable for performance reasons) to copy only the parts of the objects that need to be modified. Assays has currently 3 implementations which are formalized by concrete subclasses SimpleListAssays, ShallowSimpleListAssays, and AssaysInEnv. ShallowSimpleListAssays is the default. AssaysInEnv is a \emph{broken} alternative to ShallowSimpleListAssays that does NOT respect the \emph{copy-on-change contract}. It is only provided for illustration purposes (see source file Assays-class.R for the details). A little more detail about ShallowSimpleListAssays: a small reference class hierarchy (not exported from the \pkg{GenomicRanges} name space) defines a reference class ShallowData with a single field \code{data} of type \code{ANY}, and a derived class ShallowSimpleListAssays that specializes the type of \code{data} as \link{SimpleList}, and \code{contains=c("ShallowData", "Assays")}. The assays slot of a \link{SummarizedExperiment} object contains an instance of ShallowSimpleListAssays. } \author{Martin Morgan, \url{mtmorgan@fhcrc.org}} \seealso{ \itemize{ \item \link{SummarizedExperiment} objects. \item \link[S4Vectors]{SimpleList} objects in the \pkg{S4Vectors} package. } } \examples{ ## --------------------------------------------------------------------- ## DIRECT MANIPULATION OF Assays OBJECTS ## --------------------------------------------------------------------- m1 <- matrix(runif(24), ncol=3) m2 <- matrix(runif(24), ncol=3) a <- Assays(SimpleList(m1, m2)) a as(a, "SimpleList") length(a) a[[2]] dim(a) b <- a[-4, 2] b length(b) b[[2]] dim(b) names(a) names(a) <- c("a1", "a2") names(a) a[["a2"]] rbind(a, a) cbind(a, a) ## --------------------------------------------------------------------- ## COPY-ON-CHANGE CONTRACT ## --------------------------------------------------------------------- ## ShallowSimpleListAssays objects have copy-on-change semantics but not ## AssaysInEnv objects. For example: ssla <- as(SimpleList(m1, m2), "ShallowSimpleListAssays") aie <- as(SimpleList(m1, m2), "AssaysInEnv") ## No names on 'ssla' and 'aie': names(ssla) names(aie) ssla2 <- ssla aie2 <- aie names(ssla2) <- names(aie2) <- c("A1", "A2") names(ssla) # still NULL (as expected) names(aie) # changed! (because the names<-,AssaysInEnv method is not # implemented in a way that respects the copy-on-change # contract) } SummarizedExperiment/man/RangedSummarizedExperiment-class.Rd0000644000175400017540000002726313001230160025424 0ustar00biocbuildbiocbuild\name{RangedSummarizedExperiment-class} \docType{class} % Class \alias{class:RangedSummarizedExperiment} \alias{RangedSummarizedExperiment-class} \alias{RangedSummarizedExperiment} % Constructor \alias{SummarizedExperiment} \alias{SummarizedExperiment,SimpleList-method} \alias{SummarizedExperiment,ANY-method} \alias{SummarizedExperiment,list-method} \alias{SummarizedExperiment,missing-method} % Coercion \alias{coerce,RangedSummarizedExperiment,SummarizedExperiment-method} \alias{coerce,SummarizedExperiment,RangedSummarizedExperiment-method} % Accessors \alias{rowRanges} \alias{rowRanges,RangedSummarizedExperiment-method} \alias{rowRanges<-} \alias{rowRanges<-,SummarizedExperiment,GenomicRanges-method} \alias{rowRanges<-,SummarizedExperiment,GRangesList-method} \alias{names,RangedSummarizedExperiment-method} \alias{names<-,RangedSummarizedExperiment-method} \alias{dimnames,RangedSummarizedExperiment-method} \alias{dimnames<-,RangedSummarizedExperiment,list-method} % GenomicRanges compatibility methods \alias{Compare,ANY,RangedSummarizedExperiment-method} \alias{Compare,RangedSummarizedExperiment,ANY-method} \alias{Compare,RangedSummarizedExperiment,RangedSummarizedExperiment-method} \alias{pcompare,ANY,RangedSummarizedExperiment-method} \alias{pcompare,RangedSummarizedExperiment,ANY-method} \alias{pcompare,RangedSummarizedExperiment,RangedSummarizedExperiment-method} \alias{duplicated,RangedSummarizedExperiment-method} \alias{elementMetadata,RangedSummarizedExperiment-method} \alias{elementMetadata<-,RangedSummarizedExperiment-method} \alias{end,RangedSummarizedExperiment-method} \alias{end<-,RangedSummarizedExperiment-method} \alias{granges,RangedSummarizedExperiment-method} \alias{is.unsorted,RangedSummarizedExperiment-method} \alias{mcols,RangedSummarizedExperiment-method} \alias{mcols<-,RangedSummarizedExperiment-method} \alias{order,RangedSummarizedExperiment-method} \alias{ranges,RangedSummarizedExperiment-method} \alias{ranges<-,RangedSummarizedExperiment-method} \alias{rank,RangedSummarizedExperiment-method} \alias{seqinfo,RangedSummarizedExperiment-method} \alias{seqinfo<-,RangedSummarizedExperiment-method} \alias{seqnames,RangedSummarizedExperiment-method} \alias{sort,RangedSummarizedExperiment-method} \alias{split,RangedSummarizedExperiment-method} \alias{split,RangedSummarizedExperiment,ANY-method} \alias{start,RangedSummarizedExperiment-method} \alias{start<-,RangedSummarizedExperiment-method} \alias{strand,RangedSummarizedExperiment-method} \alias{strand<-,RangedSummarizedExperiment,ANY-method} \alias{subset,RangedSummarizedExperiment-method} \alias{width,RangedSummarizedExperiment-method} \alias{width<-,RangedSummarizedExperiment-method} \alias{updateObject,SummarizedExperiment-method} \title{RangedSummarizedExperiment objects} \description{ The RangedSummarizedExperiment class is a matrix-like container where rows represent ranges of interest (as a \link{GRanges} or \link{GRangesList} object) and columns represent samples (with sample data summarized as a \link{DataFrame}). A RangedSummarizedExperiment contains one or more assays, each represented by a matrix-like object of numeric or other mode. RangedSummarizedExperiment is a subclass of \link{SummarizedExperiment} and, as such, all the methods documented in \code{?\link{SummarizedExperiment}} also work on a RangedSummarizedExperiment object. The methods documented below are additional methods that are specific to RangedSummarizedExperiment objects. } \usage{ ## Constructor SummarizedExperiment(assays, ...) \S4method{SummarizedExperiment}{SimpleList}(assays, rowData=NULL, rowRanges=GRangesList(), colData=DataFrame(), metadata=list()) \S4method{SummarizedExperiment}{ANY}(assays, ...) \S4method{SummarizedExperiment}{list}(assays, ...) \S4method{SummarizedExperiment}{missing}(assays, ...) ## Accessors rowRanges(x, ...) rowRanges(x, ...) <- value ## Subsetting \S4method{subset}{RangedSummarizedExperiment}(x, subset, select, ...) ## rowRanges access ## see 'GRanges compatibility', below } \arguments{ \item{assays}{A \code{list} or \code{SimpleList} of matrix-like elements, or a matrix-like object. All elements of the list must have the same dimensions, and dimension names (if present) must be consistent across elements and with the row names of \code{rowRanges} and \code{colData}.} \item{rowData}{A \link[S4Vectors]{DataFrame} object describing the rows. Row names, if present, become the row names of the SummarizedExperiment object. The number of rows of the \link[S4Vectors]{DataFrame} must equal the number of rows of the matrices in \code{assays}.} \item{rowRanges}{A \link[GenomicRanges]{GRanges} or \link[GenomicRanges]{GRangesList} object describing the ranges of interest. Names, if present, become the row names of the SummarizedExperiment object. The length of the \link[GenomicRanges]{GRanges} or \link[GenomicRanges]{GRangesList} must equal the number of rows of the matrices in \code{assays}. If \code{rowRanges} is missing, a \link{SummarizedExperiment} instance is returned.} \item{colData}{An optional \link{DataFrame} describing the samples. Row names, if present, become the column names of the RangedSummarizedExperiment.} \item{metadata}{An optional \code{list} of arbitrary content describing the overall experiment.} \item{...}{For \code{SummarizedExperiment}, S4 methods \code{list} and \code{matrix}, arguments identical to those of the \code{SimpleList} method. For \code{rowRanges}, ignored. } \item{x}{A RangedSummarizedExperiment object. The \code{rowRanges} setter will also accept a \link{SummarizedExperiment} object and will first coerce it to RangedSummarizedExperiment before it sets \code{value} on it.} \item{value}{A \link[GenomicRanges]{GRanges} or \link[GenomicRanges]{GRangesList} object.} \item{subset}{An expression which, when evaluated in the context of \code{rowRanges(x)}, is a logical vector indicating elements or rows to keep: missing values are taken as false.} \item{select}{An expression which, when evaluated in the context of \code{colData(x)}, is a logical vector indicating elements or rows to keep: missing values are taken as false.} } \details{ The rows of a RangedSummarizedExperiment object represent ranges (in genomic coordinates) of interest. The ranges of interest are described by a \link{GRanges} or a \link{GRangesList} object, accessible using the \code{rowRanges} function, described below. The \link{GRanges} and \link{GRangesList} classes contains sequence (e.g., chromosome) name, genomic coordinates, and strand information. Each range can be annotated with additional data; this data might be used to describe the range or to summarize results (e.g., statistics of differential abundance) relevant to the range. Rows may or may not have row names; they often will not. } \section{Constructor}{ RangedSummarizedExperiment instances are constructed using the \code{SummarizedExperiment} function with arguments outlined above. } \section{Accessors}{ In the following code snippets, \code{x} is a RangedSummarizedExperiment object. \describe{ \item{\code{rowRanges(x)}, \code{rowRanges(x) <- value}:}{Get or set the row data. \code{value} is a \code{GenomicRanges} object. Row names of \code{value} must be NULL or consistent with the existing row names of \code{x}.} } } \section{GRanges compatibility (rowRanges access)}{ Many \link{GRanges} and \link{GRangesList} operations are supported on RangedSummarizedExperiment objects, using \code{rowRanges}. Supported operations include: \code{\link{pcompare}}, \code{\link{duplicated}}, \code{\link{end}}, \code{\link{end<-}}, \code{\link{granges}}, \code{\link{is.unsorted}}, \code{\link{match}}, \code{\link{mcols}}, \code{\link{mcols<-}}, \code{\link{order}}, \code{\link{ranges}}, \code{\link{ranges<-}}, \code{\link{rank}}, \code{\link{seqinfo}}, \code{\link{seqinfo<-}}, \code{\link{seqnames}}, \code{\link{sort}}, \code{\link{start}}, \code{\link{start<-}}, \code{\link{strand}}, \code{\link{strand<-}}, \code{\link{width}}, \code{\link{width<-}}. See also \code{?\link[SummarizedExperiment]{shift}}, \code{?\link[SummarizedExperiment]{isDisjoint}}, \code{?\link[SummarizedExperiment]{coverage}}, \code{?\link[SummarizedExperiment]{findOverlaps}}, and \code{?\link[SummarizedExperiment]{nearest}} for more \emph{GRanges compatibility methods}. Not all \link{GRanges} operations are supported, because they do not make sense for RangedSummarizedExperiment objects (e.g., length, name, as.data.frame, c, splitAsList), involve non-trivial combination or splitting of rows (e.g., disjoin, gaps, reduce, unique), or have not yet been implemented (Ops, map, window, window<-). } \section{Subsetting}{ In the code snippets below, \code{x} is a RangedSummarizedExperiment object. \describe{ \item{\code{subset(x, subset, select)}:}{Create a subset of \code{x} using an expression \code{subset} referring to columns of \code{rowRanges(x)} (including \sQuote{seqnames}, \sQuote{start}, \sQuote{end}, \sQuote{width}, \sQuote{strand}, and \code{names(rowData(x))}) and / or \code{select} referring to column names of \code{colData(x)}.} } } \section{Extension}{ RangedSummarizedExperiment is implemented as an S4 class, and can be extended in the usual way, using \code{contains="RangedSummarizedExperiment"} in the new class definition. } \author{Martin Morgan, \url{mtmorgan@fhcrc.org}} \seealso{ \itemize{ \item \link{SummarizedExperiment} objects. \item \link[SummarizedExperiment]{shift}, \link[SummarizedExperiment]{isDisjoint}, \link[SummarizedExperiment]{coverage}, \link[SummarizedExperiment]{findOverlaps}, and \link[SummarizedExperiment]{nearest} for more \emph{GRanges compatibility methods}. \item \link[GenomicRanges]{GRanges} objects in the \pkg{GenomicRanges} package. } } \examples{ nrows <- 200; ncols <- 6 counts <- matrix(runif(nrows * ncols, 1, 1e4), nrows) rowRanges <- GRanges(rep(c("chr1", "chr2"), c(50, 150)), IRanges(floor(runif(200, 1e5, 1e6)), width=100), strand=sample(c("+", "-"), 200, TRUE), feature_id=sprintf("ID\%03d", 1:200)) colData <- DataFrame(Treatment=rep(c("ChIP", "Input"), 3), row.names=LETTERS[1:6]) rse <- SummarizedExperiment(assays=SimpleList(counts=counts), rowRanges=rowRanges, colData=colData) rse dim(rse) dimnames(rse) assayNames(rse) head(assay(rse)) assays(rse) <- endoapply(assays(rse), asinh) head(assay(rse)) rowRanges(rse) rowData(rse) # same as 'mcols(rowRanges(rse))' colData(rse) rse[, rse$Treatment == "ChIP"] ## cbind() combines objects with the same ranges but different samples: rse1 <- rse rse2 <- rse1[,1:3] colnames(rse2) <- letters[1:ncol(rse2)] cmb1 <- cbind(rse1, rse2) dim(cmb1) dimnames(cmb1) ## rbind() combines objects with the same samples but different ranges: rse1 <- rse rse2 <- rse1[1:50,] rownames(rse2) <- letters[1:nrow(rse2)] cmb2 <- rbind(rse1, rse2) dim(cmb2) dimnames(cmb2) ## Coercion to/from SummarizedExperiment: se0 <- as(rse, "SummarizedExperiment") se0 as(se0, "RangedSummarizedExperiment") ## Setting rowRanges on a SummarizedExperiment object turns it into a ## RangedSummarizedExperiment object: se <- se0 rowRanges(se) <- rowRanges se # RangedSummarizedExperiment ## Sanity checks: stopifnot(identical(assays(se0), assays(rse))) stopifnot(identical(dim(se0), dim(rse))) stopifnot(identical(dimnames(se0), dimnames(rse))) stopifnot(identical(rowData(se0), rowData(rse))) stopifnot(identical(colData(se0), colData(rse))) } SummarizedExperiment/man/SummarizedExperiment-class.Rd0000644000175400017540000003612513001230160024300 0ustar00biocbuildbiocbuild\name{SummarizedExperiment-class} \docType{class} % Class \alias{class:SummarizedExperiment} \alias{SummarizedExperiment-class} \alias{class:SummarizedExperiment0} \alias{SummarizedExperiment0-class} \alias{SummarizedExperiment0} % Accessors \alias{length,SummarizedExperiment-method} \alias{names,SummarizedExperiment-method} \alias{names<-,SummarizedExperiment-method} \alias{rowData} \alias{rowData,SummarizedExperiment-method} \alias{rowData<-} \alias{rowData<-,SummarizedExperiment-method} \alias{colData} \alias{colData,SummarizedExperiment-method} \alias{colData<-} \alias{colData<-,SummarizedExperiment,DataFrame-method} \alias{assays} \alias{assays,SummarizedExperiment-method} \alias{assays<-} \alias{assays<-,SummarizedExperiment,SimpleList-method} \alias{assays<-,SummarizedExperiment,list-method} \alias{assay} \alias{assay,SummarizedExperiment,missing-method} \alias{assay,SummarizedExperiment,numeric-method} \alias{assay,SummarizedExperiment,character-method} \alias{assay<-} \alias{assay<-,SummarizedExperiment,missing-method} \alias{assay<-,SummarizedExperiment,numeric-method} \alias{assay<-,SummarizedExperiment,character-method} \alias{assayNames} \alias{assayNames,SummarizedExperiment-method} \alias{assayNames<-} \alias{assayNames<-,SummarizedExperiment,character-method} \alias{dim,SummarizedExperiment-method} \alias{dimnames,SummarizedExperiment-method} \alias{dimnames<-,SummarizedExperiment,list-method} \alias{dimnames<-,SummarizedExperiment,NULL-method} % Subsetting \alias{[,SummarizedExperiment-method} \alias{[,SummarizedExperiment,ANY-method} \alias{[,SummarizedExperiment,ANY,ANY,ANY-method} \alias{[<-,SummarizedExperiment,ANY,ANY,SummarizedExperiment-method} \alias{extractROWS,SummarizedExperiment,ANY-method} \alias{replaceROWS,SummarizedExperiment-method} % Quick colData access \alias{[[,SummarizedExperiment,ANY,missing-method} \alias{[[<-,SummarizedExperiment,ANY,missing-method} \alias{$,SummarizedExperiment-method} \alias{$<-,SummarizedExperiment-method} % Display \alias{show,SummarizedExperiment-method} % Combine \alias{rbind,SummarizedExperiment-method} \alias{cbind,SummarizedExperiment-method} \title{SummarizedExperiment objects} \description{ The SummarizedExperiment class is a matrix-like container where rows represent features of interest (e.g. genes, transcripts, exons, etc...) and columns represent samples (with sample data summarized as a \link{DataFrame}). A SummarizedExperiment object contains one or more assays, each represented by a matrix-like object of numeric or other mode. Note that SummarizedExperiment is the parent of the \link{RangedSummarizedExperiment} class which means that all the methods documented below also work on a \link{RangedSummarizedExperiment} object. } \usage{ ## Constructor # See ?RangedSummarizedExperiment for the constructor function. ## Accessors assayNames(x, ...) assayNames(x, ...) <- value assays(x, ..., withDimnames=TRUE) assays(x, ..., withDimnames=TRUE) <- value assay(x, i, ...) assay(x, i, ...) <- value rowData(x, ...) rowData(x, ...) <- value colData(x, ...) colData(x, ...) <- value #dim(x) #dimnames(x) #dimnames(x) <- value ## Quick colData access \S4method{$}{SummarizedExperiment}(x, name) \S4method{$}{SummarizedExperiment}(x, name) <- value \S4method{[[}{SummarizedExperiment,ANY,missing}(x, i, j, ...) \S4method{[[}{SummarizedExperiment,ANY,missing}(x, i, j, ...) <- value ## Subsetting \S4method{[}{SummarizedExperiment}(x, i, j, ..., drop=TRUE) \S4method{[}{SummarizedExperiment,ANY,ANY,SummarizedExperiment}(x, i, j) <- value ## Combining \S4method{cbind}{SummarizedExperiment}(..., deparse.level=1) \S4method{rbind}{SummarizedExperiment}(..., deparse.level=1) } \arguments{ \item{x}{A SummarizedExperiment object.} \item{...}{ For \code{assay}, \code{...} may contain \code{withDimnames}, which is forwarded to \code{assays}. For \code{rowData}, arguments passed thru \code{...} are forwarded to \code{\link[S4Vectors]{mcols}}. For \code{cbind}, \code{rbind}, \code{...} contains SummarizedExperiment objects to be combined. For other accessors, ignored. } \item{i, j}{ For \code{assay}, \code{assay<-}, \code{i} is an integer or numeric scalar; see \sQuote{Details} for additional constraints. For \code{[,SummarizedExperiment}, \code{[,SummarizedExperiment<-}, \code{i}, \code{j} are subscripts that can act to subset the rows and columns of \code{x}, that is the \code{matrix} elements of \code{assays}. For \code{[[,SummarizedExperiment}, \code{[[<-,SummarizedExperiment}, \code{i} is a scalar index (e.g., \code{character(1)} or \code{integer(1)}) into a column of \code{colData}. } \item{name}{A symbol representing the name of a column of \code{colData}.} \item{withDimnames}{A \code{logical(1)}, indicating whether dimnames should be applied to extracted assay elements. Setting \code{withDimnames=FALSE} increases the speed and memory efficiency with which assays are extracted. \code{withDimnames=TRUE} in the getter \code{assays<-} allows efficient complex assignments (e.g., updating names of assays, \code{names(assays(x, withDimnames=FALSE)) = ...} is more efficient than \code{names(assays(x)) = ...}); it does not influence actual assignment of dimnames to assays.} \item{drop}{A \code{logical(1)}, ignored by these methods.} \item{value}{An object of a class specified in the S4 method signature or as outlined in \sQuote{Details}.} \item{deparse.level}{See \code{?base::\link[base]{cbind}} for a description of this argument.} } \details{ The SummarizedExperiment class is meant for numeric and other data types derived from a sequencing experiment. The structure is rectangular like a \code{matrix}, but with additional annotations on the rows and columns, and with the possibility to manage several assays simultaneously. The rows of a SummarizedExperiment object represent features of interest. Information about these features is stored in a \link{DataFrame} object, accessible using the function \code{rowData}. The \link{DataFrame} must have as many rows as there are rows in the SummarizedExperiment object, with each row of the \link{DataFrame} providing information on the feature in the corresponding row of the SummarizedExperiment object. Columns of the \link{DataFrame} represent different attributes of the features of interest, e.g., gene or transcript IDs, etc. Each column of a SummarizedExperiment object represents a sample. Information about the samples are stored in a \link{DataFrame}, accessible using the function \code{colData}, described below. The \link{DataFrame} must have as many rows as there are columns in the SummarizedExperiment object, with each row of the \link{DataFrame} providing information on the sample in the corresponding column of the SummarizedExperiment object. Columns of the \link{DataFrame} represent different sample attributes, e.g., tissue of origin, etc. Columns of the \link{DataFrame} can themselves be annotated (via the \code{\link[S4Vectors]{mcols}} function). Column names typically provide a short identifier unique to each sample. A SummarizedExperiment object can also contain information about the overall experiment, for instance the lab in which it was conducted, the publications with which it is associated, etc. This information is stored as a \code{list} object, accessible using the \code{metadata} function. The form of the data associated with the experiment is left to the discretion of the user. The SummarizedExperiment container is appropriate for matrix-like data. The data are accessed using the \code{assays} function, described below. This returns a \link{SimpleList} object. Each element of the list must itself be a matrix (of any mode) and must have dimensions that are the same as the dimensions of the SummarizedExperiment in which they are stored. Row and column names of each matrix must either be \code{NULL} or match those of the SummarizedExperiment during construction. It is convenient for the elements of \link{SimpleList} of assays to be named. } \section{Constructor}{ SummarizedExperiment instances are constructed using the \code{SummarizedExperiment} function documented in \code{?\link{RangedSummarizedExperiment}}. } \section{Accessors}{ In the following code snippets, \code{x} is a SummarizedExperiment object. \describe{ \item{\code{assays(x)}, \code{assays(x) <- value}:}{Get or set the assays. \code{value} is a \code{list} or \code{SimpleList}, each element of which is a matrix with the same dimensions as \code{x}.} \item{\code{assay(x, i)}, \code{assay(x, i) <- value}:}{A convenient alternative (to \code{assays(x)[[i]]}, \code{assays(x)[[i]] <- value}) to get or set the \code{i}th (default first) assay element. \code{value} must be a matrix of the same dimension as \code{x}, and with dimension names \code{NULL} or consistent with those of \code{x}.} \item{\code{assayNames(x)}, \code{assayNames(x) <- value}:}{Get or set the names of \code{assay()} elements.} \item{\code{rowData(x)}, \code{rowData(x) <- value}:}{Get or set the row data. \code{value} is a \link{DataFrame} object. Row names of \code{value} must be NULL or consistent with the existing row names of \code{x}.} \item{\code{colData(x)}, \code{colData(x) <- value}:}{Get or set the column data. \code{value} is a \link{DataFrame} object. Row names of \code{value} must be NULL or consistent with the existing column names of \code{x}.} \item{\code{metadata(x)}, \code{metadata(x) <- value}:}{Get or set the experiment data. \code{value} is a \code{list} with arbitrary content.} \item{\code{dim(x)}:}{Get the dimensions (features of interest x samples) of the SummarizedExperiment.} \item{\code{dimnames(x)}, \code{dimnames(x) <- value}:}{Get or set the dimension names. \code{value} is usually a list of length 2, containing elements that are either \code{NULL} or vectors of appropriate length for the corresponding dimension. \code{value} can be \code{NULL}, which removes dimension names. This method implies that \code{rownames}, \code{rownames<-}, \code{colnames}, and \code{colnames<-} are all available.} } } \section{Subsetting}{ In the code snippets below, \code{x} is a SummarizedExperiment object. \describe{ \item{\code{x[i,j]}, \code{x[i,j] <- value}:}{Create or replace a subset of \code{x}. \code{i}, \code{j} can be \code{numeric}, \code{logical}, \code{character}, or \code{missing}. \code{value} must be a SummarizedExperiment object with dimensions, dimension names, and assay elements consistent with the subset \code{x[i,j]} being replaced.} } Additional subsetting accessors provide convenient access to \code{colData} columns \describe{ \item{\code{x$name}, \code{x$name <- value}}{Access or replace column \code{name} in \code{x}.} \item{\code{x[[i, ...]]}, \code{x[[i, ...]] <- value}}{Access or replace column \code{i} in \code{x}.} } } \section{Combining}{ In the code snippets below, \code{...} are SummarizedExperiment objects to be combined. \describe{ \item{\code{cbind(...)}:}{ \code{cbind} combines objects with the same features of interest but different samples (columns in \code{assays}). The colnames in \code{colData(SummarizedExperiment)} must match or an error is thrown. Duplicate columns of \code{rowData(SummarizedExperiment)} must contain the same data. Data in \code{assays} are combined by name matching; if all assay names are NULL matching is by position. A mixture of names and NULL throws an error. \code{metadata} from all objects are combined into a \code{list} with no name checking. } \item{\code{rbind(...)}:}{ \code{rbind} combines objects with the same samples but different features of interest (rows in \code{assays}). The colnames in \code{rowData(SummarizedExperiment)} must match or an error is thrown. Duplicate columns of \code{colData(SummarizedExperiment)} must contain the same data. Data in \code{assays} are combined by name matching; if all assay names are NULL matching is by position. A mixture of names and NULL throws an error. \code{metadata} from all objects are combined into a \code{list} with no name checking. } } } \section{Implementation and Extension}{ This section contains advanced material meant for package developers. SummarizedExperiment is implemented as an S4 class, and can be extended in the usual way, using \code{contains="SummarizedExperiment"} in the new class definition. In addition, the representation of the \code{assays} slot of SummarizedExperiment is as a virtual class Assays. This allows derived classes (\code{contains="Assays"}) to easily implement alternative requirements for the assays, e.g., backed by file-based storage like NetCDF or the \code{ff} package, while re-using the existing SummarizedExperiment class without modification. See \link{Assays} for more information. The current \code{assays} slot is implemented as a reference class that has copy-on-change semantics. This means that modifying non-assay slots does not copy the (large) assay data, and at the same time the user is not surprised by reference-based semantics. Updates to non-assay slots are very fast; updating the assays slot itself can be 5x or more faster than with an S4 instance in the slot. One useful technique when working with \code{assay} or \code{assays} function is use of the \code{withDimnames=FALSE} argument, which benefits speed and memory use by not copying dimnames from the row- and colData elements to each assay. } \author{Martin Morgan, \url{mtmorgan@fhcrc.org}} \seealso{ \itemize{ \item \link{RangedSummarizedExperiment} objects. \item \link[S4Vectors]{DataFrame}, \link[S4Vectors]{SimpleList}, and \link[S4Vectors]{Annotated} objects in the \pkg{S4Vectors} package. \item The \code{\link[S4Vectors]{metadata}} and \code{\link[S4Vectors]{mcols}} accessors in the \pkg{S4Vectors} package. } } \examples{ nrows <- 200; ncols <- 6 counts <- matrix(runif(nrows * ncols, 1, 1e4), nrows) colData <- DataFrame(Treatment=rep(c("ChIP", "Input"), 3), row.names=LETTERS[1:6]) se0 <- SummarizedExperiment(assays=SimpleList(counts=counts), colData=colData) se0 dim(se0) dimnames(se0) assayNames(se0) head(assay(se0)) assays(se0) <- endoapply(assays(se0), asinh) head(assay(se0)) rowData(se0) colData(se0) se0[, se0$Treatment == "ChIP"] ## cbind() combines objects with the same features of interest ## but different samples: se1 <- se0 se2 <- se1[,1:3] colnames(se2) <- letters[1:ncol(se2)] cmb1 <- cbind(se1, se2) dim(cmb1) dimnames(cmb1) ## rbind() combines objects with the same samples but different ## features of interest: se1 <- se0 se2 <- se1[1:50,] rownames(se2) <- letters[1:nrow(se2)] cmb2 <- rbind(se1, se2) dim(cmb2) dimnames(cmb2) } SummarizedExperiment/man/coverage-methods.Rd0000644000175400017540000000444513001230160022250 0ustar00biocbuildbiocbuild\name{coverage-methods} \alias{coverage-methods} \alias{coverage} \alias{coverage,RangedSummarizedExperiment-method} \title{Coverage of a RangedSummarizedExperiment object} \description{ This man page documents the \code{coverage} method for \link{RangedSummarizedExperiment} objects. } \usage{ \S4method{coverage}{RangedSummarizedExperiment}(x, shift=0L, width=NULL, weight=1L, method=c("auto", "sort", "hash")) } \arguments{ \item{x}{ A \link{RangedSummarizedExperiment} object. } \item{shift, width, weight, method}{ See \code{?\link[GenomicRanges]{coverage}} in the \pkg{GenomicRanges} package. } } \details{ This method operates on the \code{rowRanges} component of the \link{RangedSummarizedExperiment} object, which can be a \link[GenomicRanges]{GenomicRanges} or \link[GenomicRanges]{GRangesList} object. More precisely, on \link{RangedSummarizedExperiment} object \code{x}, \code{coverage(x, ...)} is equivalent to \code{coverage(rowRanges(x), ...)}. See \code{?\link[GenomicRanges]{coverage}} in the \pkg{GenomicRanges} package for the details of how \code{coverage} operates on a \link[GenomicRanges]{GenomicRanges} or \link[GenomicRanges]{GRangesList} object. } \value{ See \code{?\link[GenomicRanges]{coverage}} in the \pkg{GenomicRanges} package. } \seealso{ \itemize{ \item \link{RangedSummarizedExperiment} objects. \item The \link[GenomicRanges]{coverage} man page in the \pkg{GenomicRanges} package where the \code{coverage} methods for \link[GenomicRanges]{GenomicRanges} and \link[GenomicRanges]{GRangesList} objects are documented. } } \examples{ nrows <- 20; ncols <- 6 counts <- matrix(runif(nrows * ncols, 1, 1e4), nrows) rowRanges <- GRanges(rep(c("chr1", "chr2"), c(5, 15)), IRanges(sample(1000L, 20), width=100), strand=Rle(c("+", "-"), c(12, 8)), seqlengths=c(chr1=1800, chr2=1300)) colData <- DataFrame(Treatment=rep(c("ChIP", "Input"), 3), row.names=LETTERS[1:6]) rse <- SummarizedExperiment(assays=SimpleList(counts=counts), rowRanges=rowRanges, colData=colData) cvg <- coverage(rse) cvg stopifnot(identical(cvg, coverage(rowRanges(rse)))) } \keyword{methods} \keyword{utilities} SummarizedExperiment/man/findOverlaps-methods.Rd0000644000175400017540000000705113001230160023105 0ustar00biocbuildbiocbuild\name{findOverlaps-methods} \alias{findOverlaps-methods} \alias{findOverlaps} \alias{findOverlaps,RangedSummarizedExperiment,Vector-method} \alias{findOverlaps,Vector,RangedSummarizedExperiment-method} \alias{findOverlaps,RangedSummarizedExperiment,RangedSummarizedExperiment-method} \title{Finding overlapping ranges in RangedSummarizedExperiment objects} \description{ This man page documents the \code{findOverlaps} methods for \link{RangedSummarizedExperiment} objects. \link{RangedSummarizedExperiment} 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} methods defined in this package and documented below. } \usage{ \S4method{findOverlaps}{RangedSummarizedExperiment,Vector}(query, subject, maxgap=0L, minoverlap=1L, type=c("any", "start", "end", "within", "equal"), select=c("all", "first", "last", "arbitrary"), ignore.strand=FALSE) \S4method{findOverlaps}{Vector,RangedSummarizedExperiment}(query, subject, maxgap=0L, minoverlap=1L, type=c("any", "start", "end", "within", "equal"), select=c("all", "first", "last", "arbitrary"), ignore.strand=FALSE) } \arguments{ \item{query, subject}{ One of these two arguments must be a \link{RangedSummarizedExperiment} object. } \item{maxgap, minoverlap, type}{ See \code{?\link[GenomicRanges]{findOverlaps}} in the \pkg{GenomicRanges} package. } \item{select, ignore.strand}{ See \code{?\link[GenomicRanges]{findOverlaps}} in the \pkg{GenomicRanges} package. } } \details{ These methods operate on the \code{rowRanges} component of the \link{RangedSummarizedExperiment} object, which can be a \link[GenomicRanges]{GenomicRanges} or \link[GenomicRanges]{GRangesList} object. More precisely, if any of the above functions is passed a \link{RangedSummarizedExperiment} object thru the \code{query} and/or \code{subject} argument, then it behaves as if \code{rowRanges(query)} and/or \code{rowRanges(subject)} had been passed instead. See \code{?\link[GenomicRanges]{findOverlaps}} in the \pkg{GenomicRanges} package for the details of how \code{findOverlaps} and family operate on \link[GenomicRanges]{GenomicRanges} and \link[GenomicRanges]{GRangesList} objects. } \value{ See \code{?\link[GenomicRanges]{findOverlaps}} in the \pkg{GenomicRanges} package. } \seealso{ \itemize{ \item \link{RangedSummarizedExperiment} objects. \item The \link[GenomicRanges]{findOverlaps} man page in the \pkg{GenomicRanges} package where the \code{findOverlaps} family of methods for \link[GenomicRanges]{GenomicRanges} and \link[GenomicRanges]{GRangesList} objects is documented. } } \examples{ nrows <- 20; ncols <- 6 counts <- matrix(runif(nrows * ncols, 1, 1e4), nrows) rowRanges <- GRanges(rep(c("chr1", "chr2"), c(5, 15)), IRanges(sample(1000L, 20), width=100), strand=Rle(c("+", "-"), c(12, 8))) colData <- DataFrame(Treatment=rep(c("ChIP", "Input"), 3), row.names=LETTERS[1:6]) rse0 <- SummarizedExperiment(assays=SimpleList(counts=counts), rowRanges=rowRanges, colData=colData) rse1 <- shift(rse0, 100) hits <- findOverlaps(rse0, rse1) hits stopifnot(identical(hits, findOverlaps(rowRanges(rse0), rowRanges(rse1)))) stopifnot(identical(hits, findOverlaps(rse0, rowRanges(rse1)))) stopifnot(identical(hits, findOverlaps(rowRanges(rse0), rse1))) } \keyword{methods} \keyword{utilities} SummarizedExperiment/man/inter-range-methods.Rd0000644000175400017540000000531013001230160022660 0ustar00biocbuildbiocbuild\name{inter-range-methods} \alias{inter-range-methods} \alias{isDisjoint} \alias{isDisjoint,RangedSummarizedExperiment-method} \alias{disjointBins} \alias{disjointBins,RangedSummarizedExperiment-method} \title{Inter range transformations of a RangedSummarizedExperiment object} \description{ This man page documents the \emph{inter range transformations} that are supported on \link{RangedSummarizedExperiment} objects. } \usage{ \S4method{isDisjoint}{RangedSummarizedExperiment}(x, ignore.strand=FALSE) \S4method{disjointBins}{RangedSummarizedExperiment}(x, ignore.strand=FALSE) } \arguments{ \item{x}{ A \link{RangedSummarizedExperiment} object. } \item{ignore.strand}{ See \code{?\link[GenomicRanges]{isDisjoint}} in the \pkg{GenomicRanges} package. } } \details{ These transformations operate on the \code{rowRanges} component of the \link{RangedSummarizedExperiment} object, which can be a \link[GenomicRanges]{GenomicRanges} or \link[GenomicRanges]{GRangesList} object. More precisely, any of the above functions performs the following transformation on \link{RangedSummarizedExperiment} object \code{x}: \preformatted{ f(rowRanges(x), ...) } where \code{f} is the name of the function and \code{...} any additional arguments passed to it. See \code{?\link[GenomicRanges]{isDisjoint}} in the \pkg{GenomicRanges} package for the details of how these transformations operate on a \link[GenomicRanges]{GenomicRanges} or \link[GenomicRanges]{GRangesList} object. } \value{ See \code{?\link[GenomicRanges]{isDisjoint}} in the \pkg{GenomicRanges} package. } \seealso{ \itemize{ \item \link{RangedSummarizedExperiment} objects. \item The \link[GenomicRanges]{isDisjoint} man page in the \pkg{GenomicRanges} package where \emph{inter range transformations} of a \link[GenomicRanges]{GenomicRanges} or \link[GenomicRanges]{GRangesList} object are documented. } } \examples{ nrows <- 20; ncols <- 6 counts <- matrix(runif(nrows * ncols, 1, 1e4), nrows) rowRanges <- GRanges(rep(c("chr1", "chr2"), c(5, 15)), IRanges(sample(1000L, 20), width=100), strand=Rle(c("+", "-"), c(12, 8))) colData <- DataFrame(Treatment=rep(c("ChIP", "Input"), 3), row.names=LETTERS[1:6]) rse0 <- SummarizedExperiment(assays=SimpleList(counts=counts), rowRanges=rowRanges, colData=colData) rse1 <- shift(rse0, 99*start(rse0)) isDisjoint(rse0) # FALSE isDisjoint(rse1) # TRUE bins0 <- disjointBins(rse0) bins0 stopifnot(identical(bins0, disjointBins(rowRanges(rse0)))) bins1 <- disjointBins(rse1) bins1 stopifnot(all(bins1 == bins1[1])) } \keyword{methods} \keyword{utilities} SummarizedExperiment/man/intra-range-methods.Rd0000644000175400017540000001033513001230160022657 0ustar00biocbuildbiocbuild\name{intra-range-methods} \alias{intra-range-methods} \alias{shift} \alias{shift,RangedSummarizedExperiment-method} \alias{narrow} \alias{narrow,RangedSummarizedExperiment-method} \alias{resize} \alias{resize,RangedSummarizedExperiment-method} \alias{flank} \alias{flank,RangedSummarizedExperiment-method} \alias{promoters} \alias{promoters,RangedSummarizedExperiment-method} \alias{restrict} \alias{restrict,RangedSummarizedExperiment-method} \alias{trim,RangedSummarizedExperiment-method} \title{Intra range transformations of a RangedSummarizedExperiment object} \description{ This man page documents the \emph{intra range transformations} that are supported on \link{RangedSummarizedExperiment} objects. } \usage{ \S4method{shift}{RangedSummarizedExperiment}(x, shift=0L, use.names=TRUE) \S4method{narrow}{RangedSummarizedExperiment}(x, start=NA, end=NA, width=NA, use.names=TRUE) \S4method{resize}{RangedSummarizedExperiment}(x, width, fix="start", use.names=TRUE, ignore.strand=FALSE) \S4method{flank}{RangedSummarizedExperiment}(x, width, start=TRUE, both=FALSE, use.names=TRUE, ignore.strand=FALSE) \S4method{promoters}{RangedSummarizedExperiment}(x, upstream=2000, downstream=200) \S4method{restrict}{RangedSummarizedExperiment}(x, start=NA, end=NA, keep.all.ranges=FALSE, use.names=TRUE) \S4method{trim}{RangedSummarizedExperiment}(x, use.names=TRUE) } \arguments{ \item{x}{ A \link{RangedSummarizedExperiment} object. } \item{shift, use.names}{ See \code{?\link[GenomicRanges]{shift}} in the \pkg{GenomicRanges} package. } \item{start, end, width, fix}{ See \code{?\link[GenomicRanges]{shift}} in the \pkg{GenomicRanges} package. } \item{ignore.strand, both}{ See \code{?\link[GenomicRanges]{shift}} in the \pkg{GenomicRanges} package. } \item{upstream, downstream}{ See \code{?\link[GenomicRanges]{shift}} in the \pkg{GenomicRanges} package. } \item{keep.all.ranges}{ See \code{?\link[GenomicRanges]{shift}} in the \pkg{GenomicRanges} package. } } \details{ These transformations operate on the \code{rowRanges} component of the \link{RangedSummarizedExperiment} object, which can be a \link[GenomicRanges]{GenomicRanges} or \link[GenomicRanges]{GRangesList} object. More precisely, any of the above functions performs the following transformation on \link{RangedSummarizedExperiment} object \code{x}: \preformatted{ rowRanges(x) <- f(rowRanges(x), ...) } where \code{f} is the name of the function and \code{...} any additional arguments passed to it. See \code{?\link[GenomicRanges]{shift}} in the \pkg{GenomicRanges} package for the details of how these transformations operate on a \link[GenomicRanges]{GenomicRanges} or \link[GenomicRanges]{GRangesList} object. } \seealso{ \itemize{ \item \link{RangedSummarizedExperiment} objects. \item The \link[GenomicRanges]{shift} man page in the \pkg{GenomicRanges} package where \emph{intra range transformations} of a \link[GenomicRanges]{GenomicRanges} or \link[GenomicRanges]{GRangesList} object are documented. } } \examples{ nrows <- 20; ncols <- 6 counts <- matrix(runif(nrows * ncols, 1, 1e4), nrows) rowRanges <- GRanges(rep(c("chr1", "chr2"), c(5, 15)), IRanges(sample(1000L, 20), width=100), strand=Rle(c("+", "-"), c(12, 8))) colData <- DataFrame(Treatment=rep(c("ChIP", "Input"), 3), row.names=LETTERS[1:6]) rse0 <- SummarizedExperiment(assays=SimpleList(counts=counts), rowRanges=rowRanges, colData=colData) rse1 <- shift(rse0, 1) stopifnot(identical( rowRanges(rse1), shift(rowRanges(rse0), 1) )) se2 <- narrow(rse0, start=10, end=-15) stopifnot(identical( rowRanges(se2), narrow(rowRanges(rse0), start=10, end=-15) )) se3 <- resize(rse0, width=75) stopifnot(identical( rowRanges(se3), resize(rowRanges(rse0), width=75) )) se4 <- flank(rse0, width=20) stopifnot(identical( rowRanges(se4), flank(rowRanges(rse0), width=20) )) se5 <- restrict(rse0, start=200, end=700, keep.all.ranges=TRUE) stopifnot(identical( rowRanges(se5), restrict(rowRanges(rse0), start=200, end=700, keep.all.ranges=TRUE) )) } \keyword{methods} \keyword{utilities} SummarizedExperiment/man/makeSummarizedExperimentFromDataFrame.Rd0000644000175400017540000000627113001230160026423 0ustar00biocbuildbiocbuild\name{makeSummarizedExperimentFromDataFrame} \alias{makeSummarizedExperimentFromDataFrame} \title{Make a RangedSummarizedExperiment from a data.frame or DataFrame} \description{ \code{makeSummarizedExperimentFromDataFrame} uses \code{data.frame} or \code{DataFrame} column names to create a \link{GRanges} object for the \code{rowRanges} of the resulting \link{SummarizedExperiment} object. It requires that non-range data columns be coercible into a \code{numeric} \code{matrix} for the \link{SummarizedExperiment} constructor. All columns that are not part of the row ranges attribute are assumed to be experiment data; thus, keeping metadata columns will not be supported. Note that this function only returns \link{SummarizedExperiment} objects with a single assay. If metadata columns are to be kept, one can first construct the row ranges attribute by using the \link{makeGRangesFromDataFrame} function and subsequently creating the \link{SummarizedExperiment}. } \usage{ makeSummarizedExperimentFromDataFrame(df, ..., seqinfo = NULL, 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{...}{ Additional arguments passed on to \link{makeGRangesFromDataFrame} } \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{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{RangedSummarizedExperiment} object with rowRanges and a single assay } \author{ M. Ramos } \seealso{ \itemize{ \item \link{makeGRangesFromDataFrame} } } \examples{ ## --------------------------------------------------------------------- ## BASIC EXAMPLES ## --------------------------------------------------------------------- # Note that rownames of the data.frame are also rownames of the result df <- data.frame(chr="chr2", start = 11:15, end = 12:16, strand = c("+", "-", "+", "*", "."), expr0 = 3:7, expr1 = 8:12, expr2 = 12:16, row.names = paste0("GENE", letters[5:1])) df exRSE <- makeSummarizedExperimentFromDataFrame(df) exRSE assay(exRSE) rowRanges(exRSE) } SummarizedExperiment/man/makeSummarizedExperimentFromExpressionSet.Rd0000644000175400017540000000721313001230160027407 0ustar00biocbuildbiocbuild\name{makeSummarizedExperimentFromExpressionSet} \alias{makeSummarizedExperimentFromExpressionSet} \alias{naiveRangeMapper} \alias{probeRangeMapper} \alias{geneRangeMapper} \alias{coerce,ExpressionSet,RangedSummarizedExperiment-method} \alias{coerce,RangedSummarizedExperiment,ExpressionSet-method} \alias{coerce,SummarizedExperiment,ExpressionSet-method} \title{Make a RangedSummarizedExperiment object from an ExpressionSet and vice-versa} \description{ Coercion between \link{RangedSummarizedExperiment} and \link[Biobase]{ExpressionSet} is supported in both directions. For going from \link[Biobase]{ExpressionSet} to \link{RangedSummarizedExperiment}, the \code{makeSummarizedExperimentFromExpressionSet} function is also provided to let the user control how to map features to ranges. } \usage{ makeSummarizedExperimentFromExpressionSet(from, mapFun=naiveRangeMapper, ...) ## range mapping functions naiveRangeMapper(from) probeRangeMapper(from) geneRangeMapper(txDbPackage, key = "ENTREZID") } \arguments{ \item{from}{ An \link[Biobase]{ExpressionSet} object. } \item{mapFun}{ A function which takes an \link[Biobase]{ExpressionSet} object and returns a \link{GRanges}, or \link{GRangesList} object which corresponds to the genomic ranges used in the ExpressionSet. The \link[base]{rownames} of the returned \link[GenomicRanges]{GRanges} are used to match the \link[Biobase]{featureNames} of the \link[Biobase]{ExpressionSet}. The \code{naiveRangeMapper} function is used by default. } \item{...}{ Additional arguments passed to \code{mapFun}. } \item{txDbPackage}{ A character string with the Transcript Database to use for the mapping. } \item{key}{ A character string with the Gene key to use for the mapping. } } \value{ \code{makeSummarizedExperimentFromExpressionSet} takes an \link[Biobase]{ExpressionSet} object as input and a \emph{range mapping function} that maps the features to ranges. It then returns a \link{RangedSummarizedExperiment} object that corresponds to the input. The range mapping functions return a \link{GRanges} object, with the \code{rownames} corresponding to the \link[Biobase]{featureNames} of the \link[Biobase]{ExpressionSet} object. } \author{Jim Hester, \url{james.f.hester@gmail.com}} \seealso{ \itemize{ \item \link{RangedSummarizedExperiment} objects. \item \link[Biobase]{ExpressionSet} objects in the \pkg{Biobase} package. \item \link[GenomicFeatures]{TxDb} objects in the \pkg{GenomicFeatures} package. } } \examples{ ## --------------------------------------------------------------------- ## GOING FROM ExpressionSet TO RangedSummarizedExperiment ## --------------------------------------------------------------------- data(sample.ExpressionSet, package="Biobase") # 2 equivalent ways of doing the naive coercion makeSummarizedExperimentFromExpressionSet(sample.ExpressionSet) as(sample.ExpressionSet, "RangedSummarizedExperiment") # using probe range mapper makeSummarizedExperimentFromExpressionSet(sample.ExpressionSet, probeRangeMapper) # using the gene range mapper makeSummarizedExperimentFromExpressionSet(sample.ExpressionSet, geneRangeMapper("TxDb.Hsapiens.UCSC.hg19.knownGene")) ## --------------------------------------------------------------------- ## GOING FROM RangedSummarizedExperiment TO ExpressionSet ## --------------------------------------------------------------------- example(RangedSummarizedExperiment) # to create 'rse' rse as(rse, "ExpressionSet") } \keyword{manip} SummarizedExperiment/man/nearest-methods.Rd0000644000175400017540000001225013001230160022107 0ustar00biocbuildbiocbuild\name{nearest-methods} \alias{nearest-methods} \alias{precede} \alias{precede,RangedSummarizedExperiment,ANY-method} \alias{precede,ANY,RangedSummarizedExperiment-method} \alias{precede,RangedSummarizedExperiment,RangedSummarizedExperiment-method} \alias{follow} \alias{follow,RangedSummarizedExperiment,ANY-method} \alias{follow,ANY,RangedSummarizedExperiment-method} \alias{follow,RangedSummarizedExperiment,RangedSummarizedExperiment-method} \alias{nearest} \alias{nearest,RangedSummarizedExperiment,ANY-method} \alias{nearest,ANY,RangedSummarizedExperiment-method} \alias{nearest,RangedSummarizedExperiment,RangedSummarizedExperiment-method} \alias{distance} \alias{distance,RangedSummarizedExperiment,ANY-method} \alias{distance,ANY,RangedSummarizedExperiment-method} \alias{distance,RangedSummarizedExperiment,RangedSummarizedExperiment-method} \alias{distanceToNearest} \alias{distanceToNearest,RangedSummarizedExperiment,ANY-method} \alias{distanceToNearest,ANY,RangedSummarizedExperiment-method} \alias{distanceToNearest,RangedSummarizedExperiment,RangedSummarizedExperiment-method} \title{Finding the nearest range neighbor in RangedSummarizedExperiment objects} \description{ This man page documents the \code{nearest} methods and family (i.e. \code{precede}, \code{follow}, \code{distance}, and \code{distanceToNearest} methods) for \link{RangedSummarizedExperiment} objects. } \usage{ \S4method{precede}{RangedSummarizedExperiment,ANY}(x, subject, select=c("arbitrary", "all"), ignore.strand=FALSE) \S4method{precede}{ANY,RangedSummarizedExperiment}(x, subject, select=c("arbitrary", "all"), ignore.strand=FALSE) \S4method{follow}{RangedSummarizedExperiment,ANY}(x, subject, select=c("arbitrary", "all"), ignore.strand=FALSE) \S4method{follow}{ANY,RangedSummarizedExperiment}(x, subject, select=c("arbitrary", "all"), ignore.strand=FALSE) \S4method{nearest}{RangedSummarizedExperiment,ANY}(x, subject, select=c("arbitrary", "all"), ignore.strand=FALSE) \S4method{nearest}{ANY,RangedSummarizedExperiment}(x, subject, select=c("arbitrary", "all"), ignore.strand=FALSE) \S4method{distance}{RangedSummarizedExperiment,ANY}(x, y, ignore.strand=FALSE, ...) \S4method{distance}{ANY,RangedSummarizedExperiment}(x, y, ignore.strand=FALSE, ...) \S4method{distanceToNearest}{RangedSummarizedExperiment,ANY}(x, subject, ignore.strand=FALSE, ...) \S4method{distanceToNearest}{ANY,RangedSummarizedExperiment}(x, subject, ignore.strand=FALSE, ...) } \arguments{ \item{x, subject}{ One of these two arguments must be a \link{RangedSummarizedExperiment} object. } \item{select, ignore.strand}{ See \code{?\link[GenomicRanges]{nearest}} in the \pkg{GenomicRanges} package. } \item{y}{ For the \code{distance} methods, one of \code{x} or \code{y} must be a \link{RangedSummarizedExperiment} object. } \item{...}{Additional arguments for methods.} } \details{ These methods operate on the \code{rowRanges} component of the \link{RangedSummarizedExperiment} object, which can be a \link[GenomicRanges]{GenomicRanges} or \link[GenomicRanges]{GRangesList} object. More precisely, if any of the above functions is passed a \link{RangedSummarizedExperiment} object thru the \code{x}, \code{subject}, and/or \code{y} argument, then it behaves as if \code{rowRanges(x)}, \code{rowRanges(subject)}, and/or \code{rowRanges(y)} had been passed instead. See \code{?\link[GenomicRanges]{nearest}} in the \pkg{GenomicRanges} package for the details of how \code{nearest} and family operate on \link[GenomicRanges]{GenomicRanges} and \link[GenomicRanges]{GRangesList} objects. } \value{ See \code{?\link[GenomicRanges]{nearest}} in the \pkg{GenomicRanges} package. } \seealso{ \itemize{ \item \link{RangedSummarizedExperiment} objects. \item The \link[GenomicRanges]{nearest} man page in the \pkg{GenomicRanges} package where the \code{nearest} family of methods for \link[GenomicRanges]{GenomicRanges} and \link[GenomicRanges]{GRangesList} objects is documented. } } \examples{ nrows <- 20; ncols <- 6 counts <- matrix(runif(nrows * ncols, 1, 1e4), nrows) rowRanges <- GRanges(rep(c("chr1", "chr2"), c(5, 15)), IRanges(sample(1000L, 20), width=100), strand=Rle(c("+", "-"), c(12, 8))) colData <- DataFrame(Treatment=rep(c("ChIP", "Input"), 3), row.names=LETTERS[1:6]) rse0 <- SummarizedExperiment(assays=SimpleList(counts=counts), rowRanges=rowRanges, colData=colData) rse1 <- shift(rse0, 100) res <- nearest(rse0, rse1) res stopifnot(identical(res, nearest(rowRanges(rse0), rowRanges(rse1)))) stopifnot(identical(res, nearest(rse0, rowRanges(rse1)))) stopifnot(identical(res, nearest(rowRanges(rse0), rse1))) res <- nearest(rse0) # missing subject res stopifnot(identical(res, nearest(rowRanges(rse0)))) hits <- nearest(rse0, rse1, select="all") hits stopifnot(identical( hits, nearest(rowRanges(rse0), rowRanges(rse1), select="all") )) stopifnot(identical( hits, nearest(rse0, rowRanges(rse1), select="all") )) stopifnot(identical( hits, nearest(rowRanges(rse0), rse1, select="all") )) } \keyword{methods} \keyword{utilities} SummarizedExperiment/man/readKallisto.Rd0000644000175400017540000000762313001230160021433 0ustar00biocbuildbiocbuild\name{readKallisto} \alias{readKallisto} \alias{readKallistoBootstrap} \alias{KALLISTO_ASSAYS} \title{ Input kallisto or kallisto bootstrap results. } \description{ \code{readKallisto} inputs several kallisto output files into a single \code{SummarizedExperiment} instance, with rows corresponding to estimated transcript abundance and columns to samples. \code{readKallistoBootstrap} inputs kallisto bootstrap replicates of a single sample into a matrix of transcript x bootstrap abundance estimates. } \usage{ readKallisto(files, json = file.path(dirname(files), "run_info.json"), h5 = any(grepl("\\\\.h5$", files)), what = KALLISTO_ASSAYS, as = c("SummarizedExperiment", "list", "matrix")) readKallistoBootstrap(file, i, j) } \arguments{ \item{files}{character() paths to kallisto \sQuote{abundance.tsv} output files. The assumption is that files are organized in the way implied by kallisto, with each sample in a distinct directory, and the directory containing files abundance.tsv, run_info.json, and perhaps abundance.h5.} \item{json}{character() vector of the same length as \code{files} specifying the location of JSON files produced by kallisto and containing information on the run. The default assumes that json files are in the same directory as the corresponding abundance file.} \item{h5}{character() vector of the same length as \code{files} specifying the location of HDF5 files produced by kallisto and containing bootstrap estimates. The default assumes that HDF5 files are in the same directory as the corresponding abundance file.} \item{what}{character() vector of kallisto per-sample outputs to be input. See KALLISTO_ASSAYS for available values.} \item{as}{character(1) specifying the output format. See \code{Value} for additional detail.} \item{file}{character(1) path to a single HDF5 output file.} \item{i, j}{integer() vector of row (\code{i}) and column (\code{j}) indexes to input.} } \value{ A \code{SummarizedExperiment}, \code{list}, or \code{matrix}, depending on the value of argument \code{as}; by default a \code{SummarizedExperiment}. The \code{as="SummarizedExperiment"} \code{rowData(se)} the length of each transcript; \code{colData(se)} includes summary information on each sample, including the number of targets and bootstraps, the kallisto and index version, the start time and operating system call used to create the file. \code{assays()} contains one or more transcript x sample matrices of parameters estimated by kallisto (see \code{KALLISTO_ASSAYS}). \code{as="list"} return value contains information simillar to \code{SummarizedExperiment} with row, column and assay data as elements of the list without coordination of row and column annotations into an integrated data container. \code{as="matrix"} returns the specified assay as a simple \emph{R} matrix. } \references{ \url{http://pachterlab.github.io/kallisto} software for quantifying transcript abundance. } \author{ Martin Morgan \url{martin.morgan@roswellpark.org} } \examples{ outputs <- system.file(package="SummarizedExperiment", "extdata", "kallisto") files <- dir(outputs, pattern="abundance.tsv", full=TRUE, recursive=TRUE) stopifnot(all(file.exists(files))) ## default: input 'est_counts' (se <- readKallisto(files, as="SummarizedExperiment")) str(readKallisto(files, as="list")) str(readKallisto(files, as="matrix")) ## available assays KALLISTO_ASSAYS ## one or more assay readKallisto(files, what=c("tpm", "eff_length")) ## alternatively: read hdf5 files files <- sub(".tsv", ".h5", files, fixed=TRUE) readKallisto(files) ## input all bootstraps xx <- readKallistoBootstrap(files[1]) ridx <- head(which(rowSums(xx) != 0), 3) cidx <- c(1:5, 96:100) xx[ridx, cidx] ## selective input of rows (transcripts) and/or bootstraps readKallistoBootstrap(files[1], i=c(ridx, rev(ridx)), j=cidx) } \keyword{file} SummarizedExperiment/tests/0000755000175400017540000000000013001230160017105 5ustar00biocbuildbiocbuildSummarizedExperiment/tests/run_unitTests.R0000644000175400017540000000016613001230160022121 0ustar00biocbuildbiocbuildrequire("SummarizedExperiment") || stop("unable to load SummarizedExperiment package") SummarizedExperiment:::.test() SummarizedExperiment/vignettes/0000755000175400017540000000000013001262611017761 5ustar00biocbuildbiocbuildSummarizedExperiment/vignettes/SummarizedExperiment.Rmd0000644000175400017540000003112513001230160024602 0ustar00biocbuildbiocbuild--- title: "_SummarizedExperiment_ for Coordinating Experimental Assays, Samples, and Regions of Interest" author: "Martin Morgan, Valerie Obenchain, Jim Hester, Hervé Pagès" date: "Revised: 22 June, 2016" output: BiocStyle::html_document: toc: true vignette: > %\VignetteIndexEntry{SummarizedExperiment} %\VignetteEngine{knitr::rmarkdown} \usepackage[utf8]{inputenc} --- ```{r style, echo=FALSE, results='asis'} BiocStyle::markdown() ``` # Introduction The `SummarizedExperiment` class is used to store rectangular matrices of experimental results, which are commonly produced by sequencing and microarray experiments. Each object stores observations of one or more samples, along with additional meta-data describing both the observations (features) and samples (phenotypes). A key aspect of the `SummarizedExperiment` class is the coordination of the meta-data and assays when subsetting. For example, if you want to exclude a given sample you can do for both the meta-data and assay in one operation, which ensures the meta-data and observed data will remain in sync. Improperly accounting for meta and observational data has resulted in a number of incorrect results and retractions so this is a very desirable property. `SummarizedExperiment` is in many ways similar to the historical `ExpressionSet`, the main distinction being that `SummarizedExperiment` is more flexible in it's row information, allowing both `GRanges` based as well as those described by arbitrary `DataFrame`s. This makes it ideally suited to a variety of experiments, particularly sequencing based experiments such as RNA-Seq and ChIp-Seq. # Anatomy of a `SummarizedExperiment` The _SummarizedExperiment_ package contains two classes: `SummarizedExperiment` and `RangedSummarizedExperiment`. `SummarizedExperiment` is a matrix-like container where rows represent features of interest (e.g. genes, transcripts, exons, etc.) and columns represent samples. The objects contain one or more assays, each represented by a matrix-like object of numeric or other mode. The rows of a `SummarizedExperiment` object represent features of interest. Information about these features is stored in a `DataFrame` object, accessible using the function `rowData()`. Each row of the `DataFrame` provides information on the feature in the corresponding row of the `SummarizedExperiment` object. Columns of the DataFrame represent different attributes of the features of interest, e.g., gene or transcript IDs, etc. `RangedSummarizedExperiment` is the child of the `SummarizedExperiment` class which means that all the methods on `SummarizedExperiment` also work on a `RangedSummarizedExperiment`. The fundamental difference between the two classes is that the rows of a `RangedSummarizedExperiment` object represent genomic ranges of interest instead of a `DataFrame` of features. The `RangedSummarizedExperiment` ranges are described by a `GRanges` or a `GRangesList` object, accessible using the `rowRanges()` function. The following graphic displays the class geometry and highlights the vertical (column) and horizontal (row) relationships. ```{r include = FALSE} # download current version of SE diagram #download.file("https://docs.google.com/feeds/download/drawings/Export?id=18OcDb80FpvSGRYnFl-8vUqwNNLaNHrG1I9SWKHCselo&exportFormat=svg", "SE.svg") download.file("https://docs.google.com/feeds/download/drawings/Export?id=1kiC8Qlo1mhSnLDqkGiRNPSo6GWn3C2duBszCFbJCB-g&exportFormat=svg", "SE.svg") ``` ![Summarized Experiment](SE.svg) ## Assays The `airway` package contains an example dataset from an RNA-Seq experiment of read counts per gene for airway smooth muscles. These data are stored in a `RangedSummarizedExperiment` object which contains 8 different experimental and assays 64,102 gene transcripts. ```{r, echo=FALSE} suppressPackageStartupMessages(library(SummarizedExperiment)) suppressPackageStartupMessages(data(airway, package="airway")) ``` ```{r} library(SummarizedExperiment) data(airway, package="airway") se <- airway se ``` To retrieve the experiment data from a `SummarizedExperiment` object one can use the `assays()` accessor. An object can have multiple assay datasets each of which can be accessed using the `$` operator. The `airway` dataset contains only one assay (`counts`). Here each row represents a gene transcript and each column one of the samples. ```{r assays, eval = FALSE} assays(se)$counts ``` ```{r assays_table, echo = FALSE} knitr::kable(assays(se)$counts[1:10,]) ``` ## 'Row' (regions-of-interest) data The `rowRanges()` accessor is used to view the range information for a `RangedSummarizedExperiment`. (Note if this were the parent `SummarizedExperiment` class we'd use `rowData()`). The data are stored in a `GRangesList` object, where each list element corresponds to one gene transcript and the ranges in each `GRanges` correspond to the exons in the transcript. ```{r rowRanges} rowRanges(se) ``` ## 'Column' (sample) data Sample meta-data describing the samples can be accessed using `colData()`, and is a `DataFrame` that can store any number of descriptive columns for each sample row. ```{r colData} colData(se) ``` This sample metadata can be accessed using the `$` accessor which makes it easy to subset the entire object by a given phenotype. ```{r columnSubset} # subset for only those samples treated with dexamethasone se[, se$dex == "trt"] ``` ## Experiment-wide metadata Meta-data describing the experimental methods and publication references can be accessed using `metadata()`. ```{r metadata} metadata(se) ``` Note that `metadata()` is just a simple list, so it is appropriate for _any_ experiment wide metadata the user wishes to save, such as storing model formulas. ```{r metadata-formula} metadata(se)$formula <- counts ~ dex + albut metadata(se) ``` # Constructing a `SummarizedExperiment` Often, `SummarizedExperiment` or `RangedSummarizedExperiment` objects are returned by functions written by other packages. However it is possible to create them by hand with a call to the `SummarizedExperiment()` constructor. Constructing a `RangedSummarizedExperiment` with a `GRanges` as the _rowRanges_ argument: ```{r constructRSE} nrows <- 200 ncols <- 6 counts <- matrix(runif(nrows * ncols, 1, 1e4), nrows) rowRanges <- GRanges(rep(c("chr1", "chr2"), c(50, 150)), IRanges(floor(runif(200, 1e5, 1e6)), width=100), strand=sample(c("+", "-"), 200, TRUE), feature_id=sprintf("ID%03d", 1:200)) colData <- DataFrame(Treatment=rep(c("ChIP", "Input"), 3), row.names=LETTERS[1:6]) SummarizedExperiment(assays=list(counts=counts), rowRanges=rowRanges, colData=colData) ``` A `SummarizedExperiment` can be constructed with or without supplying a `DataFrame` for the _rowData_ argument: ```{r constructSE} SummarizedExperiment(assays=list(counts=counts), colData=colData) ``` # Common operations on `SummarizedExperiment` ## Subsetting - `[` Performs two dimensional subsetting, just like subsetting a matrix or data frame. ```{r 2d} # subset the first five transcripts and first three samples se[1:5, 1:3] ``` - `$` operates on `colData()` columns, for easy sample extraction. ```{r colDataExtraction} se[, se$cell == "N61311"] ``` ## Getters and setters - `rowRanges()` / (`rowData()`), `colData()`, `metadata()` ```{r getSet} counts <- matrix(1:15, 5, 3, dimnames=list(LETTERS[1:5], LETTERS[1:3])) dates <- SummarizedExperiment(assays=list(counts=counts), rowData=DataFrame(month=month.name[1:5], day=1:5)) # Subset all January assays dates[rowData(dates)$month == "January", ] ``` - `assay()` versus `assays()` There are two accessor functions for extracting the assay data from a `SummarizedExperiment` object. `assays()` operates on the entire list of assay data as a whole, while `assay()` operates on only one assay at a time. `assay(x, i)` is simply a convenience function which is equivalent to `assays(x)[[i]]`. ```{r assay_assays} assays(se) assays(se)[[1]][1:5, 1:5] # assay defaults to the first assay if no i is given assay(se)[1:5, 1:5] assay(se, 1)[1:5, 1:5] ``` ## Range-based operations - `subsetByOverlaps()` `SummarizedExperiment` objects support all of the `findOverlaps()` methods and associated functions. This includes `subsetByOverlaps()`, which makes it easy to subset a `SummarizedExperiment` object by an interval. ```{r overlap} # Subset for only rows which are in the interval 100,000 to 110,000 of # chromosome 1 roi <- GRanges(seqnames="1", ranges=100000:1100000) subsetByOverlaps(se, roi) ``` # Advanced: Extending `RangedSummarizedExperiment` For representing and manipulating data in their own package, Bioconductor developers are encouraged to re-use existing classes defined in other packages like the `RangedSummarizedExperiment` or `GRanges` containers defined in the `SummarizedExperiment` or `GenomicRanges` infrastructure packages, respectively. Many Bioconductor packages are designed around these basic containers, that is, they define functions that take and/or return a `RangedSummarizedExperiment` or `GRanges` object. For example the `csaw` package defines various functions that operate on `RangedSummarizedExperiment` objects, which are used to represent the number of ChIP-seq reads from each BAM file overlapping pre-specified regions. However, sometimes re-using the `RangedSummarizedExperiment` class as-is does not satisfy the needs of the package and the developer makes the choice to extend the class in order to accomodate the special needs of the package and/or the specificities of the data that it deals with. For example the `DESeq2` package defines the `DESeqDataSet` class which extends `RangedSummarizedExperiment` to add the `design` and `dispersionFunction` slots to it. The following subsections describe in a nutshell how the developer would typically proceed for extending `RangedSummarizedExperiment` in his/her own package. Some familiarity with the S4 class system is required. Readers not familiar with the S4 class system are encouraged to consult the vignette `A quick overview of the S4 class system` located in the `S4Vectors` package for the basics of implementing and extending S4 classes. The approach described below allows the developer to extend `RangedSummarizedExperiment` it in a way that remains agnostic of its internals. Keeping this separation between the responsibilities of the owners of the parent and child classes facilitate maintenance in the long run. In particular, the implementation of the child class won't be affected by changes in the internals of the parent class. ## Depend on, and import, the `SummarizedExperiment` package Add `SummarizedExperiment` to the Depends field of the DESCRIPTION file of the package and the `import(SummarizedExperiment)` directive to its NAMESPACE file. ## Define and export the `RangedSummarizedExperiment` subclass Define the subclass with something like: ```{r rseSubclass} setClass("MyRSESubclass", contains="RangedSummarizedExperiment", representation=representation( slot1="integer", slot2="function" ## ... maybe more ... ) ) ``` Export it by adding the `exportClasses(MyRSESubclass)` directive to the NAMESPACE file. ## Construct `MyRSESubclass` instances When calling `new()` for constructing a `MyRSESubclass` instance, specify only the MyRSESubclass-specific slots: `new("MyRSESubclass", rse, slot1=value1, slot2=value2)`, where `rse` is a `RangedSummarizedExperiment` object. Providing a `MyRSESubclass` constructor function (named as the class itself) is recommended. ## Define a validity method The validity method for `MyRSESubclass` only needs to take care of what's new in `MyRSESubclass` with respect to `RangedSummarizedExperiment`, that is, of the aspects of `MyRSESubclass` objects that are not already covered by the validity method for `RangedSummarizedExperiment` objects. This is because calling `validObject()` on a `MyRSESubclass` object automatically validates it as a `RangedSummarizedExperiment` object first and then calls the validity method for `MyRSESubclass` objects. In other words, validation works incrementally starting from the root of the class hierarchy and going in the parent-to-child direction. ## Use the `RangedSummarizedExperiment` accessors on `MyRSESubclass` objects Like any user of `RangedSummarizedExperiment` objects, the developer of `MyRSESubclass` should always use the `RangedSummarizedExperiment` accessors to access the `RangedSummarizedExperiment`-specific parts of his/her `MyRSESubclass` objects. ## Use `callNextMethod` In case some of the methods defined for `RangedSummarizedExperiment` objects need to be overwritten, the new methods should call `callNextMethod` internally.