MALDIquant/0000755000175000017500000000000013035270676013472 5ustar sebastiansebastianMALDIquant/man/0000755000175000017500000000000013023312270014225 5ustar sebastiansebastianMALDIquant/man/MALDIquant-deprecated.Rd0000644000175000017500000000113312624130232020511 0ustar sebastiansebastian\name{MALDIquant-deprecated} \alias{MALDIquant-deprecated} %------ PLEASE: put \alias{.} here for EACH ! %------ NOTE: ../R/Deprecated.R must be synchronized with this! \title{Deprecated Functions/Methods in Package \pkg{MALDIquant}} \description{ These functions/methods are provided for compatibility with older versions \code{\link{MALDIquant}} only, and may be defunct as soon as the next release. } %\details{ %\describe{ % % since MALDIquant x.y.z % %\item{function}{explanation} % % since MALDIquant 1.11.12 % } %} \seealso{ \code{\link{Deprecated}} } \keyword{internal} \keyword{misc} MALDIquant/man/isMassObject-functions.Rd0000644000175000017500000000210012624130232021103 0ustar sebastiansebastian\name{isMassSpectrum} \alias{isMassSpectrum} \alias{isMassPeaks} \title{ Tests for MassSpectrum or MassPeaks object. } \description{ These functions test for a \code{\linkS4class{MassSpectrum}} or \code{\linkS4class{MassPeaks}} object. } \usage{ isMassSpectrum(x) isMassPeaks(x) } \arguments{ \item{x}{ object to be tested. } } \value{ Returns \code{\link{TRUE}} or \code{\link{FALSE}} depending on whether its argument is an \code{\linkS4class{MassSpectrum}} or \code{\linkS4class{MassPeaks}} object. } \author{ Sebastian Gibb \email{mail@sebastiangibb.de} } \seealso{ \code{\linkS4class{MassPeaks}}, \code{\linkS4class{MassSpectrum}}, \code{\linkS4class{AbstractMassObject}} Website: \url{http://strimmerlab.org/software/maldiquant/} } \examples{ ## load package library("MALDIquant") ## create a MassPeaks object peaks <- createMassPeaks(mass=1:100, intensity=1:100, metaData=list(name="example 1")) ## test isMassPeaks(peaks) # returns TRUE isMassSpectrum(peaks) # returns FALSE isMassPeaks(double()) # returns FALSE } \keyword{methods} MALDIquant/man/removeBaseline-methods.Rd0000644000175000017500000000346012624130232021122 0ustar sebastiansebastian\name{removeBaseline-methods} \docType{methods} \alias{removeBaseline} \alias{removeBaseline,MassSpectrum-method} \alias{removeBaseline,list-method} \title{Removes the baseline of a MassSpectrum object.} \description{ This method removes the baseline of mass spectrometry data (represented by a \code{\linkS4class{MassSpectrum}} object).\cr The intensity of the mass spectrometry data would be reduced by \code{baseline}. } \usage{ \S4method{removeBaseline}{MassSpectrum}(object, method=c("SNIP", "TopHat", "ConvexHull", "median"), \dots) \S4method{removeBaseline}{list}(object, \dots) } \arguments{ \item{object}{\code{\linkS4class{MassSpectrum}} object or a \code{list} of \code{\linkS4class{MassSpectrum}} objects.} \item{method}{used baseline estimation method, one of \code{"SNIP"}, \code{"TopHat"}, \code{"ConvexHull"} or \code{"median"}. See \code{\link[MALDIquant]{estimateBaseline,MassSpectrum-method}} for details. } \item{\dots}{arguments to be passed to \code{\link[MALDIquant]{estimateBaseline,MassSpectrum-method}}. If \code{object} is a \code{list} \code{mc.cores} is also supported.} } \value{ Returns a modified \code{\linkS4class{MassSpectrum}} object with reduced intensities. } \author{ Sebastian Gibb \email{mail@sebastiangibb.de} } \seealso{ \code{\linkS4class{MassSpectrum}}, \code{\link[MALDIquant]{estimateBaseline,MassSpectrum-method}} \code{demo("baseline")} Website: \url{http://strimmerlab.org/software/maldiquant/} } \examples{ ## load package library("MALDIquant") ## load example data data("fiedler2009subset", package="MALDIquant") ## choose only the first mass spectrum s <- fiedler2009subset[[1]] ## plot spectrum plot(s) ## subtract baseline b <- removeBaseline(s, method="SNIP") ## draw modified spectrum on the plot lines(b, col="blue") } \keyword{methods} MALDIquant/man/MALDIquant-defunct.Rd0000644000175000017500000000262412624130232020047 0ustar sebastiansebastian\name{MALDIquant-defunct} \alias{MALDIquant-defunct} \title{Removed Functions/Methods in Package \pkg{MALDIquant}} \description{ These functions/methods are removed. } \details{ \describe{ \item{\code{plotImsSlice}}{use \code{\link[MALDIquant]{plotMsiSlice}} instead.} \item{totalIonCurrent}{\code{signature(object = "MassPeaks")}: removed.} \item{totalIonCurrent<-}{\code{signature(object = "AbstractMassObject", value = "numeric")}: use \code{\link[MALDIquant]{calibrateIntensity}} instead.} \item{calibrate}{\code{signature(x = "matrix")}: use \code{\link[MALDIquant]{calibrateIntensity}} instead.} \item{standardizeTotalIonCurrent}{\code{signature(object = "list")}: use \code{\link[MALDIquant]{calibrateIntensity}} instead.} \item{ltrim}{ \code{signature(object = "AbstractMassObject", minMass = "numeric")}: use \code{\link[MALDIquant]{trim}} instead.} \item{rtrim}{ use \code{\link[MALDIquant]{trim}} instead.} \item{mergeMassSpectra}{use \code{\link[MALDIquant]{averageMassSpectra}} instead.} \item{movingAverage}{use \code{\link[MALDIquant]{smoothIntensity}} instead.} \item{savitzkyGolay}{use \code{\link[MALDIquant]{smoothIntensity}} instead.} \item{isMassObject}{removed.} \item{isMassObjectList}{removed.} \item{intensityMatrix}{ does not support \code{\linkS4class{MassSpectrum}} objects anymore.} } } \keyword{internal} \keyword{misc} MALDIquant/man/transformIntensity-methods.Rd0000644000175000017500000000271312624130232022104 0ustar sebastiansebastian\name{transformIntensity-methods} \docType{methods} \alias{transformIntensity} \alias{transformIntensity,AbstractMassObject-method} \alias{transformIntensity,list-method} \title{Transforms intensities of an AbstractMassObject object.} \description{ This method performs a transformation (e.g. sqrt-transformation) on the intensities of an \code{\linkS4class{AbstractMassObject}} object. } \usage{ \S4method{transformIntensity}{AbstractMassObject}(object, method=c("sqrt", "log", "log2", "log10")) \S4method{transformIntensity}{list}(object, \dots) } \arguments{ \item{object}{\code{\linkS4class{AbstractMassObject}} object or a \code{list} of \code{\linkS4class{AbstractMassObject}} objects.} \item{method}{used transformation method.} \item{\dots}{arguments to be passed to underlying functions. If \code{object} is a \code{list} \code{mc.cores} is also supported.} } \author{ Sebastian Gibb \email{mail@sebastiangibb.de} } \seealso{ \code{\linkS4class{AbstractMassObject}}, \code{\linkS4class{MassSpectrum}} Website: \url{http://strimmerlab.org/software/maldiquant/} } \examples{ ## load package library("MALDIquant") ## load example data data("fiedler2009subset", package="MALDIquant") ## choose only the first mass spectrum s <- fiedler2009subset[[1]] ## transform spectrum t <- transformIntensity(s, method="sqrt") ## plot spectrum par(mfrow=c(2, 1)) plot(s, main="raw spectrum") plot(t, main="transformed spectrum") par(mfrow=c(1, 1)) } \keyword{methods} MALDIquant/man/labelPeaks-methods.Rd0000644000175000017500000000626412711716773020253 0ustar sebastiansebastian\name{labelPeaks-methods} \docType{methods} \alias{labelPeaks} \alias{labelPeaks,MassPeaks-method} \title{Draws peak labels to plot.} \description{ \code{\link[MALDIquant]{labelPeaks}} draws the corresponding mass values on top of the peaks stored in a \code{\linkS4class{MassPeaks}} object to a plot. } \usage{ \S4method{labelPeaks}{MassPeaks}(object, index, mass, labels, digits=3, underline=TRUE, verticalOffset=abs(diff(par("usr")[3:4]))*0.01, absoluteVerticalPos, adj=c(0.5, 0), cex=0.7, srt=0, avoidOverlap=FALSE, arrowLength=0, arrowLwd=0.5, arrowCol=1, \dots) } \arguments{ \item{object}{\code{\linkS4class{MassPeaks}} object.} \item{index}{\code{integer}/\code{logical}, indices of peaks to label.} \item{mass}{\code{numeric}, mass of peaks to label.} \item{labels}{\code{character}, use \code{labels} instead of mass values as peak label.} \item{digits}{\code{integer}, number of decimal places.} \item{underline}{logical, underline peak values?} \item{verticalOffset}{\code{numeric}, move label vertically (relative to peak height).} \item{absoluteVerticalPos}{\code{numeric}, absolute \code{y} value for the label. If missing \code{verticalOffset} is used.} \item{adj}{\code{numeric}, adjust text to the left, center, right and top, center, bottom; see \code{\link[graphics]{text}}.} \item{cex}{\code{numeric}, font size, see \code{\link[graphics]{par}}.} \item{srt}{\code{numeric}, the label rotation in degrees.} \item{avoidOverlap}{\code{logical}, try to find label coordinates to avoid overlap.} \item{arrowLength, arrowLwd, arrowCol}{arrow parameters, possible vectors. \code{NA} values in \code{arrowCol} cause the arrow to be omitted, see \code{\link[graphics]{arrows}}.} \item{...}{arguments to be passed to \code{\link[graphics]{text}}.} } \details{ Please note that \code{avoidOverlap = TRUE} is just supported for \code{srt \%\% 90 == 0} (means \code{srt} has to be a multiple of 90 degree). } \author{Sebastian Gibb} \seealso{ \code{\linkS4class{MassPeaks}}, \code{\link[MALDIquant]{plot,AbstractMassObject,missing-method}} Website: \url{http://strimmerlab.org/software/maldiquant/} } \examples{ ## load package library("MALDIquant") ## create a MassPeaks object from scratch p <- createMassPeaks(mass=1:20, intensity=sample(x=100:10000, size=20), metaData=list(name="example")) ## plot peaks plot(p) ## label the first 5 peaks labelPeaks(p, index=1:5) ## label all peaks in mass range 15 to 20 labelPeaks(p, mass=15:20, underline=FALSE) ## label highest peaks (top 5) top5 <- intensity(p) \%in\% sort(intensity(p), decreasing=TRUE)[1:5] labelPeaks(p, index=top5, col="red") ## real example data("fiedler2009subset") ## a simplified preprocessing r <- removeBaseline(fiedler2009subset[[1]]) p <- detectPeaks(r) plot(p) ## label highest peaks (top 10) and avoid label overlap top10 <- sort(intensity(p), decreasing=TRUE, index.return=TRUE)$ix[1:10] labelPeaks(p, index=top10, avoidOverlap=TRUE, digits=1) ## use own labels and rotate by 90 degree plot(p) labelPeaks(p, index=top10, labels=paste("TOP", 1:10), underline=FALSE, srt=90, adj=c(0, 0.5), col=2) } \keyword{methods} \keyword{Graphics|hplot} MALDIquant/man/createMassPeaks-functions.Rd0000644000175000017500000000236212624130232021602 0ustar sebastiansebastian\name{createMassPeaks} \alias{createMassPeaks} \title{ Creates a MassPeaks object. } \description{ This function creates a \code{\linkS4class{MassPeaks}} object. Normally it shouldn't called by the user. Try \code{\link[MALDIquant]{detectPeaks,MassSpectrum-method}} instead. } \usage{ createMassPeaks(mass, intensity, snr=rep.int(NA_real_, length(intensity)), metaData=list()) } \arguments{ \item{mass}{ \code{vector}, mass or mass-to-charge ratio. } \item{intensity}{ \code{vector}, intensities for measured mass-to-charge ratios. } \item{snr}{ \code{vector}, signal-to-noise ratios for intensity values. } \item{metaData}{ \code{list}, some metadata to describe the peaks. } } \value{ Returns a \code{\linkS4class{MassPeaks}} object. } \author{ Sebastian Gibb \email{mail@sebastiangibb.de} } \seealso{ \code{\link[MALDIquant]{detectPeaks,MassSpectrum-method}}, \code{\linkS4class{MassPeaks}} Website: \url{http://strimmerlab.org/software/maldiquant/} } \examples{ ## load package library("MALDIquant") ## create a MassPeaks object by default constructor s <- createMassPeaks(mass=1:100, intensity=rnorm(100)^2, metaData=list(name="example peaks")) ## show some details s } \keyword{methods} MALDIquant/man/averageMassSpectra-functions.Rd0000644000175000017500000000402612624130232022306 0ustar sebastiansebastian\name{averageMassSpectra} \alias{averageMassSpectra} \title{ Averages \code{\linkS4class{MassSpectrum}} objects. } \description{ This function averages \code{\linkS4class{MassSpectrum}} objects. } \usage{ averageMassSpectra(l, labels, method=c("mean", "median", "sum"), \dots) } \arguments{ \item{l}{ \code{list}, list of \code{\linkS4class{MassSpectrum}} objects. } \item{labels}{ \code{list}, list of \code{\link{factor}}s (one for each \code{\linkS4class{MassSpectrum}} object) to do groupwise averaging. } \item{method}{used aggregation function.} \item{\dots}{arguments to be passed to underlying functions (currently only \code{mc.cores} is supported).} } \details{ The mass of the averaged \code{\linkS4class{MassSpectrum}} object will be the mass of the first non-empty \code{\linkS4class{MassSpectrum}} object (of each group). } \value{ Returns a single (no \code{labels} given) or a \code{\link{list}} (\code{labels} given) of averaged \code{\linkS4class{MassSpectrum}} objects. } \author{ Sebastian Gibb \email{mail@sebastiangibb.de} } \seealso{ \code{\linkS4class{MassSpectrum}}, \code{\link[MALDIquant]{mergeMassPeaks}} Website: \url{http://strimmerlab.org/software/maldiquant/} } \examples{ ## load package library("MALDIquant") ## create four MassSpectrum objects and add them to a list s <- list(createMassSpectrum(mass=1:5, intensity=1:5), createMassSpectrum(mass=1:5, intensity=1:5), createMassSpectrum(mass=1:5, intensity=6:10), createMassSpectrum(mass=1:5, intensity=6:10)) ## average all four MassSpectrum objects into a single new one ## by sum their intensities ## (no labels, returns only one new MassSpectrum object) summedSpectra <- averageMassSpectra(s, method="sum") ## only average MassSpectrum objects in a group ## (e.g. useful for technical replicates) ## (two different labels, returns a list of two new MassPeaks objects) groups <- factor(c("a", "a", "b", "b"), levels=c("a", "b")) averagedSpectra <- averageMassSpectra(s, labels=groups, method="mean") } \keyword{methods} MALDIquant/man/trim-methods.Rd0000644000175000017500000000333312672203466017151 0ustar sebastiansebastian\name{trim-methods} \docType{methods} \alias{trim} \alias{trim,AbstractMassObject,numeric-method} \alias{trim,list,numeric-method} \alias{trim,list,missing-method} \title{Trim an AbstractMassObject object.} \description{ This method trims an \code{\linkS4class{AbstractMassObject}} object. That is useful if some mass ranges should be excluded from further analysis. } \usage{ \S4method{trim}{AbstractMassObject,numeric}(object, range) \S4method{trim}{list,numeric}(object, range, \dots) \S4method{trim}{list,missing}(object, range, \dots) } \arguments{ \item{object}{\code{\linkS4class{AbstractMassObject}} object or a \code{list} of \code{\linkS4class{AbstractMassObject}} objects.} \item{range}{\code{numeric}, limits of trimming (left/minimal mass, right/maximal mass). If missing it is automatically determined (largest overlapping mass range) for a \code{list} of \code{\linkS4class{AbstractMassObject}}.} \item{\dots}{arguments to be passed to underlying functions (currently only \code{mc.cores} is supported).} } \author{ Sebastian Gibb \email{mail@sebastiangibb.de} } \seealso{ \code{\linkS4class{AbstractMassObject}}, \code{\linkS4class{MassPeaks}}, \code{\linkS4class{MassSpectrum}} Website: \url{http://strimmerlab.org/software/maldiquant/} } \examples{ ## load package library("MALDIquant") ## load example data data("fiedler2009subset", package="MALDIquant") ## select only one spectrum s <- fiedler2009subset[[1]] ## remove all mass lower 3000 trim(s, range=c(3000, Inf)) ## remove all mass higher 8000 trim(s, range=c(0, 8000)) ## remove all mass lower 3000 and higher 8000 trim(s, range=c(3000, 8000)) ## choose largest overlapping mass range for all spectra trim(fiedler2009subset) } \keyword{methods} MALDIquant/man/alignSpectra-functions.Rd0000644000175000017500000000527012624130232021144 0ustar sebastiansebastian\name{alignSpectra} \alias{alignSpectra} \title{ Align MassSpectrum objects. } \description{ This function aligns a list of \code{\linkS4class{MassSpectrum}} objects (spectra alignment is also known as \emph{warping/phase correction}). \cr } \usage{ alignSpectra(spectra, halfWindowSize=20, noiseMethod="MAD", SNR=2, reference, tolerance=0.002, warpingMethod="lowess", \dots) } \arguments{ \item{spectra}{ \code{list}, list of \code{\linkS4class{MassSpectrum}} objects. } \item{halfWindowSize}{\code{numeric}, half window size; see \code{\link[MALDIquant]{detectPeaks}}. } \item{noiseMethod}{a noise estimation method; see \code{\link[MALDIquant]{detectPeaks}}. } \item{SNR}{single numeric value. \code{SNR} is an abbreviation for \emph{s}ignal-to-\emph{n}oise-\emph{r}atio; see \code{\link[MALDIquant]{detectPeaks}}. } \item{reference}{ \code{\linkS4class{MassPeaks}}, reference object to which the samples (\code{l}) should be aligned. If missing \code{\link[MALDIquant]{referencePeaks}} is used; see \code{\link[MALDIquant]{determineWarpingFunctions}}. } \item{tolerance}{ \code{double}, maximal relative deviation of a peak position (mass) to be considered as identical; see \code{\link[MALDIquant]{determineWarpingFunctions}}. } \item{warpingMethod}{ used basic warping function; see \code{\link[MALDIquant]{determineWarpingFunctions}}. } \item{\dots}{arguments to be passed to \code{\link[MALDIquant]{detectPeaks,MassSpectrum-method}}.} } \details{ \code{alignSpectra} is a wrapper function around \code{\link[MALDIquant]{detectPeaks}}, \code{\link[MALDIquant]{determineWarpingFunctions}} and \code{\link[MALDIquant]{warpMassSpectra}}. Please call these functions manually if you need finer control (e.g. plotting of warping functions). } \value{ Returns a \code{list} of aligned \code{\linkS4class{MassSpectrum}} objects. } \author{ Sebastian Gibb \email{mail@sebastiangibb.de} } \seealso{ \code{\link[MALDIquant]{detectPeaks}}, \code{\link[MALDIquant]{determineWarpingFunctions}}, \code{\link[MALDIquant]{referencePeaks}}, \code{\link[MALDIquant]{warpMassSpectra}}, \code{\linkS4class{MassSpectrum}} \code{demo("warping")} Website: \url{http://strimmerlab.org/software/maldiquant/} } \examples{ ## load package library("MALDIquant") ## load example data data("fiedler2009subset", package="MALDIquant") ## running typical workflow ## transform intensities spectra <- transformIntensity(fiedler2009subset, method="sqrt") ## smooth spectra spectra <- smoothIntensity(spectra, method="MovingAverage") ## baseline correction spectra <- removeBaseline(spectra) ## align spectra spectra <- alignSpectra(spectra) } \keyword{methods} MALDIquant/man/detectPeaks-methods.Rd0000644000175000017500000000460212700165462020425 0ustar sebastiansebastian\name{detectPeaks-methods} \docType{methods} \alias{detectPeaks} \alias{detectPeaks,MassSpectrum-method} \alias{detectPeaks,list-method} \title{Detects peaks in a MassSpectrum object.} \description{ This method looks for peaks in mass spectrometry data (represented by a \code{\linkS4class{MassSpectrum}} object).\cr A peak is a local maximum above a user defined noise threshold. } \usage{ \S4method{detectPeaks}{MassSpectrum}(object, halfWindowSize=20, method=c("MAD", "SuperSmoother"), SNR=2, \dots) \S4method{detectPeaks}{list}(object, \dots) } \arguments{ \item{object}{\code{\linkS4class{MassSpectrum}} object or a \code{list} of \code{\linkS4class{MassSpectrum}} objects.} \item{halfWindowSize}{\code{numeric}, half window size. \cr The resulting window reaches from \code{mass[currentIndex-halfWindowSize]} to \code{mass[currentIndex+halfWindowSize]}. A local maximum have to be the highest one in the given window to be recognized as peak.} \item{method}{a noise estimation function; see \code{\link[MALDIquant]{estimateNoise,MassSpectrum-method}}. } \item{SNR}{single numeric value. \code{SNR} is an abbreviation for \emph{s}ignal-to-\emph{n}oise-\emph{r}atio. A local maximum has to be higher than \code{SNR*noise} to be recognize as peak.} \item{\dots}{arguments to be passed to \code{\link[MALDIquant]{estimateNoise,MassSpectrum-method}}. If \code{object} is a \code{list} \code{mc.cores} is also supported.} } \value{ Returns a \code{\linkS4class{MassPeaks}} object. } \author{ Sebastian Gibb \email{mail@sebastiangibb.de} } \seealso{ \code{\linkS4class{MassPeaks}}, \code{\linkS4class{MassSpectrum}}, \code{\link[MALDIquant]{estimateNoise,MassSpectrum-method}} \code{demo("peaks")} Website: \url{http://strimmerlab.org/software/maldiquant/} } \examples{ ## load package library("MALDIquant") ## load example data data("fiedler2009subset", package="MALDIquant") ## choose only the first mass spectrum s <- fiedler2009subset[[1]] ## transform intensities s <- transformIntensity(s, method="sqrt") ## smoothing spectrum s <- smoothIntensity(s, method="MovingAverage") ## remove baseline s <- removeBaseline(s) ## plot spectrum plot(s) ## call peak detection p <- detectPeaks(s) ## draw peaks on the plot points(p) ## label 10 highest peaks top10 <- intensity(p) \%in\% sort(intensity(p), decreasing=TRUE)[1:10] labelPeaks(p, index=top10) } \keyword{methods} MALDIquant/man/estimateBaseline-methods.Rd0000644000175000017500000001231212624130232021434 0ustar sebastiansebastian\name{estimateBaseline-methods} \docType{methods} \alias{estimateBaseline} \alias{estimateBaseline,MassSpectrum-method} \title{Estimates the baseline of a MassSpectrum object.} \description{ This method estimates the baseline of mass spectrometry data (represented by a \code{\linkS4class{MassSpectrum}} object).\cr } \usage{ \S4method{estimateBaseline}{MassSpectrum}(object, method=c("SNIP", "TopHat", "ConvexHull", "median"), \dots) } \arguments{ \item{object}{\code{\linkS4class{MassSpectrum}} object} \item{method}{used baseline estimation method, one of \code{"SNIP"}, \code{"TopHat"}, \code{"ConvexHull"} or \code{"median"}. } \item{\dots}{arguments to be passed to \code{method}} } \details{ \describe{ \item{\code{"SNIP"}:}{ This baseline estimation is based on the Statistics-sensitive Non-linear Iterative Peak-clipping algorithm (SNIP) described in Ryan et al 1988.\cr The algorithm based on the following equation: \deqn{y_i(k) = \min \{ y_i, \frac{(y_{i-k}+y_{i+k})}{2} \}}{y_i(k) = min \{ y_i, (y_{i-k}+y_{i+k})/2 \}} It has two additional arguments namely \code{iterations} and \code{decreasing}. \code{iterations} controls the window size (\emph{k}; similar to \code{halfWindowSize} in \code{"TopHat"}, \code{"Median"}) of the algorithm. The resulting window reaches from \code{mass[cur_index-iterations]} to \code{mass[cur_index+iterations]}. \code{decreasing}: In Morhac 2009 a decreasing clipping window is suggested to get a smoother baseline. For \code{decreasing = TRUE} (\code{decreasing = FALSE}) \emph{k}=\code{iterations} is decreased (increased) by one until zero (\code{iterations}) is reached. The default setting is \code{decreasing = TRUE}. } \item{\code{"TopHat"}:}{ This algorithm applies a moving minimum (erosion filter) and subsequently a moving maximum (dilation filter) filter on the intensity values. The implementation is based on van Herk 1996. It has an additional \code{halfWindowSize} argument determining the half size of the moving window for the TopHat filter. The resulting window reaches from \code{mass[cur_index-halfWindowSize]} to \code{mass[cur_index+halfWindowSize]}. } \item{\code{"ConvexHull"}:}{ The baseline estimation is based on a convex hull constructed below the spectrum. } \item{\code{"median"}:}{ This baseline estimation uses a moving median. It is based on \code{\link[stats]{runmed}}. The additional argument \code{halfWindowSize} corresponds to the \code{k} argument in \code{\link[stats]{runmed}} (\code{k = 2 * halfWindowSize + 1}) and controls the half size of the moving window. The resulting window reaches from \code{mass[cur_index-halfWindowSize]} to \code{mass[cur_index+halfWindowSize]}. } } } \value{ Returns a two column matrix (first column: mass, second column: intensity) of the estimated baseline. } \author{ Sebastian Gibb \email{mail@sebastiangibb.de} } \references{ \code{"SNIP"}:\cr C.G. Ryan, E. Clayton, W.L. Griffin, S.H. Sie, and D.R. Cousens. 1988. Snip, a statistics-sensitive background treatment for the quantitative analysis of pixe spectra in geoscience applications. Nuclear Instruments and Methods in Physics Research Section B: Beam Interactions with Materials and Atoms, 34(3): 396-402. M. Morhac. 2009. An algorithm for determination of peak regions and baseline elimination in spectroscopic data. Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, 600(2), 478-487. \code{"TopHat"}:\cr M. van Herk. 1992. A Fast Algorithm for Local Minimum and Maximum Filters on Rectangular and Octagonal Kernels. Pattern Recognition Letters 13.7: 517-521. J. Y. Gil and M. Werman. 1996. Computing 2-Dimensional Min, Median and Max Filters. IEEE Transactions: 504-507. \code{"ConvexHull"}:\cr Andrew, A. M. 1979. Another efficient algorithm for convex hulls in two dimensions. Information Processing Letters, 9(5), 216-219. } \seealso{ \code{\linkS4class{MassSpectrum}}, \code{\link[MALDIquant]{removeBaseline,MassSpectrum-method}} \code{demo("baseline")} Website: \url{http://strimmerlab.org/software/maldiquant/} } \examples{ ## load package library("MALDIquant") ## load example data data("fiedler2009subset", package="MALDIquant") ## choose only the first mass spectrum s <- fiedler2009subset[[1]] ## SNIP plot(s) ## estimate baseline b <- estimateBaseline(s, method="SNIP", iterations=100) ## draw baseline on the plot lines(b, col="red") ## TopHat plot(s) ## estimate baseline (try different parameters) b1 <- estimateBaseline(s, method="TopHat", halfWindowSize=75) b2 <- estimateBaseline(s, method="TopHat", halfWindowSize=150) ## draw baselines on the plot lines(b1, col=2) lines(b2, col=3) ## draw legend legend(x="topright", lwd=1, legend=paste0("halfWindowSize=", c(75, 150)), col=c(2, 3)) ## ConvexHull plot(s) ## estimate baseline b <- estimateBaseline(s, method="ConvexHull") ## draw baseline on the plot lines(b, col="red") ## Median plot(s) ## estimate baseline b <- estimateBaseline(s, method="median") ## draw baseline on the plot lines(b, col="red") } \keyword{methods} MALDIquant/man/monoisotopicPeaks-methods.Rd0000644000175000017500000000624412703476727021717 0ustar sebastiansebastian\name{monoisotopicPeaks-methods} \docType{methods} \alias{monoisotopicPeaks} \alias{monoisotopicPeaks,MassPeaks-method} \alias{monoisotopicPeaks,list-method} \title{Finds monoisotopic peaks in a MassPeaks object.} \description{ This method looks for monoisotopic peaks in peak list data (represented by a \code{\linkS4class{MassPeaks}} objects).\cr It is based on the poisson model for isotopic patterns described in Breen et al 2000. } \usage{ \S4method{monoisotopicPeaks}{MassPeaks}(object, minCor=0.95, tolerance=1e-4, distance=1.00235, size=3L:10L) \S4method{monoisotopicPeaks}{list}(object, \dots) } \arguments{ \item{object}{\code{\linkS4class{MassPeaks}} object or a \code{list} of \code{\linkS4class{MassPeaks}} objects.} \item{minCor}{\code{double}, minimal correlation between the peak pattern generated by the model and the experimental peaks in the \code{\linkS4class{MassPeaks}} object to be recognized as isotopic pattern.} \item{tolerance}{\code{double}, maximal relative deviation of peaks position (mass) to be considered as isotopic distance ( \code{abs(((mass[i]+distance)-mass[i+1])/mass[i]) < tolerance}).} \item{distance}{\code{double}, distance between two consecutive peaks in an isotopic pattern (default value taken from Park et al 2008). Could contain more than one value, e.g. \code{distance=(1:3)^-1} to find isotopic patterns for multiple charged patterns (e.g. 1+, 2+, and 3+). Please note that the order matters here if there is a monoisotopic peak for charge state 1 and 3 it would be reported as charge 1 for \code{distance=(1:3)^-1} and as 3 for \code{distance=(3:1)^-1} respectively.} \item{size}{\code{double}, size (length) of isotopic pattern, longer patterns are prefered over shorter ones.} \item{\dots}{arguments to be passed to \code{\link[MALDIquant]{monoisotopicPeaks,MassPeaks-method}}. If \code{object} is a \code{list} \code{mc.cores} is also supported.} } \value{ Returns a \code{\linkS4class{MassPeaks}} object with monoisotopic peaks only. } \author{ Sebastian Gibb \email{mail@sebastiangibb.de} } \references{ K. Park, J.Y. Yoon, S. Lee, E. Paek, H. Park, H.J. Jung, and S.W. Lee. 2008. Isotopic peak intensity ratio based algorithm for determination of isotopic clusters and monoisotopic masses of polypeptides from high-resolution mass spectrometric data. Analytical Chemistry, 80: 7294-7303. E.J. Breen, F.G. Hopwood, K.L. Williams, and M.R. Wilkins. 2000. Automatic poisson peak harvesting for high throughput protein identification. Electrophoresis 21: 2243-2251. } \seealso{ \code{\linkS4class{MassPeaks}}, \code{\link[MALDIquant]{detectPeaks,MassSpectrum-method}} Website: \url{http://strimmerlab.org/software/maldiquant/} } \examples{ ## load package library("MALDIquant") ## create example peaks p <- createMassPeaks(mass=995:1005, intensity=c(100, 10, 30, 10, 40, # noise 550, 330, 110, 10, # isotopic pattern 5, 15)) # more noise m <- monoisotopicPeaks(p) as.matrix(m) ## plot the peaks and mark the monoisotopic one plot(p) points(m, col=2, pch=4) } \keyword{methods} MALDIquant/man/calibrateIntensity-methods.Rd0000644000175000017500000000621012624130232022013 0ustar sebastiansebastian\name{calibrateIntensity-methods} \docType{methods} \alias{calibrateIntensity} \alias{calibrateIntensity,MassSpectrum-method} \alias{calibrateIntensity,list-method} \title{Calibrates intensities of a MassSpectrum object.} \description{ This function calibrates (normalize) intensities of \code{\linkS4class{MassSpectrum}} objects. } \usage{ \S4method{calibrateIntensity}{MassSpectrum}(object, method=c("TIC", "PQN", "median"), range, \dots) \S4method{calibrateIntensity}{list}(object, method=c("TIC", "PQN", "median"), range, \dots) } \arguments{ \item{object}{\code{\linkS4class{MassSpectrum}} object or a \code{list} of \code{\linkS4class{MassSpectrum}} objects.} \item{method}{the calibration method to be used. This should be one of \code{"TIC"}, \code{"PQN"} or \code{"median"}. See \sQuote{Details} section.} \item{range}{\code{numeric} of length 2, if given the scaling factor is calculated on the mass range from \code{range[1L]} to \code{range[2L]} and applied to the whole spectrum.} \item{\dots}{arguments to be passed to other functions. Currently only \code{mc.cores} is supported if \code{object} is a \code{list}.} } \details{ A number of different calibration methods are provided: \describe{ \item{\code{"TIC"}:}{The TIC (\emph{T}otal \emph{I}on \emph{C}urrent) of a \code{\linkS4class{MassSpectrum}} object is set to one. If \code{range} is given the \emph{TIC} is only calculated for the intensities in the specified mass range.} \item{\code{"PQN"}:}{The PQN (\emph{P}robabilistic \emph{Q}uotient \emph{N}ormalization) is described in \emph{Dieterle et al 2006}. \code{calibrateIntensity} uses the following algorithm: \enumerate{ \item{Calibrate all spectra using the \code{"TIC"} calibration.} \item{Calculate a median reference spectrum.} \item{Calculate the quotients of all intensities of the spectra with those of the reference spectrum.} \item{Calculate the median of these quotients for each spectrum.} \item{Divide all intensities of each spectrum by its median of quotients.} } } \item{\code{"median"}:}{The median of intensities of a \code{\linkS4class{MassSpectrum}} object is set to one.} } } \value{ Returns a modified \code{\linkS4class{MassSpectrum}} object with calibrated intensities. } \references{ F. Dieterle, A. Ross, G. Schlotterbeck, and Hans Senn. 2006. Probabilistic quotient normalization as robust method to account for dilution of complex biological mixtures. Application in 1H NMR metabonomics. Analytical Chemistry 78(13): 4281-4290. } \author{ Sebastian Gibb \email{mail@sebastiangibb.de} } \seealso{ \code{\linkS4class{MassSpectrum}} Website: \url{http://strimmerlab.org/software/maldiquant/} } \examples{ ## load package library("MALDIquant") ## load example data data("fiedler2009subset", package="MALDIquant") ## baseline correction b <- removeBaseline(fiedler2009subset) ## calibrate intensity values calibrateIntensity(b, method="TIC") ## calibrate intensity values using TIC for a specific mass range calibrateIntensity(b, method="TIC", range=c(3000, 5000)) } \keyword{methods} MALDIquant/man/match.closest.Rd0000644000175000017500000000371413023312270017270 0ustar sebastiansebastian% Generated by roxygen2: do not edit by hand % Please edit documentation in R/match.closest-functions.R \name{match.closest} \alias{match.closest} \title{Relaxed Value Matching} \usage{ match.closest(x, table, tolerance = Inf, nomatch = NA_integer_) } \arguments{ \item{x}{\code{numeric}, the values to be matched.} \item{table}{\code{numeric}, the values to be matched against. In contrast to \code{\link{match}} \code{table} has to be sorted in increasing order.} \item{tolerance}{\code{numeric}, accepted tolerance. Use \code{Inf} to match without restrictions. Could be of length one or the same length as \code{table}.} \item{nomatch}{\code{numeric}, if the difference between the value in \code{x} and \code{table} is larger than \code{tolerance} \code{nomatch} is returned. Has to be of length one.} } \value{ An \code{integer} vector of the same length as \code{x} giving the closest position in \code{table} of the first match or \code{nomatch} if there is no match. } \description{ \code{match.closest} returns a vector of the positions of (first) matches its first arguments in its second. In contrast to the similar \code{\link{match}} it just accept \code{numeric} arguments but has an additional \code{tolerance} argument that allows relaxed matching. } \examples{ library("MALDIquant") match.closest(c(1.1, 1.4, 9.8), 1:10) # [1] 1 1 10 match.closest(c(1.1, 1.4, 9.8), 1:10, tolerance=0.25) # [1] 1 NA 10 match.closest(c(1.1, 1.4, 9.8), 1:10, tolerance=0.25, nomatch=0) # [1] 1 0 10 ## this function is most useful if you want to subset an intensityMatrix ## by a few (reference) peaks ## create an example intensityMatrix im <- matrix(1:10, nrow=2, dimnames=list(NULL, 1:5)) attr(im, "mass") <- 1:5 im # 1 2 3 4 5 # [1,] 1 3 5 7 9 # [2,] 2 4 6 8 10 # attr(,"mass") # [1] 1 2 3 4 5 ## reference peaks ref <- c(2.2, 4.8) im[, match.closest(ref, attr(im, "mass"), tolerance=0.25, nomatch=0)] # 2 5 # [1,] 3 9 # [2,] 4 10 } \seealso{ \code{\link{match}} } MALDIquant/man/filterPeaks-functions.Rd0000644000175000017500000001055512624130232021003 0ustar sebastiansebastian\name{filterPeaks} \alias{filterPeaks} \title{ Removes less frequent peaks. } \description{ This function removes infrequently occuring peaks in a list of \code{\linkS4class{MassPeaks}} objects. } \usage{ filterPeaks(l, minFrequency, minNumber, labels, mergeWhitelists=FALSE) } \arguments{ \item{l}{ \code{list}, list of \code{\linkS4class{MassPeaks}} objects. } \item{minFrequency}{ \code{double}, remove all peaks which occur in less than \code{minFrequency*length(l)} \code{\linkS4class{MassPeaks}} objects. It is a relative threshold. } \item{minNumber}{ \code{double}, remove all peaks which occur in less than \code{minNumber} \code{\linkS4class{MassPeaks}} objects. It is an absolute threshold. } \item{labels}{ \code{factor}, (one for each \code{\linkS4class{MassPeaks}} object) to do groupwise filtering. The \emph{levels} of the factor \code{label} define the groups. If not specified a single group is assumed. } \item{mergeWhitelists}{ \code{logical}, if \code{FALSE} the filtering criteria are applied groupwise. If \code{TRUE} peaks that survive the filtering in one group (level of \code{labels}) these peaks are also kept in other groups even if their frequencies are below \code{minFrequency}. } } \details{ For \code{mergeWhitelists=FALSE} the filtering uses a separate peak whitelist for each group specified by \code{labels}, and is done independently in each group. For \code{mergeWhitelists=TRUE} the peak whitelists are combined, which means that peaks that occur frequently in at least one group are also kept in all other groups. If both \code{minFrequency} and \code{minNumber} arguments are specified the more stringent threshold is used. } \value{ Returns a \code{\link{list}} of filtered \code{\linkS4class{MassPeaks}} objects. } \author{ Sebastian Gibb \email{mail@sebastiangibb.de} } \seealso{ \code{\link[MALDIquant]{intensityMatrix}}, \code{\linkS4class{MassPeaks}} Website: \url{http://strimmerlab.org/software/maldiquant/} } \examples{ ## load package library("MALDIquant") ## create four MassPeaks objects and add them to the list p <- list(createMassPeaks(mass=1:2, intensity=1:2), createMassPeaks(mass=1:3, intensity=1:3), createMassPeaks(mass=1:4, intensity=1:4), createMassPeaks(mass=1:5, intensity=1:5)) ## only keep peaks which occur in all MassPeaks objects filteredPeaks <- filterPeaks(p, minFrequency=1) ## compare result intensities <- intensityMatrix(filteredPeaks) ## peaks at mass 3,4,5 are removed all(dim(intensities) == c(4, 2)) # TRUE all(intensities[,1] == 1) # TRUE all(intensities[,2] == 2) # TRUE ## only keep peaks which occur in all MassPeaks objects in a group ## (e.g. useful for technical replicates) groups <- factor(c("a", "a", "b", "b"), levels=c("a", "b")) filteredPeaks <- filterPeaks(p, minFrequency=1, labels=groups) ## peaks at mass 3 were removed in group "a" filteredPeaks[groups == "a"] ## peaks at mass 5 were removed in group "b" filteredPeaks[groups == "b"] ## only keep peaks which occur at least twice in a group groups <- factor(c("a", "a", "b", "b", "b"), levels=c("a", "b")) filteredPeaks <- filterPeaks(c(p, p[[3]]), minNumber=2, labels=groups) ## peaks at mass 3 were removed in group "a" filteredPeaks[groups == "a"] ## peaks at mass 5 were removed in group "b" filteredPeaks[groups == "b"] ## apply different minFrequency arguments to each group groups <- factor(c("a", "a", "b", "b", "b"), levels=c("a", "b")) filteredPeaks <- filterPeaks(c(p, p[[3]]), minFrequency=c(1, 2/3), labels=groups) intensityMatrix(filteredPeaks) # 1 2 3 4 #[1,] 1 2 NA NA #[2,] 1 2 NA NA #[3,] 1 2 3 4 #[4,] 1 2 3 4 #[4,] 1 2 3 4 ## demonstrate the use of mergeWhitelists groups <- factor(c("a", "a", "b", "b"), levels=c("a", "b")) ## default behaviour filteredPeaks <- filterPeaks(p, minNumber=2, labels=groups) intensityMatrix(filteredPeaks) # 1 2 3 4 #[1,] 1 2 NA NA #[2,] 1 2 NA NA #[3,] 1 2 3 4 #[4,] 1 2 3 4 ## use mergeWhitelists=TRUE to keep peaks of group "a" that match all filtering ## criteria in group "b" ## (please note that mass == 3 is not removed in the second MassPeaks object) filteredPeaks <- filterPeaks(p, minNumber=2, labels=groups, mergeWhitelists=TRUE) intensityMatrix(filteredPeaks) # 1 2 3 4 #[1,] 1 2 NA NA #[2,] 1 2 3 NA #[3,] 1 2 3 4 #[4,] 1 2 3 4 } \keyword{methods} MALDIquant/man/msiSlices-functions.Rd0000644000175000017500000000507512624130232020466 0ustar sebastiansebastian\name{msiSlices} \title{Turn a list of AbstractMassObjects into a mass spectrometry imaging slice.} \alias{msiSlices} \description{ This function turns a mass spectrometry imaging dataset represented by a \code{list} of \code{\linkS4class{AbstractMassObject}} objects into an \code{\link[MALDIquant]{intensityMatrix}} for each slice (stored in an \code{array}). } \usage{ msiSlices(x, center, tolerance, method=c("sum", "mean", "median"), adjust=TRUE) } \arguments{ \item{x}{a \code{list} of \code{\linkS4class{MassSpectrum}}/ \code{\linkS4class{MassPeaks}} objects.} \item{center}{\code{double}, the \code{center} mas value of each slice.} \item{tolerance}{\code{double}, specifies the thickness of the slices (\code{center + c(-tolerance, tolerance)}).} \item{method}{used aggregation function.} \item{adjust}{\code{logical}, if \code{TRUE} the lowest coordinates of the mass spectrometry imaging dataset are set to \code{c(x=1, y=1)} to avoid \code{NA} values at the borders.} } \details{ Each \code{\linkS4class{MassSpectrum}}/\code{\linkS4class{MassPeaks}} object in \code{x} must contain a \code{list} named \code{imaging} with an element \code{pos} that stores the \code{x} and \code{y} value of the spectrum, e.g.: \preformatted{ > metaData(spectra[[1]])$imaging$pos x y 1 5 } } \value{ Returns an \code{\link{array}} of three dimensions. The first and second dimensions contains the x and y coordinates of the image. The third dimension represents the index of the \code{center} of each slice. There are two additional attributes, namely \code{"center"} and \code{"tolerance"} which store the original \code{center} and \code{tolerance} information. } \author{ Sebastian Gibb \email{mail@sebastiangibb.de} } \seealso{ \code{\linkS4class{AbstractMassObject}}, \code{\linkS4class{MassSpectrum}}, \code{\linkS4class{MassPeaks}}, \code{\link[MALDIquant]{coordinates,AbstractMassObject-method}}, \code{\link[MALDIquant]{plotMsiSlice,list-method}} Please find real examples on: Website: \url{http://strimmerlab.org/software/maldiquant/} Vignette: \url{https://github.com/sgibb/MALDIquantExamples/raw/master/inst/doc/nyakas2013.pdf} Shiny: \url{https://github.com/sgibb/ims-shiny/} } \examples{ ## load package library("MALDIquant") ## load example data data("fiedler2009subset", package="MALDIquant") ## please note: this is NOT a MSI data set ## we just add some coordinates for demonstration coordinates(fiedler2009subset) <- cbind(x=rep(1:4, 2), y=rep(1:2, each=4)) slices <- msiSlices(fiedler2009subset, center=c(5864.49, 8936.97), tolerance=0.25) slices } MALDIquant/man/AbstractMassObject-class.Rd0000644000175000017500000001341712672203466021362 0ustar sebastiansebastian\name{AbstractMassObject-class} \Rdversion{1.1} \docType{class} \alias{AbstractMassObject-class} \alias{[,AbstractMassObject,logical,missing-method} \alias{[,AbstractMassObject,numeric,missing-method} \alias{[,MassPeaks,logical,missing-method} \alias{[,MassPeaks,numeric,missing-method} \alias{as.matrix,AbstractMassObject-method} \alias{intensity} \alias{intensity<-} \alias{intensity,AbstractMassObject-method} \alias{intensity<-,AbstractMassObject,numeric-method} \alias{isEmpty} \alias{isEmpty,AbstractMassObject-method} \alias{length,AbstractMassObject-method} \alias{lines,AbstractMassObject-method} \alias{mass} \alias{mass<-} \alias{mass,AbstractMassObject-method} \alias{mass<-,AbstractMassObject,numeric-method} \alias{mz} \alias{mz<-} \alias{mz,AbstractMassObject-method} \alias{mz<-,AbstractMassObject,numeric-method} \alias{metaData} \alias{metaData<-} \alias{metaData,AbstractMassObject-method} \alias{metaData<-,AbstractMassObject-method} \alias{coordinates} \alias{coordinates<-} \alias{coordinates,AbstractMassObject-method} \alias{coordinates<-,AbstractMassObject,numeric-method} \alias{coordinates<-,AbstractMassObject,matrix-method} \alias{coordinates,list-method} \alias{coordinates<-,list,matrix-method} \alias{points,AbstractMassObject-method} \title{Class "AbstractMassObject"} \description{ \code{\linkS4class{AbstractMassObject}} is an abstract (means pure virtual) class. It is the parent class of \code{\linkS4class{MassSpectrum}} and \code{\linkS4class{MassPeaks}}. It shouldn't create or handle by the user because it is for internal use only. } \section{Derived classes}{ \code{\linkS4class{MassPeaks}}, \code{\linkS4class{MassSpectrum}} } \section{Slots}{ \describe{ \item{\code{mass}:}{\code{numeric}, mass or mass-to-charge ratio} \item{\code{intensity}:}{\code{numeric}, intensities for measured mass-to-charge ratios} \item{\code{metaData}:}{\code{list}, some metadata to describe the spectrum} } } \section{Methods}{ \describe{ \item{[}{\code{signature(x = "AbstractMassObject", i = "numeric")}: Extracts a range of an \code{\linkS4class{AbstractMassObject}} object and returns a new one.} \item{as.matrix}{\code{signature(x = "AbstractMassObject")}: Converts an \code{\linkS4class{AbstractMassObject}} object to a matrix with 2 columns (\code{mass}, \code{intensity}).} \item{coordinates}{\code{signature(object = "AbstractMassObject")}: Accessor function for coordinates stored in object generated from imaging mass spectrometry data.} \item{coordinates<-}{\code{signature(object = "AbstractMassObject", value = "numeric|matrix")} Replacement function for coordinates used in imaging mass spectrometry datasets.} \item{intensity}{\code{signature(object = "AbstractMassObject")}: Accessor function for slot \code{intensity}.} \item{intensity<-}{\code{signature(object = "AbstractMassObject", value = "numeric")} Replacement function for slot \code{intensity}.} \item{isEmpty}{\code{signature(object = "AbstractMassObject")}: Returns \code{TRUE} if length of \code{intensity} is 0 or all \code{intensity} values are 0.} \item{length}{\code{signature(x = "AbstractMassObject")}: Returns length of slot \code{intensity}.} \item{lines}{\code{signature(x = "AbstractMassObject")}: Extented function for adding \code{AbstractMassObject} object as a line to a specific plot. See \code{\link{lines}} for details.} \item{mass}{\code{signature(object = "AbstractMassObject")}: Accessor function for slot \code{mass}.} \item{mass<-}{\code{signature(object = "AbstractMassObject", value = "numeric")} Replacement function for slot \code{mass}.} \item{mz}{\code{signature(object = "AbstractMassObject")}: Accessor function for slot \code{mass}.} \item{mz<-}{\code{signature(object = "AbstractMassObject", value = "numeric")} Replacement function for slot \code{mass}.} \item{metaData}{\code{signature(object = "AbstractMassObject")}: Accessor function for slot \code{metaData}.} \item{metaData<-}{\code{signature(object = "AbstractMassObject")}: Replacement function for slot \code{metaData}.} \item{plot}{\code{signature(x = "AbstractMassObject", y = "missing")}: Extented function for plotting an \code{AbstractMassObject} object. See \code{\link{plot,AbstractMassObject,missing-method}} for details.} \item{points}{\code{signature(x = "AbstractMassObject")}: Extented function for adding \code{\linkS4class{AbstractMassObject}} object as points to a specific plot. See \code{\link{points}} for details.} \item{trim}{\code{signature(object = "AbstractMassObject", range = "numeric")}: Trim an \code{AbstractMassObject} object. See \code{\link[MALDIquant]{trim,AbstractMassObject,numeric-method}} for details.} \item{transformIntensity}{\code{signature(object = "AbstractMassObject")}: Transforms the intensities of an \code{AbstractMassObject} object. See \code{\link[MALDIquant]{transformIntensity,AbstractMassObject-method}} for details.} } } \author{ Sebastian Gibb \email{mail@sebastiangibb.de} } \seealso{ \code{\linkS4class{MassPeaks}}, \code{\linkS4class{MassSpectrum}}, \code{\link[MALDIquant]{plot,AbstractMassObject,missing-method}}, \code{\link[MALDIquant]{transformIntensity,AbstractMassObject-method}}, \code{\link[MALDIquant]{trim,AbstractMassObject,numeric-method}} Website: \url{http://strimmerlab.org/software/maldiquant/} } \examples{ ## load package library("MALDIquant") ## create example spectrum s <- createMassSpectrum(mass=1:10, intensity=11:20, metaData=list(name="Example Spectrum")) ## get intensity intensity(s) ## get mass mass(s) ## get metaData metaData(s) ## replace metaData metaData(s) <- list(name="Spectrum") ## trim spectrum trim(s, c(2, 9)) ## select a range s[3:6] } \keyword{classes} MALDIquant/man/warp-functions.Rd0000644000175000017500000000302512624130232017475 0ustar sebastiansebastian\name{warpMassSpectra} \alias{warpMassSpectra} \alias{warpMassPeaks} \title{ Run warping functions on AbstractMassObject objects. } \description{ These functions run warping functions on \code{\linkS4class{AbstractMassObject}} objects (warping is also known as \emph{phase correction}). } \usage{ warpMassPeaks(l, w) warpMassSpectra(l, w) } \arguments{ \item{l}{ \code{list}, list of \code{\linkS4class{MassPeaks}} or \code{\linkS4class{MassSpectrum}} objects. } \item{w}{ a \code{list} of warping functions determined by \code{\link[MALDIquant]{determineWarpingFunctions}}. Has to be of the same length as \code{l}. } } \details{ The warping function \code{w} is called in the following way: \deqn{newMass = oldMass + w(oldMass)} } \value{ Returns a \code{list} of warped \code{\linkS4class{MassPeaks}} or \code{\linkS4class{MassSpectrum}} objects. } \author{ Sebastian Gibb \email{mail@sebastiangibb.de} } \seealso{ \code{\link[MALDIquant]{determineWarpingFunctions}}, \code{\linkS4class{MassPeaks}}, \code{\linkS4class{MassSpectrum}} Website: \url{http://strimmerlab.org/software/maldiquant/} } \examples{ ## load package library("MALDIquant") ## create a MassPeaks object p <- createMassPeaks(mass=1:5, intensity=1:5) ## stupid warping function for demonstration ## (please use determineWarpingFunctions in real life applications) simpleWarp <- function(x) { return(1) } ## run warping function w <- warpMassPeaks(list(p), list(simpleWarp))[[1]] ## compare results all(mass(w) == mass(p)+1) # TRUE } \keyword{methods} MALDIquant/man/plotMsiSlice-methods.Rd0000644000175000017500000001204612624130232020571 0ustar sebastiansebastian\name{plotMsiSlice-methods} \docType{methods} \title{Plots a Mass Spectrometry Imaging dataset.} \alias{plotMsiSlice} \alias{plotMsiSlice,list-method} \alias{plotMsiSlice,array-method} \alias{plotMsiSlice,matrix-method} \description{ This function allows to plot a slice of a mass spectrometry imaging dataset represented by a \code{list} of \code{\linkS4class{AbstractMassObject}} objects or an \code{array} or a \code{matrix}. } \usage{ \S4method{plotMsiSlice}{list}(x, center, tolerance, colRamp=colorRamp(c("black", "blue", "green", "yellow", "red")), interpolate=FALSE, legend=TRUE, alignLabels=FALSE, combine=FALSE, \dots) \S4method{plotMsiSlice}{array}(x, colRamp=colorRamp(c("black", "blue", "green", "yellow", "red")), interpolate=FALSE, legend=TRUE, alignLabels=FALSE, combine=FALSE, plotInteractive=FALSE, \dots) \S4method{plotMsiSlice}{matrix}(x, colRamp=colorRamp(c("black", "blue", "green", "yellow", "red")), interpolate=FALSE, scale=TRUE, legend=scale, \dots) } \arguments{ \item{x}{The mass spectrometry imaging dataset. It could be a \code{list} of \code{\linkS4class{MassSpectrum}}/\code{\linkS4class{MassPeaks}} objects or an \code{array} (e.g. generated by \code{\link[MALDIquant]{msiSlices}}) or a \code{matrix}.} \item{center}{\code{double}, if \code{x} is a \code{list} of \code{\linkS4class{MassSpectrum}}/\code{\linkS4class{MassPeaks}} objects this argument represent the \emph{center} mass value of the slices, see \code{\link[MALDIquant]{msiSlices}} for details.} \item{tolerance}{\code{double}, if \code{center} is given \code{tolerance} specifies the thickness of the slices (\code{center + c(-tolerance, tolerance)}), see \code{\link[MALDIquant]{msiSlices}} for details.} \item{colRamp}{colours as \code{colorRamp} \code{function}, see \code{\link[grDevices]{colorRamp}} for details. If \code{combine=TRUE} multiple colour functions must be applied as \code{list} with an length that equals the number of given centers.} \item{interpolate}{\code{logical}, use linear interpolation when drawing the image, see \code{\link[graphics]{rasterImage}} for details.} \item{scale}{\code{logical}, if \code{TRUE} all values are divided by the maximal value of the slice to get values between 0 and 1.} \item{legend}{\code{logical}, if \code{TRUE} a reference color gradient is plotted on the right hand side of the plot. The upper color represents the highest value in the slice and the lower color the lowest value respectively. The \code{legend} is disabled if \code{scale=FALSE}.} \item{alignLabels}{\code{logical}, if \code{combine=TRUE} and \code{alignLabels=TRUE} the center positions below the legend are aligned on the right margin otherwise the aligned to their corresponding gradient.} \item{combine}{\code{logical}, if \code{TRUE} multiple centers are plotted in one image. Therefore it would be necessary to apply a list of \code{colRamp} functions (one function for each center). The intensity values for each center of each pixel are compared against each other and the highest scaled intensity determines the center (and the corresponding \code{colRamp}).} \item{plotInteractive}{ \code{logical}, if the slice \code{array} contains multiple centers, \code{combine=FALSE} and an interactive plotting device is used a warning is thrown and only the first center would be plotted. Use \code{plotInteractive=TRUE} to overwrite this behaviour and to plot multiple centers on an interactive device.} \item{\dots}{arguments to be passed to \code{\link[graphics]{plot}}, e.g. \code{main}.} } \details{ Each \code{\linkS4class{MassSpectrum}}/\code{\linkS4class{MassPeaks}} object in \code{x} must contain a \code{list} named \code{imaging} with an element \code{pos} that stores the \code{x} and \code{y} value of the spectrum, e.g.: \preformatted{ > metaData(spectra[[1]])$imaging$pos x y 1 5 } } \author{ Sebastian Gibb \email{mail@sebastiangibb.de} } \seealso{ \code{\linkS4class{AbstractMassObject}}, \code{\linkS4class{MassSpectrum}}, \code{\linkS4class{MassPeaks}}, \code{\link[MALDIquant]{coordinates,AbstractMassObject-method}}, \code{\link[MALDIquant]{msiSlices}}, \code{\link[MALDIquant]{plot,MassSpectrum,missing-method}} Please find real examples on: Website: \url{http://strimmerlab.org/software/maldiquant/} Vignette: \url{https://github.com/sgibb/MALDIquantExamples/raw/master/inst/doc/nyakas2013.pdf} Shiny: \url{https://github.com/sgibb/ims-shiny/} } \examples{ ## load package library("MALDIquant") ## load example data data("fiedler2009subset", package="MALDIquant") ## please note: this is NOT a MSI data set ## we just add some coordinates for demonstration coordinates(fiedler2009subset) <- cbind(x=rep(1:4, 2), y=rep(1:2, each=4)) plotMsiSlice(fiedler2009subset, center=8936.97, tolerance=0.25) plotMsiSlice(fiedler2009subset, center=c(5864.49, 8936.97), tolerance=0.25, combine=TRUE, colRamp=list(colorRamp(c("#000000", "#FF00FF")), colorRamp(c("#000000", "#00FF00")))) } \keyword{Graphics|hplot} MALDIquant/man/MassSpectrum-class.Rd0000644000175000017500000000707112624130232020254 0ustar sebastiansebastian\name{MassSpectrum-class} \Rdversion{1.1} \docType{class} \alias{MassSpectrum} \alias{MassSpectrum-class} \alias{isRegular} \alias{isRegular,MassSpectrum-method} \alias{totalIonCurrent} \alias{totalIonCurrent,MassSpectrum-method} \title{Class "MassSpectrum"} \description{ \code{\linkS4class{MassSpectrum}} represents a single spectrum of a MALDI-TOF mass spectrometry measurement. It provides an easy framework for doing some preprocessing steps like peak detection, baseline correction and much more. \cr } \section{Objects from the Class}{ \code{\link[MALDIquant]{createMassSpectrum}}: Creates a \code{\linkS4class{MassSpectrum}} object. } \section{Extends}{ Class \code{\linkS4class{AbstractMassObject}}, directly. } \section{Methods}{ \describe{ \item{calibrateIntensity}{\code{signature(x = "MassSpectrum")}: Calibrates the intensity of a \code{\linkS4class{MassSpectrum}} object. See \code{\link[MALDIquant]{calibrateIntensity,MassSpectrum-method}} for details.} \item{detectPeaks}{\code{signature(x = "MassSpectrum")}: Look for local maxima and estimate noise to extract peaks out of a \code{\linkS4class{MassSpectrum}} object. See \code{\link[MALDIquant]{detectPeaks,MassSpectrum-method}} for details.} \item{estimateBaseline}{\code{signature(x = "MassSpectrum")}: Estimates the baseline of a \code{\linkS4class{MassSpectrum}} object. See \code{\link[MALDIquant]{estimateBaseline,MassSpectrum-method}} for details.} \item{estimateNoise}{\code{signature(x = "MassSpectrum")}: Estimates the noise of a \code{\linkS4class{MassSpectrum}} object. See \code{\link[MALDIquant]{estimateNoise,MassSpectrum-method}} for details.} \item{isRegular}{\code{signature(object = "MassSpectrum")}: Returns \code{FALSE} if the frequency of mass values with irregular intervals is greater than \code{threshold} (because \code{object} was measured in \emph{centroid} mode or some \code{intensity} values were filtered).} \item{removeBaseline}{\code{signature(x = "MassSpectrum")}: Estimates and removes the baseline of a \code{\linkS4class{MassSpectrum}} object. See \code{\link[MALDIquant]{removeBaseline,MassSpectrum-method}} for details.} \item{smoothIntensity}{\code{signature(object = "MassSpectrum")}: Smoothes the intensities of an \code{MassSpectrum} object. See \code{\link[MALDIquant]{smoothIntensity,MassSpectrum-method}} for details.} \item{totalIonCurrent}{\code{signature(object = "MassSpectrum")}: Accessor function for Total Ion Current (TIC, area under the curve).} } } \author{ Sebastian Gibb \email{mail@sebastiangibb.de} } \seealso{ \code{\link[MALDIquant]{createMassSpectrum}}, \code{\link[MALDIquant]{calibrateIntensity,MassSpectrum-method}}, \code{\link[MALDIquant]{detectPeaks,MassSpectrum-method}}, \code{\link[MALDIquant]{estimateBaseline,MassSpectrum-method}}, \code{\link[MALDIquant]{estimateNoise,MassSpectrum-method}}, \code{\link[MALDIquant]{removeBaseline,MassSpectrum-method}}, \code{\link[MALDIquant]{smoothIntensity,MassSpectrum-method}}, \code{\linkS4class{AbstractMassObject}} Website: \url{http://strimmerlab.org/software/maldiquant/} } \examples{ ## load package library("MALDIquant") ## create a MassSpectrum object by default constructor s <- createMassSpectrum(mass=1:100, intensity=rnorm(100)^2, metaData=list(name="example")) ## show some details s ## plot spectrum plot(s) ## get TIC totalIonCurrent(s) ## modify intensity and metaData intensity(s)[1:50] <- 0 metaData(s) <- list(name="modified example") ## plot again plot(s) } \keyword{classes} MALDIquant/man/MALDIquant-package.Rd0000644000175000017500000000407412624130232020013 0ustar sebastiansebastian\name{MALDIquant-package} \alias{MALDIquant-package} \alias{MALDIquant} \docType{package} \title{ Quantitative Analysis of Mass Spectrometry Data } \description{ MALDIquant provides a complete analysis pipeline for matrix-assisted laser desorption/ionization-time-of-flight (MALDI-TOF) and other two-dimensional mass spectrometry data. In addition to commonly used plotting and processing methods it includes distinctive features, namely baseline subtraction methods such as morphological filters (TopHat) or the statistics-sensitive non-linear iterative peak-clipping algorithm (SNIP), peak alignment using warping functions, handling of replicated measurements as well as allowing spectra with different resolutions. For a first overview see \code{vignette("MALDIquant-intro", package="MALDIquant")} and/or run \code{demo("MALDIquant")}. } \details{ \tabular{ll}{ Package: \tab MALDIquant\cr License: \tab GPL (>= 3)\cr URL: \tab http://strimmerlab.org/software/maldiquant/\cr } Main classes: \itemize{ \item \code{\linkS4class{MassPeaks}}: Represents a peak list of a single spectrum. \item \code{\linkS4class{MassSpectrum}}: Represents a single spectrum. } The accompanying website (see below) provides example R scripts to illustrate the functionality of this package, too. } \author{ Sebastian Gibb Maintainer: Sebastian Gibb \email{mail@sebastiangibb.de} } \references{ S. Gibb and K. Strimmer. 2012. MALDIquant: a versatile R package for the analysis of mass spectrometry data. Bioinformatics 28: 2270-2271. \url{http://bioinformatics.oxfordjournals.org/content/28/17/2270.abstract} Website: \url{http://strimmerlab.org/software/maldiquant/} } \seealso{ \itemize{ \item Introduction: \code{vignette("MALDIquant-intro", package="MALDIquant")}. \item Run demo files: \code{demo("MALDIquant")}. \item List all available manual pages: \code{library(help="MALDIquant")}. \item MALDIquant website: \url{http://strimmerlab.org/software/maldiquant/}. \item more MALDIquant examples and complete analyses: \url{https://github.com/sgibb/MALDIquantExamples/}. } } MALDIquant/man/findEmptyMassObjects-functions.Rd0000644000175000017500000000365112624130232022626 0ustar sebastiansebastian\name{findEmptyMassObjects} \alias{findEmptyMassObjects} \alias{removeEmptyMassObjects} \title{ Finds or removes empty AbstractMassObject objects in a list. } \description{ These functions looks for empty \code{\linkS4class{AbstractMassObject}} objects in a \code{\link{list}}. } \usage{ findEmptyMassObjects(l) removeEmptyMassObjects(l) } \arguments{ \item{l}{ \code{list}, list of \code{\linkS4class{AbstractMassObject}} where empty objects should be found or removed. } } \value{ \code{findEmptyMassObjects}: Returns a \code{\link{vector}} of indices referring to empty \code{\linkS4class{AbstractMassObject}} objects. \code{removeEmptyMassObjects}: Returns a \code{\link{list}} of \code{\linkS4class{AbstractMassObject}} objects but without empty ones. } \author{ Sebastian Gibb \email{mail@sebastiangibb.de} } \seealso{ \code{\link[MALDIquant]{isEmpty,AbstractMassObject-method}}, \code{\linkS4class{AbstractMassObject}} Website: \url{http://strimmerlab.org/software/maldiquant/} } \examples{ ## load package library("MALDIquant") ## create list peakList <- list() ## create two MassPeaks objects and add them to the list peakList[[1]] <- createMassPeaks(mass=1:100, intensity=1:100, metaData=list(name="example 1")) peakList[[2]] <- createMassPeaks(mass=1:100, intensity=1:100, metaData=list(name="example 2")) ## find empty objects (there should not be any one) findEmptyMassObjects(peakList) ## add an empty MassPeaks object to the list peakList[[3]] <- createMassPeaks(mass=double(), intensity=double(), metaData=list(name="empty MassPeaks object")) ## look for empty objects (isEmptyIdx == 3) (isEmptyIdx <- findEmptyMassObjects(peakList)) ## to remove all empty MassObjects from a list length(peakList) # 3 peakList <- removeEmptyMassObjects(peakList) length(peakList) # 2; WARNING: all indices could changed } \keyword{methods} MALDIquant/man/plot-methods.Rd0000644000175000017500000000420712672203466017155 0ustar sebastiansebastian\name{plot-methods} \docType{methods} \alias{plot,AbstractMassObject,missing-method} \alias{plot,MassSpectrum,missing-method} \title{Plots an AbstractMassObject object.} \description{ This is an overloaded method to allow plotting of an \code{\linkS4class{AbstractMassObject}} object. } \usage{ \S4method{plot}{AbstractMassObject,missing}(x, col="black", xlab=expression(italic(m/z)), ylab="intensity", type=ifelse(isMassPeaks(x), "h", "l"), xlim=c(ifelse(length(x@mass), min(x@mass, na.rm=TRUE), 0), ifelse(length(x@mass), max(x@mass, na.rm=TRUE), 1)), ylim=c(0, ifelse(length(x@intensity), max(x@intensity, na.rm=TRUE), 1)), main=x@metaData$name, sub=x@metaData$file, cex.sub=0.75, col.sub="#808080", abline.col="#808080", \dots) } \arguments{ \item{x}{\code{\linkS4class{MassSpectrum}} object.} \item{col}{line colour, see \code{\link[graphics]{par}}.} \item{xlab}{title for the x-axis, see \code{\link[graphics]{title}}.} \item{ylab}{title for the y-axis, see \code{\link[graphics]{title}}.} \item{type}{type of plot: see \code{\link[graphics]{plot}}.} \item{xlim}{the x limits (x1, x2) of the plot, see \code{\link[graphics]{plot.default}}.} \item{ylim}{the y limits (y1, y2) of the plot, see \code{\link[graphics]{plot.default}}.} \item{main}{title for the plot, see \code{\link[graphics]{title}}.} \item{sub}{sub title for the plot, see \code{\link[graphics]{title}}.} \item{cex.sub}{sub title font size, see \code{\link[graphics]{par}}.} \item{col.sub}{sub title color, see \code{\link[graphics]{par}}.} \item{abline.col}{colour for horizontal line at y=0.} \item{\dots}{arguments to be passed to \code{\link[graphics]{plot}}.} } \author{ Sebastian Gibb \email{mail@sebastiangibb.de} } \seealso{ \code{\linkS4class{AbstractMassObject}} Website: \url{http://strimmerlab.org/software/maldiquant/} } \examples{ ## load package library("MALDIquant") ## create a MassSpectrum object by default constructor s <- createMassSpectrum(mass=1:100, intensity=rnorm(100)^2, metaData=list(name="example")) ## show some details s ## plot spectrum plot(s) } \keyword{methods} \keyword{Graphics|hplot} MALDIquant/man/fiedler2009subset.Rd0000644000175000017500000000476712624130232017707 0ustar sebastiansebastian\encoding{utf8} \name{fiedler2009subset} \docType{data} \alias{fiedler2009subset} \title{Example Mass Spectra (raw)} \description{ This dataset contains 16 example mass spectra. It is used to demonstrate the usage of \code{\link{MALDIquant-package}}. } \usage{fiedler2009subset} \format{ A list containing 16 \code{\link[MALDIquant]{MassSpectrum-class}} objects.} \details{ The dataset is a subset of data used in \emph{Fiedler et al 2009}. \cr It contains spectra of 8 different patients (each one has 2 technical replicates). \tabular{rlllll}{ list_index \tab laboratory \tab patient_id \tab sex \tab age \tab type \cr 1 \tab Leipzig \tab LC77 \tab male \tab 37 \tab control\cr 2 \tab Leipzig \tab LC77 \tab male \tab 37 \tab control\cr 3 \tab Leipzig \tab LC213 \tab female \tab 51 \tab control\cr 4 \tab Leipzig \tab LC213 \tab female \tab 51 \tab control\cr 5 \tab Leipzig \tab LT178 \tab male \tab 58 \tab cancer \cr 6 \tab Leipzig \tab LT178 \tab male \tab 58 \tab cancer \cr 7 \tab Leipzig \tab LT157 \tab male \tab 60 \tab cancer \cr 8 \tab Leipzig \tab LT157 \tab male \tab 60 \tab cancer \cr 9 \tab Heidelberg \tab HC49 \tab male \tab 43 \tab control\cr 10 \tab Heidelberg \tab HC49 \tab male \tab 43 \tab control\cr 11 \tab Heidelberg \tab HC54 \tab female \tab 71 \tab control\cr 12 \tab Heidelberg \tab HC54 \tab female \tab 71 \tab control\cr 13 \tab Heidelberg \tab HT151 \tab male \tab 53 \tab cancer \cr 14 \tab Heidelberg \tab HT151 \tab male \tab 53 \tab cancer \cr 15 \tab Heidelberg \tab HT429 \tab female \tab 58 \tab cancer \cr 16 \tab Heidelberg \tab HT429 \tab female \tab 58 \tab cancer \cr } } \references{ G.M. Fiedler, A.B. Leichtle, J. Kase, S. Baumann, U. Ceglarek, K. Felix, T. Conrad, H. Witzigmann, A. Weimann, C. Schütte, J. Hauss, M. Büchler and J. Thiery \cr \dQuote{Serum Peptidome Profiling Revealed Platelet Factor 4 as a Potential Discriminating Peptide Associated with Pancreatic Cancer} \cr Clinical Cancer Research, 11(15): 3812-3819, 2009 \cr ISSN 1557-3265; doi:10.1158/1078-0432.CCR-08-2701 \cr \url{http://clincancerres.aacrjournals.org/content/15/11/3812} } \seealso{ \code{\link[MALDIquant]{MassSpectrum-class}} Website: \url{http://strimmerlab.org/software/maldiquant/} } \keyword{datasets} MALDIquant/man/smoothIntensity-methods.Rd0000644000175000017500000000464112624130232021404 0ustar sebastiansebastian\name{smoothIntensity-methods} \docType{methods} \alias{smoothIntensity} \alias{smoothIntensity,MassSpectrum-method} \alias{smoothIntensity,list-method} \title{Smoothes intensities of a MassSpectrum object.} \description{ This method smoothes the intensity values of a \code{\linkS4class{MassSpectrum}} object. } \usage{ \S4method{smoothIntensity}{MassSpectrum}(object, method=c("SavitzkyGolay", "MovingAverage"), halfWindowSize, \dots) } \arguments{ \item{object}{\code{\linkS4class{AbstractMassObject}} object or a \code{list} of \code{\linkS4class{AbstractMassObject}} objects.} \item{method}{used smoothing method, one of \code{"SavitzkyGolay"} or \code{"MovingAverage"}.} \item{halfWindowSize}{half window size. The resulting window reaches from \code{mass[currentIndex-halfWindowSize]} to \code{mass[currentIndex+halfWindowSize]} (window size is \code{2*halfWindowSize+1}). The best size differs depending on the selected \code{method}.} \item{\dots}{arguments to be passed to \code{method}. \code{SavitzkyGolay} has an additional \code{polynomialOrder} argument (default: \code{3}) to control the order of the filter. Unused for \code{MovingAverage}} } \details{ \code{halfWindowSize}: Depends on the selected \code{method}. For the \code{SavitzkyGolay} the \code{halfWindowSize} should be smaller than \emph{FWHM} of the peaks (full width at half maximum; please find details in Bromba and Ziegler 1981). In general the \code{halfWindowSize} for the \code{MovingAverage} has to be much smaller than for \code{SavitzkyGolay} to conserve the peak shape. } \author{ Sebastian Gibb \email{mail@sebastiangibb.de} } \references{ A. Savitzky and M. J. Golay. 1964. Smoothing and differentiation of data by simplified least squares procedures. Analytical chemistry, 36(8), 1627-1639. M. U. Bromba and H. Ziegler. 1981. Application hints for Savitzky-Golay digital smoothing filters. Analytical Chemistry, 53(11), 1583-1586. } \seealso{ \code{\linkS4class{MassSpectrum}} Website: \url{http://strimmerlab.org/software/maldiquant/} } \examples{ ## load package library("MALDIquant") ## load example data data("fiedler2009subset", package="MALDIquant") ## smooth spectra s <- smoothIntensity(fiedler2009subset, method="MovingAverage", halfWindowSize=2) ## or s <- smoothIntensity(fiedler2009subset, method="SavitzkyGolay", halfWindowSize=10) } \keyword{methods} MALDIquant/man/MassPeaks-class.Rd0000644000175000017500000000256412703476727017543 0ustar sebastiansebastian\name{MassPeaks-class} \Rdversion{1.1} \docType{class} \alias{MassPeaks} \alias{MassPeaks-class} \alias{snr} \alias{snr,MassPeaks-method} \title{Class "MassPeaks"} \description{ \code{\linkS4class{MassPeaks}} represents extracted peaks of a single spectrum of a MALDI-TOF mass spectrometry measurement. } \section{Objects from the Class}{ \code{\link[MALDIquant]{createMassPeaks}}: Creates a \code{\linkS4class{MassPeaks}} object. } \section{Extends}{ Class \code{\linkS4class{AbstractMassObject}}, directly. } \section{Slots}{ \describe{ \item{\code{snr}:}{\code{vector}, \emph{s}ignal-to-\emph{n}oise \emph{r}atio} } } \section{Methods}{ \describe{ \item{labelPeaks}{\code{signature(x = "MassPeaks")}: Draws peak labels to plot. See \code{\link{labelPeaks,MassPeaks-method}} for details.} \item{monoisotopicPeaks}{\code{signature(x = "MassPeaks")}: Finds monoisotopic peaks in peak lists. See \code{\link{monoisotopicPeaks,MassPeaks-method}} for details.} } } \author{ Sebastian Gibb \email{mail@sebastiangibb.de} } \seealso{ \code{\link[MALDIquant]{createMassPeaks}}, \code{\link[MALDIquant]{detectPeaks,MassSpectrum-method}}, \code{\link[MALDIquant]{labelPeaks,MassPeaks-method}}, \code{\linkS4class{AbstractMassObject}} Website: \url{http://strimmerlab.org/software/maldiquant/} } %\examples{ %## load package %library("MALDIquant") %} \keyword{classes} MALDIquant/man/binPeaks-functions.Rd0000644000175000017500000000546412703476727020315 0ustar sebastiansebastian\name{binPeaks} \alias{binPeaks} \title{ Align Peaks into discrete bins. } \description{ This function looks for similar peaks (mass) across \code{\linkS4class{MassPeaks}} objects and equalizes their mass. } \usage{ binPeaks(l, method=c("strict", "relaxed"), tolerance=0.002) } \arguments{ \item{l}{ \code{list}, list of \code{\linkS4class{MassPeaks}} objects. } \item{method}{ bin creation rule. \code{"strict"} creates bins never containing two or more peaks of the sampe sample. \code{"relaxed"} allows multiple peaks of the same sample in one bin.} \item{tolerance}{ \code{double}, maximal relative deviation of a peak position (mass) to be considered as identical. } } \details{ The algorithm is based on the following workflow: \enumerate{ \item{Put all mass in a sorted vector.} \item{Calculate differences between each neighbor.} \item{Divide the mass vector at the largest gap (largest difference) and form a left and a right bin.} \item{Rerun step 3 for the left and/or the right bin if they don't fulfill the following criteria: \itemize{ \item{All peaks in a bin are near to the mean (\code{abs(mass-meanMass)/meanMass < tolerance}).} \item{\code{method == "strict"}: The bin doesn't contain two or more peaks of the same sample.} } } } \code{method == "strict"}: The new peak positions (mass value) are the mean mass of a bin. \cr \code{method == "relaxed"}: The new peak positions for the highest peaks of each sample in a bin are generated by the mean mass of this peaks. The lower peaks are not changed. } \value{ Returns a \code{\link{list}} of mass adjusted \code{\linkS4class{MassPeaks}} objects. } \author{ Sebastian Gibb \email{mail@sebastiangibb.de} } \seealso{ \code{\link[MALDIquant]{intensityMatrix}}, \code{\linkS4class{MassPeaks}} Website: \url{http://strimmerlab.org/software/maldiquant/} } \examples{ ## load package library("MALDIquant") ## create two MassPeaks objects p <- list(createMassPeaks(mass=seq(100, 500, 100), intensity=1:5), createMassPeaks(mass=c(seq(100.2, 300.2, 100), 395), intensity=1:4)) binnedPeaks <- binPeaks(p, tolerance=0.002) ## compare result iM1 <- intensityMatrix(p) iM2 <- intensityMatrix(binnedPeaks) all(dim(iM1) == c(2, 9)) # TRUE all(dim(iM2) == c(2, 6)) # TRUE show(iM2) ## increase tolerance binnedPeaks <- binPeaks(p, tolerance=0.1) iM3 <- intensityMatrix(binnedPeaks) all(dim(iM3) == c(2, 5)) # TRUE show(iM3) ## differences between "strict" and "relaxed" p <- c(createMassPeaks(mass=c(1, 1.01, 3), intensity=c(2, 1, 1)), createMassPeaks(mass=c(0.99, 3), intensity=rep(1, 2)), createMassPeaks(mass=c(1.02, 3), intensity=rep(1, 2))) intensityMatrix(binPeaks(p, method="strict", tolerance=0.05)) intensityMatrix(binPeaks(p, method="relaxed", tolerance=0.05)) } \keyword{methods} MALDIquant/man/intensityMatrix-functions.Rd0000644000175000017500000000463612624130232021750 0ustar sebastiansebastian\name{intensityMatrix} \alias{intensityMatrix} \title{ Converts a list of MassPeaks objects into a matrix. } \description{ This function converts a \code{\link{list}} of \code{\linkS4class{MassPeaks}} objects into a \code{\link{matrix}}. } \usage{ intensityMatrix(peaks, spectra) } \arguments{ \item{peaks}{ \code{list}, list of \code{\linkS4class{MassPeaks}} objects. } \item{spectra}{ \code{list}, list of \code{\linkS4class{MassSpectrum}} objects. If a peak is missing the corresponding intensity value of the spectrum is used. If \code{spectra} is missing \code{NA} is used instead. } } \details{ \code{peaks} have to be binned by \code{\link[MALDIquant]{binPeaks}} before calling \code{\link[MALDIquant]{intensityMatrix}}. } \value{ Returns a \code{\link{matrix}} containing intensities of all \code{\linkS4class{MassPeaks}} objects of \code{peaks} and interpolated intensity values for missing peaks if \code{spectra} was given or \code{NA} otherwise. \cr The \code{\link{matrix}} has \code{length(peaks)} rows (one row for each sample) and \code{length(unique(mass))} columns. There is an additional attribute \code{"mass"} that stores the mass values. } \author{ Sebastian Gibb \email{mail@sebastiangibb.de} } \seealso{ \code{\link[MALDIquant]{binPeaks}}, \code{\linkS4class{MassPeaks}}, \code{\linkS4class{MassSpectrum}} Website: \url{http://strimmerlab.org/software/maldiquant/} } \examples{ ## load package library("MALDIquant") ## create example MassPeaks objects p <- list(createMassPeaks(mass=1:4, intensity=11:14, metaData=list(name="test mass peaks 1")), createMassPeaks(mass=2:5, intensity=22:25, metaData=list(name="test mass peaks 2"))) ## converts MassPeaks objects into a matrix intensityMatrix(p) ## realistic example ## load example data data("fiedler2009subset", package="MALDIquant") ## transform intensities s <- transformIntensity(fiedler2009subset, method="sqrt") ## smoothing spectrum s <- smoothIntensity(s, method="MovingAverage") ## remove baseline s <- removeBaseline(s) ## call peak detection p <- detectPeaks(s) ## bin peaks p <- binPeaks(p) ## convert MassPeaks objects into a matrix with missing intensity ## values intensityMatrix(p) ## convert MassPeaks and MassSpectrum objects into a matrix without ## missing intensity values intensityMatrix(p, s) } \keyword{methods} MALDIquant/man/estimateNoise-methods.Rd0000644000175000017500000000404112624130232020767 0ustar sebastiansebastian\name{estimateNoise-methods} \docType{methods} \alias{estimateNoise} \alias{estimateNoise,MassSpectrum-method} \title{Estimates the noise of a MassSpectrum object.} \description{ This method estimates the noise of mass spectrometry data (represented by a \code{\linkS4class{MassSpectrum}} object).\cr } \usage{ \S4method{estimateNoise}{MassSpectrum}(object, method=c("MAD", "SuperSmoother"), \dots) } \arguments{ \item{object}{\code{\linkS4class{MassSpectrum}} object} \item{method}{used noise estimation method, one of \code{"MAD"} or \code{"SuperSmoother"}. } \item{\dots}{arguments to be passed to \code{method}.} } \details{ \describe{ \item{\code{"MAD"}:}{ This function estimates the noise of mass spectrometry data by calculating the median absolute deviation, see also \code{\link[stats]{mad}}. } \item{\code{"SuperSmoother"}:}{ This function estimates the noise of mass spectrometry data using Friedman's Super Smoother. Please refer \code{\link[stats]{supsmu}} for details and additional arguments. } } } \value{ Returns a two column matrix (first column: mass, second column: intensity) of the estimated noise. } \author{ Sebastian Gibb \email{mail@sebastiangibb.de} } \seealso{ \code{\linkS4class{MassSpectrum}}, \code{\link[MALDIquant]{detectPeaks,MassSpectrum-method}}, \code{\link[stats]{mad}}, \code{\link[stats]{supsmu}} Website: \url{http://strimmerlab.org/software/maldiquant/} } \examples{ ## load package library("MALDIquant") ## load example data data("fiedler2009subset", package="MALDIquant") ## choose only the first mass spectrum s <- fiedler2009subset[[1]] ## transform intensities s <- transformIntensity(s, method="sqrt") ## remove baseline s <- removeBaseline(s) ## plot spectrum plot(s) ## estimate noise nm <- estimateNoise(s, method="MAD") nss <- estimateNoise(s, method="SuperSmoother") ## draw noise on the plot lines(nm, col=2) lines(nss, col=4) ## draw legend legend(x="topright", lwd=1, legend=c("MAD", "SuperSmoother"), col=c(2, 4)) } \keyword{methods} MALDIquant/man/isMassObjectList-functions.Rd0000644000175000017500000000247112624130232021752 0ustar sebastiansebastian\name{isMassSpectrumList} \alias{isMassSpectrumList} \alias{isMassPeaksList} \title{ Tests a list of MassSpectrum or MassPeaks objects. } \description{ These functions test a \code{\link{list}} whether containing \code{\linkS4class{MassSpectrum}} or \code{\linkS4class{MassSpectrum}} objects. } \usage{ isMassSpectrumList(x) isMassPeaksList(x) } \arguments{ \item{x}{ object to be tested. } } \value{ Returns \code{\link{TRUE}} or \code{\link{FALSE}} depending on whether its argument is a \code{\link{list}} of \code{\linkS4class{MassSpectrum}} or \code{\linkS4class{MassPeaks}} objects. } \author{ Sebastian Gibb \email{mail@sebastiangibb.de} } \seealso{ \code{\linkS4class{MassPeaks}}, \code{\linkS4class{MassSpectrum}}, \code{\linkS4class{AbstractMassObject}} Website: \url{http://strimmerlab.org/software/maldiquant/} } \examples{ ## load package library("MALDIquant") ## create list p <- list() ## test list isMassPeaksList(p) # returns FALSE ## create two MassPeaks objects and add them to the list p <- createMassPeaks(mass=1:100, intensity=1:100, metaData=list(name="example 1")) p <- createMassPeaks(mass=1:100, intensity=1:100, metaData=list(name="example 2")) ## test list isMassPeaksList(p) # returns TRUE isMassSpectrumList(p) # returns FALSE } \keyword{methods} MALDIquant/man/referencePeaks-functions.Rd0000644000175000017500000000364612700164610021461 0ustar sebastiansebastian\name{referencePeaks} \alias{referencePeaks} \title{ Creates a reference \code{\linkS4class{MassPeaks}} object. } \description{ This function creates a reference \code{\linkS4class{MassPeaks}} object (also called \emph{Anchor Peaks}) from a list of \code{\linkS4class{MassPeaks}} objects.\cr Generally it is a combination of \code{\link[MALDIquant]{binPeaks}} and \code{\link[MALDIquant]{filterPeaks}} } \usage{ referencePeaks(l, method=c("strict", "relaxed"), minFrequency=0.9, tolerance=0.002) } \arguments{ \item{l}{ \code{list}, list of \code{\linkS4class{MassPeaks}} objects. } \item{method}{ bin creation rule (see \code{\link[MALDIquant]{binPeaks}}). } \item{minFrequency}{ \code{double}, remove all peaks which occur in less than \code{minFrequency*length(l)} \code{\linkS4class{MassPeaks}} objects. } \item{tolerance}{ \code{double}, maximal relative deviation of a peak position (mass) to be considered as identical. } } \value{ Returns a new \code{\linkS4class{MassPeaks}} objects. \cr The \code{\link[MALDIquant]{intensity}} slot of the returned \code{\linkS4class{MassPeaks}} represents the frequency of this mass position in all samples. } \author{ Sebastian Gibb \email{mail@sebastiangibb.de} } \seealso{ \code{\link[MALDIquant]{binPeaks}}, \code{\link[MALDIquant]{filterPeaks}}, \code{\linkS4class{MassPeaks}} Website: \url{http://strimmerlab.org/software/maldiquant/} } \examples{ ## load package library("MALDIquant") ## create four MassPeaks objects and add them to the list p<- list(createMassPeaks(mass=1:2, intensity=1:2), createMassPeaks(mass=1:3, intensity=1:3), createMassPeaks(mass=1:4, intensity=1:4), createMassPeaks(mass=1:5, intensity=1:5)) ## only use peaks which occur in all MassPeaks objects as reference peaks refPeaks <- referencePeaks(p, minFrequency=1) mass(refPeaks) # 1:2 intensity(refPeaks) # c(1, 1) } \keyword{methods} MALDIquant/man/mergeMassPeaks-functions.Rd0000644000175000017500000000403012624130232021430 0ustar sebastiansebastian\name{mergeMassPeaks} \alias{mergeMassPeaks} \title{ Merges \code{\linkS4class{MassPeaks}} objects. } \description{ This function merges \code{\linkS4class{MassPeaks}} objects. } \usage{ mergeMassPeaks(l, labels, method=c("mean", "median", "sum"), ignore.na=TRUE, \dots) } \arguments{ \item{l}{ \code{list}, list of \code{\linkS4class{MassPeaks}} objects. } \item{labels}{ \code{list}, list of \code{\link{factor}}s (one for each \code{\linkS4class{MassPeaks}} object) to do groupwise merging. } \item{method}{used merge method.} \item{ignore.na}{Should \code{NA} (positions where a peak is missing) ignored (\code{ignore.na=TRUE}) or treated as zero (\code{ignore.na=FALSE})?} \item{\dots}{arguments to be passed to underlying functions (currently only \code{mc.cores} is supported).} } \value{ Returns a single (no \code{labels} given) or a \code{\link{list}} (\code{labels} given) of merged \code{\linkS4class{MassPeaks}} objects. } \author{ Sebastian Gibb \email{mail@sebastiangibb.de} } \seealso{ \code{\linkS4class{MassPeaks}}, \code{\link[MALDIquant]{averageMassSpectra}} Website: \url{http://strimmerlab.org/software/maldiquant/} } \examples{ ## load package library("MALDIquant") ## create four MassPeaks objects and add them to the list p <- list(createMassPeaks(mass=1:2, intensity=1:2), createMassPeaks(mass=1:3, intensity=1:3), createMassPeaks(mass=1:4, intensity=1:4), createMassPeaks(mass=1:5, intensity=1:5)) ## merge all four MassPeaks objects into a single new one ## by sum their intensities ## (no labels, returns only one new MassPeaks object) mergedPeaks <- mergeMassPeaks(p, method="sum") ## only merge MassPeaks objects in a group ## (two different labels, returns a list of two new MassPeaks objects) groups <- factor(c("a", "a", "b", "b"), levels=c("a", "b")) mergedPeaks <- mergeMassPeaks(p, labels=groups, method="mean") ## the same, but treat NA as zero mergedPeaks <- mergeMassPeaks(p, labels=groups, method="mean", ignore.na=FALSE) } \keyword{methods} MALDIquant/man/createMassSpectrum-functions.Rd0000644000175000017500000000173312624130232022342 0ustar sebastiansebastian\name{createMassSpectrum} \alias{createMassSpectrum} \title{ Creates a MassSpectrum object. } \description{ This function creates a \code{\linkS4class{MassSpectrum}} object. } \usage{ createMassSpectrum(mass, intensity, metaData=list()) } \arguments{ \item{mass}{ \code{vector}, mass or mass-to-charge ratio } \item{intensity}{ \code{vector}, intensities for measured mass-to-charge ratios } \item{metaData}{ \code{list}, some metadata to describe the spectrum } } \value{ Returns a \code{\linkS4class{MassSpectrum}} object. } \author{ Sebastian Gibb \email{mail@sebastiangibb.de} } \seealso{ \code{\linkS4class{MassSpectrum}} Website: \url{http://strimmerlab.org/software/maldiquant/} } \examples{ ## load package library("MALDIquant") ## create a MassSpectrum object by default constructor s <- createMassSpectrum(mass=1:100, intensity=rnorm(100)^2, metaData=list(name="example spectrum")) ## show some details s } \keyword{methods} MALDIquant/man/determineWarpingFunctions-functions.Rd0000644000175000017500000001072412624130232023725 0ustar sebastiansebastian\name{determineWarpingFunctions} \alias{determineWarpingFunctions} \title{ Determine warping functions of MassPeaks objects. } \description{ This function determines a warping function for a list of \code{\linkS4class{AbstractMassObject}} objects (warping is also known as \emph{phase correction/spectra alignment}). } \usage{ determineWarpingFunctions(l, reference, tolerance=0.002, method=c("lowess", "linear", "quadratic", "cubic"), plot=FALSE, plotInteractive=FALSE, \dots) } \arguments{ \item{l}{ \code{list}, list of \code{\linkS4class{MassPeaks}} objects. } \item{reference}{ \code{\linkS4class{MassPeaks}}, reference object to which the samples (\code{l}) should be aligned. If missing \code{\link[MALDIquant]{referencePeaks}} is used. } \item{tolerance}{ \code{double}, maximal relative deviation of a peak position (mass) to be considered as identical. } \item{method}{ used basic warping function. } \item{plot}{ \code{logical}, if \code{TRUE} a warping plot is drawn for each sample. } \item{plotInteractive}{ \code{logical}, if \code{FALSE} a non-interactive device (e.g. \code{pdf}) is used for warping plots. } \item{\dots}{ arguments to be passed to \code{warpingFunction} } } \details{ \code{warpingFunction}: \code{determineWarpingFunctions} estimates a warping function to overcome the difference between mass in \code{reference} and in the current sample. To calculate the differences each reference peak would match with the highest sample peak in the nearer neighborhood (defined by \code{mass of reference peak*tolerance}). \cr \code{plotInteractive}: If \code{plot} is \code{TRUE} a lot of output is created (each sample in \code{l} gets its own plot). That's why an non-interactive devices is recommended: \preformatted{ ## create a device pdf() ## calculate warping functions w <- determineWarpingFunctions(p, plot=TRUE) ## close device dev.off() } } \value{ Returns a \code{list} of individual warping functions. } \author{ Sebastian Gibb \email{mail@sebastiangibb.de} } \seealso{ \code{\link[MALDIquant]{referencePeaks}}, \code{\link[MALDIquant]{warpMassPeaks}}, \code{\link[MALDIquant]{warpMassSpectra}}, \code{\linkS4class{MassPeaks}} \code{demo("warping")} Website: \url{http://strimmerlab.org/software/maldiquant/} } \examples{ ## load package library("MALDIquant") ## create a reference MassPeaks object r <- createMassPeaks(mass=1:5, intensity=1:5) ## create test samples p <- list(createMassPeaks(mass=((1:5)*1.01), intensity=1:5), createMassPeaks(mass=((1:5)*0.99), intensity=1:5)) ## create an interactive device with 2 rows par(mfrow=c(2, 1)) ## calculate warping function ## (using a linear function as basic warping function) ## and show warping plot w <- determineWarpingFunctions(p, tolerance=0.02, method="linear", plot=TRUE, plotInteractive=TRUE) par(mfrow=c(1, 1)) ## w contains the individual warping functions warpedPeaks <- warpMassPeaks(p, w) ## compare results all(mass(r) == mass(warpedPeaks[[1]])) # TRUE all(mass(r) == mass(warpedPeaks[[2]])) # TRUE ## realistic example ## load example data data("fiedler2009subset", package="MALDIquant") ## running typical workflow ## use only four spectra of the subset spectra <- fiedler2009subset[1:4] ## transform intensities spectra <- transformIntensity(spectra, method="sqrt") ## smooth spectra spectra <- smoothIntensity(spectra, method="MovingAverage") ## baseline correction spectra <- removeBaseline(spectra) ## detect peaks peaks <- detectPeaks(spectra) ## create an interactive device with 2 rows par(mfrow=c(4, 1)) ## calculate warping functions (using LOWESS based basic function [default]) w <- determineWarpingFunctions(peaks, plot=TRUE, plotInteractive=TRUE) par(mfrow=c(1, 1)) ## realistic example with user defined reference/calibration peaks ## use the workflow above for fiedler2009subset ## create reference peaks refPeaks <- createMassPeaks(mass=c(1207, 1264, 1351, 1466, 1616, 2769, 2932, 3191, 3262, 4091, 4209, 5904, 7762, 9285), intensity=rep(1, 14)) ## create an interactive device with 2 rows par(mfrow=c(4, 1)) ## calculate warping functions (using a quadratic function as basic function) w <- determineWarpingFunctions(peaks, reference=refPeaks, method="quadratic", plot=TRUE, plotInteractive=TRUE) par(mfrow=c(1, 1)) } \keyword{methods} MALDIquant/man/MALDIquant-parallel.Rd0000644000175000017500000000636112624130232020215 0ustar sebastiansebastian\name{MALDIquant-parallel} \alias{MALDIquant-parallel} \title{Parallel Support in Package \pkg{MALDIquant}} \description{ \code{\link{MALDIquant}} offers multi-core support using \code{\link[parallel]{mclapply}} and \code{\link[parallel]{mcmapply}}. This approach is limited to unix-based platforms. Please note that not all functions benfit from parallelisation. Often the overhead to create/copy objects outrun the time saving of parallel runs. This is true for functions that are very fast to compute (e.g. \code{sqrt}-transformation). That's why the default value for the \code{mc.cores} argument in all functions is \code{1L}. It depends on the size of the dataset which step (often only \code{\link[MALDIquant]{removeBaseline}} and \code{\link[MALDIquant]{detectPeaks}}) benefits from parallelisation. \cr In general it is faster to encapsulate the complete workflow into a function and parallelise it using \code{\link[parallel]{mclapply}} instead of using the \code{mc.cores} argument of each method. The reason is the reduced overhead for object management (only one split/combine is needed instead of doing these operations in each function again and again). } \details{ \describe{ The following functions/methods support the \code{mc.cores} argument: \itemize{ \item{\code{\link[MALDIquant]{trim,list,numeric-method}}} \item{\code{\link[MALDIquant]{transformIntensity,list-method}}} \item{\code{\link[MALDIquant]{smoothIntensity,list-method}}} \item{\code{\link[MALDIquant]{removeBaseline,list-method}}} \item{\code{\link[MALDIquant]{calibrateIntensity,list-method}}} \item{\code{\link[MALDIquant]{detectPeaks,list-method}}} \item{\code{\link[MALDIquant]{alignSpectra}}} \item{\code{\link[MALDIquant]{averageMassSpectra}}} \item{\code{\link[MALDIquant]{mergeMassPeaks}}} } } } \seealso{ \code{\link[parallel]{mclapply}}, \code{\link[parallel]{mcmapply}} } \examples{ ## load package library("MALDIquant") ## load example data data("fiedler2009subset", package="MALDIquant") ## run single-core baseline correction print(system.time( b1 <- removeBaseline(fiedler2009subset, method="SNIP") )) if(.Platform$OS.type == "unix") { ## run multi-core baseline correction print(system.time( b2 <- removeBaseline(fiedler2009subset, method="SNIP", mc.cores=2) )) stopifnot(all.equal(b1, b2)) } ## parallelise complete workflow workflow <- function(spectra, cores) { s <- transformIntensity(spectra, method="sqrt", mc.cores=cores) s <- smoothIntensity(s, method="SavitzkyGolay", halfWindowSize=10, mc.cores=cores) s <- removeBaseline(s, method="SNIP", iterations=100, mc.cores=cores) s <- calibrateIntensity(s, method="TIC", mc.cores=cores) detectPeaks(s, method="MAD", halfWindowSize=20, SNR=2, mc.cores=cores) } if(.Platform$OS.type == "unix") { ## parallelise the complete workflow is often faster because the overhead is ## reduced print(system.time( p1 <- unlist(parallel::mclapply(fiedler2009subset, function(x)workflow(list(x), cores=1), mc.cores=2), use.names=FALSE) )) print(system.time( p2 <- workflow(fiedler2009subset, cores=2) )) stopifnot(all.equal(p1, p2)) } } \keyword{misc} MALDIquant/inst/0000755000175000017500000000000013035216066014440 5ustar sebastiansebastianMALDIquant/inst/CITATION0000644000175000017500000000165112624130232015571 0ustar sebastiansebastianbibentry( bibtype="Article", title = "{MALDI}quant: a versatile {R} package for the analysis of mass spectrometry data", author = c(person("Sebastian", "Gibb", role=c("aut", "cre"), email="mail@sebastiangibb.de"), person("Korbinian", "Strimmer", role="ths")), volume = "28", number = "17", pages = "2270--2271", year = "2012", doi = "10.1093/bioinformatics/bts447", url = "http://bioinformatics.oxfordjournals.org/content/28/17/2270.abstract", eprint = "http://bioinformatics.oxfordjournals.org/content/28/17/2270.full.pdf+html", journal = "Bioinformatics", publisher= "Oxford University Press", key = "MALDIquant", header = "To cite the MALDIquant package in publications use:", textVersion = paste("S. Gibb and K. Strimmer. 2012.", "MALDIquant: a versatile R package for the analysis of mass spectrometry data.", "Bioinformatics 28, 2270-2271.") ) MALDIquant/inst/doc/0000755000175000017500000000000013035216066015205 5ustar sebastiansebastianMALDIquant/inst/doc/MALDIquant-intro.R0000644000175000017500000001000013035216066020347 0ustar sebastiansebastian## ----setup, include=FALSE, cache=FALSE----------------------------------- library("knitr") opts_chunk$set(tidy.opts=list(width.cutoff=45), tidy=FALSE, fig.align="center", fig.height=4.25, comment=NA, prompt=TRUE) ## ----env, echo=FALSE----------------------------------------------------- suppressPackageStartupMessages(library("MALDIquant")) ## ----mqsetup, eval=FALSE------------------------------------------------- # install.packages(c("MALDIquant", "MALDIquantForeign")) ## ----mqlibrary, eval=FALSE----------------------------------------------- # library("MALDIquant") ## ----mqobjects----------------------------------------------------------- s <- createMassSpectrum(mass=1:10, intensity=1:10, metaData=list(name="Spectrum1")) s ## ----mqaccess------------------------------------------------------------ mass(s) intensity(s) metaData(s) ## ----mqdataimport-------------------------------------------------------- data(fiedler2009subset) ## ----mqdataimport2------------------------------------------------------- length(fiedler2009subset) fiedler2009subset[1:2] ## ----mqqclength---------------------------------------------------------- any(sapply(fiedler2009subset, isEmpty)) table(sapply(fiedler2009subset, length)) ## ----mqqcregular--------------------------------------------------------- all(sapply(fiedler2009subset, isRegular)) ## ----mqqcplots----------------------------------------------------------- plot(fiedler2009subset[[1]]) plot(fiedler2009subset[[16]]) ## ----mqvs---------------------------------------------------------------- spectra <- transformIntensity(fiedler2009subset, method="sqrt") ## ----mqsm---------------------------------------------------------------- spectra <- smoothIntensity(spectra, method="SavitzkyGolay", halfWindowSize=10) ## ----mqve---------------------------------------------------------------- baseline <- estimateBaseline(spectra[[16]], method="SNIP", iterations=100) plot(spectra[[16]]) lines(baseline, col="red", lwd=2) ## ----mqbc---------------------------------------------------------------- spectra <- removeBaseline(spectra, method="SNIP", iterations=100) plot(spectra[[1]]) ## ----mqcb---------------------------------------------------------------- spectra <- calibrateIntensity(spectra, method="TIC") ## ----mqpa---------------------------------------------------------------- spectra <- alignSpectra(spectra, halfWindowSize=20, SNR=2, tolerance=0.002, warpingMethod="lowess") ## ----mqav1--------------------------------------------------------------- samples <- factor(sapply(spectra, function(x)metaData(x)$sampleName)) ## ----mqav2--------------------------------------------------------------- avgSpectra <- averageMassSpectra(spectra, labels=samples, method="mean") ## ----mqpd1--------------------------------------------------------------- noise <- estimateNoise(avgSpectra[[1]]) plot(avgSpectra[[1]], xlim=c(4000, 5000), ylim=c(0, 0.002)) lines(noise, col="red") lines(noise[,1], noise[, 2]*2, col="blue") ## ----mqpd2--------------------------------------------------------------- peaks <- detectPeaks(avgSpectra, method="MAD", halfWindowSize=20, SNR=2) plot(avgSpectra[[1]], xlim=c(4000, 5000), ylim=c(0, 0.002)) points(peaks[[1]], col="red", pch=4) ## ----mqpb---------------------------------------------------------------- peaks <- binPeaks(peaks, tolerance=0.002) ## ----mqfp---------------------------------------------------------------- peaks <- filterPeaks(peaks, minFrequency=0.25) ## ----mqim---------------------------------------------------------------- featureMatrix <- intensityMatrix(peaks, avgSpectra) head(featureMatrix[, 1:3]) ## ----sessioninfo, echo=FALSE, results="asis"----------------------------- toLatex(sessionInfo(), locale=FALSE) MALDIquant/inst/doc/MALDIquant-intro.pdf0000644000175000017500000361711413035216066020745 0ustar sebastiansebastian%PDF-1.5 % 73 0 obj << /Length 672 /Filter /FlateDecode >> stream xڅTn0+xj÷ȞuРAķ$Ze!Ht}\8n1;KJ*BՌ~r~_.,'\0ʉ ,7d&qy!] ƌ4T'r;_L3AyrwWdrcH%Xǜw]ۦ[;&D0 Bj L(Mʕ|2Ě\ի)?ZsS(+Y J,"ZCyrp#@,hp .\ j@!܀0]@لr54AƐ8J~ 6yyԯ۔^(6q(ڦە.)KB"g0|-H1JXs{< YIeFָ`3ڪC)t< #q~۷j;1P㭫 ]_ŃrE9 (S@»=0°+ EfvH !kC­d No l.L|$_Hqta%(|*h6kݷ9'l4Ը,, WaL{LWOs endstream endobj 107 0 obj << /Length 1222 /Filter /FlateDecode >> stream xXKs6WX$Sθm:iIɬPwe\ƽX~b!6L Ƴم'hR-i,n N1RP]d$*×uc]龫f2*U(Hc0˷@<۔CW!Ԉ~4xֻ-rܵSf}v<G0Heeٗ<"{b#pp'TFgۮmC1x*Orաcmk蒆)QEdtU o~rcqL eƇXu`Kb.UM TW? 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If you use it, please support the project by citing it in publications: \begin{quote} \bibentry{MALDIquant} \end{quote} If you have any questions, bugs, or suggestions do not hesitate to contact me (\email{mail@sebastiangibb.de}). \\ Please visit \url{http://strimmerlab.org/software/maldiquant/}. \section{Introduction} \Mq{} comprising all steps from importing of raw data, preprocessing (e.g. baseline removal), peak detection and non-linear peak alignment to calibration of mass spectra. \Mq{} was initially developed for clinical proteomics using Matrix-Assisted Laser Desorption/Ionization (MALDI) technology. However, the algorithms implemented in \Mq{} are generic and may be equally applied to other 2D mass spectrometry data. \Mq{} was carefully designed to be independent of any specific mass spectrometry hardware. Nonetheless, a lot of open and native file formates, e.g. binary data files from Bruker flex series instruments, mzXML, mzML, etc. are supported through the associated \R{} package \Rpackage{MALDIquantForeign}. \section{Setup} After starting \R{} we could install \Mq{} and \Rpackage{MALDIquantForeign} directly from \CRAN{} using \Rfunction{install.packages}: <>= install.packages(c("MALDIquant", "MALDIquantForeign")) @ Before we can use \Mq{} we have to load the package. <>= library("MALDIquant") @ \section{MALDIquant objects} \Mq{} is written in an object-oriented programming approach and uses \R{}'s S4 objects. A spectrum is represented by an \Robject{MassSpectrum} and a list of peaks by an \Robject{MassPeaks} instance. To create such objects manually we could use \Rfunction{createMassSpectrum} and \Rfunction{createMassPeaks}. In general we do not need these functions because \Rpackage{MALDIquantForeign's} import routines will generate the \Robject{MassSpectrum}/\Robject{MassPeaks} objects. <>= s <- createMassSpectrum(mass=1:10, intensity=1:10, metaData=list(name="Spectrum1")) s @ Each \Robject{MassSpectrum}/\Robject{MassPeaks} stores the mass and intensity values of a spectrum respective of the peaks. Additionally they contain a list of metadata. To access these information we use \Rfunction{mass}, \Rfunction{intensity} and \Rfunction{metaData}. <>= mass(s) intensity(s) metaData(s) @ \section{Workflow} A Mass Spectrometry Analysis often follows the same workflow (see also Fig. \ref{fig:workflow}). After importing the raw data (see also the \Rpackage{MALDIquantForeign} package) we control the quality of the spectra and draw some plots. We apply a variance-stabilizing transformation and smoothing filter. Next we remove the chemical background using a Baseline Correction method. To compare the intensities across spectra we calibrate the intensity values (often called normalization) and the mass values (warping, alignment). Subsequently we perfom a Peak Detection and do some post processing like filtering etc. \begin{figure}[htbp] \centering \begin{small} \workflow{} \caption{MS Analysis Workflow} \label{fig:workflow} \end{small} \end{figure} \clearpage \subsection{Data Import} Normally we will use some of the import methods provided by \Rpackage{MALDIquantForeign}, e.g. \Rfunction{importBrukerFlex}, \Rfunction{importMzMl}, etc. But in this vignette we will use a small example dataset shipped with \Mq{}. This dataset is a subset of MALDI-TOF data described in \cite{Fiedler2009}. <>= data(fiedler2009subset) @ \Robject{fiedler2009subset} is a \Robject{list} of 16 \Robject{MassSpectrum} objects. The 16 spectra are 8 biological samples with 2 technical replicates. <>= length(fiedler2009subset) fiedler2009subset[1:2] @ \subsection{Quality Control} For a basic quality control we test whether all spectra contain the same number of data points and are not empty. <>= any(sapply(fiedler2009subset, isEmpty)) table(sapply(fiedler2009subset, length)) @ Subsequently we control the mass difference between each data point (should be equal or monotonically increasing) because \Mq{} is designed for profile data and not for centroided data. <>= all(sapply(fiedler2009subset, isRegular)) @ Finally we draw some plots and inspect the spectra visually. <>= plot(fiedler2009subset[[1]]) plot(fiedler2009subset[[16]]) @ \subsection{Variance Stabilization} We use the square root transformation to simplify graphical visualization and to overcome the potential dependency of the variance from the mean. <>= spectra <- transformIntensity(fiedler2009subset, method="sqrt") @ \subsection{Smoothing} Next we use a 21 point Savitzky-Golay-Filter \citep{Savitzky1964} to smooth the spectra. <>= spectra <- smoothIntensity(spectra, method="SavitzkyGolay", halfWindowSize=10) @ \subsection{Baseline Correction} Before we correct the baseline we visualize it. Here we use the SNIP algorithm \citep{Ryan1988}. <>= baseline <- estimateBaseline(spectra[[16]], method="SNIP", iterations=100) plot(spectra[[16]]) lines(baseline, col="red", lwd=2) @ If we are satisfied with our estimated baseline we remove it. <>= spectra <- removeBaseline(spectra, method="SNIP", iterations=100) plot(spectra[[1]]) @ \subsection{Intensity Calibration/Normalization} For better comparison and to overcome (very) small batch effects we equalize the intensity values using the Total-Ion-Current-Calibration (often called normalization). <>= spectra <- calibrateIntensity(spectra, method="TIC") @ \subsection{Warping/Alignment} Now we (re)calibrate the mass values. Our alignment procedure is a peak based warping algorithm. If you need a finer control or want to investigate the impact of different parameters please use \Rfunction{determineWarpingFunctions} instead of the easier \Rfunction{alignSpectra}. <>= spectra <- alignSpectra(spectra, halfWindowSize=20, SNR=2, tolerance=0.002, warpingMethod="lowess") @ Before we call the Peak Detection we want to average the technical replicates. Therefore we look for the sample name that is stored in the metadata because each technical replicate has the same sample name. <>= samples <- factor(sapply(spectra, function(x)metaData(x)$sampleName)) @ Next we use \Rfunction{averageMassSpectra} to create a mean spectrum for each biological sample. <>= avgSpectra <- averageMassSpectra(spectra, labels=samples, method="mean") @ \subsection{Peak Detection} The next crucial step is the Peak Detection. Before we perform the peak detection algorithm we estimate the noise of the spectra to get a feeling for the signal-to-noise ratio. <>= noise <- estimateNoise(avgSpectra[[1]]) plot(avgSpectra[[1]], xlim=c(4000, 5000), ylim=c(0, 0.002)) lines(noise, col="red") lines(noise[,1], noise[, 2]*2, col="blue") @ We decide to use a signal-to-noise ratio of 2 (blue line). <>= peaks <- detectPeaks(avgSpectra, method="MAD", halfWindowSize=20, SNR=2) plot(avgSpectra[[1]], xlim=c(4000, 5000), ylim=c(0, 0.002)) points(peaks[[1]], col="red", pch=4) @ \subsection{Peak Binning} After the alignment the peak positions (mass) are very similar but not identical. The binning is needed to make similar peak mass values identical. <>= peaks <- binPeaks(peaks, tolerance=0.002) @ \subsection{Feature Matrix} We choose a very low signal-to-noise ratio to keep as much features as possible. To remove some false positive peaks we remove less frequent peaks. <>= peaks <- filterPeaks(peaks, minFrequency=0.25) @ At the end of the analysis we create a feature matrix that could be used in further statistical analysis. Please note that missing values (not detected peaks) are imputed/interpolated from the corresponding spectrum. <>= featureMatrix <- intensityMatrix(peaks, avgSpectra) head(featureMatrix[, 1:3]) @ \section{Summary} We shortly described a complete example workflow of a mass spectrometry data analysis. Please note that this workflow is only an example and could not cover every use case.\\ \Mq{} provides a lot of more functions than we mentioned in this vignette. The described functions are the most used ones but they have a lot of more parameters which could/need adjust to your data (e.g. \Robject{halfWindowSize}, \Robject{SNR}, \Robject{tolerance}, etc.). That's why we suggest the user to read the manual pages of theses functions carefully.\\ We also provide more examples in the demo directory and at: \begin{quote} \url{http://strimmerlab.org/software/maldiquant/} \end{quote} Please do not hesitate to contact me (\email{mail@sebastiangibb.de}) if you have any questions. \section{Session Information} <>= toLatex(sessionInfo(), locale=FALSE) @ \bibliographystyle{apalike} \bibliography{bibliography} \end{document} MALDIquant/NAMESPACE0000644000175000017500000000466213022761357014716 0ustar sebastiansebastianimport("methods") importFrom("parallel", "mclapply", "mcmapply") importFrom("graphics", "abline", "arrows", "lines", "par", "plot", "plot.default", "points", "rasterImage", "rect", "strheight", "strwidth", "text") importFrom("grDevices", "as.raster", "colorRamp", "dev.cur", "dev.interactive", "dev.new", "dev.off", "pdf", "rgb") importFrom("stats", "coef", "dpois", "filter", "lm", "lowess", "mad", "median", "runmed", "supsmu") importFrom("utils", "head", "modifyList", "object.size", "packageVersion", "relist", "tail") exportClasses("MassPeaks", "MassSpectrum") export("alignSpectra", "averageMassSpectra", "binPeaks", "createMassPeaks", "createMassSpectrum", "determineWarpingFunctions", "filterPeaks", "findEmptyMassObjects", "intensityMatrix", "isMassPeaks", "isMassPeaksList", "isMassSpectrum", "isMassSpectrumList", "match.closest", "mergeMassPeaks", "msiSlices", "referencePeaks", "removeEmptyMassObjects", "warpMassPeaks", "warpMassSpectra") exportMethods("as.matrix", "calibrateIntensity", "coordinates", "coordinates<-", "detectPeaks", "estimateBaseline", "estimateNoise", "intensity", "intensity<-", "isEmpty", "isRegular", "labelPeaks", "length", "lines", "mass", "mass<-", "metaData", "metaData<-", "monoisotopicPeaks", "mz", "mz<-", "plot", "plotMsiSlice", "points", "removeBaseline", "smoothIntensity", "snr", "totalIonCurrent", "transformIntensity", "trim") useDynLib("MALDIquant") 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*vignettes/MALDIquant-intro.Rnw 5c072541fc956025aecacd9dc2129c6a *vignettes/bibliography.bib MALDIquant/DESCRIPTION0000644000175000017500000000262713035270676015207 0ustar sebastiansebastianPackage: MALDIquant Version: 1.16.1 Date: 2016-12-23 Title: Quantitative Analysis of Mass Spectrometry Data Authors@R: c(person("Sebastian", "Gibb", role=c("aut", "cre"), email="mail@sebastiangibb.de"), person("Korbinian", "Strimmer", role="ths")) Depends: R (>= 3.2.0), methods Imports: parallel Suggests: knitr, testthat (>= 0.8) Description: A complete analysis pipeline for matrix-assisted laser desorption/ionization-time-of-flight (MALDI-TOF) and other two-dimensional mass spectrometry data. In addition to commonly used plotting and processing methods it includes distinctive features, namely baseline subtraction methods such as morphological filters (TopHat) or the statistics-sensitive non-linear iterative peak-clipping algorithm (SNIP), peak alignment using warping functions, handling of replicated measurements as well as allowing spectra with different resolutions. License: GPL (>= 3) URL: http://strimmerlab.org/software/maldiquant/ https://github.com/sgibb/MALDIquant/ BugReports: https://github.com/sgibb/MALDIquant/issues/ LazyLoad: yes VignetteBuilder: knitr RoxygenNote: 5.0.1 NeedsCompilation: yes Packaged: 2017-01-10 17:39:02 UTC; sebastian Author: Sebastian Gibb [aut, cre], Korbinian Strimmer [ths] Maintainer: Sebastian Gibb Repository: CRAN Date/Publication: 2017-01-11 00:43:58 MALDIquant/demo/0000755000175000017500000000000012721405447014413 5ustar sebastiansebastianMALDIquant/demo/baseline.R0000644000175000017500000000155212624130232016310 0ustar sebastiansebastian## This is a MALDIquant example file. It is released into public domain with the ## right to use it for any purpose but without any warranty. ## baseline demo ## load necessary packages ## requires MALDIquant >= 1.9 library("MALDIquant") ## load example spectra data("fiedler2009subset", package="MALDIquant") ## choose only spectrum 1 s <- fiedler2009subset[[1]] ## test different baseline estimation methods bSnip <- estimateBaseline(s, method="SNIP") plot(s, main="SNIP Baseline [default]") lines(bSnip, lwd=2, col=2) bTopHat <- estimateBaseline(s, method="TopHat") plot(s, main="TopHat Baseline") lines(bTopHat, lwd=2, col=2) bConvexHull <- estimateBaseline(s, method="ConvexHull") plot(s, main="ConvexHull Baseline") lines(bConvexHull, lwd=2, col=2) bMedian <- estimateBaseline(s, method="median") plot(s, main="Median Baseline") lines(bMedian, lwd=2, col=2) MALDIquant/demo/workflow.R0000644000175000017500000000724412624130232016404 0ustar sebastiansebastian## This is a MALDIquant example file. It is released into public domain with the ## right to use it for any purpose but without any warranty. ## workflow demo ## load necessary packages ## requires MALDIquant >= 1.9 library("MALDIquant") ## load example spectra data("fiedler2009subset", package="MALDIquant") ## check raw data ## any empty spectra? (empty spectra are ignored in subsequent baseline ## correction/peak detection; you could find/remove them by calling ## findEmptyMassObjects/removeEmptyMassObjects) ## see ?isEmpty, ?findEmptyMassObjects, ?removeEmptyMassObjects any(sapply(fiedler2009subset, isEmpty)) # FALSE ## any spectra with irregular mass values/intervals? (spectra with ## missing/filtered mass values/irregular mass intervals may compromise ## subsequent baseline correction and peak detection.) any(!sapply(fiedler2009subset, isRegular)) # FALSE ## do length of spectra differ? (if they differ you have to adjust the ## corresponding halfWindowSize in subsequent baseline correction and peak ## detection.) table(sapply(fiedler2009subset, length)) # 42388 # 16 ## preprocessing ## sqrt transform (for variance stabilization) spectra <- transformIntensity(fiedler2009subset, method="sqrt") ## 21 point Savitzky-Golay-Filter for smoothing spectra ## (maybe you have to adjust the halfWindowSize; ## you could use a simple moving average instead) ## see ?smoothIntensity spectra <- smoothIntensity(spectra, method="SavitzkyGolay", halfWindowSize=10) ## remove baseline ## (maybe you have to adjust iterations to your spectra; high resolution ## spectra need a much lower iteration number (halfWindowSize, for some other ## baseline estimation algorithms) ## see ?removeBaseline, ?estimateBaseline spectra <- removeBaseline(spectra, method="SNIP", iterations=100) ## calibrate (normalize) intensities (different calibration methods available) ## see ?calibrateIntensity spectra <- calibrateIntensity(spectra, method="TIC") ## spectra alignment ## (the spectra alignment is peak based, maybe you need to adjust ## halfWindowSize, SNR, tolerance, warpingMethod) ## see ?alignSpectra spectra <- alignSpectra(spectra, halfWindowSize=20, SNR=2, tolerance=0.002, warpingMethod="lowess") ## average technical replicates ## 1. create factors for correct assignment ## (e.g. sample name is stored in metaData(x)$sampleName; maybe you have to ## adjust the metaData or use your own ID table for your data here) samples <- factor(sapply(spectra, function(x)metaData(x)$sampleName)) ## 2. average technical replicates ## see ?averageMassSpectra avgSpectra <- averageMassSpectra(spectra, labels=samples, method="mean") ## run peak detection ## (maybe you need to adjust halfWindowSize [decreasing it for high resolution ## spectra] and SNR [a higher value increase the True-Positive-Rate but decrease ## sensitivity]) ## see ?detectPeaks, ?estimateNoise peaks <- detectPeaks(avgSpectra, method="MAD", halfWindowSize=20, SNR=2) ## bin peaks ## (After alignment peak positions (mass) are similar but not identical. Binning ## is needed to make similar peak mass values identical.) ## see ?binPeaks peaks <- binPeaks(peaks, tolerance=0.002) ## prepare for statistical analysis ## 1. get cancer/control indices filenames <- sapply(peaks, function(x)metaData(x)$file[1]) cancer <- grepl(pattern="/tumor/", x=filenames) classes <- factor(ifelse(cancer, "cancer", "control"), levels=c("cancer", "control")) ## 2. export expression/training matrix ## (and fill missing peaks by interpolated values) ## see ?intensityMatrix training <- intensityMatrix(peaks, avgSpectra) ## 'training' and 'classes' could now used by any statistical tool e.g. sda MALDIquant/demo/MALDIquant.R0000644000175000017500000000062312523633731016435 0ustar sebastiansebastian## This is a MALDIquant example file. It is released into public domain with the ## right to use it for any purpose but without any warranty. ## all MALDIquant demos library("MALDIquant") demo("baseline", package="MALDIquant") demo("peaks", package="MALDIquant") demo("warping", package="MALDIquant") message("demo(\"workflow\") produces no graphical output") demo("workflow", package="MALDIquant") MALDIquant/demo/warping.R0000644000175000017500000000403512624130232016174 0ustar sebastiansebastian## This is a MALDIquant example file. It is released into public domain with the ## right to use it for any purpose but without any warranty. ## warping demo ## load necessary packages ## requires MALDIquant >= 1.9 library("MALDIquant") ## load example spectra data("fiedler2009subset", package="MALDIquant") ## use only 4 spectra spectra <- fiedler2009subset[seq(1, 16, by=4)] ## some preprocessing ## sqrt transform (for variance stabilization) spectra <- transformIntensity(spectra, method="sqrt") ## 21 point Savitzky-Golay-Filter for smoothing spectra spectra <- smoothIntensity(spectra, method="SavitzkyGolay", halfWindowSize=10) ## remove baseline spectra <- removeBaseline(spectra, method="SNIP", iterations=100) ## calibrate intensity values by "Total Ion Current" spectra <- calibrateIntensity(spectra, method="TIC") ## run peak detection peaks <- detectPeaks(spectra, method="MAD", halfWindowSize=20, SNR=2) ## warping par(mfrow=c(2, 2)) warpingFunctions <- determineWarpingFunctions(peaks, tolerance=0.001, plot=TRUE, plotInteractive=TRUE) ## warp spectra warpedSpectra <- warpMassSpectra(spectra, warpingFunctions) ## warp peaks warpedPeaks <- warpMassPeaks(peaks, warpingFunctions) ## compare some regions in a plot par(mfrow=c(2, 2)) ## helper function to avoid double coding plotSpectra <- function(unwarped, warped, range) { plot(unwarped[[1]], main=paste0("unwarped spectra (mass ", paste0(range, collapse=":"), " Da)"), xlim=range, ylim=c(0, 2e-3), type="n") color <- rainbow(length(unwarped)) for (i in seq(along=unwarped)) { lines(unwarped[[i]], col=color[i]) } plot(unwarped[[1]], main=paste0("warped spectra (mass ", paste0(range, collapse=":"), " Da)"), xlim=range, ylim=c(0, 2e-3), type="n") for (i in seq(along=warped)) { lines(warped[[i]], col=color[i]) } } plotSpectra(spectra, warpedSpectra, c(4180, 4240)) plotSpectra(spectra, warpedSpectra, c(9200, 9400)) par(mfrow=c(1, 1)) MALDIquant/demo/peaks.R0000644000175000017500000000366712624130232015642 0ustar sebastiansebastian## This is a MALDIquant example file. It is released into public domain with the ## right to use it for any purpose but without any warranty. ## peaks demo ## load necessary packages ## requires MALDIquant >= 1.9 library("MALDIquant") ## load example spectra data("fiedler2009subset", package="MALDIquant") ## choose only spectrum 1 s1 <- fiedler2009subset[[1]] ## preprocessing ## sqrt transform (for variance stabilization) s2 <- transformIntensity(s1, method="sqrt") ## 21 point Savitzky-Golay-Filter for smoothing spectra ## (maybe you have to adjust the halfWindowSize; ## you could use a simple moving average instead) ## see ?smoothIntensity s3 <- smoothIntensity(s2, method="SavitzkyGolay", halfWindowSize=10) ## remove baseline ## (maybe you have to adjust iterations to your spectra; high resolution ## spectra need a much lower iteration number (halfWindowSize, for some other ## baseline estimation algorithms) ## see ?removeBaseline, ?estimateBaseline s4 <- removeBaseline(s3, method="SNIP", iterations=100) ## run peak detection ## (maybe you need to adjust halfWindowSize [decreasing it for high resolution ## spectra] and SNR [a higher value increase the True-Positive-Rate but decrease ## sensitivity]) ## see ?detectPeaks, ?estimateNoise p <- detectPeaks(s4, method="MAD", halfWindowSize=20, SNR=2) ## produce some plots par(mfrow=c(2,3)) xlim <- range(mass(s1)) # use same xlim on all plots for better comparison plot(s1, main="1: raw", sub="", xlim=xlim) plot(s2, main="2: variance stabilization", sub="", xlim=xlim) plot(s3, main="3: smoothing", sub="", xlim=xlim) plot(s4, main="4: baseline correction", sub="", xlim=xlim) plot(s4, main="5: peak detection", sub="", xlim=xlim) points(p) ## label top 20 peaks top20 <- intensity(p) %in% sort(intensity(p), decreasing=TRUE)[1:20] labelPeaks(p, index=top20, underline=TRUE) plot(p, main="6: peak plot", sub="", xlim=xlim) labelPeaks(p, index=top20, underline=TRUE) par(mfrow=c(1,1)) MALDIquant/demo/00Index0000644000175000017500000000037012325510750015537 0ustar sebastiansebastianMALDIquant MALDIquant Demonstration baseline Demonstrate different baseline estimation methods. peaks Demonstrate peak detection. warping Demonstrate influence of warping. workflow Complete example workflow (no graphical output). MALDIquant/vignettes/0000755000175000017500000000000013035216066015473 5ustar sebastiansebastianMALDIquant/vignettes/bibliography.bib0000644000175000017500000000444512624130232020624 0ustar sebastiansebastian@MANUAL{RPROJECT, title = {R: A Language and Environment for Statistical Computing}, author = {{R Core Team}}, organization = {R Foundation for Statistical Computing}, address = {Vienna, Austria}, year = {2014}, url = {http://www.R-project.org/}, } @ARTICLE{MALDIquant, title = {{MALDI}quant: a versatile {R} package for the analysis of mass spectrometry data}, author = {Sebastian Gibb and Korbinian Strimmer}, volume = {28}, number = {17}, pages = {2270-2271}, year = {2012}, doi = {10.1093/bioinformatics/bts447}, url = {http://bioinformatics.oxfordjournals.org/content/28/17/2270.abstract}, eprint = {http://bioinformatics.oxfordjournals.org/content/28/17/2270.full.pdf+html}, journal = {Bioinformatics}, publisher = {Oxford Univ Press}, } @ARTICLE{Fiedler2009, author = {Georg Martin Fiedler and Alexander Benedikt Leichtle and Julia Kase and Sven Baumann and Uta Ceglarek and Klaus Felix and Tim Conrad and Helmut Witzigmann and Arved Weimann and Christof Schütte and Johann Hauss and Markus Büchler and Joachim Thiery}, title = {Serum peptidome profiling revealed platelet factor 4 as a potential discriminating Peptide associated with pancreatic cancer.}, journal = {Clin Cancer Res}, year = {2009}, volume = {15}, pages = {3812--3819}, number = {11}, month = {Jun}, url = {http://dx.doi.org/10.1158/1078-0432.CCR-08-2701} } @ARTICLE{Savitzky1964, author = {Savitzky, Abraham. and Golay, M. J. E.}, title = {Smoothing and Differentiation of Data by Simplified Least Squares Procedures.}, journal = {Analytical Chemistry}, year = {1964}, volume = {36}, pages = {1627-1639}, number = {8}, url = {http://pubs.acs.org/doi/abs/10.1021/ac60214a047} } @ARTICLE{Ryan1988, author = {C.G. Ryan and E. Clayton and W.L. Griffin and S.H. Sie and D.R. Cousens}, title = {SNIP, a statistics-sensitive background treatment for the quantitative analysis of PIXE spectra in geoscience applications}, journal = {Nuclear Instruments and Methods in Physics Research Section B: Beam Interactions with Materials and Atoms}, year = {1988}, volume = {34}, pages = {396 - 402}, number = {3}, doi = {doi: 10.1016/0168-583X(88)90063-8}, issn = {0168-583X}, url = {http://www.sciencedirect.com/science/article/B6TJN-46YSYTJ-30/2/e0d015ceb8ea8a7bc0702a857a19750b} } MALDIquant/vignettes/MALDIquant-intro.Rnw0000644000175000017500000002660612624130232021216 0ustar sebastiansebastian%\VignetteEngine{knitr} %\VignetteIndexEntry{MALDIquant: Quantitative Analysis of Mass Spectrometry Data} %\VignetteKeywords{Bioinformatics, Proteomics, Mass Spectrometry} %\VignettePackage{MALDIquant} \documentclass[12pt]{article} \usepackage{natbib} \usepackage{hyperref} \usepackage{tikz} \usepackage{bibentry} % inline bibentries \nobibliography* % no special bibliography for bibentry \newcommand{\R}{\texttt{R}} \newcommand{\CRAN}{\texttt{CRAN}} \newcommand{\Rfunction}[1]{{\texttt{#1}}} \newcommand{\Robject}[1]{{\texttt{#1}}} \newcommand{\Rpackage}[1]{{\texttt{#1}}} \newcommand{\Mq}{\Rpackage{MALDIquant}} \newcommand{\email}[1]{\href{mailto:#1}{\normalfont\texttt{#1}}} \newcommand{\workflow}{ \tikzstyle{line} = [draw, -latex] \tikzstyle{mqbox}=[rectangle, draw, text width=8.5em, text centered, minimum height=2.0em, fill=white] \begin{tikzpicture}[node distance=3.2em] % nodes \node [mqbox] (di) {Data Import}; \node [mqbox, below of=di] (qc) {Quality Control}; \node [mqbox, below of=qc] (vs) {Transformation \& Smoothing}; \node [mqbox, below of=vs] (bc) {Baseline Correction}; \node [mqbox, below of=bc] (cb) {Intensity Calibration}; \node [mqbox, below of=cb] (sa) {Spectra Alignment}; \node [mqbox, below of=sa] (pd) {Peak Detection}; \node [mqbox, below of=pd] (pb) {Peak Binning}; \node [mqbox, below of=pb] (fm) {Feature Matrix}; % edges \path [line] (di) -- (qc); \path [line] (qc) -- (vs); \path [line] (vs) -- (bc); \path [line] (bc) -- (cb); \path [line] (cb) -- (sa); \path [line] (sa) -- (pd); \path [line] (pd) -- (pb); \path [line] (pb) -- (fm); \end{tikzpicture} } \title{\Mq{}: Quantitative Analysis of Mass Spectrometry Data} \author{ Sebastian Gibb% \thanks{\email{mail@sebastiangibb.de}} } \date{\today} \begin{document} <>= library("knitr") opts_chunk$set(tidy.opts=list(width.cutoff=45), tidy=FALSE, fig.align="center", fig.height=4.25, comment=NA, prompt=TRUE) @ <>= suppressPackageStartupMessages(library("MALDIquant")) @ \maketitle \begin{abstract} \Mq{} provides a complete analysis pipeline for MALDI-TOF and other 2D mass spectrometry data.\\ This vignette describes the usage of the \Mq{} package and guides the user through a typical preprocessing workflow. \end{abstract} \clearpage \tableofcontents \section*{Foreword} \Mq{} is free and open source software for the \R{} \citep{RPROJECT} environment and under active development. If you use it, please support the project by citing it in publications: \begin{quote} \bibentry{MALDIquant} \end{quote} If you have any questions, bugs, or suggestions do not hesitate to contact me (\email{mail@sebastiangibb.de}). \\ Please visit \url{http://strimmerlab.org/software/maldiquant/}. \section{Introduction} \Mq{} comprising all steps from importing of raw data, preprocessing (e.g. baseline removal), peak detection and non-linear peak alignment to calibration of mass spectra. \Mq{} was initially developed for clinical proteomics using Matrix-Assisted Laser Desorption/Ionization (MALDI) technology. However, the algorithms implemented in \Mq{} are generic and may be equally applied to other 2D mass spectrometry data. \Mq{} was carefully designed to be independent of any specific mass spectrometry hardware. Nonetheless, a lot of open and native file formates, e.g. binary data files from Bruker flex series instruments, mzXML, mzML, etc. are supported through the associated \R{} package \Rpackage{MALDIquantForeign}. \section{Setup} After starting \R{} we could install \Mq{} and \Rpackage{MALDIquantForeign} directly from \CRAN{} using \Rfunction{install.packages}: <>= install.packages(c("MALDIquant", "MALDIquantForeign")) @ Before we can use \Mq{} we have to load the package. <>= library("MALDIquant") @ \section{MALDIquant objects} \Mq{} is written in an object-oriented programming approach and uses \R{}'s S4 objects. A spectrum is represented by an \Robject{MassSpectrum} and a list of peaks by an \Robject{MassPeaks} instance. To create such objects manually we could use \Rfunction{createMassSpectrum} and \Rfunction{createMassPeaks}. In general we do not need these functions because \Rpackage{MALDIquantForeign's} import routines will generate the \Robject{MassSpectrum}/\Robject{MassPeaks} objects. <>= s <- createMassSpectrum(mass=1:10, intensity=1:10, metaData=list(name="Spectrum1")) s @ Each \Robject{MassSpectrum}/\Robject{MassPeaks} stores the mass and intensity values of a spectrum respective of the peaks. Additionally they contain a list of metadata. To access these information we use \Rfunction{mass}, \Rfunction{intensity} and \Rfunction{metaData}. <>= mass(s) intensity(s) metaData(s) @ \section{Workflow} A Mass Spectrometry Analysis often follows the same workflow (see also Fig. \ref{fig:workflow}). After importing the raw data (see also the \Rpackage{MALDIquantForeign} package) we control the quality of the spectra and draw some plots. We apply a variance-stabilizing transformation and smoothing filter. Next we remove the chemical background using a Baseline Correction method. To compare the intensities across spectra we calibrate the intensity values (often called normalization) and the mass values (warping, alignment). Subsequently we perfom a Peak Detection and do some post processing like filtering etc. \begin{figure}[htbp] \centering \begin{small} \workflow{} \caption{MS Analysis Workflow} \label{fig:workflow} \end{small} \end{figure} \clearpage \subsection{Data Import} Normally we will use some of the import methods provided by \Rpackage{MALDIquantForeign}, e.g. \Rfunction{importBrukerFlex}, \Rfunction{importMzMl}, etc. But in this vignette we will use a small example dataset shipped with \Mq{}. This dataset is a subset of MALDI-TOF data described in \cite{Fiedler2009}. <>= data(fiedler2009subset) @ \Robject{fiedler2009subset} is a \Robject{list} of 16 \Robject{MassSpectrum} objects. The 16 spectra are 8 biological samples with 2 technical replicates. <>= length(fiedler2009subset) fiedler2009subset[1:2] @ \subsection{Quality Control} For a basic quality control we test whether all spectra contain the same number of data points and are not empty. <>= any(sapply(fiedler2009subset, isEmpty)) table(sapply(fiedler2009subset, length)) @ Subsequently we control the mass difference between each data point (should be equal or monotonically increasing) because \Mq{} is designed for profile data and not for centroided data. <>= all(sapply(fiedler2009subset, isRegular)) @ Finally we draw some plots and inspect the spectra visually. <>= plot(fiedler2009subset[[1]]) plot(fiedler2009subset[[16]]) @ \subsection{Variance Stabilization} We use the square root transformation to simplify graphical visualization and to overcome the potential dependency of the variance from the mean. <>= spectra <- transformIntensity(fiedler2009subset, method="sqrt") @ \subsection{Smoothing} Next we use a 21 point Savitzky-Golay-Filter \citep{Savitzky1964} to smooth the spectra. <>= spectra <- smoothIntensity(spectra, method="SavitzkyGolay", halfWindowSize=10) @ \subsection{Baseline Correction} Before we correct the baseline we visualize it. Here we use the SNIP algorithm \citep{Ryan1988}. <>= baseline <- estimateBaseline(spectra[[16]], method="SNIP", iterations=100) plot(spectra[[16]]) lines(baseline, col="red", lwd=2) @ If we are satisfied with our estimated baseline we remove it. <>= spectra <- removeBaseline(spectra, method="SNIP", iterations=100) plot(spectra[[1]]) @ \subsection{Intensity Calibration/Normalization} For better comparison and to overcome (very) small batch effects we equalize the intensity values using the Total-Ion-Current-Calibration (often called normalization). <>= spectra <- calibrateIntensity(spectra, method="TIC") @ \subsection{Warping/Alignment} Now we (re)calibrate the mass values. Our alignment procedure is a peak based warping algorithm. If you need a finer control or want to investigate the impact of different parameters please use \Rfunction{determineWarpingFunctions} instead of the easier \Rfunction{alignSpectra}. <>= spectra <- alignSpectra(spectra, halfWindowSize=20, SNR=2, tolerance=0.002, warpingMethod="lowess") @ Before we call the Peak Detection we want to average the technical replicates. Therefore we look for the sample name that is stored in the metadata because each technical replicate has the same sample name. <>= samples <- factor(sapply(spectra, function(x)metaData(x)$sampleName)) @ Next we use \Rfunction{averageMassSpectra} to create a mean spectrum for each biological sample. <>= avgSpectra <- averageMassSpectra(spectra, labels=samples, method="mean") @ \subsection{Peak Detection} The next crucial step is the Peak Detection. Before we perform the peak detection algorithm we estimate the noise of the spectra to get a feeling for the signal-to-noise ratio. <>= noise <- estimateNoise(avgSpectra[[1]]) plot(avgSpectra[[1]], xlim=c(4000, 5000), ylim=c(0, 0.002)) lines(noise, col="red") lines(noise[,1], noise[, 2]*2, col="blue") @ We decide to use a signal-to-noise ratio of 2 (blue line). <>= peaks <- detectPeaks(avgSpectra, method="MAD", halfWindowSize=20, SNR=2) plot(avgSpectra[[1]], xlim=c(4000, 5000), ylim=c(0, 0.002)) points(peaks[[1]], col="red", pch=4) @ \subsection{Peak Binning} After the alignment the peak positions (mass) are very similar but not identical. The binning is needed to make similar peak mass values identical. <>= peaks <- binPeaks(peaks, tolerance=0.002) @ \subsection{Feature Matrix} We choose a very low signal-to-noise ratio to keep as much features as possible. To remove some false positive peaks we remove less frequent peaks. <>= peaks <- filterPeaks(peaks, minFrequency=0.25) @ At the end of the analysis we create a feature matrix that could be used in further statistical analysis. Please note that missing values (not detected peaks) are imputed/interpolated from the corresponding spectrum. <>= featureMatrix <- intensityMatrix(peaks, avgSpectra) head(featureMatrix[, 1:3]) @ \section{Summary} We shortly described a complete example workflow of a mass spectrometry data analysis. Please note that this workflow is only an example and could not cover every use case.\\ \Mq{} provides a lot of more functions than we mentioned in this vignette. The described functions are the most used ones but they have a lot of more parameters which could/need adjust to your data (e.g. \Robject{halfWindowSize}, \Robject{SNR}, \Robject{tolerance}, etc.). That's why we suggest the user to read the manual pages of theses functions carefully.\\ We also provide more examples in the demo directory and at: \begin{quote} \url{http://strimmerlab.org/software/maldiquant/} \end{quote} Please do not hesitate to contact me (\email{mail@sebastiangibb.de}) if you have any questions. \section{Session Information} <>= toLatex(sessionInfo(), locale=FALSE) @ \bibliographystyle{apalike} \bibliography{bibliography} \end{document} MALDIquant/build/0000755000175000017500000000000013035216066014562 5ustar sebastiansebastianMALDIquant/build/vignette.rds0000644000175000017500000000042413035216066017121 0ustar sebastiansebastianQn0 MhۉBBcvZW&]zH;ϐJ}S|![ j/v\G!VFVyb[6<0Lelōa%$XksmWSB_ 'd $/8eF1 &2}JL$k,i;NϛW+Wa "VE6^nf4G<8e?(8MALDIquant/R/0000755000175000017500000000000013027254751013670 5ustar sebastiansebastianMALDIquant/R/transformIntensity-methods.R0000644000175000017500000000307713022575752021407 0ustar sebastiansebastian## AbstractMassObject setMethod(f="transformIntensity", signature=signature(object="AbstractMassObject"), definition=function(object, method=c("sqrt", "log", "log2", "log10")) { ## empty spectrum? if (.isEmptyWarning(object)) { return(object) } method <- match.arg(method) fun <- switch(method, "sqrt" = { sqrt }, "log" = { log }, "log2" = { log2 }, "log10" = { log10 } ) .transformIntensity(object, fun=fun) }) ## AbstractMassObject setMethod(f=".transformIntensity", signature=signature(object="AbstractMassObject"), definition=function(object, fun, na.rm=TRUE, ...) { if (!isEmpty(object)) { fun <- match.fun(fun) object@intensity <- fun(object@intensity, ...) if (na.rm) { naIdx <- which(!is.na(object@intensity)) object@intensity <- object@intensity[naIdx] object@mass <- object@mass[naIdx] } object <- .replaceNegativeIntensityValues(object) } object }) ## list setMethod(f="transformIntensity", signature=signature(object="list"), definition=function(object, ...) { ## test arguments .stopIfNotIsMassObjectList(object) .lapply(object, transformIntensity, ...) }) ## list setMethod(f=".transformIntensity", signature=signature(object="list"), definition=function(object, ...) { ## test arguments .stopIfNotIsMassObjectList(object) .lapply(object, .transformIntensity, ...) }) MALDIquant/R/estimateNoise-functions.R0000644000175000017500000000221113022575752020630 0ustar sebastiansebastian## .estimateNoise ## estimating the noise of a spectrum ## ## params: ## x: vector of x values (mass) ## y: vector of y values (intensity) ## method: method to use ## ...: further arguments passed to "method" ## ## returns: ## numeric, estimated noise (y) ## .estimateNoise <- function(x, y, method=c("MAD", "SuperSmoother"), ...) { method <- match.arg(method) switch(method, "MAD" = { .estimateNoiseMad(x, y) }, "SuperSmoother" = { .estimateNoiseSuperSmoother(x, y, ...) } ) } ## estimateNoiseMad ## estimate noise by calculating mad over intensity values ## ## params: ## x: vector of x values ## y: vector of y values ## ## returns: ## numeric, estimated noise (y) ## .estimateNoiseMad <- function(x, y) { rep.int(stats::mad(y), length(x)) } ## estimateNoiseSuperSmoother ## estimate noise by using Friedman's super smoother ## ## params: ## x: vector of x values ## y: vector of y values ## ...: further arguments to passed to supsmu ## ## returns: ## numeric, estimated noise (y) ## .estimateNoiseSuperSmoother <- function(x, y, ...) { stats::supsmu(x=x, y=y, ...)$y } MALDIquant/R/replaceNegativeIntensityValues-functions.R0000644000175000017500000000075213022575752024214 0ustar sebastiansebastian## .replaceNegativeIntensityValues ## replace negative intensity valus by zeros ## ## params: ## object AbstractMass Object ## warn: throw a warning? ## ## returns: ## a AbstractMass object ## .replaceNegativeIntensityValues <- function(object, warn=TRUE) { if (any(object@intensity < 0L, na.rm=TRUE) && !isEmpty(object)) { if (warn) { warning("Negative intensity values are replaced by zeros.") } object@intensity[which(object@intensity < 0L)] <- 0L } object } MALDIquant/R/isMassObject-functions.R0000644000175000017500000000033013022575752020405 0ustar sebastiansebastian.isMassObject <- function(x) { inherits(x=x, what="AbstractMassObject") } isMassSpectrum <- function(x) { is(object=x, class2="MassSpectrum") } isMassPeaks <- function(x) { is(object=x, class2="MassPeaks") } MALDIquant/R/warpingFunction-functions.R0000644000175000017500000000371513022575752021206 0ustar sebastiansebastian## warpingFunction functions called by determineWarpingFunction ## ## requirements: ## A function with three arguments: x, difference, ... . ## This function has to return a function with only an argument (x) which does ## the warping. ## .warpingFunctionLowess ## lowess based determination of warping function [default] ## ## params: ## x: double, original mass ## d: double, corresponding difference to reference ## ...: additional arguments possible (see ?lowess for details) ## ## returns: ## function(x) ## .warpingFunctionLowess <- function(x, d, ...) { lo <- lowess(x=x, y=d, ...) approxfun(x=lo$x, y=lo$y, rule=2L) } ## .warpingFunctionLinear ## 1st order polynomial based determination of warping function ## ## params: ## x: double, original mass ## d: double, corresponding difference to reference ## ...: additional arguments possible (see ?lm for details) ## ## returns: ## function(x) ## .warpingFunctionLinear <- function(x, d, ...) { l <- lm(y ~ x1, data=list(x1=x, y=d), ...) co <- coef(l) function(x) { co[1L]+x*co[2L] } } ## .warpingFunctionQuadratic ## 2nd order polynomial based determination of warping function ## ## params: ## x: double, original mass ## d: double, corresponding difference to reference ## ...: additional arguments possible (see ?lm for details) ## ## returns: ## function(x) ## .warpingFunctionQuadratic <- function(x, d, ...) { l <- lm(y ~ x1+x2, data=list(x1=x, x2=x*x, y=d), ...) co <- coef(l) function(x) { co[1L]+x*co[2L]+x*x*co[3L] } } ## .warpingFunctionCubic ## 3rd order polynomial based determination of warping function ## ## params: ## x: double, original mass ## d: double, corresponding difference to reference ## ...: additional arguments possible (see ?lm for details) ## ## returns: ## function(x) ## .warpingFunctionCubic <- function(x, d, ...) { l <- lm(y ~ x1+x2+x3, data=list(x1=x, x2=x*x, x3=x*x*x, y=d), ...) co <- coef(l) function(x) { co[1L]+x*co[2L]+x*x*co[3L]+x*x*x*co[4L] } } MALDIquant/R/subset-methods.R0000644000175000017500000000215513022575752016766 0ustar sebastiansebastian## AbstractMassObject setMethod(f="[", signature=signature(x="AbstractMassObject", i="numeric", j="missing"), definition=function(x, i, j, ..., drop=TRUE) { x@mass <- x@mass[i] x@intensity <- x@intensity[i] x }) ## AbstractMassObject setMethod(f="[", signature=signature(x="AbstractMassObject", i="logical", j="missing"), definition=function(x, i, j, ..., drop=TRUE) { ## seems to be faster than evaluating the logical expression twice i <- which(i) x@mass <- x@mass[i] x@intensity <- x@intensity[i] x }) setMethod(f="[", signature=signature(x="MassPeaks", i="numeric", j="missing"), definition=function(x, i, j, ..., drop=TRUE) { x@mass <- x@mass[i] x@intensity <- x@intensity[i] x@snr <- x@snr[i] x }) ## AbstractMassObject setMethod(f="[", signature=signature(x="MassPeaks", i="logical", j="missing"), definition=function(x, i, j, ..., drop=TRUE) { ## seems to be faster than evaluating the logical expression twice i <- which(i) x@mass <- x@mass[i] x@intensity <- x@intensity[i] x@snr <- x@snr[i] x }) MALDIquant/R/intensityMatrix-functions.R0000644000175000017500000000151413022575752021237 0ustar sebastiansebastian## intensityMatrix ## converts a list of MassPeaks into an expression matrix ## ## params: ## peaks: list of MassPeaks objects ## spectra: list of MassSpectrum objects ## ## returns: ## a matrix ## intensityMatrix <- function(peaks, spectra) { ## test arguments .stopIfNotIsMassPeaksList(peaks) m <- .as.matrix.MassObjectList(peaks) ## lookup corresponding intensity values in spectra for missing peaks if (!missing(spectra)) { .stopIfNotIsMassSpectrumList(spectra) if (length(peaks) != length(spectra)) { stop("Incompatible number of spectra!") } isNa <- is.na(m) uniqueMass <- as.double(colnames(m)) approxSpectra <- lapply(spectra, approxfun, yleft=0L, yright=0L) for (i in seq_along(approxSpectra)) { m[i, isNa[i, ]] <- approxSpectra[[i]](uniqueMass[isNa[i, ]]) } } m } MALDIquant/R/as.matrix-functions.R0000644000175000017500000000175313022575752017737 0ustar sebastiansebastian## .as.matrix.MassObjectList ## internal function to convert a list of AbstractMassObject objects into a ## matrix ## ## params: ## l: list of AbstractMassObject objects ## ## returns: ## a matrix .as.matrix.MassObjectList <- function(l) { .stopIfNotIsMassObjectList(l) mass <- .unlist(lapply(l, function(x)x@mass)) intensity <- .unlist(lapply(l, function(x)x@intensity)) uniqueMass <- sort.int(unique(mass), method="quick") n <- lengths(l) r <- rep.int(seq_along(l), n) i <- findInterval(mass, uniqueMass) m <- matrix(NA_real_, nrow=length(l), ncol=length(uniqueMass), dimnames=list(NULL, uniqueMass)) m[cbind(r, i)] <- intensity attr(m, "mass") <- uniqueMass m } ## .as.binary.matrix ## internal function to convert a matrix with NA to a binary one ## ## params: ## m: matrix ## ## returns: ## a binary matrix .as.binary.matrix <- function(m) { stopifnot(is.matrix(m)) isNA <- which(is.na(m)) m[] <- 1L m[isNA] <- 0L mode(m) <- "integer" m } MALDIquant/R/AllGenerics.R0000644000175000017500000001173113022575752016210 0ustar sebastiansebastian## AbstractMassObject if (is.null(getGeneric("as.matrix"))) { setGeneric("as.matrix", function(x, ...) standardGeneric("as.matrix")) } if (is.null(getGeneric("length"))) { setGeneric("length", function(x) standardGeneric("length")) } if (is.null(getGeneric("lines"))) { setGeneric("lines", function(x, ...) standardGeneric("lines")) } if (is.null(getGeneric("plot"))) { setGeneric("plot", function(x, y, ...) standardGeneric("plot")) } if (is.null(getGeneric("plotMsiSlice"))) { setGeneric("plotMsiSlice", function(x, ...) standardGeneric("plotMsiSlice")) } if (is.null(getGeneric("points"))) { setGeneric("points", function(x, ...) standardGeneric("points")) } if (is.null(getGeneric(".prepareShow"))) { setGeneric(".prepareShow", function(object) standardGeneric(".prepareShow")) } if (is.null(getGeneric("transformIntensity"))) { setGeneric("transformIntensity", function(object, ...) standardGeneric("transformIntensity")) } if (is.null(getGeneric(".transformIntensity"))) { setGeneric(".transformIntensity", function(object, ...) standardGeneric(".transformIntensity")) } if (is.null(getGeneric("trim"))) { setGeneric("trim", function(object, range, ...) standardGeneric("trim")) } ## get/set slots if (is.null(getGeneric("mass"))) { setGeneric("mass", function(object, ...) standardGeneric("mass")) } if (is.null(getGeneric("mass<-"))) { setGeneric("mass<-", function(object, value) standardGeneric("mass<-")) } # from ProtGenerics (same as mass) if (is.null(getGeneric("mz"))) { setGeneric("mz", function(object, ...) standardGeneric("mz")) } if (is.null(getGeneric("mz<-"))) { setGeneric("mz<-", function(object, value) standardGeneric("mz<-")) } if (is.null(getGeneric("intensity"))) { setGeneric("intensity", function(object, ...) standardGeneric("intensity")) } if (is.null(getGeneric("intensity<-"))) { setGeneric("intensity<-", function(object, value) standardGeneric("intensity<-")) } if (is.null(getGeneric("isEmpty"))) { setGeneric("isEmpty", function(x) standardGeneric("isEmpty")) } if (is.null(getGeneric(".isEmptyWarning"))) { setGeneric(".isEmptyWarning", function(x) standardGeneric(".isEmptyWarning")) } if (is.null(getGeneric("metaData"))) { setGeneric("metaData", function(object) standardGeneric("metaData")) } if (is.null(getGeneric("metaData<-"))) { setGeneric("metaData<-", function(object, value) standardGeneric("metaData<-")) } if (is.null(getGeneric("coordinates"))) { setGeneric("coordinates", function(object, ...) standardGeneric("coordinates")) } if (is.null(getGeneric("coordinates<-"))) { setGeneric("coordinates<-", function(object, value) standardGeneric("coordinates<-")) } ## end of AbstractMassObject ## MassSpectrum if (is.null(getGeneric("approxfun"))) { setGeneric("approxfun", function(x, y=NULL, method="linear", yleft, yright, rule=1, f=0, ties=mean) standardGeneric("approxfun")) } if (is.null(getGeneric("calibrateIntensity"))) { setGeneric("calibrateIntensity", function(object, ...) standardGeneric("calibrateIntensity")) } if (is.null(getGeneric("detectPeaks"))) { setGeneric("detectPeaks", function(object, ...) standardGeneric("detectPeaks")) } if (is.null(getGeneric("estimateBaseline"))) { setGeneric("estimateBaseline", function(object, method=c("SNIP", "ConvexHull", "Median"), ...) standardGeneric("estimateBaseline")) } if (is.null(getGeneric("estimateNoise"))) { setGeneric("estimateNoise", function(object, ...) standardGeneric("estimateNoise")) } if (is.null(getGeneric(".findLocalMaxima"))) { setGeneric(".findLocalMaxima", function(object, halfWindowSize=20L) standardGeneric(".findLocalMaxima")) } if (is.null(getGeneric(".findLocalMaximaLogical"))) { setGeneric(".findLocalMaximaLogical", function(object, halfWindowSize=20L) standardGeneric(".findLocalMaximaLogical")) } if (is.null(getGeneric("isRegular"))) { setGeneric("isRegular", function(object, ...) standardGeneric("isRegular")) } if (is.null(getGeneric("removeBaseline"))) { setGeneric("removeBaseline", function(object, ...) standardGeneric("removeBaseline")) } if (is.null(getGeneric("smoothIntensity"))) { setGeneric("smoothIntensity", function(object, ...) standardGeneric("smoothIntensity")) } if (is.null(getGeneric("totalIonCurrent"))) { setGeneric("totalIonCurrent", function(object) standardGeneric("totalIonCurrent")) } ## end of MassSpectrum ## MassPeaks if (is.null(getGeneric("labelPeaks"))) { setGeneric("labelPeaks", function(object, ...) standardGeneric("labelPeaks")) } if (is.null(getGeneric("monoisotopicPeaks"))) { setGeneric("monoisotopicPeaks", function(object, ...) standardGeneric("monoisotopicPeaks")) } if (is.null(getGeneric("snr"))) { setGeneric("snr", function(object) standardGeneric("snr")) } ## end of MassPeaks MALDIquant/R/length-methods.R0000644000175000017500000000024013022575752016733 0ustar sebastiansebastian## AbstractMassObject setMethod(f="length", signature=signature(x="AbstractMassObject"), definition=function(x) { length(x@intensity) }) MALDIquant/R/unlist-functions.R0000644000175000017500000000026313022575752017342 0ustar sebastiansebastian## .unlist ## wrapper for unlist ## ## params: ## x: an R object ## ## returns: ## see also ?unlist ## .unlist <- function(x) { unlist(x, recursive=FALSE, use.names=FALSE) } MALDIquant/R/Deprecated.R0000644000175000017500000000036613022761357016060 0ustar sebastiansebastian## deprecated since MALDIquant 1.15.2 ## We have to keep it until May 2017 because the current MSnbase 2.0.x depends ## on this internal function. .which.closest <- function(x, vec) { match.closest(x, vec, tolerance=Inf, nomatch=NA_integer_) } MALDIquant/R/estimateBaseline-methods.R0000644000175000017500000000074613022575752020743 0ustar sebastiansebastian## MassSpectrum setMethod(f="estimateBaseline", signature=signature(object="MassSpectrum"), definition=function(object, method=c("SNIP", "TopHat", "ConvexHull", "median"), ...) { if (.isEmptyWarning(object)) { return(NA) } cbind(mass=object@mass, intensity=.estimateBaseline(x=object@mass, y=object@intensity, method=method, ...)) }) MALDIquant/R/merge-functions.R0000644000175000017500000000413713022575752017127 0ustar sebastiansebastian## mergeMassPeaks ## merge MassPeaks objects ## ## params: ## l: list of MassPeaks objects ## labels: factor, labels for samples ## fun: merge function ## ## returns: ## a new MassPeaks object or a list of new MassPeaks objects ## mergeMassPeaks <- function(l, labels, method=c("mean", "median", "sum"), ignore.na=TRUE, ...) { ## test arguments .stopIfNotIsMassPeaksList(l) method <- match.arg(method) fun <- switch(method, "mean" = { colMeans }, "median" = { .colMedians }, "sum" = { colSums } ) .doByLabels(l=l, labels=labels, FUN=.mergeMassPeaks, fun=fun, ignore.na=ignore.na, ...) } ## .mergeMassPeaks ## merge MassPeaks objects ## ## params: ## l: list of MassPeaks objects ## fun: merge function ## ## returns: ## a new MassPeaks object ## .mergeMassPeaks <- function(l, fun=colMeans, ignore.na=TRUE) { fun <- match.fun(fun) ## create a matrix which could merged m <- .as.matrix.MassObjectList(l) mass <- attr(m, "mass") ## avoid named intensity/snr slot colnames(m) <- NULL isNA <- is.na(m) if (!ignore.na) { m[isNA] <- 0L } ## merge intensities intensity <- fun(m, na.rm=TRUE) ## merge snr for (i in seq_along(l)) { m[i, !isNA[i, ]] <- l[[i]]@snr } snr <- fun(m, na.rm=TRUE) ## merge metaData metaData <- .mergeMetaData(lapply(l, function(x)x@metaData)) createMassPeaks(mass=mass, intensity=intensity, snr=snr, metaData=metaData) } ## merge different metaData by equal list names ## ## params ## m: list of metaData ## ## returns: ## merged list ## .mergeMetaData <- function(m) { .flat <- function(x)unname(unlist(x)) nm <- names(m[[1L]]) names(nm) <- nm lapply(nm, function(n) { cur <- m[[1L]][[n]] all <- lapply(m, "[[", n) len <- lengths(all) if (!all(length(cur) == len) || !all(.flat(cur) == .flat(all))) { if (!is.list(cur)) { all <- unlist(all) } return(unname(all)) } else { return(cur) } }) } MALDIquant/R/localMaxima-functions.R0000644000175000017500000000074213022575752020255 0ustar sebastiansebastian## local maxima function called by .findLocalMaxima ## ## This function looks for local maxima in a numeric vector. ## ## params: ## y: double, intensity ## halfWindowSize: numeric, half window size. ## ## returns: ## logical vector of local maxima ## .localMaxima <- function(y, halfWindowSize=1L) { y <- c(rep.int(0L, halfWindowSize), y, rep.int(0L, halfWindowSize)) i <- (halfWindowSize + 1L):(length(y) - halfWindowSize) .Call("C_localMaxima", y, halfWindowSize)[i] } MALDIquant/R/deprecated-functions.R0000644000175000017500000000343113022575752020124 0ustar sebastiansebastian## .deprecated ## mark a function as deprecated ## throw an error if current major version or minor is larger than major, or ## minor+1; if minor == minor+1L wrote warning; otherwise message ## ## params: ## version: last working version ## ...: arguments to be passed to stop, warning, message ## ## returns: ## nothing ## .deprecated <- function(version, ...) { current <- packageVersion("MALDIquant") version <- as.package_version(version) if (current$major > version$major || current$minor > version$minor + 1L) { stop(..., call.=FALSE) } else if (current$minor > version$minor) { warning(..., call.=FALSE) } else { message(...) } invisible() } ## .deprecatedFunction ## mark a function as deprecated and show default message ## ## params: ## version: last working version ## old: old function ## new: new function ## ## returns: ## nothing ## .deprecatedFunction <- function(version, old, new) { if (missing(old)) { old <- sys.call(-1L)[[1]] } msg <- paste0("\"", old , "\" is deprecated.") if (!missing(new)) { msg <- paste0(msg, "\nUse \"", new, "\" instead. See help(\"", new ,"\").") } .deprecated(version, msg) } ## .deprecatedArgument ## mark an function as deprecated and show default message ## ## params: ## version: last working version ## old: old argument ## new: new argument ## help: help file ## ## returns: ## nothing ## .deprecatedArgument <- function(version, old, new, help) { msg <- paste0("Argument \"", old, "\" is deprecated.") if (!missing(new)) { if (missing(help)) { parentCall <- sys.call(-1L)[[1]] } else { parentCall <- call(help)[[1]] } msg <- paste0(msg, "\nUse \"", new, "\" instead. See help(\"", deparse(parentCall),"\").") } .deprecated(version, msg) } MALDIquant/R/show-functions.R0000644000175000017500000000054313022575752017005 0ustar sebastiansebastian## .prepareShowGroupName ## prepares a group name for show (appends numbers to name if needed) ## ## params: ## x: vector ## name: group name ## ## returns: ## .prepareShowGroupName <- function(x, name) { if (!is.null(x)) { n <- length(x) if (n > 1L) { name <- paste0(name, seq_len(n)) } } else { name <- NULL } name } MALDIquant/R/smoothingFilters-functions.R0000644000175000017500000000576213022575752021375 0ustar sebastiansebastian ## .movingAverage ## runs a simple 2-side moving average. ## ## params: ## y: double, intensity values ## halfWindowSize integer, half window size. ## ## returns: ## double ## .movingAverage <- function(y, halfWindowSize=2L) { .stopIfNotIsValidHalfWindowSize(halfWindowSize, n=length(y)) windowSize <- 2L * halfWindowSize + 1L .filter(y, hws=halfWindowSize, coef=matrix(1L / windowSize, nrow=windowSize, ncol=windowSize)) } ## .savitzkyGolay ## runs a savitzky golay filter ## ## Savitzky, A., & Golay, M. J. (1964). Smoothing and differentiation of data ## by simplified least squares procedures. Analytical chemistry, 36(8), 1627-1639. ## ## params: ## y: double, intensity values ## halfWindowSize: integer, half window size. ## polynomialOrder: integer, polynomial order of sg-filter ## ## returns: ## double ## .savitzkyGolay <- function(y, halfWindowSize=10L, polynomialOrder=3L) { .stopIfNotIsValidHalfWindowSize(halfWindowSize, n=length(y)) windowSize <- 2L * halfWindowSize + 1L if (windowSize < polynomialOrder) { stop("The window size has to be larger than the polynomial order.") } .filter(y, hws=halfWindowSize, coef=.savitzkyGolayCoefficients(m=halfWindowSize, k=polynomialOrder)) } ## .savitzkyGolayCoefficients ## ## Savitzky, A., & Golay, M. J. (1964). Smoothing and differentiation of data ## by simplified least squares procedures. Analytical chemistry, 36(8), 1627-1639. ## ## Implementation based on: ## Steinier, J., Termonia, Y., & Deltour, J. (1972). Comments on Smoothing and ## differentiation of data by simplified least square procedure. ## Analytical Chemistry, 44(11), 1906-1909. ## ## Implemention of left/right extrema based on: ## sgolay in signal 0.7-3/R/sgolay.R by Paul Kienzle ## modified by Sebastian Gibb ## ## params: ## m: integer, half window size ## k: integer, polynomial order (k == 0 = moving average) .savitzkyGolayCoefficients <- function(m, k=3L) { k <- 0L:k nm <- 2L * m + 1L nk <- length(k) K <- matrix(k, nrow=nm, ncol=nk, byrow=TRUE) ## filter is applied to -m:m around current data point ## to avoid removing (NA) of left/right extrema ## lhs: 0:2*m ## rhs: (n-2m):n ## filter matrix contains 2*m+1 rows ## row 1:m == lhs coef ## row m+1 == typical sg coef ## row (n-m-1):n == rhs coef F <- matrix(double(), nrow=nm, ncol=nm) for (i in seq_len(m + 1L)) { M <- matrix(seq_len(nm) - i, nrow=nm, ncol=nk, byrow=FALSE) X <- M^K T <- solve(t(X) %*% X) %*% t(X) F[i, ] <- T[1L, ] } ## rhs (row (n-m):n) are equal to reversed lhs F[(m + 2L):nm, ] <- rev(F[seq_len(m), ]) F } ## .filter ## remove time series attributes and NA at left/right extrema .filter <- function(x, hws, coef) { n <- length(x) w <- 2L * hws + 1L y <- stats::filter(x=x, filter=coef[hws + 1L, ], sides=2L) attributes(y) <- NULL ## fix left/right extrema y[seq_len(hws)] <- head(coef, hws) %*% head(x, w) y[(n - hws + 1L):n] <- tail(coef, hws) %*% tail(x, w) y } MALDIquant/R/findEmptyMassObjects-functions.R0000644000175000017500000000055413022575752022124 0ustar sebastiansebastian## findEmptyMassObjects ## find empty AbstractMassObject objects in a MassObjectsList ## ## params: ## l: list of AbstractMassObject objects ## ## returns: ## a vector of indices refer to empty AbstractMassObject objects ## findEmptyMassObjects <- function(l) { .stopIfNotIsMassObjectList(l) isEmpty <- vapply(l, isEmpty, logical(1L)) which(isEmpty) } MALDIquant/R/lapply-functions.R0000644000175000017500000000034113022575752017322 0ustar sebastiansebastian## .lapply ## wrapper for lapply with different defaults ## ## params: ## see ?lapply ## ## returns: ## see ?lapply ## .lapply <- function(X, FUN, ..., mc.cores=1L) { parallel::mclapply(X, FUN, ..., mc.cores=mc.cores) } MALDIquant/R/plot-methods.R0000644000175000017500000000275713022575752016447 0ustar sebastiansebastian## AbstractMassObject setMethod(f="plot", signature=signature(x="AbstractMassObject", y="missing"), definition=function(x, col="black", xlab=expression(italic(m/z)), ylab="intensity", type=ifelse(isMassPeaks(x), "h", "l"), xlim=c(ifelse(length(x@mass), min(x@mass, na.rm=TRUE), 0L), ifelse(length(x@mass), max(x@mass, na.rm=TRUE), 1L)), ylim=c(0, ifelse(length(x@intensity), max(x@intensity, na.rm=TRUE), 1L)), main=x@metaData$name, sub=x@metaData$file, cex.sub=0.75, col.sub="#808080", abline.col="#808080", ...) { if (all(sub == x@metaData$file) && length(x@metaData$file) > 1L) { sub <- paste0(ifelse(isMassSpectrum(x), "averaged spectrum", "merged peaks"), " composed of ", length(x@metaData$file), " ", ifelse(isMassSpectrum(x), "MassSpectrum", "MassPeaks"), " objects") } plot(x=x@mass, y=x@intensity, col=col, type=type, xlab=xlab, ylab=ylab, xlim=xlim, ylim=ylim, main=main, sub=sub, cex.sub=cex.sub, col.sub=col.sub, ...) abline(h=0L, col=abline.col) }) MALDIquant/R/mass-methods.R0000644000175000017500000000106413022575752016422 0ustar sebastiansebastian## AbstractMassObject setMethod(f="mass", signature=signature(object="AbstractMassObject"), definition=function(object, ...) { object@mass }) ## AbstractMassObject setReplaceMethod(f="mass", signature=signature(object="AbstractMassObject", value="numeric"), definition=function(object, value) { if (length(object@mass) == length(value)) { object@mass <- as.double(value) } else { stop("Lengths of mass (", length(object@mass), ") and value (", length(value), ") have to be equal.") } object }) MALDIquant/R/isMassObjectList-functions.R0000644000175000017500000000232413022575752021246 0ustar sebastiansebastian.isMassObjectList <- function(x) { if (!is.list(x)) { return(FALSE) } length(x) && all(unname(vapply(x, .isMassObject, logical(1L)))) } .stopIfNotIsMassObjectList <- function(x) { if (!.isMassObjectList(x)) { parentCall <- deparse(sys.call(-1L)) stop(parentCall, " : ", sQuote(deparse(substitute(x))), " is no list of MALDIquant::AbstractMassObject objects!", call.=FALSE) } TRUE } isMassSpectrumList <- function(x) { if (!is.list(x)) { return(FALSE) } length(x) && all(unname(vapply(x, isMassSpectrum, logical(1L)))) } .stopIfNotIsMassSpectrumList <- function(x) { if (!isMassSpectrumList(x)) { parentCall <- deparse(sys.call(-1L)) stop(parentCall, " : ", sQuote(deparse(substitute(x))), " is no list of MALDIquant::MassSpectrum objects!", call.=FALSE) } TRUE } isMassPeaksList <- function(x) { if (!is.list(x)) { return(FALSE) } length(x) && all(unname(vapply(x, isMassPeaks, logical(1L)))) } .stopIfNotIsMassPeaksList <- function(x) { if (!isMassPeaksList(x)) { parentCall <- deparse(sys.call(-1L)) stop(parentCall, " : ", sQuote(deparse(substitute(x))), " is no list of MALDIquant::MassPeaks objects!", call.=FALSE) } TRUE } MALDIquant/R/estimateNoise-methods.R0000644000175000017500000000063413022575752020272 0ustar sebastiansebastian## MassSpectrum setMethod(f="estimateNoise", signature=signature(object="MassSpectrum"), definition=function(object, method=c("MAD", "SuperSmoother"), ...) { if (.isEmptyWarning(object)) { return(0L) } cbind(mass=object@mass, intensity=.estimateNoise(x=object@mass, y=object@intensity, method=method, ...)) }) MALDIquant/R/totalIonCurrent-methods.R0000644000175000017500000000047313022575752020616 0ustar sebastiansebastian## MassSpectrum setMethod(f="totalIonCurrent", signature=signature(object="MassSpectrum"), definition=function(object) { left <- as.double(head(object@intensity, -1L)) right <- as.double(tail(object@intensity, -1L)) as.double(sum((left + right) / 2L * diff(object@mass), na.rm=TRUE)) }) MALDIquant/R/as-methods.R0000644000175000017500000000047713022575752016071 0ustar sebastiansebastian## convert MassPeaks into MassSpectrum objects ## ## returns: ## a MassSpectrum object setAs(from="MassPeaks", to="MassSpectrum", function (from)createMassSpectrum(mass=from@mass, intensity=from@intensity, metaData=from@metaData)) MALDIquant/R/estimateBaseline-functions.R0000644000175000017500000000713213022575752021304 0ustar sebastiansebastian## .estimateBaseline ## estimating the baseline of a spectrum ## ## params: ## x: vector of x values (mass) ## y: vector of y values (intensity) ## method: method to use ## ...: further arguments passed to "method" ## ## returns: ## numeric, estimated baseline (y) ## .estimateBaseline <- function(x, y, method=c("SNIP", "TopHat", "ConvexHull", "median"), ...) { method <- match.arg(method) switch(method, "SNIP" = { .estimateBaselineSnip(x, y, ...) }, "TopHat" = { .estimateBaselineTopHat(x, y, ...) }, "ConvexHull" = { .estimateBaselineConvexHull(x, y, ...) }, "median" = { .estimateBaselineMedian(x, y, ...) } ) } ## estimateBaselineConvexHull ## estimate baseline by creating a convex hull ## ## A. M. Andrew, "Another Efficient Algorithm for Convex Hulls in Two ## Dimensions", Info. Proc. Letters 9, 216-219 (1979). ## only calculate lower hull (we don't need the upper one) ## ## params: ## x: vector of x values ## y: vector of y values ## ## returns: ## numeric, estimated baseline (y) ## .estimateBaselineConvexHull <- function(x, y) { .Call("C_lowerConvexHull", x, y) } ## estimateBaselineMedian ## estimate baseline by computing moving median ## ## params: ## x: vector of x values ## y: vector of y values ## halfWindowSize: size of local window ## ## returns: ## numeric, estimated baseline (y) ## .estimateBaselineMedian <- function(x, y, halfWindowSize=100L) { .stopIfNotIsValidHalfWindowSize(halfWindowSize=halfWindowSize, n=length(x)) as.vector(runmed(y, k=2L * halfWindowSize + 1L)) } ## estimateBaselineSnip ## estimate baseline by SNIP algorithm ## ## SNIP algorithm based on: ## C.G. Ryan, E. Clayton, W.L. Griffin, S.H. Sie, and D.R. Cousens. ## "Snip, a statistics-sensitive background treatment for the quantitative ## analysis of pixe spectra in geoscience applications." ## Nuclear Instruments and Methods in Physics Research Section B: ## Beam Interactions with Materials and Atoms, 34(3):396-402, 1988. ## ISSN 0168-583X. doi:10.1016/0168-583X(88)90063-8. ## URL http://www.sciencedirect.com/science/article/B6TJN-46YSYTJ-30/2/e0d015ceb8ea8a7bc0702a857a19750b ## ## decreasing clipping window adapted from: ## M. Morhac. 2009. ## "An algorithm for determination of peak regions and baseline elimination in ## spectroscopic data." ## Nuclear Instruments and Methods in Physics Research Section A: ## Accelerators, Spectrometers, Detectors and Associated Equipment, 600(2), 478-487. ## ISSN 0168-9002. doi:10.1016/S0168-9002(97)01023-1. ## URL http://www.sciencedirect.com/science/article/pii/S0168900297010231 ## ## params: ## x: vector of x values (only needed for create a matrix as return value) ## y: vector of y values ## iterations: number of iterations ## decreasing: use a decreasing clipping window ## ## returns: ## numeric, estimated baseline (y) ## .estimateBaselineSnip <- function(x, y, iterations=100L, decreasing=TRUE) { .Call("C_snip", y, iterations, decreasing) } ## estimateBaselineTopHat ## estimate baseline by TopHat filter (erosion + dilation) ## ## params: ## x: vector of x values (only needed for create a matrix as return value) ## y: vector of y values ## halfWindowSize: size of local window ## ## returns: ## numeric, estimated baseline (y) ## .estimateBaselineTopHat <- function(x, y, halfWindowSize=100L) { .stopIfNotIsValidHalfWindowSize(halfWindowSize=halfWindowSize, n=length(x)) .dilation(.erosion(y, halfWindowSize=halfWindowSize), halfWindowSize=halfWindowSize) } MALDIquant/R/intensity-methods.R0000644000175000017500000000121113022575752017477 0ustar sebastiansebastian## AbstractMassObject setMethod(f="intensity", signature=signature(object="AbstractMassObject"), definition=function(object, ...) { object@intensity }) ## AbstractMassObject setReplaceMethod(f="intensity", signature=signature(object="AbstractMassObject", value="numeric"), definition=function(object, value) { if (length(object@intensity) == length(value)) { object@intensity <- as.double(value) } else { stop("Lengths of intensity(", length(object@intensity), ") and value (", length(value), ") have to be equal.") } object }) MALDIquant/R/memoryUsage-functions.R0000644000175000017500000000056213022575752020323 0ustar sebastiansebastian## .memoryUsageStr.object_size ## pretty string of memory usage ## ## params: ## x: object_size ## ## returns: ## character ## .memoryUsageStr <- function(x) { os <- object.size(x) iec <- c("B", "KiB", "MiB", "GiB", "TiB", "PiB", "EiB", "ZiB", "YiB") l <- trunc(log(os) / log(1024L)) i <- pmin(l + 1L, 9L) paste(round(os / (1024L^l), digits=3L), iec[i]) } MALDIquant/R/constructor-functions.R0000644000175000017500000000166613022575752020421 0ustar sebastiansebastian## MassSpectrum ## createMassSpectrum ## default constructor: MassSpectrum class ## ## params: ## mass: vector, spectrum mass ## intensity: vector, spectrum intensities ## metaData: list, metadata ## ## returns: ## a MassSpectrum object ## createMassSpectrum <- function(mass, intensity, metaData=list()) { new(Class="MassSpectrum", mass=mass, intensity=intensity, metaData=metaData) } ## end of MassSpectrum ## MassPeaks ## createMassPeaks ## default constructor: MassPeaks class ## ## params: ## mass: vector, peaks mass ## intensity: vector, peaks intensities ## snr: vector, peaks snr ## metaData: list, metadata ## ## returns: ## a MassPeaks object ## createMassPeaks <- function(mass, intensity, snr=rep.int(NA_real_, length(intensity)), metaData=list()) { new(Class="MassPeaks", mass=mass, intensity=intensity, snr=snr, metaData=metaData) } ## end of MassPeaks MALDIquant/R/msiSlices-functions.R0000644000175000017500000000317713022575752017766 0ustar sebastiansebastian## msiSlices ## create intensity matrix for each slice from a list of MassSpectrum/MassPeaks ## objects ## ## params: ## x: list of MassSpectrum/MassPeaks objects ## center: position(s) of interest (in mass values) ## tolerance: aggregate data around center +/- tolerance ## method: aggregate method ## adjust: set coordinates to 1,1 (if there are empty/missing spectra before) ## ## returns: ## an array (dim: x, y, z=slice nr) ## msiSlices <- function(x, center, tolerance, method=c("sum", "mean", "median"), adjust=TRUE) { x <- suppressWarnings(trim(x, range=range(center) + c(-tolerance, tolerance))) .msiSlices(m=.as.matrix.MassObjectList(x), coord=coordinates(x, adjust=adjust), center=center, tolerance=tolerance, method=method) } .msiSlices <- function(m, coord, center, tolerance, method=c("sum", "mean", "median")) { method <- match.arg(method) fun <- switch(method, "sum" = rowSums, "mean" = rowMeans, "median" = function(x, ...).colMedians(t(x), ...)) n <- unname(apply(coord, MARGIN=2L, FUN=max)) l <- pmin(findInterval(center - tolerance - .Machine$double.eps, attr(m, "mass")) + 1L, ncol(m)) r <- findInterval(center + tolerance + .Machine$double.eps, attr(m, "mass")) slices <- array(NA_real_, dim=c(x=n[1L], y=n[2L], z=length(center))) for (i in seq_along(center)) { slices[cbind(coord, i)] <- fun(m[, l[i]:r[i], drop=FALSE], na.rm=TRUE) } attr(slices, "center") <- center attr(slices, "tolerance") <- tolerance attr(slices, "method") <- method slices } MALDIquant/R/removeBaseline-methods.R0000644000175000017500000000150513022575752020417 0ustar sebastiansebastian## MassSpectrum setMethod(f="removeBaseline", signature=signature(object="MassSpectrum"), definition=function(object, method=c("SNIP", "TopHat", "ConvexHull", "median"), ...) { ## empty spectrum? if (.isEmptyWarning(object)) { return(object) } ## estimate baseline baseline <- .estimateBaseline(x=object@mass, y=object@intensity, method=method, ...) ## substract baseline object@intensity <- object@intensity - baseline object }) ## list setMethod(f="removeBaseline", signature=signature(object="list"), definition=function(object, ...) { ## test arguments .stopIfNotIsMassSpectrumList(object) .mapply(removeBaseline, object, ...) }) MALDIquant/R/show-methods.R0000644000175000017500000000411313022575752016435 0ustar sebastiansebastian## AbstractMassObject setMethod(f="show", signature=signature(object="AbstractMassObject"), definition=function(object) { l <- .prepareShow(object) isFilename <- grepl(pattern="^File.*", x=l$groups) ## to avoid newlines in other values don't format filenames ## (they could be very long) l$values[!isFilename] <- format(l$values[!isFilename], justify="left") l$groups <- format(l$groups, justify="left") cat(paste0(l$groups, ": ", l$values, collapse="\n"), sep="\n") }) setMethod(f=".prepareShow", signature=signature(object="AbstractMassObject"), definition=function(object) { groups <- c("S4 class type", "Number of m/z values", "Range of m/z values", "Range of intensity values", "Memory usage") values <- class(object)[1L] if (isEmpty(object)) { values <- c(values, 0L, NA, NA) } else { values <- c(values, length(object@mass), paste0(round(range(object@mass), digits=3L), collapse=" - "), paste0(format(min(object@intensity), digits=4L, scientific=TRUE), " - ", format(max(object@intensity), digits=4L, scientific=TRUE))) } values <- c(values, .memoryUsageStr(object)) groups <- c(groups, .prepareShowGroupName(object@metaData$name, "Name")) values <- c(values, object@metaData$name) groups <- c(groups, .prepareShowGroupName(object@metaData$file, "File")) values <- c(values, object@metaData$file) list(groups=groups, values=values) }) setMethod(f=".prepareShow", signature=signature(object="MassPeaks"), definition=function(object) { l <- callNextMethod(object) groups <- "Range of snr values" if (isEmpty(object)) { values <- NA } else { values <- paste0(round(range(object@snr), digits=3), collapse=" - ") } ## append snr info after intensity l$groups <- append(l$groups, groups, after=4L) l$values <- append(l$values, values, after=4L) list(groups=l$groups, values=l$values) }) MALDIquant/R/determineWarpingFunctions-functions.R0000644000175000017500000001421213022575752023220 0ustar sebastiansebastian## determineWarpingFunctions ## calculates warping functions for each MassPeaks object ## ## params: ## l: list, list of MassPeaks objects ## reference: MassPeaks, a reference MassPeaks object to which all other ## MassPeaks objects should be aligned ## tolerance: double, maximal deviation of a peak position to be ## considered as same peak ## method: choose type of base warping function ## plot: logical, plots warping function ## ## returns: ## a list of warping functions ## determineWarpingFunctions <- function(l, reference, tolerance=0.002, method=c("lowess", "linear", "quadratic", "cubic"), plot=FALSE, plotInteractive=FALSE, ...) { ## test arguments if (!isMassPeaksList(l) && !isMassPeaks(l)) { stop(sQuote("l"), " is no list of MALDIquant::MassPeaks objects!") } method <- match.arg(method) warpingFunction <- switch(method, "lowess" = { .warpingFunctionLowess }, "linear" = { .warpingFunctionLinear }, "quadratic" = { .warpingFunctionQuadratic }, "cubic" = { .warpingFunctionCubic } ) optArgs <- list(...) ## find reference peaks if (missing(reference)) { arguments <- list(l=l, tolerance=tolerance) argumentNames <- c("method", "minFrequency") arguments <- modifyList(arguments, optArgs[argumentNames]) optArgs[argumentNames] <- NULL reference <- do.call(referencePeaks, arguments) } if (isEmpty(reference)) { stop("Reference MassPeaks object contains no peaks!") } if (length(reference) < 10L) { warning("Reference MassPeaks object contains very few peaks (n == ", length(reference), "). The warping could be instable. ", "Consider to reduce ", sQuote("minFrequency"), " or/and to increase ", sQuote("tolerance"), ".") } ## fetch plot.default arguments (debug plot) if (plot) { plotNames <- c("xlim", "ylim", "xlab", "ylab", "type", "lwd", "col", "col.sub", "cex.main", "cex.sub", "main", "sub") givenPlotArgs <- optArgs[plotNames] optArgs[plotNames] <- NULL } ## reference has to become sample no 1 tmpPeakList <- c(reference, l) ## same procedure as in binPeaks ## fetch all mass mass <- unname(.unlist(lapply(tmpPeakList, function(x)x@mass))) ## fetch all intensities intensities <- .unlist(lapply(tmpPeakList, function(x)x@intensity)) ## store original mass sample number/id samples <- rep.int(seq_along(tmpPeakList), lengths(tmpPeakList)) ## sort values by mass s <- sort.int(mass, method="quick", index.return=TRUE) mass <- s$x intensities <- intensities[s$ix] samples <- samples[s$ix] ## run peak binning and use relaxed grouper which choose the highest test ## sample peaks binnedMass <- .binPeaks(mass=mass, intensities=intensities, samples=samples, tolerance=tolerance, grouper=.grouperRelaxedHighestAtReference) ## group mass/intensities by sample ids lIdx <- split(seq_along(binnedMass), samples) ## calculate differences binnedMass[binnedMass == 0L] <- NA_real_ d <- binnedMass - mass ## each function which determines a warping function uses these 3 arguments arguments <- list(x=NULL, d=NULL) if (length(optArgs)) { arguments <- c(arguments, optArgs) } ## determine warping functions warpingFunctions <- lapply(lIdx[-1L], function(i) { ## fetch changed mass == aligned peaks notNA <- !is.na(binnedMass[i]) arguments$x <- mass[i][notNA] ## original mass arguments$d <- d[i][notNA] ## difference to reference if (!length(arguments$x)) { stop("Could not match any peak in spectrum ", samples[i[1L]] - 1L, " to a reference peak.") } do.call(warpingFunction, arguments) }) ## clean misleading names (names == idx+1 because reference is idx == 1) names(warpingFunctions) <- NULL ## debug plot if (plot) { ## non interactive device (pdf, png, ...) available? isNonInteractivePlot <- dev.cur() != 1L && !dev.interactive() if (!isNonInteractivePlot && !plotInteractive) { warning(sQuote("plot"), " is ", sQuote("TRUE"), " but no non-interactive device is available. ", "Using pdf() to create a default one.") pdf(paper="a4r", width=12) } else if (dev.cur() == 1L && plotInteractive) { warning(sQuote("plot"), " is ", sQuote("TRUE"), " but no interactive device is available. ", "Using dev.new() to create a default one.") dev.new() } ## set default plot arguments plotArgsDefaults <- list(xlim=range(mass), ylim=range(d, na.rm=TRUE), xlab="mass", ylab="difference", type="p", lwd=1L, col=1L, cex.main=0.8, cex.sub=0.75, col.sub="#808080") plotArgs <- modifyList(plotArgsDefaults, givenPlotArgs) nReference <- length(reference) x <- plotArgs$xlim[1L]:plotArgs$xlim[2L] ## workaround to avoid error: ## Error in l[[i]] : this S4 class is not subsettable if (!is.list(l)) { l <- list(l) } for (i in seq_along(l)) { ## fetch changed mass == aligned peaks notNA <- !is.na(binnedMass[lIdx[[i + 1L]]]) if (is.null(givenPlotArgs$main)) { plotArgs$main <- paste0("sample ", i, " vs reference\n", "(matched peaks: ", sum(notNA), "/", nReference, ")") } if (is.null(givenPlotArgs$sub)) { plotArgs$sub <- l[[i]]@metaData$file } ## plot reference vs sample plotArgs$x <- l[[i]]@mass[notNA] plotArgs$y <- d[lIdx[[i + 1L]]][notNA] do.call(plot.default, plotArgs) ## draw warping function lines(x, warpingFunctions[[i]](x), lwd=plotArgs$lwd, col=plotArgs$col) } if (!isNonInteractivePlot && !plotInteractive) { dev.off() } } warpingFunctions } MALDIquant/R/referencePeaks-functions.R0000644000175000017500000000213013022575752020741 0ustar sebastiansebastian## referencePeaks ## calculate reference peaks (a wrapper around filterPeaks and binPeaks) ## ## params: ## l: list of MassPeaks objects ## method: character, grouper to used (strict: don't allow multiple peaks of ## the same sample in the same bin, relaxed: allow them) ## minFrequency: double, minimal frequency of a peak to be not removed ## tolerance: double, maximal deviation of a peak position to be ## considered as same peak ## ## returns: ## a new MassPeaks object ## referencePeaks <- function(l, method=c("strict", "relaxed"), minFrequency=0.9, tolerance=0.002) { .stopIfNotIsMassPeaksList(l) ## find reference peaks by binning and filtering referencePeaks <- filterPeaks(binPeaks(l, method=method, tolerance=tolerance), minFrequency=minFrequency) m <- .as.binary.matrix(.as.matrix.MassObjectList(referencePeaks)) ## set peak intensity to number of occurrence intensity <- unname(colMeans(m)) createMassPeaks(mass=attr(m, "mass"), intensity=intensity) } MALDIquant/R/mapply-functions.R0000644000175000017500000000056313022575752017331 0ustar sebastiansebastian## .mapply ## wrapper for mapply with different defaults ## ## params: ## see ?mapply ## ## returns: ## see ?mapply ## .mapply <- function(FUN, ..., MoreArgs=NULL, SIMPLIFY=FALSE, USE.NAMES=FALSE, mc.cores=1L) { parallel::mcmapply(FUN=FUN, ..., MoreArgs=MoreArgs, SIMPLIFY=SIMPLIFY, USE.NAMES=USE.NAMES, mc.cores=mc.cores) } MALDIquant/R/averageMassSpectra-functions.R0000644000175000017500000000363113022575752021606 0ustar sebastiansebastian## averageMassSpectra ## averages MassSpectrum objects ## ## params: ## l: list of MassSpectrum objects ## labels: factor, labels for samples ## method: aggregation method ## ## returns: ## a new MassSpectrum object or a list of new MassSpectra objects ## averageMassSpectra <- function(l, labels, method=c("mean", "median", "sum"), ...) { ## test parameters .stopIfNotIsMassSpectrumList(l) method <- match.arg(method) fun <- switch(method, "mean" = { colMeans }, "median" = { .colMedians }, "sum" = { colSums } ) .doByLabels(l=l, labels=labels, FUN=.averageMassSpectra, fun=fun, ...) } ## .averageMassSpectra ## average MassSpectrum objects ## ## params: ## l: list of MassSpectrum objects ## fun: aggregation function ## ignore.na: ignore NA/set them to 0 ## ## returns: ## a new MassSpectrum object ## .averageMassSpectra <- function(l, fun=colMeans, mergeMetaData=TRUE) { fun <- match.fun(fun) ## merge metaData if (mergeMetaData) { metaData <- .mergeMetaData(lapply(l, function(x)x@metaData)) } else { metaData <- list() } ## use the first non empty spectrum as reference i <- which(!vapply(l, isEmpty, logical(1L)))[1L] if (!is.na(i)) { mass <- l[[i]]@mass } else { mass <- NA_real_ } ## interpolate not existing masses approxSpectra <- lapply(l, approxfun) ## get interpolated intensities intensityList <- lapply(approxSpectra, function(x)x(mass)) ## create a matrix which could merged m <- do.call(rbind, intensityList) ## merge intensities intensity <- fun(m, na.rm=TRUE) ## create an empty spectrum if all intensities are NaN if (is.nan(intensity[1L])) { intensity <- double() mass <- double() } createMassSpectrum(mass=mass, intensity=intensity, metaData=metaData) } MALDIquant/R/metaData-methods.R0000644000175000017500000000060113022575752017173 0ustar sebastiansebastian## AbstractMassObject setMethod(f="metaData", signature=signature(object="AbstractMassObject"), definition=function(object) { object@metaData }) ## AbstractMassObject setReplaceMethod(f="metaData", signature=signature(object="AbstractMassObject"), definition=function(object, value) { object@metaData <- value object }) MALDIquant/R/findLocalMaxima-methods.R0000644000175000017500000000147713022575752020517 0ustar sebastiansebastian## MassSpectrum setMethod(f=".findLocalMaxima", signature=signature(object="MassSpectrum"), definition=function(object, halfWindowSize=20L) { if (.isEmptyWarning(object)) { return(matrix(ncol=2L, dimnames=list(list(), list("mass", "intensity")))) } localMaxima <- .findLocalMaximaLogical(object, halfWindowSize=halfWindowSize) cbind(mass=object@mass, intensity=object@intensity)[localMaxima,] }) setMethod(f=".findLocalMaximaLogical", signature=signature(object="MassSpectrum"), definition=function(object, halfWindowSize=20L) { if (.isEmptyWarning(object)) { return(logical()) } .stopIfNotIsValidHalfWindowSize(halfWindowSize=halfWindowSize, n=length(object)) .localMaxima(object@intensity, halfWindowSize=halfWindowSize) }) MALDIquant/R/trim-methods.R0000644000175000017500000000210013022575752016422 0ustar sebastiansebastian## AbstractMassObject setMethod("trim", signature=signature(object="AbstractMassObject", range="numeric"), definition=function(object, range) { if (length(range) != 2L) { stop(sQuote("range"), " has to be a vector of length 2.") } range <- .reorderRange(range) sel <- which(findInterval(object@mass, range, rightmost.closed=TRUE) == 1L) if (!length(sel)) { warning("The mass range (", paste0(range, collapse=":"), ") is outside of the stored mass values. No data points left.") } object[sel] }) ## list setMethod("trim", signature=signature(object="list", range="numeric"), definition=function(object, range, ...) { .stopIfNotIsMassObjectList(object) .lapply(X=object, FUN=trim, range=range, ...) }) setMethod("trim", signature=signature(object="list", range="missing"), definition=function(object, ...) { .stopIfNotIsMassObjectList(object) range <- .overlap(object) if (all(range == 0L)) { stop("No overlap found!") } .lapply(X=object, FUN=trim, range=range, ...) }) MALDIquant/R/irregular-functions.R0000644000175000017500000000035513022575752020022 0ustar sebastiansebastian## .irregularScore ## calculate frequency of irregular data ## ## params: ## x: double ## ## returns: ## double, frequency of irregular data ## .irregularScore <- function(x) { d <- diff(x) d <- d[-1L] < head(d, -1L) mean(d) } MALDIquant/R/onAttach.R0000644000175000017500000000044413022575752015560 0ustar sebastiansebastian.onAttach <- function(libname, pkgname) { packageStartupMessage("\nThis is MALDIquant version ", utils::packageVersion("MALDIquant"), "\n", "Quantitative Analysis of Mass Spectrometry Data\n", " See ", sQuote("?MALDIquant"), " for more information about this package.\n") } MALDIquant/R/AllClasses.R0000644000175000017500000000111013022575752016034 0ustar sebastiansebastian## basic class for all spectra based information setClass("AbstractMassObject", slots=list(mass="numeric", intensity="numeric", metaData="list"), prototype=list(mass=numeric(), intensity=numeric(), metaData=list()), contains="VIRTUAL") ## represent a spectrum setClass("MassSpectrum", contains="AbstractMassObject") ## represent a peak list from a single spectrum setClass("MassPeaks", slots=list(snr="numeric"), prototype=list(snr=numeric()), contains="AbstractMassObject") MALDIquant/R/removeEmptyMassObjects-functions.R0000644000175000017500000000064313022575752022500 0ustar sebastiansebastian## removeEmptyMassObjects ## find and remove empty AbstractMassObject objects in a MassObjectsList ## ## params: ## l: list of AbstractMassObject objects ## ## returns: ## a list without empty objects ## removeEmptyMassObjects <- function(l) { .stopIfNotIsMassObjectList(l) ## find empty MassPeaks objects notEmpty <- !(seq_along(l) %in% findEmptyMassObjects(l)) ## exclude empty elements l[notEmpty] } MALDIquant/R/grouper-functions.R0000644000175000017500000000660113022575752017511 0ustar sebastiansebastian## grouper functions called by .binPeaks ## .grouperStrict ## strict grouping function ## Don't allow peaks of one sample in the same bin. ## ## params: ## mass: double, sorted mass ## intensities: double, corresponding intensities ## samples: double, corresponding sample id numbers ## tolerance: double, maximal deviation of a peak position to be ## considered as same peak ## ## returns: ## NA if further splitting is needed ## meanMass (double) if all criteria are matched ## .grouperStrict <- function(mass, intensities, samples, tolerance) { ## don't accept two or more peaks of the same sample if (anyDuplicated(samples)) { return(NA) } meanMass <- mean(mass) ## all peaks in range? if (any(abs(mass - meanMass) / meanMass > tolerance)) { return(NA) } meanMass } ## .grouperRelaxed ## relaxed grouping function (more than one peak of one sample per bin possible) ## choose highest peak in range. ## ## params: ## mass: double, sorted mass ## intensities: double, corresponding intensities ## samples: double, corresponding sample id numbers ## tolerance: double, maximal deviation of a peak position to be ## considered as same peak ## meanMass: double, mean of mass (new peak position) ## ## returns: ## NA if further splitting is needed ## meanMass (double) if all criteria are matched ## .grouperRelaxed <- function(mass, intensities, samples, tolerance) { meanMass <- mean(mass) ## all peaks in range? if (any(abs(mass - meanMass) / meanMass > tolerance)) { return(NA) } ## choose highest peak in duplicates if (anyDuplicated(samples)) { s <- sort.int(intensities, method="quick", decreasing=TRUE, index.return=TRUE) samples <- samples[s$ix] noDup <- !duplicated(samples) noDup[s$ix] <- noDup ## replace mass corresponding to highest intensity mass[noDup] <- mean(mass[noDup]) return(mass) } meanMass } ## .grouperRelaxedHighestAtReference ## relaxed grouping function (more than one peak of one sample per bin possible) ## Choose highest test sample peaks in range around a reference peak. ## ## params: ## mass: double, sorted mass ## intensities: double, corresponding intensities ## samples: double, corresponding sample id numbers (1==reference) ## tolerance: double, maximal deviation of a peak position to be ## considered as same peak ## ## returns: ## NA if further splitting is needed ## meanMass (double) if all criteria are matched else 0 ## .grouperRelaxedHighestAtReference <- function(mass, intensities, samples, tolerance) { ## any reference peaks in current samples? ref <- samples == 1L nRef <- sum(ref) if (nRef == 0L) { ## no reference peak return(0L) } else if (nRef > 1L) { ## too many reference peaks => further splitting needed return(NA) } ## only one mass should left as reference mass meanMass <- mass[ref] ## all peaks in range? if (any(abs(mass - meanMass) / meanMass > tolerance)) { return(NA) } ## choose highest peak in duplicates if (anyDuplicated(samples)) { s <- sort.int(intensities, method="quick", decreasing=TRUE, index.return=TRUE) sSamples <- samples[s$ix] noDup <- !duplicated(sSamples) sMass <- double(length(sSamples)) sMass[noDup] <- meanMass mass[s$ix] <- sMass return(mass) } meanMass } MALDIquant/R/monoisotopicPeaks-methods.R0000644000175000017500000000117713022575752021172 0ustar sebastiansebastian## MassPeaks setMethod(f="monoisotopicPeaks", signature=signature(object="MassPeaks"), definition=function(object, minCor=0.95, tolerance=1e-4, distance=1.00235, size=3L:10L) { object[.monoisotopic(x=mass(object), y=intensity(object), minCor=minCor, tolerance=tolerance, distance=distance, size=size)] }) ## list setMethod(f="monoisotopicPeaks", signature=signature(object="list"), definition=function(object, ...) { ## test arguments .stopIfNotIsMassPeaksList(object) .lapply(object, monoisotopicPeaks, ...) }) MALDIquant/R/approxfun-methods.R0000644000175000017500000000064513022575752017505 0ustar sebastiansebastian## MassSpectrum setMethod(f="approxfun", signature=signature(x="MassSpectrum"), definition=function(x, y=NULL, method="linear", yleft, yright, rule=1L, f=0L, ties=mean) { if (isEmpty(x)) { function(x)rep.int(NA, length(x)) } else { approxfun(x=x@mass, y=x@intensity, method=method, yleft=yleft, yright=yright, rule=rule, f=f, ties=ties) } }) MALDIquant/R/calibrateIntensity-methods.R0000644000175000017500000000227713022575752021323 0ustar sebastiansebastian## MassSpectrum setMethod(f="calibrateIntensity", signature=signature(object="MassSpectrum"), definition=function(object, method=c("TIC", "PQN", "median"), range, ...) { method <- match.arg(method) switch(method, "TIC" = , "median" = { .transformIntensity(object, fun=.calibrateIntensitySimple, offset=0L, scaling=.scalingFactor(object, method=method, range=range)) }, "PQN" = { stop(dQuote("PQN"), " is not supported for a single MassSpectrum object.") }) }) ## list setMethod(f="calibrateIntensity", signature=signature(object="list"), definition=function(object, method=c("TIC", "PQN", "median"), range, ...) { ## test arguments .stopIfNotIsMassSpectrumList(object) method <- match.arg(method) switch(method, "TIC" = , "median" = { lapply(object, calibrateIntensity, method=method, range=range, ...) }, "PQN" = { .calibrateProbabilisticQuotientNormalization(object, range=range) } ) }) MALDIquant/R/labelPeaks-methods.R0000644000175000017500000000500613022761357017520 0ustar sebastiansebastiansetMethod(f="labelPeaks", signature=signature(object="MassPeaks"), definition=function(object, index, mass, labels, digits=3L, underline=TRUE, ## verticalOffset ca. 0.01 of plot height verticalOffset=abs(diff(par("usr")[3L:4L]))*0.01, absoluteVerticalPos, adj=c(0.5, 0L), cex=0.7, srt=0L, avoidOverlap=FALSE, arrowLength=0L, arrowLwd=0.5, arrowCol=1L, ...) { ## index if (missing(index) && missing(mass)) { index <- seq_along(object@mass) } else if (!missing(index) && is.logical(index)) { index <- which(index) } if (!missing(mass) && is.numeric(mass)) { massIdx <- match.closest(mass, object@mass) if (missing(index)) { index <- massIdx } else { index <- c(index, massIdx) } ## remove duplicated indices index <- unique(index) } isValidIndex <- length(index) && length(index) <= length(object@mass) && (min(index) >= 1L && max(index) <= length(object@mass)) if (!isValidIndex) { stop("No valid ", sQuote("index"), " nor ", sQuote("mass"), " given.") } x <- object@mass[index] ## labels if (missing(labels)) { labels <- round(x=x, digits=digits) } else if (!missing(labels) && length(index) != length(labels)) { stop("Lenghts of ", sQuote("index"), "/", sQuote("mass"), " and ", sQuote("labels"), " have to be equal.") } if (underline) { labels <- as.expression(sapply(labels, function(x)substitute(underline(a), list(a=x)))) } if (missing(absoluteVerticalPos)) { y <- object@intensity[index] + verticalOffset } else { y <- absoluteVerticalPos } if (avoidOverlap) { if (srt %% 90L != 0L) { stop(sQuote("avoidOverlap = TRUE"), " and ", sQuote("srt != x * 90"), " is not supported.") } ## inspired by Ian Fellows' wordcloud::wordlayout p <- .calculateLabelPositions(object, x, y, labels, adj=adj, cex=cex, srt=srt) ## create arrows from label to peak arrows(x0=p[, "x"], y0=p[, "y"], x1=x, y1=y, col=arrowCol, length=arrowLength, lwd=arrowLwd) ## no transparent background rect(xleft=p[, "x0"], ybottom=p[, "y0"], xright=p[, "x1"], ytop=p[, "y1"], col="white", border=NA, density=-1L) x <- p[, "x"] y <- p[, "y"] } text(x=x, y=y, labels=labels, adj=adj, cex=cex, srt=srt, ...) }) MALDIquant/R/snr-methods.R0000644000175000017500000000021413022575752016255 0ustar sebastiansebastian## MassPeaks setMethod(f="snr", signature=signature(object="MassPeaks"), definition=function(object) { object@snr }) MALDIquant/R/isFunctionList-functions.R0000644000175000017500000000057513022575752021007 0ustar sebastiansebastian.isFunctionList <- function(x) { if (!is.list(x)) { return(FALSE) } length(x) && all(.unlist(vapply(x, is.function, logical(1L)))) } .stopIfNotIsFunctionList <- function(x) { if (!.isFunctionList(x)) { parentCall <- deparse(sys.call(-1L)) stop(parentCall, " : ", sQuote(deparse(substitute(x))), " is no list of functions!", call.=FALSE) } TRUE } MALDIquant/R/range-functions.R0000644000175000017500000000151713022575752017123 0ustar sebastiansebastian## .overlap ## returns largest overlapping mass range of a list of AbstractMassObject ## objects. ## ## params: ## l: list of AbstractMassObject objects ## ## returns: ## double, minimal and maximal mass ## .overlap <- function(l) { ## test argument .stopIfNotIsMassObjectList(l) ## mass values are already sorted leftMass <- .unlist(lapply(l, function(x)x@mass[1L])) rightMass <- .unlist(lapply(l, function(x)x@mass[length(x@mass)])) if (length(rightMass)) { r <- c(max(leftMass, na.rm=TRUE), min(rightMass, na.rm=TRUE)) if (r[1L] < r[2L]) { return(r) } } ## no overlap c(0L, 0L) } ## .reorderRange ## swap range values if needed ## ## params: ## x: range values ## ## returns: ## corrected range values ## .reorderRange <- function(x) { ## sort range if (x[1L] > x[2L]) { x <- x[2L:1L] } x } MALDIquant/R/monoisotopic-functions.R0000644000175000017500000001171313022761357020546 0ustar sebastiansebastian#' .pseudoCluster #' #' Find possible isotopic cluster in mass/mz data. #' #' @param x double, mass #' @param size integer, cluster size, number of peaks per cluster #' @param distance double, distance between isotopes (mass of a neutron; see #' Park et al 2008); could be of length > 1 (if > 1: order will affect later #' removal in .monoisotopicPattern). #' @param tolerance double, mass tolerance #' @return a matrix of indices (nrow(x) == n) of potential clusters #' @references #' K. Park, J.Y. Yoon, S. Lee, E. Paek, H. Park, H.J. Jung, and S.W. Lee. 2008. #' Isotopic peak intensity ratio based algorithm for determination of isotopic #' clusters and monoisotopic masses of polypeptides from high-resolution #' mass spectrometric data. #' Analytical Chemistry, 80: 7294-7303. #' @noRd .pseudoCluster <- function(x, size=3L, distance=1.00235, tolerance=1e-4) { if (size < 2L) { stop("The ", sQuote("size"), " of a cluster has to be at least 2!") } mm <- matrix(x, nrow=size, ncol=length(x) * length(distance), byrow=TRUE) ms <- mm + (rep(distance, each=size) * 0L:(size - 1L)) i <- match.closest(ms, x, tolerance=mm * tolerance) dim(i) <- dim(ms) i[, !is.na(colSums(i)), drop=FALSE] } #' .F #' #' Map mass to poisson mean/lambda. #' #' @param mass double, mass from experimental peak list #' @return double suitable to pass to `dpois` #' @references #' E.J. Breen, F.G. Hopwood, K.L. Williams, and M.R. Wilkins. 2000. #' Automatic poisson peak harvesting for high throughput protein identification. #' Electrophoresis 21 (2000): 2243-2251. #' @noRd .F <- function(x)0.000594 * x + 0.03091 #' .P #' #' Model isotopic distribution by poisson distribution. #' #' @param mass double, mass from experimental peak list #' @param isotopes integer, which isotopes #' @return double, isotopic distribution #' @references #' E.J. Breen, F.G. Hopwood, K.L. Williams, and M.R. Wilkins. 2000. #' Automatic poisson peak harvesting for high throughput protein identification. #' Electrophoresis 21 (2000): 2243-2251. #' @noRd .P <- function(x, isotopes)dpois(isotopes, .F(x)) #' .Psum #' #' Model isotopic distribution by poisson distribution and sum to 1 (similar to #' TIC). #' #' @param mass double, mass from experimental peak list #' @param isotopes integer, which isotopes #' @return double, isotopic distribution #' @references #' E.J. Breen, F.G. Hopwood, K.L. Williams, and M.R. Wilkins. 2000. #' Automatic poisson peak harvesting for high throughput protein identification. #' Electrophoresis 21 (2000): 2243-2251. #' @noRd .Psum <- function(x, isotopes) { ni <- length(isotopes) nx <- length(x) p <- .P(rep.int(x, rep.int(ni, nx)), isotopes) dim(p) <- c(ni, nx) t(t(p) / colSums(p)) } #' .monoisotopicPattern #' #' Model isotopic distribution by poisson distribution. #' #' @param x double, mass from experimental peak list #' @param y double, intensity from experimental peak list #' @param tolerance double, mass tolerance for .pseudoCluster #' @param minCor double, minimal correlation between experimental and model #' intensities #' @param distance double, distance between isotopes (mass of a neutron; see #' Park et al 2008); could be of length > 1; if length > 1 the order matters. #' The first distance elements are prefered (the last elements are possible #' removed because the contain duplicated indices). #' @param size integer, cluster size (number of peaks for a possible cluster), #' see .pseudoCluster #' @return matrix, index of monoisotopic masses in first row #' @references #' E.J. Breen, F.G. Hopwood, K.L. Williams, and M.R. Wilkins. 2000. #' Automatic poisson peak harvesting for high throughput protein identification. #' Electrophoresis 21 (2000): 2243-2251. #' @noRd .monoisotopicPattern <- function(x, y, minCor=0.95, tolerance=1e-4, distance=1.00235, size=3L) { pc <- .pseudoCluster(x, size=size, distance=distance, tolerance=tolerance) y <- y[pc] dim(y) <- dim(pc) y <- t(t(y)/colSums(y)) p <- .Psum(x[pc[1L,]], isotopes=0L:(size-1L)) cr <- .colCors(y, p) pc <- pc[, cr > minCor, drop=FALSE] pc[duplicated(as.vector(pc))] <- NA_real_ pc[, !is.na(colSums(pc)), drop=FALSE] } #' .monoisotopic #' #' Loop through multiple .monoisotopicPattern outputs and remove duplicated #' peaks. #' #' @param x double, mass from experimental peak list #' @param y double, intensity from experimental peak list #' @param size integer vector, cluster size #' @param \ldots further arguments passed to .monoisotopicPattern #' @return double, index of monoisotopic masses #' @noRd .monoisotopic <- function(x, y, size=3L:10L, ...) { if (length(x) && length(x) == length(y)) { pattern <- lapply(sort.int(size, decreasing=TRUE), function(s).monoisotopicPattern(x=x, y=y, size=s, ...)) upattern <- .unlist(pattern) upattern[duplicated(upattern)] <- NA_real_ upattern <- relist(upattern, pattern) sort.int(.unlist(lapply(upattern, function(p)p[1L, !is.na(colSums(p))]))) } else { double() } } MALDIquant/R/alignSpectra-functions.R0000644000175000017500000000240713022575752020442 0ustar sebastiansebastian## alignSpectra ## wrapper around detectPeaks, determineWarpingFunctions and warpMassSpectra ## ## params: ## spectra: list, list of MassSpectrum objects ## halfWindowSize: numeric, half window size. ## noiseMethod: character, noise estimation method ## SNR: double, signal-to-noise ratio ## reference: MassPeaks, a reference MassPeaks object to which all other ## MassPeaks objects should be aligned ## tolerance: double, maximal deviation of a peak position to be ## considered as same peak ## warpingMethod: choose type of base warping function ## ## returns: ## a list of aligned MassSpectrum objects ## alignSpectra <- function(spectra, ## peak detection halfWindowSize=20, noiseMethod="MAD", SNR=2, ## warping reference, tolerance=0.002, warpingMethod="lowess", ...) { ## test arguments .stopIfNotIsMassSpectrumList(spectra) peaks <- detectPeaks(spectra, halfWindowSize=halfWindowSize, method=noiseMethod, SNR=SNR, ...) wf <- determineWarpingFunctions(peaks, reference=reference, tolerance=tolerance, method=warpingMethod) warpMassSpectra(spectra, wf) } MALDIquant/R/isRegular-methods.R0000644000175000017500000000033413022575752017413 0ustar sebastiansebastian## MassSpectrum setMethod(f="isRegular", signature=signature(object="MassSpectrum"), definition=function(object, threshold=1e-3) { s <- .irregularScore(object@mass) <= threshold !is.na(s) & s }) MALDIquant/R/warp-functions.R0000644000175000017500000000203313022575752016772 0ustar sebastiansebastian## warpMassSpectra ## warp MassSpectrum objects ## ## params: ## l: list of MassSpectrum objects ## w: list of warping functions determined by determineWarpingFunctions ## ## returns: ## a list of warped MassSpectrum objects ## warpMassSpectra <- function(l, w) { .stopIfNotIsMassSpectrumList(l) .stopIfNotIsFunctionList(w) .warp(l, w) } ## warpMassPeaks ## warp MassPeaks objects ## ## params: ## l: list of MassPeaks objects ## w: list of warping functions determined by determineWarpingFunctions ## ## returns: ## a list of warped MassPeaks objects ## warpMassPeaks <- function(l, w) { .stopIfNotIsMassPeaksList(l) .stopIfNotIsFunctionList(w) .warp(l, w) } ## .warp ## .warp bstractMassObject objects ## ## params: ## l: list of AbstractMassObject objects ## w: list of warping functions determined by determineWarpingFunctions ## ## returns: ## a list of warped AbstractMassObject objects ## .warp <- function(l, w) { .mapply(function(m, wf) { m@mass <- m@mass + wf(m@mass) m }, m=l, wf=w) } MALDIquant/R/lines-methods.R0000644000175000017500000000034713022575752016574 0ustar sebastiansebastian## AbstractMassObject setMethod(f="lines", signature=signature(x="AbstractMassObject"), definition=function(x, type=ifelse(isMassPeaks(x), "h", "l"), ...) { lines(x=x@mass, y=x@intensity, type, ...) }) MALDIquant/R/plotMsiSlice-functions.R0000644000175000017500000000713013022575752020433 0ustar sebastiansebastian.plotMsiSlice <- function(x, center=attr(x, "center"), tolerance=attr(x, "tolerance"), colRampList=list(colorRamp(c("black", "blue", "green", "yellow", "red"))), xlab="", ylab="", interpolate=FALSE, scale=TRUE, legend=scale, alignLabels=FALSE, label.cex=0.75, label.col=NULL, ...) { stopifnot(is.array(x)) d <- dim(x) tolerance <- rep_len(tolerance, length(center)) xlim <- c(0L, d[1L] + (2L * d[3L] * legend)) ylim <- c(0L, d[2L]) ## prepare plot area plot(NA, type="n", xlim=xlim, ylim=ylim, axes=FALSE, xlab=xlab, ylab=ylab, asp=1L, ...) if (d[3L] > 1L) { col <- x for (i in seq_len(d[3L])) { col[,, i] <- .colorMatrix(.array2matrix(x, z=i), colRamp=colRampList[[i]], scale=scale) } x <- .combineColorMatrices(x, col) } else { x <- .colorMatrix(.array2matrix(x), colRamp=colRampList[[1L]], scale=scale) } ## plot image .rasterSlice(x, interpolate=interpolate) if (legend) { if (!is.null(center)) { labels <- .mapply(function(cnt, tol)bquote(.(cnt) %+-% .(tol)), cnt=center, tol=tolerance) strh <- max(strheight(labels, cex=label.cex)) * 1.2 } else { labels <- character(d[3L]) strh <- 0L } xleft <- xlim[2L] - seq(from=d[3L] * 2L - 1L, to=1L, by=-2L) xright <- xleft + 1L ybottom <- rep.int(d[3L] * strh, d[3L]) ytext <- (d[3L] - 1L):0L * strh xtext <- if (alignLabels) { rep.int(xlim[2L], 3L) } else { xright } if (is.null(label.col) && d[3L] == 1L) { label.col <- "black" } else if (is.null(label.col) && d[3L] > 1L) { label.col <- lapply(colRampList, function(x).rgb(x(1L))) } for (i in seq_len(d[3L])) { .msiLegend(xleft=xleft[i], xright=xright[i], ybottom=ybottom[1L], ytop=ylim[2L], colRamp=colRampList[[i]], interpolate=interpolate) text(x=xtext[i], y=ytext[i], labels=as.expression(labels[i]), col=label.col[[i]], cex=label.cex, adj=c(1L, 0L)) } } } .rasterSlice <- function(x, interpolate=FALSE) { rasterImage(as.raster(t(x)), xleft=0L, xright=nrow(x), ybottom=0L, ytop=ncol(x), interpolate=interpolate) } .array2matrix <- function(a, z=1L) { ## subset function that preserves a matrix even if x or y 1 ## ([,,drop=TRUE]) creates a vector d <- dim(a) matrix(a[,, z, drop=TRUE], nrow=d[1L], ncol=d[2L]) } .msiLegend <- function(xleft, xright, ybottom, ytop, colRamp=colorRamp(c("black", "blue", "green", "yellow", "red")), interpolate=FALSE) { gradient <- matrix(.rgb(colRamp(seq.int(1L, 0L, length.out=100L))), ncol=1L) rect(xleft=xleft, xright=xright, ybottom=ybottom, ytop=ytop, col="black") rasterImage(as.raster(gradient), xleft=xleft, xright=xright, ybottom=ybottom, ytop=ytop, interpolate=interpolate) } .colorMatrix <- function(x, colRamp, scale=TRUE) { if (scale) { x <- x / max(x, na.rm=TRUE) } notNA <- which(!is.na(x)) x[notNA] <- .rgb(colRamp(x[notNA])) x } .combineColorMatrices <- function(x, col) { i <- apply(x, 2L, max.col, ties.method="first") j <- cbind(x=rep.int(seq_len(nrow(x)), ncol(x)), y=rep(seq_len(ncol(x)), each=nrow(x)), z=as.vector(i)) y <- .array2matrix(col) y[] <- col[j] y } .rgb <- function(x) { rgb(x, maxColorValue=255L) } MALDIquant/R/points-methods.R0000644000175000017500000000025513022575752016774 0ustar sebastiansebastian## MassPeaks setMethod(f="points", signature=signature(x="AbstractMassObject"), definition=function(x, ...) { points(x=x@mass, y=x@intensity, ...) }) MALDIquant/R/doByLabels-functions.R0000644000175000017500000000203013022575752020036 0ustar sebastiansebastian## .doByLabels ## run a specific function labelwise ## ## params: ## l: list of AbstractMassObject objects ## labels: factor, labels for samples ## fun: function ## ## returns: ## list of modified AbstractMassObject objects ## .doByLabels <- function(l, labels, FUN, ..., mc.cores=1L) { ## test parameters .stopIfNotIsMassObjectList(l) FUN <- match.fun(FUN) if (!missing(labels)) { ## drop unused levels and turn argument into factor if (is.factor(labels)) { labels <- droplevels(labels) } else { ## preserve order in labels labels <- factor(labels, levels=unique(labels)) } if (length(labels) != length(l)) { stop("For each item in ", sQuote("l"), " there must be a label in ", sQuote("labels"), "!") } ## replace tapply by split to preserve order tmp <- .lapply(split(unlist(l), labels), FUN=FUN, ..., mc.cores=mc.cores) k <- unlist(tmp) if (length(k) != length(tmp)) { k <- unsplit(tmp, labels) } } else { k <- FUN(l, ...) } k } MALDIquant/R/filterPeaks-functions.R0000644000175000017500000000713513022575752020302 0ustar sebastiansebastian## filterPeaks ## filter peaks which are not frequently represented in different samples ## ## params: ## l: list of MassPeaks objects ## minFrequency: double, minimal frequency of a peak to be not removed ## minNumber: double, minimal (absolute) number of peaks to be not removed ## labels: factor, labelwise filtering ## mergeWhitelists: logical, apply whitelists local (FALSE) or global (TRUE) ## ## returns: ## a list of adjusted MassPeaks objects ## filterPeaks <- function(l, minFrequency, minNumber, labels, mergeWhitelists=FALSE) { ## test arguments .stopIfNotIsMassPeaksList(l) ## labels if (missing(labels)) { labels <- rep.int(0L, length(l)) } ## drop unused levels and turn argument into factor if (is.factor(labels)) { labels <- droplevels(labels) } else { ## preserve order in labels labels <- factor(labels, levels=unique(labels)) } if (missing(minFrequency)) { minFrequency <- NA } if (missing(minNumber)) { minNumber <- NA } ll <- levels(labels) nl <- length(ll) if (length(labels) != length(l)) { stop("For each item in ", sQuote("l"), " there must be a label in ", sQuote("labels"), "!") } ## recycle arguments if needed minFrequency <- rep_len(minFrequency, nl) minNumber <- rep_len(minNumber, nl) mergeWhitelists <- mergeWhitelists[1] ## binary peak matrix (mask) m <- .as.binary.matrix(.as.matrix.MassObjectList(l)) ## whitelist w <- matrix(0L, nrow=nrow(m), ncol=ncol(m)) ## group indices by labels idx <- lapply(ll, function(x)which(labels == x)) ## collect whitelists for (i in seq_along(idx)) { wl <- .whitelist(m, idx[[i]], minFrequency=minFrequency[i], minNumber=minNumber[i]) if (sum(wl)) { if (mergeWhitelists) { ## R uses columnwise recycling w <- t(t(w) | wl) } else { ## R uses columnwise recycling w[idx[[i]], ] <- t(t(w[idx[[i]], , drop=FALSE]) | wl) } } else { warning("Empty peak whitelist for level ", sQuote(ll[i]), ".") } } ## apply whitelist w <- w & m ## turn matrix back into MassPeaks objects for (i in seq_along(l)) { j <- which(as.logical(m[i, ])) include <- which(w[i, j]) l[[i]]@mass <- l[[i]]@mass[include] l[[i]]@intensity <- l[[i]]@intensity[include] l[[i]]@snr <- l[[i]]@snr[include] } l } ## .whitelist ## helper function to create whitelists for filtering ## ## params: ## m: matrix ## rows: index of rows which should filtered ## minFrequency: double, minimal frequency of a peak to be not removed ## minNumber: double, minimal (absolute) number of peaks to be not removed ## ## returns: ## a logical vector representing the whitelist ## .whitelist <- function(m, rows, minFrequency, minNumber) { ## test arguments if (is.na(minFrequency) && is.na(minNumber)) { stop(sQuote(minFrequency), " or ", sQuote(minNumber), " has to be a meaningful number!") } if (!is.na(minFrequency) && minFrequency < 0L) { minFrequency <- 0L warning(sQuote("minFrequency"), " < 0 does not make sense! Using 0 instead.") } if (!is.na(minNumber) && minNumber < 0L) { minNumber <- 0L warning(sQuote("minNumber"), " < 0 does not make sense! Using 0 instead.") } if (!is.na(minFrequency) && !is.na(minNumber)) { warning(sQuote("minFrequency"), " and ", sQuote("minNumber"), " arguments are given. Choosing the higher one.") } ## calculate minimal number of peaks minPeakNumber <- max(minFrequency * length(rows), minNumber, na.rm=TRUE) colSums(m[rows, , drop=FALSE]) >= minPeakNumber } MALDIquant/R/morphologicalFilter-functions.R0000644000175000017500000000212313022575752022026 0ustar sebastiansebastian## dilation ## calculates moving max (dilation filter) ## ## M. van Herk. "A Fast Algorithm for Local Minimum and Maximum Filters on ## Rectangular and Octagonal Kernels." ## Pattern Recognition Letters 13.7 (1992): 517-521. ## ## J. Y. Gil and M. Werman. "Computing 2-Dimensional Min, Median and Max ## Filters." IEEE Transactions (1996): 504-507. ## ## params: ## x: vector of x values ## halfWindowSize: size of local window ## ## returns: ## moving max ## .dilation <- function(x, halfWindowSize) { .Call("C_dilation", x, halfWindowSize) } ## erosion ## calculates moving min (erosion filter) ## ## M. van Herk. "A Fast Algorithm for Local Minimum and Maximum Filters on ## Rectangular and Octagonal Kernels." ## Pattern Recognition Letters 13.7 (1992): 517-521. ## ## J. Y. Gil and M. Werman. "Computing 2-Dimensional Min, Median and Max ## Filters." IEEE Transactions (1996): 504-507. ## ## params: ## x: vector of x values ## halfWindowSize: size of local window ## ## returns: ## moving min ## .erosion <- function(x, halfWindowSize) { .Call("C_erosion", x, halfWindowSize) } MALDIquant/R/match.closest-functions.R0000644000175000017500000000512313027254751020571 0ustar sebastiansebastian#' Relaxed Value Matching #' #' \code{match.closest} returns a vector of the positions of (first) matches #' its first arguments in its second. In contrast to the similar #' \code{\link{match}} it just accept \code{numeric} arguments but #' has an additional \code{tolerance} argument that allows relaxed #' matching. #' #' @param x \code{numeric}, the values to be matched. #' @param table \code{numeric}, the values to be matched against. In contrast to #' \code{\link{match}} \code{table} has to be sorted in increasing order. #' @param tolerance \code{numeric}, accepted tolerance. Use \code{Inf} to match without #' restrictions. Could be of length one or the same length as \code{table}. #' @param nomatch \code{numeric}, if the difference #' between the value in \code{x} and \code{table} is larger than #' \code{tolerance} \code{nomatch} is returned. Has to be of length one. #' #' @return An \code{integer} vector of the same length as \code{x} giving the #' closest position in \code{table} of the first match or \code{nomatch} if #' there is no match. #' #' @seealso \code{\link{match}} #' @aliases match.closest #' @export #' @examples #' library("MALDIquant") #' match.closest(c(1.1, 1.4, 9.8), 1:10) #' # [1] 1 1 10 #' match.closest(c(1.1, 1.4, 9.8), 1:10, tolerance=0.25) #' # [1] 1 NA 10 #' match.closest(c(1.1, 1.4, 9.8), 1:10, tolerance=0.25, nomatch=0) #' # [1] 1 0 10 #' #' ## this function is most useful if you want to subset an intensityMatrix #' ## by a few (reference) peaks #' #' ## create an example intensityMatrix #' im <- matrix(1:10, nrow=2, dimnames=list(NULL, 1:5)) #' attr(im, "mass") <- 1:5 #' im #' # 1 2 3 4 5 #' # [1,] 1 3 5 7 9 #' # [2,] 2 4 6 8 10 #' # attr(,"mass") #' # [1] 1 2 3 4 5 #' #' ## reference peaks #' ref <- c(2.2, 4.8) #' #' im[, match.closest(ref, attr(im, "mass"), tolerance=0.25, nomatch=0)] #' # 2 5 #' # [1,] 3 9 #' # [2,] 4 10 #' match.closest <- function(x, table, tolerance=Inf, nomatch=NA_integer_) { lIdx <- findInterval(x, table, rightmost.closed=FALSE, all.inside=TRUE) rIdx <- lIdx + 1L lIdx[lIdx == 0L] <- 1L lDiff <- abs(table[lIdx] - x) rDiff <- abs(table[rIdx] - x) d <- which(lDiff >= rDiff) lIdx[d] <- rIdx[d] if (any(is.finite(tolerance))) { if (any(tolerance < 0L)) { warning(sQuote("tolerance"), " < 0 is meaningless. Set to zero.") tolerance[tolerance < 0L] <- 0L } if (length(nomatch) != 1L) { stop("Length of ", sQuote("nomatch"), " has to be one.") } tolerance <- rep_len(tolerance, length(table)) lDiff[d] <- rDiff[d] lIdx[lDiff > tolerance[lIdx]] <- nomatch } lIdx } MALDIquant/R/calculateLabelPositions-functions.R0000644000175000017500000001330713022575752022634 0ustar sebastiansebastian## .calculateLabelPositions ## calculate label positions to avoid collisions ## ## params: ## object: a single MassPeaks object ## x: original x coordinates for labels ## y: original y coordinates for labels ## peakLabels: peak labels ## adj: text alignment ?par("adj") ## cex: font size ?par("cex") ## srt: rotation in degree ## maxSteps: max tries to avoid collisons ## ## returns: ## a matrix of coordinates ## .calculateLabelPositions <- function(object, x, y, peakLabels, adj, cex, srt, maxSteps=100L) { ## start with smallest peak i <- sort.int(y, index.return=TRUE, method="quick")$ix ## calculate label rectangles rects <- .textLabelRects(x[i], y[i], peakLabels[i], adj=adj, cex=cex, srt=srt) ## move rectangles around to avoid collisons for (j in seq_along(x)) { rects[j, ] <- .testLabelOverlap(object, rects, currentIndex=j, maxSteps=maxSteps) } ## undo sorting rects[i, ] <- rects rects } ## .testLabelOverlap ## try to avoid overlap/collisions ## ## params: ## object: a single MassPeaks object ## rects: a matrix of coordinates (created by .textRects) ## currentIndex: which coordinates should moved around ## maxSteps: max tries to avoid collisons ## ## returns: ## a vector of coordinates ## .testLabelOverlap <- function(object, rects, currentIndex, maxSteps) { r <- pi / 180L * c(90, as.vector(rbind(seq(80L, 40L, by=-10L), seq(100L, 140L, by=10L)))) testrects <- rbind(matrix(c(object@mass, rep(0L, length(object)), object@mass, object@intensity), ncol=4L), rects[seq_len(currentIndex - 1L), c("x0", "y0", "x1", "y1")]) for (k in 0L:maxSteps) { ## move up s <- k / 4L cur <- rects[currentIndex, ] cur[c("y0", "y1", "y")] <- cur[c("y0", "y1", "y")] + s * cur["h"] for (l in r) { ## move in curve oldcur <- cur cur[c("y0", "y1", "y")] <- cur[c("y0", "y1", "y")] + sin(l) * s * cur["h"] cur[c("x0", "x1", "x")] <- cur[c("x0", "x1", "x")] + cos(l) * s * cur["w"] ## success if (!.overlaps(cur, testrects)) { return(cur) } cur <- oldcur } } ## no success, return original pos rects[currentIndex, ] } ## .textLabelRects ## calculate label rectangles ## ## params: ## x: original x coordinates for labels ## y: original y coordinates for labels ## text: peak labels ## adj: text alignment ?par("adj") ## cex: font size ?par("cex") ## srt: rotation in degree ## offset: offset ## ## returns: ## a matrix of coordinates ## .textLabelRects <- function(x, y, text, adj=c(0.5, 0L), cex=0.7, srt=0, offset=c(0.0, 0.2)) { wh <- .strWH(text, srt=srt, cex=cex) ## extra calculation of offsets to be independend of adj offset <- t(t(wh) * offset) m <- matrix(c(x - wh[, "w"] * adj[1L] - offset[, "w"], y - wh[, "h"] * adj[2L] - offset[, "h"], x + wh[, "w"] * (1L - adj[1L]) + offset[, "w"], y + wh[, "h"] * (1L - adj[2L]) + offset[, "h"], x, y, wh[, "w"], wh[, "h"]), ncol = 8L, dimnames = list(c(), c("x0", "y0", "x1", "y1", "x", "y", "w", "h"))) if (srt) { m[, c("x0", "x1", "y0", "y1")] <- .rotate(rbind(m[, c("x0", "y0")], m[, c("x1", "y1")]), center=m[, c("x", "y")], srt=srt) if (m[1L, "x0"] > m[1L, "x1"]) { m[, c("x1", "x0")] <- m[, c("x0", "x1")] } if (m[1L, "y0"] > m[1L, "y1"]) { m[, c("y1", "y0")] <- m[, c("y0", "y1")] } m[, c("w", "h")] <- m[, c("x1", "y1")] - m[, c("x0", "y0")] } m } ## .overlaps ## does rectangles overlap? ## ## HINT: doesn't work for rotated rectangles (!= 0, 90, 180, 270, 360) ## ## params: ## a: vector, length 4, c(x0, y0, x1, y1); c(x0, y0) bottom left ## b: matrix, at least 4 columns, c(x0, y0, x1, y1) ## ## returns: ## TRUE/FALSE .overlaps <- function(a, b) { if (is.vector(b)) { b <- t(b) } any(# rectangles on left/right of each other? !((a[1L] >= b[, 3L] | a[3L] <= b[, 1L]) | # rectangles on top/bottom of each other? (a[2L] >= b[, 4L] | a[4L] <= b[, 2L]))) } ## .scaleFactor ## get height to width scale factor of a graphic device ## ## returns: ## double, scale factor ## .scaleFactor <- function() { usr <- par("usr") pin <- par("pin") dx <- usr[2L] - usr[1L] dy <- usr[4L] - usr[3L] (dy * pin[1L]) / (dx * pin[2L]) } ## .rotate ## rotate rectangle ## ## params: ## x: matrix, 2 columns, x,y coordinates ## center: matrix, 2 columns, rotation center x,y coordinate ## srt: rotation in degree ## returns: ## double, length 2, x, y, rotated ## .rotate <- function(x, center, srt) { stopifnot(all(dim(x)) == all(dim(center))) a <- pi * srt / 180L cosa <- cos(a) sina <- sin(a) dx <- x[, 1L] - center[, 1L] dy <- x[, 2L] - center[, 2L] x[, 1L] <- center[, 1L] + dx * cosa - dy * sina x[, 2L] <- center[, 2L] + dx * sina + dy * cosa x } ## .strWH ## string width and height scaled to device scale and rotatet ## ## TODO: doesn't work for srt != 0, 90, 180, ... (.overlaps doesn't work for ## rotated rectangles) ## ## params: ## text: text ## cex: character scaling factor (see ?par) ## srt: rotation in degree ## scale: scale factor ## ## returns: ## double, length 2, w, h ## .strWH <- function(text, srt, cex=0.7, scale=.scaleFactor()) { a <- pi * srt / 180L scale <- abs(cos(a) + sin(a) * scale) matrix(c(strwidth(text, cex=cex) * scale, strheight(text, cex=cex) / scale), ncol = 2L, dimnames = list(c(), c("w", "h"))) } MALDIquant/R/isEmpty-methods.R0000644000175000017500000000116413022575752017112 0ustar sebastiansebastian## AbstractMassObject setMethod(f="isEmpty", signature=signature(x="AbstractMassObject"), definition=function(x) { !sum(as.double(x@intensity), na.rm=TRUE) }) setMethod(f=".isEmptyWarning", signature=signature(x="AbstractMassObject"), definition=function(x) { if (isEmpty(x)) { msg <- paste0(class(x)[1L], " object") if (!is.null(x@metaData$file)) { msg <- paste0(msg, " (file: ", x@metaData$file, ")") } parentCall <- sys.call(-1L) warning("In ", deparse(parentCall), " : ", msg, " is empty!", call.=FALSE) return(TRUE) } FALSE }) MALDIquant/R/as.matrix-methods.R0000644000175000017500000000041713022575752017366 0ustar sebastiansebastian## AbstractMassObject setMethod(f="as.matrix", signature=signature(x="AbstractMassObject"), definition=function(x, index) { matrix(c(x@mass[index], x@intensity[index]), ncol=2L, byrow=FALSE, dimnames=list(NULL, c("mass", "intensity"))) }) MALDIquant/R/plotMsiSlice-methods.R0000644000175000017500000000561013022575752020067 0ustar sebastiansebastiansetMethod(f="plotMsiSlice", signature=signature(x="list"), definition=function(x, center, tolerance, colRamp=colorRamp(c("black", "blue", "green", "yellow", "red")), interpolate=FALSE, legend=TRUE, alignLabels=FALSE, combine=FALSE, ...) { .stopIfNotIsMassObjectList(x) slides <- msiSlices(x, center=center, tolerance=tolerance) plotMsiSlice(slides, colRamp=colRamp, interpolate=interpolate, legend=legend, alignLabels=alignLabels, combine=combine, ...) }) setMethod(f="plotMsiSlice", signature=signature(x="array"), definition=function(x, colRamp=colorRamp(c("black", "blue", "green", "yellow", "red")), interpolate=FALSE, legend=TRUE, alignLabels=FALSE, combine=FALSE, plotInteractive=FALSE, ...) { n <- dim(x)[3L] if (!is.list(colRamp)) { colRamp <- rep_len(list(colRamp), n) } if (n != length(colRamp)) { stop(sQuote("dim(x)[3L]"), " (number of centers) has to be the same as ", "the length of the list ", sQuote("colRamp"), "!\n", "See ", sQuote("?plotMsiSlice"), " for details.") } if (combine) { .plotMsiSlice(x, colRampList=colRamp, interpolate=interpolate, legend=legend, alignLabels=alignLabels, ...) } else { isNonInteractivePlot <- dev.cur() != 1L && !dev.interactive() if (n > 1L && !isNonInteractivePlot && !plotInteractive) { warning(sQuote("plotMsiSlice"), " was called for multiple slices on an ", "interactive device. Only the first slice is plotted. Use ", sQuote("pdf"), " or a similar device to plot all slices at once.", " Alternatively use ", dQuote("combine=TRUE"), " to plot ", "multiple centers in one plot.\n", "See ", sQuote("?plotMsiSlice"), " for details.") n <- 1L } tolerance <- rep_len(attr(x, "tolerance"), n) for (i in seq_len(n)) { .plotMsiSlice(x[,, i, drop=FALSE], center=attr(x, "center")[i], tolerance=tolerance[i], colRampList=colRamp[i], interpolate=interpolate, legend=legend, ...) } } }) setMethod(f="plotMsiSlice", signature=signature(x="matrix"), definition=function(x, colRamp=colorRamp(c("black", "blue", "green", "yellow", "red")), interpolate=FALSE, scale=TRUE, legend=scale, ...) { if (!is.list(colRamp)) { colRamp <- list(colRamp) } dim(x) <- c(dim(x), 1L) .plotMsiSlice(x, colRampList=colRamp, interpolate=interpolate, scale=scale, legend=legend, ...) }) MALDIquant/R/coordinates-methods.R0000644000175000017500000000324313022575752017772 0ustar sebastiansebastian## AbstractMassObject setMethod(f="coordinates", signature=signature(object="AbstractMassObject"), definition=function(object, ...) { object@metaData$imaging$pos }) ## AbstractMassObject setReplaceMethod(f="coordinates", signature=signature(object="AbstractMassObject", value="numeric"), definition=function(object, value) { if (!length(value) %in% c(2L, 3L)) { stop("2 or 3 coordinates are needed!") } object@metaData$imaging$pos <- value object }) setReplaceMethod(f="coordinates", signature=signature(object="AbstractMassObject", value="matrix"), definition=function(object, value) { if (!ncol(value) %in% c(2L, 3L)) { stop("2 or 3 coordinates are needed!") } if (nrow(value) != 1L) { warning("all rows but the first are ignored!") } object@metaData$imaging$pos <- value[1L, ] object }) ## list setMethod(f="coordinates", signature=signature(object="list"), definition=function(object, adjust=FALSE) { ## test arguments .stopIfNotIsMassObjectList(object) m <- do.call(rbind, lapply(object, coordinates)) colnames(m) <- c("x", "y", "z")[seq_len(ncol(m))] if (adjust) { m <- apply(m, MARGIN=2L, function(x)x - min(x) + 1L) } m }) ## list setReplaceMethod(f="coordinates", signature=signature(object="list", value="matrix"), definition=function(object, value) { ## test arguments .stopIfNotIsMassObjectList(object) .mapply("coordinates<-", object, split(value, row(value))) }) MALDIquant/R/mz-methods.R0000644000175000017500000000056513022575752016112 0ustar sebastiansebastian## AbstractMassObject setMethod(f="mz", signature=signature(object="AbstractMassObject"), definition=function(object, ...) { mass(object) }) ## AbstractMassObject setReplaceMethod(f="mz", signature=signature(object="AbstractMassObject", value="numeric"), definition=function(object, value) { mass(object) <- value object }) MALDIquant/R/colMedians-functions.R0000644000175000017500000000176413022575752020111 0ustar sebastiansebastian.colMedians <- function(x, na.rm=FALSE) { stopifnot(is.matrix(x), is.logical(na.rm)) .Call("C_colMedians", x, na.rm) } #' .colMaxs #' #' Similar to apply(x, 2, max). #' #' @param x matrix/data.frame #' @return double #' @author Sebastian Gibb #' @noRd .colMaxs <- function(x) { x[max.col(t(x), ties.method="first") + 0L:(ncol(x) - 1L) * nrow(x)] } #' .colCors #' #' Calculate the correlation for two matrices columnwise. #' #' @param x matrix/data.frame #' @return double #' @author Sebastian Gibb #' @noRd .colCors <- function(x, y, na.rm=FALSE) { stopifnot(is.matrix(x) && is.matrix(y)) stopifnot(all(dim(x) == dim(y))) if (na.rm) { isNA <- is.na(x) | is.na(y) x[isNA] <- NA_real_ y[isNA] <- NA_real_ } cmX <- colMeans(x, na.rm=na.rm) cmY <- colMeans(y, na.rm=na.rm) (colMeans(x * y, na.rm=na.rm) - (cmX * cmY)) / (sqrt(colMeans(x * x, na.rm=na.rm) - cmX * cmX) * sqrt(colMeans(y * y, na.rm=na.rm) - cmY * cmY)) } MALDIquant/R/valid-methods.R0000644000175000017500000000177113022575752016563 0ustar sebastiansebastian## AbstractMassObject .validAbstractMassObject <- function(object) { if (length(object@mass) != length(object@intensity)) { return(paste0("Lengths of mass (", length(object@mass), ") and intensity (", length(object@intensity), ") have to be equal.")) } if (is.numeric(object@mass) && length(object@mass) && any(object@mass < 0L)) { warning("Negative mass values found.") } if (is.numeric(object@intensity) && !isEmpty(object) && any(object@intensity < 0L)) { warning("Negative intensity values found.") } TRUE } setValidity("AbstractMassObject", method=.validAbstractMassObject) .validMassPeaks <- function(object) { if (length(object@intensity) != length(object@snr)) { return(paste0("Lengths of intensity (", length(object@intensity), ") and snr (", length(object@snr), ") have to be equal.")) } .validAbstractMassObject(object) } setValidity("MassPeaks", method=.validMassPeaks) MALDIquant/R/binPeaks-functions.R0000644000175000017500000001134413022575752017562 0ustar sebastiansebastian## binPeaks ## binning peaks by splitting at the largest gap ## This is a wrapper function around .binPeaks which prepares the peak list ## before and recreates a correct peak list after binning. ## ## params: ## l: list of MassPeaks objects ## method: character, grouper to used (strict: don't allow multiple peaks of ## the same sample in the same bin, relaxed: allow them) ## tolerance: double, maximal deviation of a peak position to be ## considered as same peak ## ## returns: ## a list of adjusted MassPeaks objects ## binPeaks <- function(l, method=c("strict", "relaxed"), tolerance=0.002) { ## test arguments .stopIfNotIsMassPeaksList(l) method <- match.arg(method) ## fetch all mass mass <- unname(.unlist(lapply(l, function(x)x@mass))) ## fetch all intensities intensities <- .unlist(lapply(l, function(x)x@intensity)) ## fetch all snr snr <- .unlist(lapply(l, function(x)x@snr)) ## store original mass sample number/id samples <- rep.int(seq_along(l), lengths(l)) ## sort values by mass s <- sort.int(mass, method="quick", index.return=TRUE) mass <- s$x intensities <- intensities[s$ix] snr <- snr[s$ix] samples <- samples[s$ix] ## select grouper grouper <- switch(method, "strict" = { .grouperStrict }, "relaxed" = { .grouperRelaxed } ) ## binning mass <- .binPeaks(mass=mass, intensities=intensities, samples=samples, tolerance=tolerance, grouper=grouper) ## resort mass (order could change if "relaxed" is used) if (method == "relaxed") { s <- sort.int(mass, method="quick", index.return=TRUE) mass <- s$x intensities <- intensities[s$ix] snr <- snr[s$ix] samples <- samples[s$ix] } ## group mass/intensities/snr by sample ids lIdx <- split(seq_along(mass), samples) ## create adjusted peak list l <- .mapply(FUN=function(p, i) { p@mass <- mass[i] p@intensity <- intensities[i] p@snr <- snr[i] p }, p=l, i=lIdx) l } ## .binPeaks ## binning peaks by splitting at the largest gap ## ## params: ## mass: double, sorted mass ## intensities: double, corresponding intensities ## samples: double, corresponding sample id numbers ## tolerance: double, maximal deviation of a peak position to be ## considered as same peak ## grouper: grouping function ## ...: arguments passed to grouping function ## ## returns: ## vector (double) of modified mass ## .binPeaks <- function(mass, intensities, samples, tolerance, grouper=.grouperStrict, ...) { n <- length(mass) ## calculate difference d <- diff(mass) ## grouper function grouper <- match.fun(grouper) ## stack based implementation taken from ## caMassClass 1.9 R/msc.peaks.clust.R written by ## Jarek Tuszynski ## it is a lot of faster than recursion ## store boundaries in a stack nBoundaries <- max(20L, floor(3L * log(n))) boundary <- list(left=double(nBoundaries), right=double(nBoundaries)) currentBoundary <- 1L boundary$left[currentBoundary] <- 1L boundary$right[currentBoundary] <- n ## workhorse loop while (currentBoundary > 0L) { ## find largest gap left <- boundary$left[currentBoundary] right <- boundary$right[currentBoundary] currentBoundary <- currentBoundary - 1L gaps <- d[left:(right - 1L)] gapIdx <- which.max(gaps) + left - 1L ## left side l <- grouper(mass=mass[left:gapIdx], intensities=intensities[left:gapIdx], samples=samples[left:gapIdx], tolerance=tolerance, ...) ## further splitting needed? if (is.na(l[1L])) { currentBoundary <- currentBoundary + 1L boundary$left[currentBoundary] <- left boundary$right[currentBoundary] <- gapIdx } else { mass[left:gapIdx] <- l } ## right side r <- grouper(mass=mass[(gapIdx + 1L):right], intensities=intensities[(gapIdx + 1L):right], samples=samples[(gapIdx + 1L):right], tolerance=tolerance, ...) ## further splitting needed? if (is.na(r[1L])) { currentBoundary <- currentBoundary + 1L boundary$left[currentBoundary] <- gapIdx + 1L boundary$right[currentBoundary] <- right } else { mass[(gapIdx + 1L):right] <- r } ## stack size have to be increased? ## (should rarely happen because recursion deep is mostly < 20) if (currentBoundary == nBoundaries) { nBoundaries <- floor(nBoundaries * 1.5) boundary$left <- c(boundary$left, double(nBoundaries - currentBoundary)) boundary$right <- c(boundary$right, double(nBoundaries - currentBoundary)) } } mass } MALDIquant/R/smoothIntensity-methods.R0000644000175000017500000000213713022575752020701 0ustar sebastiansebastian## AbstractMassObject setMethod(f="smoothIntensity", signature=signature(object="MassSpectrum"), definition=function(object, method=c("SavitzkyGolay", "MovingAverage"), halfWindowSize, ...) { ## empty spectrum? if (.isEmptyWarning(object)) { return(object) } method <- match.arg(method) fun <- switch(method, "SavitzkyGolay" = { if (missing(halfWindowSize)) { halfWindowSize <- 10L } .savitzkyGolay }, "MovingAverage" = { if (missing(halfWindowSize)) { halfWindowSize <- 2L } .movingAverage } ) .transformIntensity(object, fun=fun, halfWindowSize=halfWindowSize, ...) }) ## list setMethod(f="smoothIntensity", signature=signature(object="list"), definition=function(object, ...) { ## test arguments .stopIfNotIsMassSpectrumList(object) .mapply(smoothIntensity, object, ...) }) MALDIquant/R/detectPeaks-methods.R0000644000175000017500000000236713022575752017722 0ustar sebastiansebastian## MassSpectrum setMethod(f="detectPeaks", signature=signature(object="MassSpectrum"), definition=function(object, halfWindowSize=20L, method=c("MAD", "SuperSmoother"), SNR=2L, ...) { ## empty spectrum? if (.isEmptyWarning(object)) { return(createMassPeaks(mass=object@mass, intensity=object@intensity, metaData=object@metaData)) } ## estimate noise noise <- .estimateNoise(x=object@mass, y=object@intensity, method=method, ...) ## find local maxima isLocalMaxima <- .findLocalMaximaLogical(object, halfWindowSize=halfWindowSize) ## include only local maxima which are above the noise isAboveNoise <- object@intensity > (SNR * noise) peakIdx <- which(isAboveNoise & isLocalMaxima) createMassPeaks(mass=object@mass[peakIdx], intensity=object@intensity[peakIdx], snr=object@intensity[peakIdx] / noise[peakIdx], metaData=object@metaData) }) ## list setMethod(f="detectPeaks", signature=signature(object="list"), definition=function(object, ...) { ## test arguments .stopIfNotIsMassSpectrumList(object) .mapply(detectPeaks, object, ...) }) MALDIquant/R/calibrateIntensity-functions.R0000644000175000017500000000512513022575752021663 0ustar sebastiansebastian## .scalingFactor ## calculate scaling factor for TIC/median ranged calibration ## ## params: ## object: AbstractMassObject ## method: character ## range: double ## ## returns: ## double ## .scalingFactor <- function(object, method=c("TIC", "median"), range) { method <- match.arg(method) if (!missing(range)) { object <- trim(object, range=range) } switch(method, "TIC" = { totalIonCurrent(object) }, "median" = { median(object@intensity) } ) } ## .calibrateIntensitySimple ## calibrate intensity values by offset and scaling factor ## ## params: ## y: double, intensity values ## offset: double ## scaling: double ## ## returns: ## double, calibrated intensity values ## .calibrateIntensitySimple <- function(y, offset=0L, scaling=1L) { if (scaling == 0L) { warning("Scaling factor is zero. No calibration applied.") y } else { (y - offset) / scaling } } ## .calibrateProbabilisticQuotientNormalization ## calibrate intensity values by Probabilistic Quotient Normalization ## ## F. Dieterle, A. Ross, G. Schlotterbeck, and Hans Senn. ## "Probabilistic quotient normalization as robust method to account for ## dilution of complex biological mixtures. Application in 1H NMR ## metabonomics." ## Analytical Chemistry 78, no. 13 (2006): 4281-4290. ## ## 1. Perform an integral normalization (typically a constant ## integral of 100 is used). ## 2. Choose/calculate the reference spectrum (the best approach ## is the calculation of the median spectrum of control samples). ## 3. Calculate the quotients of all variables of interest of the test ## spectrum with those of the reference spectrum. ## 4. Calculate the median of these quotients. ## 5. Divide all variables of the test spectrum by this median. ## ## params: ## l: list of MassSpectrum objects ## range: double ## ## returns: ## list of calibrated MassSpectrum objects ## .calibrateProbabilisticQuotientNormalization <- function(l, range) { ## 1. integral normalization (==TIC) l <- calibrateIntensity(l, method="TIC", range=range) ## 2. median reference spectrum if (missing(range)) { reference <- .averageMassSpectra(l, fun=.colMedians, mergeMetaData=FALSE) } else { reference <- .averageMassSpectra(trim(l, range=range), fun=.colMedians, mergeMetaData=FALSE) } lapply(l, function(x) { ## 3. quotient calculation q <- approxfun(x)(reference@mass) / reference@intensity ## 4. median m <- median(q, na.rm=TRUE) ## 5. divide by median .transformIntensity(x, fun=.calibrateIntensitySimple, offset=0L, scaling=m) }) } MALDIquant/R/isValidHalfWindowSize-functions.R0000644000175000017500000000112513022575752022233 0ustar sebastiansebastian## .stopIfNotIsValidHalfWindowSize ## test for too small/large half window size ## ## params: ## halfWindowSize: half window size ## n: length of data ## ## returns: ## TRUE if valid ## .stopIfNotIsValidHalfWindowSize <- function(halfWindowSize, n) { parentCall <- deparse(sys.call(-1L)) if (halfWindowSize < 1L) { stop(parentCall, " : ", sQuote("halfWindowSize"), " is too small!", call.=FALSE) } windowSize <- halfWindowSize * 2L + 1L if (windowSize > n) { stop(parentCall, " : ", sQuote("halfWindowSize"), " is too large!", call.=FALSE) } TRUE } MALDIquant/tests/0000755000175000017500000000000012624130232014616 5ustar sebastiansebastianMALDIquant/tests/testthat.R0000644000175000017500000000005512624130232016601 0ustar sebastiansebastianlibrary("testthat") test_check("MALDIquant") MALDIquant/tests/testthat/0000755000175000017500000000000013035270676016474 5ustar sebastiansebastianMALDIquant/tests/testthat/test_findLocalMaximaLogical-methods.R0000644000175000017500000000333712624130232025632 0ustar sebastiansebastiancontext(".findLocalMaximaLogical") s <- createMassSpectrum(mass=1:5, intensity=c(1, 2, 1, 2, 1)) test_that(".findLocalMaximaLogical throws errors", { expect_error(MALDIquant:::.findLocalMaximaLogical(s, halfWindowSize=0), "too small") expect_error(MALDIquant:::.findLocalMaximaLogical(s, halfWindowSize=3), "too large") }) test_that(".findLocalMaxima(Logical) shows warnings", { expect_warning(MALDIquant:::.findLocalMaximaLogical( createMassSpectrum(mass=double(), intensity=double()), "empty")) expect_warning(MALDIquant:::.findLocalMaxima( createMassSpectrum(mass=double(), intensity=double()), "empty")) }) test_that(".findLocalMaximaLogical works with different window sizes", { expect_identical(suppressWarnings(MALDIquant:::.findLocalMaximaLogical( createMassSpectrum(mass=double(), intensity=double()), halfWindowSize=1)), logical()) expect_identical(MALDIquant:::.findLocalMaximaLogical(s, halfWindowSize=1), c(FALSE, TRUE, FALSE, TRUE, FALSE)) expect_identical(MALDIquant:::.findLocalMaximaLogical(s, halfWindowSize=2), c(FALSE, TRUE, FALSE, FALSE, FALSE)) }) test_that(".findLocalMaxima returns matrix", { m <- matrix(ncol=2, dimnames=list(list(), list("mass", "intensity"))) expect_equal(suppressWarnings(MALDIquant:::.findLocalMaxima( createMassSpectrum(mass=double(), intensity=double()), halfWindowSize=1)), m) m <- matrix(c(2, 2, 4, 2), ncol=2, byrow=TRUE, dimnames=list(list(), list("mass", "intensity"))) expect_identical(MALDIquant:::.findLocalMaxima(s, halfWindowSize=1), m) }) MALDIquant/tests/testthat/test_averageMassSpectra-functions.R0000644000175000017500000000426712624130232025437 0ustar sebastiansebastiancontext("averageMassSpectra") s <- c(createMassSpectrum(mass=1:3, intensity=1:3, metaData=list(a=1, b=2)), createMassSpectrum(mass=1:3, intensity=4:6, metaData=list(a=1, b=3))) meanS <- createMassSpectrum(mass=1:3, intensity=(2:4)+0.5, metaData=list(a=1, b=2:3)) sumS <- createMassSpectrum(mass=1:3, intensity=c(5, 7, 9), metaData=list(a=1, b=2:3)) p <- c(createMassPeaks(mass=1:3, intensity=1:3, snr=1:3, metaData=list(a=1, b=2)), createMassPeaks(mass=1:3, intensity=4:6, snr=4:6, metaData=list(a=1, b=3))) test_that("averageMassSpectra", { expect_equal(unname(averageMassSpectra(list(s[[1]], s[[1]], s[[2]], s[[2]]), labels=c(1:2, 1:2))), list(meanS, meanS)) expect_equal(unname(averageMassSpectra(list(s[[1]], s[[1]], s[[2]], s[[2]]), labels=c(1:2, 1:2), method="median")), list(meanS, meanS)) expect_equal(unname(averageMassSpectra(list(s[[1]], s[[1]], s[[2]], s[[2]]), labels=c(1, 1, 2, 2), method="median")), s) expect_equal(unname(averageMassSpectra(list(s[[1]], s[[1]], s[[2]], s[[2]]), labels=c(1:2, 1:2), method="sum")), list(sumS, sumS)) }) test_that("averageMassSpectra throws errors", { expect_error(averageMassSpectra(1:3), "no list of MALDIquant::MassSpectrum objects") expect_error(averageMassSpectra(list()), "no list of MALDIquant::MassSpectrum objects") expect_error(averageMassSpectra(p), "no list of MALDIquant::MassSpectrum objects") }) test_that("averageMassSpectra works with empty spectra", { e <- createMassSpectrum(mass=double(), intensity=double(), metaData=list(a=1, b=2)) expect_equal(unname(averageMassSpectra(list(s[[1]], e))), s[[1]]) expect_equal(unname(averageMassSpectra(list(e, e))), e) }) test_that(".averageMassSpectra", { expect_equal(MALDIquant:::.averageMassSpectra(s), meanS) expect_equal(MALDIquant:::.averageMassSpectra(s, fun=colSums), sumS) }) MALDIquant/tests/testthat/test_isEmpty-methods.R0000644000175000017500000000165612624130232022743 0ustar sebastiansebastiancontext("isEmpty") s <- c(createMassSpectrum(mass=1:10, intensity=11:20), createMassSpectrum(mass=1:2, intensity=rep.int(.Machine$integer.max, 2))) e <- c(createMassSpectrum(mass=double(), intensity=double(), metaData=list(file="fid")), createMassSpectrum(mass=1:10, intensity=rep(0, 10)), createMassSpectrum(mass=1:10, intensity=as.double(rep(NA, 10)))) test_that("isEmpty", { for (i in seq(along=e)) { expect_true(isEmpty(e[[i]])) } for (i in seq(along=s)) { expect_false(isEmpty(s[[i]])) } }) test_that("isEmptyWarning", { expect_warning(MALDIquant:::.isEmptyWarning(e[[1]]), "MassSpectrum object \\(file: fid\\) is empty") expect_warning(MALDIquant:::.isEmptyWarning(e[[2]]), "MassSpectrum object is empty") expect_true(suppressWarnings(MALDIquant:::.isEmptyWarning(e[[1]]))) expect_false(MALDIquant:::.isEmptyWarning(s[[1]])) }) MALDIquant/tests/testthat/test_mz-methods.R0000644000175000017500000000063312672203466021746 0ustar sebastiansebastiancontext("mz") s <- createMassSpectrum(mass=1:10, intensity=11:20) test_that("mz", { expect_identical(mz(s), 1:10) expect_identical(mz(s)[1:3], 1:3) }) test_that("mz<- throws errors", { expect_error(mz(s) <- LETTERS[1:10]) expect_error(mz(s) <- 1, "have to be equal") }) test_that("mz<-", { mz(s) <- 11:20 expect_equal(mz(s), 11:20) mz(s)[5:10] <- 5:10 expect_equal(mz(s), c(11:14, 5:10)) }) MALDIquant/tests/testthat/test_plotMsiSlice-methods.R0000644000175000017500000000146212703476727023737 0ustar sebastiansebastiancontext("plotMsiSlice") m <- c(createMassSpectrum(mass=1:10, intensity=1:10), createMassSpectrum(mass=2:11, intensity=2:11)) coordinates(m) <- cbind(1:2, c(1, 1)) test_that("plotMsiSlice throws errors", { expect_error(plotMsiSlice(m, center=c(3, 5), tolerance=0.5, colRamp=list(A=1, B=2, C=3)), paste0(".*number of centers.* has to be the same ", "as the length of the list .*colRamp.*")) }) test_that("plotMsiSlice throws warnings", { expect_error(tryCatch(plotMsiSlice(m, center=c(3, 5), tolerance=0.5), warning=function(w)stop(conditionMessage(w))), paste0(".*plotMsiSlice.* was called for multiple slices on an ", "interactive device. Only the first slice is plotted.")) }) MALDIquant/tests/testthat/test_monoisotopic-functions.R0000644000175000017500000000477513020334160024402 0ustar sebastiansebastiancontext(".monoisotopic") test_that(".pseudoCluster", { x <- c(1, 2, 3, 5, 8, 9, 10, 12, 15) m1s2 <- matrix(c(1, 2, 2, 3, 5, 6, 6, 7), nrow=2) m1s3 <- matrix(c(1, 2, 3, 5, 6, 7), nrow=3) m3s3 <- matrix(c(2, 4, 5, 6, 8, 9), nrow=3) m5s4 <- matrix(NA_real_, nrow=4, ncol=0) m12s3 <- matrix(c(1:3, 5:7, 1, 3, 4, 5, 7, 8), nrow=3) expect_error(MALDIquant:::.pseudoCluster(x, size=1), "The .*size.* of a cluster has to be at least 2") expect_equal(MALDIquant:::.pseudoCluster(x, size=2, distance=1), m1s2) expect_equal(MALDIquant:::.pseudoCluster(x, size=3, distance=1), m1s3) expect_equal(MALDIquant:::.pseudoCluster(x, size=3, distance=3), m3s3) expect_equal(MALDIquant:::.pseudoCluster(x, size=4, distance=5), m5s4) expect_equal(MALDIquant:::.pseudoCluster(x, size=3, distance=1:2), m12s3) }) test_that(".F", { x <- seq(1000, 5000, by=10) expect_equal(MALDIquant:::.F(x), 0.000594 * x + 0.03091) }) test_that(".P", { x <- seq(1000, 5000, by=10) expect_equal(MALDIquant:::.P(x, 0:3), dpois(0:3, MALDIquant:::.F(x))) }) test_that(".Psum", { x <- c(1000, 2000) isotopes <- 0:3 p <- sapply(x, function(xx) { pp <- MALDIquant:::.P(xx, isotopes=isotopes) pp/sum(pp) }) expect_equal(MALDIquant:::.Psum(x, isotopes), p) }) test_that(".monoisotopicPattern", { x <- c(1, 2, 3, 5, 8, 9, 10, 12, 15) y <- c(96, 3, 1, 5, 78, 20, 2, 12, 15) expect_equal(MALDIquant:::.monoisotopicPattern(1:10, 1:10), matrix(NA_real_, nrow=3, ncol=0)) expect_equal(MALDIquant:::.monoisotopicPattern(x, y, distance=1, size=3), cbind(1:3, 5:7)) expect_equal(MALDIquant:::.monoisotopicPattern(x, y, distance=1:2, size=3), cbind(1:3, 5:7)) expect_equal(MALDIquant:::.monoisotopicPattern(x, y, distance=2:1, size=3), cbind(c(1, 3, 4), c(5, 7, 8))) expect_equal(MALDIquant:::.monoisotopicPattern(x, y, distance=1, size=2), cbind(1:2, 5:6)) expect_equal(MALDIquant:::.monoisotopicPattern(x, y, distance=1, minCor=0.99), as.matrix(1:3)) }) test_that(".monoisotopic", { x <- c(1, 2, 3, 5, 8, 9, 10, 12, 15) y <- c(96, 3, 1, 5, 78, 20, 2, 12, 15) expect_equal(MALDIquant:::.monoisotopic(double(), double()), numeric()) expect_equal(MALDIquant:::.monoisotopic(1:10, 1:5), numeric()) expect_equal(MALDIquant:::.monoisotopic(1:10, 1:10), numeric()) expect_equal(MALDIquant:::.monoisotopic(x, y, distance=1, size=2:5), c(1, 5)) expect_equal(MALDIquant:::.monoisotopic(x, y, distance=1, minCor=0.99), 1) }) MALDIquant/tests/testthat/test_merge-functions.R0000644000175000017500000000606012624130232022747 0ustar sebastiansebastiancontext("merge") s <- c(createMassSpectrum(mass=1:3, intensity=1:3, metaData=list(a=1, b=2)), createMassSpectrum(mass=1:3, intensity=4:6, metaData=list(a=1, b=3))) p <- c(createMassPeaks(mass=1:3, intensity=1:3, snr=1:3, metaData=list(a=1, b=2)), createMassPeaks(mass=1:3, intensity=4:6, snr=4:6, metaData=list(a=1, b=3))) pNA <- c(createMassPeaks(mass=1:3, intensity=1:3, snr=1:3, metaData=list(a=1, b=2)), createMassPeaks(mass=1:2, intensity=4:5, snr=4:5, metaData=list(a=1, b=3))) meanP <- createMassPeaks(mass=1:3, intensity=(2:4)+0.5, snr=(2:4)+0.5, metaData=list(a=1, b=2:3)) meanPNA <- createMassPeaks(mass=1:3, intensity=c(2.5, 3.5, 1.5), snr=c(2.5, 3.5, 1.5), metaData=list(a=1, b=2:3)) sumP <- createMassPeaks(mass=1:3, intensity=c(5, 7, 9), snr=c(5, 7, 9), metaData=list(a=1, b=2:3)) test_that("mergeMassPeaks", { expect_equal(unname(mergeMassPeaks(list(p[[1]], p[[1]], p[[2]], p[[2]]), labels=c(1:2, 1:2))), list(meanP, meanP)) expect_equal(unname(mergeMassPeaks(pNA[rep(1:2, each=2)], labels=rep(1:2, times=2), ignore.na=FALSE)), list(meanPNA, meanPNA)) expect_equal(unname(mergeMassPeaks(list(p[[1]], p[[1]], p[[2]], p[[2]]), labels=c(1:2, 1:2), method="sum")), list(sumP, sumP)) expect_equal(unname(mergeMassPeaks(pNA[rep(1:2, each=2)], method="median", ignore.na=FALSE)), createMassPeaks(mass=1:3, intensity=c(2.5, 3.5, 1.5), snr=c(2.5, 3.5, 1.5), metaData=list(a=1, b=rep(2:3, each=2)))) }) test_that("mergeMassPeaks throws errors", { expect_error(mergeMassPeaks(1:3), "no list of MALDIquant::MassPeaks objects") expect_error(mergeMassPeaks(list()), "no list of MALDIquant::MassPeaks objects") expect_error(mergeMassPeaks(s), "no list of MALDIquant::MassPeaks objects") }) test_that(".mergeMassPeaks", { expect_equal(MALDIquant:::.mergeMassPeaks(p), meanP) expect_equal(MALDIquant:::.mergeMassPeaks(p, fun=colSums), sumP) }) test_that(".mergeMetaData", { l <- list(a=list(numbers=1:3, lists=list(a=1, b=2), characters=c("a", "b")), b=list(numbers=1:3, lists=list(a=1, b=2), characters=c("b", "c"))) r <- list(numbers=1:3, lists=list(a=1, b=2), characters=c("a", "b", "b", "c")) expect_identical(MALDIquant:::.mergeMetaData(l), r) l <- list(a=list(numbers=1:2, lists=list(a=1, b=2, d=4), characters=c("a", "b", "c")), b=list(numbers=1:3, lists=list(a=1, b=2), characters=c("b", "c"))) r <- list(numbers=c(1:2, 1:3), lists=list(list(a=1, b=2, d=4), list(a=1, b=2)), characters=c("a", "b", "c", "b", "c")) expect_identical(MALDIquant:::.mergeMetaData(l), r) }) MALDIquant/tests/testthat/test_subset-methods.R0000644000175000017500000000213512624130232022607 0ustar sebastiansebastiancontext("subset") s <- createMassSpectrum(mass=1:10, intensity=11:20) p <- createMassPeaks(mass=1:10, intensity=11:20, snr=21:30) test_that("numerical index based subsetting works on MassSpectrum", { expect_equal(s[2:9], createMassSpectrum(mass=2:9, intensity=12:19)) expect_equal(s[-1], createMassSpectrum(mass=2:10, intensity=12:20)) }) test_that("logical index based subsetting works on MassSpectrum", { expect_equal(s[mass(s) <= 9], createMassSpectrum(mass=1:9, intensity=11:19)) expect_equal(s[intensity(s) <= 15], createMassSpectrum(mass=1:5, intensity=11:15)) }) test_that("numerical index based subsetting works on MassPeaks", { expect_equal(p[2:9], createMassPeaks(mass=2:9, intensity=12:19, snr=22:29)) expect_equal(p[-1], createMassPeaks(mass=2:10, intensity=12:20, snr=22:30)) }) test_that("logical index based subsetting works on MassPeaks", { expect_equal(p[mass(p) <= 9], createMassPeaks(mass=1:9, intensity=11:19, snr=21:29)) expect_equal(p[intensity(p) <= 15], createMassPeaks(mass=1:5, intensity=11:15, snr=21:25)) }) MALDIquant/tests/testthat/test_isMassObjectList-functions.R0000644000175000017500000000230112624130232025064 0ustar sebastiansebastiancontext("isMassObjectList") s <- createMassSpectrum(mass=1:10, intensity=1:10) p <- createMassPeaks(mass=1:10, intensity=1:10) test_that(".isMassObjectList", { expect_false(MALDIquant:::.isMassObjectList(double(2))) expect_false(MALDIquant:::.isMassObjectList(list())) expect_false(MALDIquant:::.isMassObjectList(s)) expect_false(MALDIquant:::.isMassObjectList(p)) expect_true(MALDIquant:::.isMassObjectList(list(s, s))) expect_true(MALDIquant:::.isMassObjectList(list(s, p))) expect_true(MALDIquant:::.isMassObjectList(list(p, p))) }) test_that("isMassSpectrumList", { expect_false(isMassSpectrumList(double(2))) expect_false(isMassSpectrumList(list())) expect_false(isMassSpectrumList(s)) expect_false(isMassSpectrumList(p)) expect_false(isMassSpectrumList(list(s, p))) expect_false(isMassSpectrumList(list(p, p))) expect_true(isMassSpectrumList(list(s, s))) }) test_that("isMassPeaksList", { expect_false(isMassPeaksList(double(2))) expect_false(isMassPeaksList(list())) expect_false(isMassPeaksList(s)) expect_false(isMassPeaksList(p)) expect_false(isMassPeaksList(list(s, s))) expect_false(isMassPeaksList(list(s, p))) expect_true(isMassPeaksList(list(p, p))) }) MALDIquant/tests/testthat/test_alignSpectra-functions.R0000644000175000017500000000075612624130232024272 0ustar sebastiansebastiancontext("alignSpectra") test_that("alignSpectra throws errors", { expect_error(alignSpectra(list()), ".*spectra.* is no list of MALDIquant::MassSpectrum objects") }) test_that("alignSpectra", { data(fiedler2009subset) spectra <- fiedler2009subset[1:8] peaks <- detectPeaks(spectra, halfWindowSize=20, method="MAD", SNR=2) wf <- determineWarpingFunctions(peaks, tolerance=0.002, method="lowess") expect_equal(alignSpectra(spectra), warpMassSpectra(spectra, wf)) }) MALDIquant/tests/testthat/test_length-methods.R0000644000175000017500000000037612624130232022570 0ustar sebastiansebastiancontext("length") test_that("length", { expect_identical(length(createMassSpectrum(mass=1:10, intensity=1:10)), 10L) expect_identical(length(createMassSpectrum(mass=double(), intensity=double())), 0L) }) MALDIquant/tests/testthat/test_localMaxima-functions.R0000644000175000017500000000032012624130232024070 0ustar sebastiansebastiancontext(".localMaxima") y <- c(1, 1, 2, 1, 1, 3, 4) set.seed(1) b <- rnorm(5e4) test_that(".localMaxima", { expect_identical(MALDIquant:::.localMaxima(y, 1), c(T, F, T, F, F, F, T)) }) MALDIquant/tests/testthat/test_determineWarpingFunctions-functions.R0000644000175000017500000000377512703476727027103 0ustar sebastiansebastiancontext("determineWarpingFunctions") r <- createMassPeaks(mass=1:10, intensity=1:10) p <- createMassPeaks(mass=(1:10)+0.01, intensity=1:10) test_that("determineWarpingFunctions throws errors", { expect_error(determineWarpingFunctions(1:10), "no list of MALDIquant::MassPeaks objects") expect_error(determineWarpingFunctions(p, reference=createMassPeaks(double(), double())), "Reference MassPeaks object contains no peaks") expect_error(determineWarpingFunctions(createMassPeaks(mass=20, intensity=20), reference=r), "Could not match any peak in spectrum 1 to a reference peak") }) test_that("determineWarpingFunctions throws warnings", { expect_warning(determineWarpingFunctions(p, reference=r[6:10]), "Reference MassPeaks object contains very few peaks") expect_error(tryCatch(determineWarpingFunctions(list(p, p), plot=TRUE), warning=function(w)stop(conditionMessage(w))), paste0(".*plot.* is .*TRUE.* but no non-interactive device ", "is available. Using pdf.* to create a default one.")) expect_error(tryCatch(determineWarpingFunctions(list(p, p), plot=TRUE, plotInteractive=TRUE), warning=function(w)stop(conditionMessage(w))), paste0(".*plot.* is .*TRUE.* but no interactive device is ", "available. Using dev.new.* to create a default one.")) }) test_that("determineWarpingFunctions works with single MassPeaks object", { w <- determineWarpingFunctions(p, reference=r, method="linear") wp <- warpMassPeaks(list(p), w)[[1]] expect_equal(r, wp) }) test_that("determineWarpingFunctions works with list of MassPeaks objects", { w <- determineWarpingFunctions(list(p, p), reference=r, method="linear") wp <- warpMassPeaks(list(p, p), w) expect_equal(list(r, r), wp) }) MALDIquant/tests/testthat/test_removeEmptyMassObjects-functions.R0000644000175000017500000000130712624130232026321 0ustar sebastiansebastiancontext("removeEmptyMassObjects") s <- createMassSpectrum(mass=1:10, intensity=1:10) e <- createMassSpectrum(double(), double()) test_that("removeEmptyMassObjects throws errors", { expect_error(removeEmptyMassObjects(e), "no list of MALDIquant::AbstractMassObject objects") expect_error(removeEmptyMassObjects(list()), "no list of MALDIquant::AbstractMassObject objects") }) test_that("removeEmptyMassObjects", { expect_identical(removeEmptyMassObjects(list(e, e)), list()) expect_identical(removeEmptyMassObjects(list(s, s)), list(s, s)) expect_identical(removeEmptyMassObjects(list(s, e)), list(s)) expect_identical(removeEmptyMassObjects(list(e, s)), list(s)) }) MALDIquant/tests/testthat/test_unlist-functions.R0000644000175000017500000000035612624130232023170 0ustar sebastiansebastiancontext(".unlist") test_that(".unlist doesn't work recursive", { expect_identical(MALDIquant:::.unlist(list(a=list(a=1:3, b=4:6), b=list(c=7:9))), list(1:3, 4:6, 7:9)) }) MALDIquant/tests/testthat/test_smoothingFilters-functions.R0000644000175000017500000000501012624130232025202 0ustar sebastiansebastiancontext("smoothingFilters") test_that(".movingAverage", { values <- c(rep(3, 3), 4:7, rep(8, 3)) expect_equal(MALDIquant:::.movingAverage(1:10, halfWindowSize=2), values) values <- c(rep(4, 4), 5:6, rep(7, 4)) expect_equal(MALDIquant:::.movingAverage(1:10, 3), values) }) test_that(".movingAverage throws errors", { expect_error(MALDIquant:::.movingAverage(1:10, halfWindowSize=0), "too small") expect_error(MALDIquant:::.movingAverage(1:10, halfWindowSize=100), "too large") }) test_that(".savitzkyGolayCoefficients", { values <- list(rep(1, 5)/5, c(-3, 12, 17, 12, -3)/35, c(-2, 3, 6, 7, 6, 3, -2)/21, c(-21, 14, 39, 54, 59, 54, 39, 14, -21)/231, c(5, -30, 75, 131, 75, -30, 5)/231, c(15, -55, 30, 135, 179, 135, 30, -55, 15)/429) hws <- c(2, 2, 3, 4, 3, 4) order <- c(0, 2, 3, 3:5) for (i in seq(along=values)) { expect_equal(.savitzkyGolayCoefficients(m=hws[i], k=order[i])[hws[i]+1, ], values[[i]]) } }) test_that(".savitzkyGolay", { expect_equal(MALDIquant:::.savitzkyGolay(1:10, halfWindowSize=2, polynomialOrder=0), MALDIquant:::.movingAverage(1:10, halfWindowSize=2)) values <- c(8L, 1L, 7L, 6L, 3L, 13L, 5L, 2L, 19L, 11L, 15L, 18L, 9L, 10L, 20L, 12L, 17L, 14L, 16L, 4L) result <- c(7.64285714285716, 2.42857142857143, 4.85714285714286, 5.14285714285714, 6.94285714285714, 8.37142857142857, 5.68571428571428, 7.14285714285714, 11.9714285714286, 15.2857142857143, 14.8285714285714, 15.1714285714286, 10.9714285714286, 12.2285714285714, 15.0285714285714, 16.4571428571429, 14.0857142857143, 16.7428571428571, 14.1714285714286, 4.45714285714283) expect_equal(MALDIquant:::.savitzkyGolay(values, halfWindowSize=2, polynomialOrder=3), result) }) test_that(".savitzkyGolay throws errors", { expect_error(MALDIquant:::.savitzkyGolay(1:10, halfWindowSize=0, polynomialOrder=3), "too small") expect_error(MALDIquant:::.savitzkyGolay(1:10, halfWindowSize=100, polynomialOrder=3), "too large") expect_error(MALDIquant:::.savitzkyGolay(1:10, halfWindowSize=2, polynomialOrder=10), "The window size has to be larger than the polynomial order.") }) MALDIquant/tests/testthat/test_coordinates-methods.R0000644000175000017500000000311212624130232023610 0ustar sebastiansebastiancontext("coordinates") s <- list(createMassSpectrum(mass=1:5, intensity=6:10, metaData=list(imaging=list(pos=c(5, 2)))), createMassSpectrum(mass=1:5, intensity=6:10, metaData=list(imaging=list(pos=c(6, 2))))) test_that("coordinates", { expect_identical(coordinates(s[[1]]), c(5, 2)) expect_identical(coordinates(s), cbind(x=5:6, y=c(2, 2))) expect_identical(coordinates(s, adjust=TRUE), cbind(x=1:2, y=c(1, 1))) }) test_that("coordinates<- throws errors", { expect_error(coordinates(s[[1]]) <- LETTERS[1:10]) expect_error(coordinates(s[[1]]) <- 1, "2 or 3 coordinates are needed!") expect_error(coordinates(s[[1]]) <- 4, "2 or 3 coordinates are needed!") expect_error(coordinates(s[[1]]) <- cbind(x=1, y=1, z=1, zz=1), "2 or 3 coordinates are needed!") expect_error(coordinates(s) <- cbind(x=1:4), "2 or 3 coordinates are needed!") l <- list(1:10) expect_error(coordinates(l) <- cbind(x=2:3, y=3:4), "is no list of MALDIquant::AbstractMassObject objects!") }) test_that("coordinates<- throws warnings", { expect_warning(coordinates(s[[1]]) <- cbind(x=1:2, y=1:2), "all rows but the first are ignored") }) test_that("coordinates<-", { coordinates(s[[1]]) <- 2:3 expect_equal(coordinates(s[[1]]), 2:3) coordinates(s[[1]]) <- cbind(x=2, y=3) expect_equal(coordinates(s[[1]]), c(x=2, y=3)) coordinates(s[[1]])[2] <- 5 expect_equal(coordinates(s[[1]]), c(x=2, y=5)) coordinates(s) <- cbind(x=2:3, y=3:4) expect_equal(coordinates(s), cbind(x=2:3, y=3:4)) }) MALDIquant/tests/testthat/test_msiSlices-functions.R0000644000175000017500000000371212624130232023604 0ustar sebastiansebastiancontext("msiSlices") test_that("msiSlices", { p <- list(createMassPeaks(mass=1:5, intensity=1:5), createMassPeaks(mass=1:5, intensity=2:6), createMassPeaks(mass=1:5, intensity=3:7)) coordinates(p) <- cbind(x=c(2, 2, 3), y=c(2, 3, 2)) r <- array(c(3, 5, 4, NA), dim=c(x=2, y=2, z=1)) attr(r, "center") <- 3 attr(r, "tolerance") <- 0.5 attr(r, "method") <- "sum" expect_equal(msiSlices(p, center=3, tolerance=0.5), r) r <- array(c(NA, NA, NA, NA, 3, 5, NA, 4, NA), dim=c(x=3, y=3, z=1)) attr(r, "center") <- 3 attr(r, "tolerance") <- 0.5 attr(r, "method") <- "sum" expect_equal(msiSlices(p, center=3, tolerance=0.5, adjust=FALSE), r) }) test_that(".msiSlices", { m <- matrix(c(1:5, 2:6, 3:7), byrow=TRUE, nrow=3) attr(m, "mass") <- 1:5 coord <- cbind(x=c(1, 1, 2), y=c(1, 2, 1)) r <- array(c(3, 5, 4, NA), dim=c(x=2, y=2, z=1)) attr(r, "center") <- 3 attr(r, "tolerance") <- 0.5 attr(r, "method") <- "sum" expect_equal(MALDIquant:::.msiSlices(m, coord, center=3, tolerance=0.5), r) r[,,1] <- c(9, 15, 12, NA) attr(r, "tolerance") <- 1 expect_equal(MALDIquant:::.msiSlices(m, coord, center=3, tolerance=1), r) r[,,1] <- c(3, 5, 4, NA) attr(r, "tolerance") <- 1 attr(r, "method") <- "mean" expect_equal(MALDIquant:::.msiSlices(m, coord, center=3, tolerance=1, method="mean"), r) r[,,1] <- c(3, 5, 4, NA) attr(r, "tolerance") <- 1 attr(r, "method") <- "median" expect_equal(MALDIquant:::.msiSlices(m, coord, center=3, tolerance=1, method="median"), r) r <- array(c(6, 12, 9, NA, 9, 15, 12, NA), dim=c(x=2, y=2, z=2)) attr(r, "center") <- 2:3 attr(r, "tolerance") <- 1 attr(r, "method") <- "sum" expect_equal(MALDIquant:::.msiSlices(m, coord, center=2:3, tolerance=1), r) r[,,2] <- c(15, 25, 20, NA) attr(r, "tolerance") <- 1:2 expect_equal(MALDIquant:::.msiSlices(m, coord, center=2:3, tolerance=1:2), r) }) MALDIquant/tests/testthat/test_warp-functions.R0000644000175000017500000000321712624130232022622 0ustar sebastiansebastiancontext("warp") s <- createMassSpectrum(mass=1:10, intensity=1:10) p <- createMassPeaks(mass=1:10, intensity=1:10, snr=1:10) test_that("warpMassSpectra throws errors", { expect_error(warpMassSpectra(p, function(x)1), "no list of MALDIquant::MassSpectrum objects") expect_error(warpMassSpectra(s, function(x)1), "no list of MALDIquant::MassSpectrum objects") expect_error(warpMassSpectra(list(s), 1), "no list of functions") }) test_that("warpMassSpectra", { expect_equal(warpMassSpectra(list(s, s), list(function(x)1, function(x)x+1)), list(createMassSpectrum(mass=2:11, intensity=1:10), createMassSpectrum(mass=seq(3, 21, by=2), intensity=1:10))) }) test_that("warpMassPeaks throws errors", { expect_error(warpMassPeaks(p, function(x)1), "no list of MALDIquant::MassPeaks objects") expect_error(warpMassPeaks(s, function(x)1), "no list of MALDIquant::MassPeaks objects") expect_error(warpMassPeaks(list(p), 1), "no list of functions") }) test_that("warpMassPeaks", { expect_equal(warpMassPeaks(list(p, p), list(function(x)1, function(x)x+1)), list(createMassPeaks(mass=2:11, intensity=1:10, snr=1:10), createMassPeaks(mass=seq(3, 21, by=2), intensity=1:10, snr=1:10))) }) test_that(".warp", { expect_equal(MALDIquant:::.warp(list(s, s), list(function(x)1, function(x)x+1)), list(createMassSpectrum(mass=2:11, intensity=1:10), createMassSpectrum(mass=seq(3, 21, by=2), intensity=1:10))) }) MALDIquant/tests/testthat/test_mapply-functions.R0000644000175000017500000000036512624130232023154 0ustar sebastiansebastiancontext(".mapply") test_that(".mapply", { a <- setNames(1L:10L, LETTERS[1L:10L]) b <- 1L:2L f <- function(x, y)x+y expect_identical(MALDIquant:::.mapply(f, a, b), mapply(f, a, b, USE.NAMES=FALSE, SIMPLIFY=FALSE)) }) MALDIquant/tests/testthat/test_filterPeaks-functions.R0000644000175000017500000001164312624130232024124 0ustar sebastiansebastiancontext("filterPeaks") p <- createMassPeaks(mass=1:5, intensity=1:5) l <- list(p, p[1:4], p[1:3], p[1:2]) test_that("filterPeaks throws errors", { expect_error(filterPeaks(p), "no list of MALDIquant::MassPeaks") expect_error(filterPeaks(list()), "no list of MALDIquant::MassPeaks") expect_error(filterPeaks(l, minFrequency=NA, minNumber=NA), " has to be a meaningful number") expect_error(filterPeaks(l=l, labels=as.factor("a"), minFrequency=1), "For each item in .*l.* there must be a label in .*labels.*") }) test_that("filterPeaks shows warnings", { expect_warning(filterPeaks(l, minFrequency=2), "Empty peak whitelist for level") expect_warning(filterPeaks(l, minFrequency=-1), " < 0 does not make sense! Using 0 instead") expect_warning(filterPeaks(l, minNumber=10), "Empty peak whitelist for level") expect_warning(filterPeaks(l, minNumber=-1), " < 0 does not make sense! Using 0 instead") expect_warning(filterPeaks(l, minFrequency=2/3, minNumber=2), " arguments are given. Choosing the higher one.") expect_warning(filterPeaks(l, minNumber=2, labels=c(1, 2, 2, 2)), "Empty peak whitelist for level") }) test_that("filterPeaks", { expect_identical(filterPeaks(l, minFrequency=1), list(p[1:2], p[1:2], p[1:2], p[1:2])) expect_identical(filterPeaks(l, minFrequency=0.5), list(p[1:4], p[1:4], p[1:3], p[1:2])) expect_identical(filterPeaks(l, minFrequency=0), l) expect_identical(filterPeaks(l, minFrequency=1, labels=factor(rep(letters[1:2], each=2), levels=letters[1:2])), list(p[1:4], p[1:4], p[1:2], p[1:2])) ## test unused levels expect_identical(filterPeaks(l, minFrequency=1, labels=factor(rep(letters[1:2], each=2), levels=letters[1:5])), list(p[1:4], p[1:4], p[1:2], p[1:2])) ## test numbers expect_identical(filterPeaks(l, minFrequency=1, labels=rep(1:2, each=2)), list(p[1:4], p[1:4], p[1:2], p[1:2])) ## test case for #22 (unexpected results for different number of technical ## replicates because using of floor(minFrequency)) expect_identical(filterPeaks(list(p, p[1:4], p[1:3], p, p[1:4], p[1:3], p[1:2]), minFrequency=2/3, labels=c(rep(1, 3), rep(2, 4))), list(p[1:4], p[1:4], p[1:3], p[1:3], p[1:3], p[1:3], p[1:2])) ## use absolute threshold expect_identical(filterPeaks(list(p, p[1:4], p[1:3], p, p[1:4], p[1:3], p[1:2]), minNumber=2, labels=c(rep(1, 3), rep(2, 4))), list(p[1:4], p[1:4], p[1:3], p[1:4], p[1:4], p[1:3], p[1:2])) ## test case for #26 (minNumber > n removes all peaks) ## since MALDIquant 1.8.16 min{Frequency,minNumber} could be vectors so this ## is not needed any more and we could use min{Frequency,Number} as feature to ## remove all peaks (or avoid the generation of a whitelist if ## mergeWhitelists=TRUE) expect_equal(suppressWarnings(filterPeaks(list(p, p[1:4], p), minNumber=2, labels=c(1, 2, 2))), list(createMassPeaks(double(), double()), p[1:4], p[1:4])) }) test_that("filterPeaks mergeWhitelists argument works", { p2 <- list( createMassPeaks(1:5, 1:5), createMassPeaks(1:4, 1:4), createMassPeaks(4:9, 4:9), createMassPeaks(4:8, 4:8), createMassPeaks(1:5, 1:5), createMassPeaks(2:6, 2:6)) expect_identical(filterPeaks(p2, minFrequency=1, labels=rep(1:3, each=2), mergeWhitelists=FALSE), c(p2[[2]], p2[[2]], p2[[4]], p2[[4]], createMassPeaks(2:5, 2:5), createMassPeaks(2:5, 2:5))) expect_identical(filterPeaks(p2, minFrequency=1, labels=rep(1:3, each=2), mergeWhitelists=TRUE), c(p2[1:2], p2[[4]], p2[[4]], p2[5:6])) expect_identical(suppressWarnings(filterPeaks(p2, minFrequency=c(1, 1, 2), labels=rep(1:3, each=2), mergeWhitelists=TRUE)), c(p2[1:2], p2[[4]], p2[[4]], p2[5:6])) ## test with different numbers and frequencies expect_identical(suppressWarnings(filterPeaks(p2, minFrequency=c(1, NA, NA), minNumber=c(NA, 3, 1), labels=rep(1:3, each=2), mergeWhitelists=TRUE)), c(p2[1:2], createMassPeaks(4:6, 4:6), createMassPeaks(4:6, 4:6), p2[5:6])) }) MALDIquant/tests/testthat/test_replaceNegativeIntensityValues.R0000644000175000017500000000066212624130232026031 0ustar sebastiansebastiancontext(".replaceNegativeIntensityValues") test_that(".replaceNegativeIntensityValues", { s <- suppressWarnings(createMassSpectrum(mass=1:5, intensity=-(1:5))) expect_warning(MALDIquant:::.replaceNegativeIntensityValues(s), "Negative intensity values are replaced by zeros.") expect_equal(intensity( MALDIquant:::.replaceNegativeIntensityValues(s, warn=FALSE)), rep(0L, 5L)) }) MALDIquant/tests/testthat/test_metaData-methods.R0000644000175000017500000000046712624130232023030 0ustar sebastiansebastiancontext("metaData") s <- createMassSpectrum(mass=1:10, intensity=11:20, metaData=list(name="test spectrum")) test_that("metaData", { expect_identical(metaData(s)$name, "test spectrum") }) test_that("metaData<-", { l <- list(a=1:3, b=letters[1:3]) metaData(s) <- l expect_identical(metaData(s), l) }) MALDIquant/tests/testthat/test_smoothIntensity-methods.R0000644000175000017500000000267112624130232024527 0ustar sebastiansebastiancontext("smoothIntensity") i <- c(1:20, 20:1)^2 s <- createMassSpectrum(mass=1:40, intensity=i) test_that("smoothIntensity shows warnings", { expect_warning(smoothIntensity( createMassSpectrum(mass=double(), intensity=double())), "empty") }) test_that("smoothIntensity", { expect_equal(intensity(smoothIntensity(s, method="SavitzkyGolay", halfWindowSize=4)), MALDIquant:::.savitzkyGolay(i, halfWindowSize=4)) expect_equal(intensity(smoothIntensity(s, method="SavitzkyGolay")), MALDIquant:::.savitzkyGolay(i, halfWindowSize=10)) expect_equal(length(smoothIntensity(s, method="MovingAverage", halfWindowSize=2)), 40) expect_equal(intensity(smoothIntensity(s, method="MovingAverage", halfWindowSize=2)), MALDIquant:::.movingAverage(i, halfWindowSize=2)) }) test_that("smoothIntensity works with list of MassSpectrum objects", { expect_error(smoothIntensity(list(x=1, y=1)), "no list of MALDIquant::MassSpectrum objects") expect_error(smoothIntensity(list(s, createMassPeaks(1, 1)), "no list of MALDIquant::MassSpectrum objects")) r <- createMassSpectrum(mass=1:40, intensity= MALDIquant:::.movingAverage(i)) expect_equal(smoothIntensity(list(s, s), method="MovingAverage"), list(r, r)) }) MALDIquant/tests/testthat/test_calculateLabelPositions-functions.R0000644000175000017500000000575412672203466026503 0ustar sebastiansebastiancontext("calculateLabelPositions") test_that(".textLabelRects", { pdf(NULL) on.exit(dev.off()) plot.new() words <- c("foo", "foobar") cex <- 1 r <- matrix(c(1:4, strwidth(words, cex=cex), strheight(words, cex=cex)), nrow = 2, dimnames = list(c(), c("x", "y", "w", "h"))) r <- cbind(x0=r[, "x"] - r[, "w"]/2, y0=r[, "y"], x1=r[, "x"] + r[, "w"]/2, y1=r[, "y"] + r[, "h"], r) s <- MALDIquant:::.scaleFactor() r90 <- r r90[, "x1"] <- r90[, "x"] r90[, "x0"] <- r90[, "x"] - r[, "h"] / s r90[, "y0"] <- r90[, "y"] - r[, "w"]/2 * s r90[, "y1"] <- r90[, "y"] + r[, "w"]/2 * s r90[, c("w", "h")] <- r90[, c("x1", "y1")] - r90[, c("x0", "y0")] expect_equal(MALDIquant:::.textLabelRects(1:2, 3:4, c("foo", "foobar"), offset = c(0, 0), cex=1), r) expect_equal(MALDIquant:::.textLabelRects(1:2, 3:4, c("foo", "foobar"), offset = c(0, 0), cex=1, srt=90), r90) }) test_that(".overlaps", { target <- c(2, 2, 4, 3) # x1,y1 == bottom left dest <- list(a=target, # identical b=c(1, 1, 3, 2.5), # overlap c=c(3, 1, 6, 4), # overlap d=c(2, 4, 3, 5), # no overlap e=c(6, 0, 8, 1)) # no overlap dest2 <- list(rbind(dest$a, dest$b), # overlap rbind(dest$b, dest$e), # overlap rbind(dest$d, dest$e)) # no overlap r <- c(TRUE, TRUE, TRUE, FALSE, FALSE) r2 <- c(TRUE, TRUE, FALSE) for (i in seq(along=dest)) { expect_identical(MALDIquant:::.overlaps(target, dest[[i]]), r[i]) } for (i in seq(along=dest2)) { expect_identical(MALDIquant:::.overlaps(target, dest2[[i]]), r2[i]) } }) test_that(".scaleFactor", { pdf(NULL) on.exit(dev.off()) plot.new() expect_equal(MALDIquant:::.scaleFactor(), 1.116279, tolerance = 1e-5) }) test_that(".strWH", { pdf(NULL) on.exit(dev.off()) plot.new() expect_equal(as.vector(MALDIquant:::.strWH("foo", srt=0, cex=1, scale=0.5)), c(strwidth("foo", cex=1), strheight("foo", cex=1))) expect_equal(as.vector(MALDIquant:::.strWH("foo", srt=90, cex=1, scale=0.5)), c(strwidth("foo", cex=1)/2, strheight("foo", cex=1)*2)) }) test_that(".rotate", { .m <- function(x)matrix(x, ncol=2, byrow=TRUE) p <- .m(c(1, 2, 4, 4, 3, 2)) center <- p - 1 srt <- c(0, 90, 180, 270, 360) r <- list("0" = p, "90" = .m(c(-1, 2, 2, 4, 1, 2)), "180" = .m(c(-1, 0, 2, 2, 1, 0)), "270" = .m(c(1, 0, 4, 2, 3, 0)), "360" = p) for (i in seq(along=srt)) { expect_equal(MALDIquant:::.rotate(p, center, srt=srt[i]), r[[i]]) } }) MALDIquant/tests/testthat/test_intensity-methods.R0000644000175000017500000000076012624130232023332 0ustar sebastiansebastiancontext("intensity") s <- createMassSpectrum(mass=1:10, intensity=11:20) test_that("intensity", { expect_identical(intensity(s), 11:20) expect_identical(intensity(s)[1:3], 11:13) }) test_that("intensity<- throws errors", { expect_error(intensity(s) <- LETTERS[1:10]) expect_error(intensity(s) <- 1, "have to be equal") }) test_that("intensity<-", { intensity(s) <- 1:10 expect_equal(intensity(s), 1:10) intensity(s)[5:10] <- 15:20 expect_equal(intensity(s), c(1:4, 15:20)) }) MALDIquant/tests/testthat/test_referencePeaks-functions.R0000644000175000017500000000343412624130232024574 0ustar sebastiansebastiancontext("referencePeaks") p <- c(createMassPeaks(mass=1:5, intensity=1:5), createMassPeaks(mass=1:4, intensity=1:4), createMassPeaks(mass=1:3, intensity=1:3), createMassPeaks(mass=1:2, intensity=1:2)) p2 <- c(createMassPeaks(mass=c(1, 1.001, 3), intensity=c(2, 1, 1)), createMassPeaks(mass=c(0.99, 3), intensity=rep(1, 2)), createMassPeaks(mass=c(1.02, 3), intensity=rep(1, 2))) test_that("referencePeaks throws errors", { expect_error(referencePeaks(list(a="a", b="b"), minFrequency=0.5), "no list of MALDIquant::MassPeaks objects") expect_error(referencePeaks(p[[1]], minFrequency=0.5), "no list of MALDIquant::MassPeaks objects") }) test_that("referencePeaks shows warnings", { expect_warning(referencePeaks(p, minFrequency=-1), " < 0 does not make sense! Using 0 instead") expect_warning(referencePeaks(p, minFrequency=10), "Empty peak whitelist for level") }) test_that("referencePeaks works with different frequencies", { expect_equal(referencePeaks(p, minFrequency=1), createMassPeaks(mass=1:2, intensity=rep(1, 2))) expect_equal(referencePeaks(p, minFrequency=0.5), createMassPeaks(mass=1:4, intensity=c(1, 1, 3/4, 0.5))) }) test_that("referencePeaks works with different methods", { expect_equal(referencePeaks(p2, minFrequency=1, tolerance=0.05), createMassPeaks(mass=3, intensity=1)) expect_equal(referencePeaks(p2, method="strict", minFrequency=1, tolerance=0.05), createMassPeaks(mass=3, intensity=1)) expect_equal(referencePeaks(p2, method="relaxed", minFrequency=1, tolerance=0.05), createMassPeaks(mass=c(1+1/300, 3), intensity=rep(1, 2))) }) MALDIquant/tests/testthat/test_approxfun-methods.R0000644000175000017500000000063412624130232023326 0ustar sebastiansebastiancontext("approxfun") test_that("approxfun", { s <- createMassSpectrum(mass=1:10, intensity=11:20) e <- createMassSpectrum(mass=double(), intensity=double()) p <- createMassPeaks(mass=1:10, intensity=11:20) expect_error(MALDIquant:::approxfun(p)) expect_equal(approxfun(s)(seq(0, 11, by=0.5)), c(NA, NA, seq(11, 20, by=0.5), NA, NA)) expect_equal(approxfun(e)(1:20), rep(NA, 20)) }) MALDIquant/tests/testthat/test_estimateNoise-methods.R0000644000175000017500000000306512667542035024135 0ustar sebastiansebastiancontext("estimateNoise-methods") s <- createMassSpectrum(mass=1:20, intensity=rep(10:1, 2)) test_that("estimateNoise throws errors", { expect_error(estimateNoise(s, method="foobar"), ".*arg.* should be one of .*MAD.*, .*SuperSmoother.*") }) test_that("estimateNoise shows warnings", { expect_warning(estimateNoise( createMassSpectrum(mass=double(), intensity=double())), "empty") expect_identical(suppressWarnings(estimateNoise( createMassSpectrum(mass=double(), intensity=double()))), 0L) }) test_that(".estimateNoiseMad", { n <- rep(stats::mad(intensity(s)), length(s)) m <- cbind(mass=mass(s), intensity=n) expect_identical(MALDIquant:::.estimateNoiseMad(1:20, intensity(s)), n) expect_identical(MALDIquant:::.estimateNoise(mass(s), intensity(s)), n) expect_identical(MALDIquant:::.estimateNoise(mass(s), intensity(s), method="MAD"), n) expect_identical(estimateNoise(s), m) expect_identical(estimateNoise(s, method="MAD"), m) }) test_that(".estimateNoiseSuperSmoother", { n <- stats::supsmu(x=1:20, y=intensity(s))$y m <- cbind(mass=mass(s), intensity=n) expect_identical(MALDIquant:::.estimateNoiseSuperSmoother(mass(s), intensity(s)), n) expect_identical(MALDIquant:::.estimateNoise(mass(s), intensity(s), method="SuperSmoother"), n) expect_identical(estimateNoise(s, method="SuperSmoother"), m) }) MALDIquant/tests/testthat/test_range-functions.R0000644000175000017500000000101312624130232022735 0ustar sebastiansebastiancontext("range") test_that(".overlap", { l1 <- c(createMassSpectrum(1L:10L, 1L:10L), createMassSpectrum(5L:15L, 5L:15L)) l2 <- c(createMassSpectrum(1L:10L, 1L:10L), createMassSpectrum(15L:25L, 15L:25L)) expect_identical(MALDIquant:::.overlap(l1), c(5L, 10L)) expect_identical(MALDIquant:::.overlap(l2), c(0L, 0L)) }) test_that(".reorderRange", { expect_identical(MALDIquant:::.reorderRange(c(5L, 10L)), c(5L, 10L)) expect_identical(MALDIquant:::.reorderRange(c(10L, 5L)), c(5L, 10L)) }) MALDIquant/tests/testthat/test_show-methods.R0000644000175000017500000000621312624130232022263 0ustar sebastiansebastiancontext("show") test_that("show", { x <- createMassSpectrum(mass=1:10, intensity=11:20, metaData=list(name="example", file="example.mzML")) r <- paste0(format(c("S4 class type", "Number of m/z values", "Range of m/z values", "Range of intensity values", "Memory usage", "Name", "File"), justify="left"), ": ", c(format(c("MassSpectrum", "10", "1 - 10", "11 - 20", MALDIquant:::.memoryUsageStr(x), "example"), justify="left"), "example.mzML")) expect_identical(capture.output(x), r) x <- createMassPeaks(mass=1:10, intensity=11:20, metaData=list(name="example")) r <- paste0(format(c("S4 class type", "Number of m/z values", "Range of m/z values", "Range of intensity values", "Range of snr values", "Memory usage", "Name"), justify="left"), ": ", format(c("MassPeaks", "10", "1 - 10", "11 - 20", "NA - NA", MALDIquant:::.memoryUsageStr(x), "example"), justify="left")) expect_identical(capture.output(x), r) x <- createMassPeaks(mass=1:10, intensity=11:20, snr=1:10, metaData=list(name=c("example1", "example2"), file=c("example1.txt", "example2.txt"))) r <- paste0(format(c("S4 class type", "Number of m/z values", "Range of m/z values", "Range of intensity values", "Range of snr values", "Memory usage", "Name1", "Name2", "File1", "File2"), justify="left"), ": ", c(format(c("MassPeaks", "10", "1 - 10", "11 - 20", "1 - 10", MALDIquant:::.memoryUsageStr(x), "example1", "example2"), justify="left"), "example1.txt", "example2.txt")) expect_identical(capture.output(x), r) x <- createMassPeaks(double(), double()) r <- paste0(format(c("S4 class type", "Number of m/z values", "Range of m/z values", "Range of intensity values", "Range of snr values", "Memory usage"), justify="left"), ": ", format(c("MassPeaks", "0", "NA", "NA", "NA", MALDIquant:::.memoryUsageStr(x)), justify="left")) expect_identical(capture.output(x), r) }) MALDIquant/tests/testthat/test_irregular-functions.R0000644000175000017500000000025512624130232023644 0ustar sebastiansebastiancontext(".irregular") test_that(".irregularScore", { expect_equal(MALDIquant:::.irregularScore(1:10), 0) expect_equal(MALDIquant:::.irregularScore((1:10)[-8]), 1/7) }) MALDIquant/tests/testthat/test_valid-functions.R0000644000175000017500000000157112624130232022751 0ustar sebastiansebastiancontext("valid") test_that(".validAbstractMassObject", { s <- createMassSpectrum(mass=1:10, intensity=1:10) expect_true(MALDIquant:::.validAbstractMassObject(s)) s@intensity <- 1 expect_identical(MALDIquant:::.validAbstractMassObject(s), "Lengths of mass (10) and intensity (1) have to be equal.") s@mass <- -1 expect_warning(MALDIquant:::.validAbstractMassObject(s), "Negative mass values found.") s@mass <- 1 s@intensity <- -1 expect_warning(MALDIquant:::.validAbstractMassObject(s), "Negative intensity values found.") }) test_that(".validMassPeaks", { p <- createMassPeaks(mass=1:10, intensity=1:10, snr=1:10) expect_true(MALDIquant:::.validMassPeaks(p)) p@intensity <- 1 expect_identical(MALDIquant:::.validMassPeaks(p), "Lengths of intensity (1) and snr (10) have to be equal.") }) MALDIquant/tests/testthat/test_transformIntensity-methods.R0000644000175000017500000000454212624130232025230 0ustar sebastiansebastiancontext("transformIntensity") s <- createMassSpectrum(mass=1:10, intensity=(1:10)^2) test_that("transformIntensity shows warnings", { expect_warning(transformIntensity( createMassSpectrum(mass=double(), intensity=double()), method="sqrt"), "empty") }) test_that("transformIntensity", { expect_equal(intensity(transformIntensity(s, method="sqrt")), 1:10) expect_equal(intensity(transformIntensity(s, method="log")), log((1:10)^2)) expect_equal(intensity(transformIntensity(s, method="log2")), log2((1:10)^2)) expect_equal(intensity(transformIntensity(s, method="log10")), log10((1:10)^2)) }) test_that("transformIntensity works with list of MassSpectrum objects", { expect_error(transformIntensity(list(x=1, y=1)), "no list of MALDIquant::AbstractMassObject objects") expect_error(transformIntensity(list(s, createMassPeaks(1, 1)), "no list of MALDIquant::AbstractMassObject objects")) r <- createMassSpectrum(mass=1:10, intensity=1:10) expect_equal(transformIntensity(list(s, s), method="sqrt"), list(r, r)) }) test_that(".transformIntensity throws errors", { expect_error(.transformIntensity(s), "is missing") }) test_that(".transformIntensity shows warnings", { expect_warning(.transformIntensity(s, fun=function(x)return(-x)), "Negative intensity values are replaced by zeros.") }) test_that(".transformIntensity", { expect_equal(intensity(.transformIntensity(s, sqrt)), 1:10) expect_equal(length(.transformIntensity(s, MALDIquant:::.movingAverage, halfWindowSize=2)), 10) expect_equal(length(.transformIntensity(s, function(x)as.double(filter(x, rep(1, 5)/5, sides=2)))), 6) expect_equal(intensity(suppressWarnings( .transformIntensity(s, fun=function(x)return(-x)))), rep(0, 10)) }) test_that(".transformIntensity works with list of AbstractMassObject objects", { expect_error(.transformIntensity(list(x=1, y=1)), "no list of MALDIquant::AbstractMassObject objects") expect_error(.transformIntensity(list(s, createMassPeaks(1, 1)), "no list of MALDIquant::AbstractMassObject objects")) r <- createMassSpectrum(mass=1:10, intensity=1:10) expect_equal(.transformIntensity(list(s, s), fun=sqrt), list(r, r)) }) MALDIquant/tests/testthat/test_plotMsiSlice-functions.R0000644000175000017500000000230612671065367024300 0ustar sebastiansebastiancontext(".plotMsiSlice") test_that(".array2matrix", { x1 <- array(1:12, dim=c(x=2, y=3, z=2)) x2 <- array(1:12, dim=c(1, 12, 1)) r1 <- matrix(1:6, nrow=2, ncol=3) r2 <- matrix(7:12, nrow=2, ncol=3) r3 <- matrix(1:12, nrow=1, ncol=12) expect_identical(MALDIquant:::.array2matrix(x1), r1) expect_identical(MALDIquant:::.array2matrix(x1, z=2), r2) expect_identical(MALDIquant:::.array2matrix(x2), r3) }) test_that(".colorMatrix", { x <- matrix(c(NA, 1:8, NA), nrow=2) colRamp1 <- colorRamp(c("black", "green")) colRamp2 <- function(x)cbind(0, 0, 30*x) r1 <- matrix(c(NA, rgb(colRamp1(1:8/8), maxColorValue=255), NA), nrow=2) r2 <- matrix(c(NA, rgb(colRamp2(1:8), maxColorValue=255), NA), nrow=2) expect_equal(MALDIquant:::.colorMatrix(x, colRamp1), r1) expect_equal(MALDIquant:::.colorMatrix(x, colRamp2, scale=FALSE), r2) }) test_that(".combineColorMatrices", { x <- array(c(1:8, 8:1), dim=c(2, 4, 2)) col <- array(rep(1:2, each=8), dim=c(2, 4, 2)) r <- matrix(rep(2:1, each=4), nrow=2, ncol=4) expect_equal(MALDIquant:::.combineColorMatrices(x, col), r) }) test_that(".rgb", { r <- cbind(1:255, 1:255, 1:255) expect_equal(MALDIquant:::.rgb(r), rgb(r, maxColorValue=255)) }) MALDIquant/tests/testthat/test_snr-methods.R0000644000175000017500000000021712624130232022103 0ustar sebastiansebastiancontext("snr") s <- createMassPeaks(mass=1:10, intensity=11:20, snr=rep(2, 10)) test_that("snr", { expect_identical(snr(s), rep(2, 10)) }) MALDIquant/tests/testthat/test_intensityMatrix-functions.R0000644000175000017500000000205312624130232025061 0ustar sebastiansebastiancontext("intensityMatrix") p <- list(createMassPeaks(mass=1:4, intensity=11:14), createMassPeaks(mass=2:5, intensity=22:25)) s <- list(createMassSpectrum(mass=1:5, intensity=11:15), createMassSpectrum(mass=1:5, intensity=21:25)) m <- matrix(c(11:14, NA_real_, NA_real_, 22:25), byrow=TRUE, ncol=5, nrow=2, dimnames=list(NULL, 1:5)) e <- matrix(c(11:15, 21:25), byrow=TRUE, ncol=5, nrow=2, dimnames=list(NULL, 1:5)) attr(m, "mass") <- attr(e, "mass") <- 1:5 test_that("intensityMatrix throws errors", { expect_error(intensityMatrix(p[[1]]), "no list of MALDIquant::MassPeaks objects") expect_error(intensityMatrix(list()), "no list of MALDIquant::MassPeaks objects") expect_error(intensityMatrix(p, list()), "no list of MALDIquant::MassSpectrum objects") expect_error(intensityMatrix(p, s[1]), "Incompatible number of spectra") }) test_that("intensityMatrix", { expect_identical(intensityMatrix(p), m) expect_equal(intensityMatrix(p, s), e) }) MALDIquant/tests/testthat/test_findEmptyMassObjects-functions.R0000644000175000017500000000125112624130232025742 0ustar sebastiansebastiancontext("findEmptyMassObjects") s <- createMassSpectrum(mass=1:10, intensity=1:10) e <- createMassSpectrum(double(), double()) test_that("findEmptyMassObjects throws errors", { expect_error(findEmptyMassObjects(e), "no list of MALDIquant::AbstractMassObject objects") expect_error(findEmptyMassObjects(list()), "no list of MALDIquant::AbstractMassObject objects") }) test_that("findEmptyMassObjects", { expect_identical(findEmptyMassObjects(list(e, e)), 1L:2L) expect_identical(findEmptyMassObjects(list(s, s)), integer()) expect_identical(findEmptyMassObjects(list(s, e)), 2L) expect_identical(findEmptyMassObjects(list(e, s)), 1L) }) MALDIquant/tests/testthat/test_isMassObject-functions.R0000644000175000017500000000127512624130232024241 0ustar sebastiansebastiancontext("isMassObject") s <- createMassSpectrum(mass=1:10, intensity=1:10) p <- createMassPeaks(mass=1:10, intensity=1:10) test_that(".isMassObject", { expect_false(MALDIquant:::.isMassObject(double())) expect_false(MALDIquant:::.isMassObject(list())) expect_true(MALDIquant:::.isMassObject(s)) expect_true(MALDIquant:::.isMassObject(p)) }) test_that("isMassSpectrum", { expect_false(isMassSpectrum(double())) expect_false(isMassSpectrum(list())) expect_false(isMassSpectrum(p)) expect_true(isMassSpectrum(s)) }) test_that("isMassPeaks", { expect_false(isMassPeaks(double())) expect_false(isMassPeaks(list())) expect_false(isMassPeaks(s)) expect_true(isMassPeaks(p)) }) MALDIquant/tests/testthat/test_colMedians-functions.R0000644000175000017500000000275312711716773023754 0ustar sebastiansebastiancontext(".colMedians") test_that(".colMedians throws errors", { expect_error(MALDIquant:::.colMedians(list())) expect_error(MALDIquant:::.colMedians(matrix(1:10), na.rm="foobar")) }) test_that(".colMedians", { colMedians <- function(x, na.rm=FALSE)apply(x, 2, median, na.rm=na.rm) set.seed(1234) ## even nrow m <- matrix(rnorm(1e5), ncol=1e2) expect_equal(MALDIquant:::.colMedians(m), colMedians(m)) ## odd nrow nr <- nrow(m) m <- m[1:(nr-1), ] expect_equal(MALDIquant:::.colMedians(m), colMedians(m)) ## NA na <- sample(1:length(m), size=1e2) m[na] <- NA expect_equal(MALDIquant:::.colMedians(m), colMedians(m)) expect_equal(MALDIquant:::.colMedians(m, TRUE), colMedians(m, TRUE)) }) test_that(".colMaxs", { colMaxs <- function(x)apply(x, 2, max) set.seed(1234) ## even nrow m <- matrix(rnorm(1e5), ncol=1e2) expect_equal(MALDIquant:::.colMaxs(m), colMaxs(m)) }) test_that(".colCors", { colCors <- function(x, y, use="everything") { z <- double(ncol(x)) for (i in 1:ncol(x)) { z[i] <- stats::cor(x[,i], y[,i], use=use) } z } set.seed(1234) m <- matrix(rnorm(1e5), ncol=1e2) n <- matrix(rnorm(1e5), ncol=1e2) mna <- m nna <- n mna[sample(1e5, 1e3)] <- NA nna[sample(1e5, 1e3)] <- NA expect_equal(MALDIquant:::.colCors(m, n), colCors(m, n)) expect_equal(MALDIquant:::.colCors(mna, nna), colCors(mna, nna)) expect_equal(MALDIquant:::.colCors(mna, nna, na.rm=TRUE), colCors(mna, nna, use="na.or.complete")) }) MALDIquant/tests/testthat/test_binPeaks-functions.R0000644000175000017500000000345212711717475023427 0ustar sebastiansebastiancontext("binPeaks") p <- list(createMassPeaks(mass=seq(100, 500, 100), intensity=1:5), createMassPeaks(mass=c(seq(100.2, 300.2, 100), 395), intensity=1:4)) p2 <- c(createMassPeaks(mass=c(1.009, 1.01, 3), intensity=c(2, 1, 1), snr=1:3), createMassPeaks(mass=c(1, 3), intensity=1:2, snr=1:2), createMassPeaks(mass=c(1.03, 3), intensity=1:2, snr=1:2)) test_that("binPeaks throws errors", { expect_error(binPeaks(list()), "no list of MALDIquant::MassPeaks") expect_error(binPeaks(p, method="foobar"), ".*arg.* should be one of .*strict.*, .*relaxed.*") }) test_that("binPeaks bins peaks strict", { b <- binPeaks(p, tolerance=0.002) expect_true(all(b[[1]]@mass[1:3]==b[[2]]@mass[1:3])) expect_false(all(b[[1]]@mass[4]==b[[2]]@mass[4])) expect_true(length(b[[1]])==5) expect_true(length(b[[2]])==4) expect_false(all(p[[1]]@mass==b[[1]]@mass)) expect_false(all(p[[2]]@mass==b[[2]]@mass)) b <- binPeaks(p, tolerance=0.1) expect_true(all(b[[1]]@mass[1:4]==b[[2]]@mass[1:4])) expect_true(length(b[[1]])==5) expect_true(length(b[[2]])==4) expect_false(all(p[[1]]@mass==b[[1]]@mass)) expect_false(all(p[[2]]@mass==b[[2]]@mass)) }) test_that("binPeaks bins peaks releaxed", { b <- binPeaks(p2, method="relaxed", tolerance=0.05) expect_true(all(b[[1]]@mass==c(1.01, 1.013, 3))) ip <- sort(p2[[1]]@mass, index.return=TRUE) ib <- sort(b[[1]]@mass, index.return=TRUE) expect_equal(ip$ix, ib$ix) expect_false(all(p2[[1]]@intensity == b[[1]]@intensity)) expect_false(all(p2[[1]]@snr== b[[1]]@snr)) expect_true(all(b[[1]]@intensity == c(1, 2, 1))) expect_true(all(b[[2]]@intensity == 1:2)) expect_true(all(b[[3]]@intensity == 1:2)) expect_true(all(b[[1]]@snr == c(2, 1, 3))) expect_true(all(b[[2]]@snr == 1:2)) expect_true(all(b[[3]]@snr == 1:2)) }) MALDIquant/tests/testthat/test_match.closest-functions.R0000644000175000017500000000261613027254751024435 0ustar sebastiansebastiancontext("match.closest") test_that("match.closest", { expect_error(match.closest(1, c(0, -1, 3)), "sorted non-decreasingly") expect_equal(match.closest(1.001, 1:10), 1) expect_equal(match.closest(1.4, 1:10), 1) expect_equal(match.closest(9.8, 1:10), 10) expect_equal(match.closest(11.1, 1:10), 10) expect_equal(match.closest(4:5, 4.8, tolerance=1), c(1, 1)) expect_equal(match.closest(c(0.5, 1.5, exp(1), pi), 1:10), c(1, 2, 3, 3)) }) test_that("match.closest, length(table) == 1", { expect_equal(match.closest(1:3, 0, nomatch=0, tolerance=0), c(0, 0, 0)) expect_equal(match.closest(1:3, 1, nomatch=0, tolerance=0), c(1, 0, 0)) expect_equal(match.closest(1:3, 2, nomatch=0, tolerance=0), c(0, 1, 0)) expect_equal(match.closest(1:3, 3, nomatch=0, tolerance=0), c(0, 0, 1)) expect_equal(match.closest(1:3, 4, nomatch=0, tolerance=0), c(0, 0, 0)) }) test_that("match.closest, tolerance", { expect_error(match.closest(1, 1, tolerance=1, nomatch=1:2), "Length of .*nomatch.* has to be one") expect_warning(match.closest(1, 1, tolerance=-1), ".*tolerance.* < 0 is meaningless") expect_equal(match.closest(1.001, 1:10, tolerance=0), NA_integer_) expect_equal(suppressWarnings(match.closest(c(1,2,3.1), 1:3, tolerance=-1)), c(1, 2, NA_integer_)) expect_equal(match.closest(1.001, 1:10, tolerance=0, nomatch=-1), -1) expect_equal(match.closest(1.4, 1:10, tolerance=0.4), 1) }) MALDIquant/tests/testthat/test_monoisotopicPeaks-methods.R0000644000175000017500000000126712703476727025041 0ustar sebastiansebastiancontext("monoisotopicPeaks") p <- createMassPeaks(mass=995:1005, intensity=c(100, 10, 30, 10, 40, 550, 330, 110, 10, 5, 15)) m <- createMassPeaks(mass=1000, intensity=550) test_that("monoisotopicPeaks", { expect_equal(monoisotopicPeaks(p), m) }) test_that("detectPeaks works with list of MassPeaks objects", { expect_error(monoisotopicPeaks(list(x=1, y=1)), "no list of MALDIquant::MassPeaks objects") expect_error(monoisotopicPeaks(list(createMassSpectrum(1, 1), createMassSpectrum(1, 1)), "no list of MALDIquant::MassPeaks objects")) expect_equal(monoisotopicPeaks(list(p, p)), list(m, m)) }) MALDIquant/tests/testthat/test_as.matrix-methods.R0000644000175000017500000000077212624130232023215 0ustar sebastiansebastiancontext("as.matrix") m <- matrix(c(1:10, 11:20), ncol=2, dimnames=list(c(), c("mass", "intensity"))) s <- createMassSpectrum(mass=1:10, intensity=11:20) p <- createMassPeaks(mass=1:10, intensity=11:20) test_that("as.matrix works without indices", { expect_equal(as.matrix(s), m) expect_equal(as.matrix(p), m) }) test_that("as.matrix works with indices", { expect_equal(as.matrix(s)[3:4,], m[3:4,]) expect_equal(as.matrix(s)[7, 1], m[7, 1]) expect_equal(as.matrix(s)[mass(s) > 5, 2], 16:20) }) MALDIquant/tests/testthat/test_calibrateIntensity-methods.R0000644000175000017500000000525412624130232025144 0ustar sebastiansebastiancontext("calibrateIntensity") s <- list(createMassSpectrum(mass=1:5, intensity=1:5), createMassSpectrum(mass=1:5, intensity=2:6), createMassSpectrum(mass=1:5, intensity=3:7)) test_that("calibrateIntensity,MassSpectrum throws errors", { expect_error(calibrateIntensity(s[[1]], method="foobar"), ".*arg.* should be one of .*TIC.*, .*PQN.*, .*median.*") expect_error(calibrateIntensity(s[[1]], method="PQN"), ".*PQN.* is not supported for a single MassSpectrum object") }) test_that("calibrateIntensity,list throws errors", { expect_error(calibrateIntensity(list(1:10), method="TIC"), "no list of MALDIquant::MassSpectrum objects") expect_error(calibrateIntensity(s, method="foobar"), ".*arg.* should be one of .*TIC.*, .*PQN.*, .*median.*") }) test_that("calibrateIntensity,MassSpectrum throws warnings", { # median == zero; see #51 m <- createMassSpectrum(mass=1:5, intensity=c(rep(0, 4), 5)) expect_warning(calibrateIntensity(m, method="median"), "Scaling factor is zero. No calibration applied.") }) test_that("calibrateIntensity works with TIC", { sTIC <- calibrateIntensity(s[[1]], method="TIC") expect_equal(totalIonCurrent(sTIC), 1) sTIC <- calibrateIntensity(s, method="TIC") expect_equal(unlist(lapply(sTIC, totalIonCurrent)), rep(1, 3)) sTIC <- calibrateIntensity(s, method="TIC", range=c(2, 4)) expect_equal(unlist(lapply(sTIC, totalIonCurrent)), rep(2, 3)) }) test_that("calibrateIntensity works with median", { sMed <- calibrateIntensity(s[[1]], method="median") expect_equal(intensity(sMed), intensity(s[[1]])/median(intensity(s[[1]]))) sMed <- calibrateIntensity(s, method="median") expect_equal(lapply(sMed, intensity), lapply(s, function(x)intensity(x)/median(intensity(x)))) sMed <- calibrateIntensity(s, method="median", range=c(2, 4)) expect_equal(unlist(lapply(sMed, intensity)), as.vector(mapply(function(sp, m)intensity(sp)/m, sp=s, m=3:5))) # median == zero; see #51 m <- createMassSpectrum(mass=1:5, intensity=c(rep(0, 4), 5)) expect_equal(suppressWarnings(calibrateIntensity(m, method="median")), m) }) test_that("calibrateIntensity works with PQN", { sPQN <- calibrateIntensity(s, method="PQN") expect_equal(sPQN, calibrateIntensity(s, method="TIC")) m <- list(s[[1]], createMassSpectrum(1:5, rep(2, 5)), createMassSpectrum(1:5, c(4:6, 2:1))) mPQN <- calibrateIntensity(m, method="PQN") expect_equal(unlist(lapply(mPQN, totalIonCurrent)), c(1, 1, 0.96875)) sPQN <- calibrateIntensity(s, method="PQN", range=c(2, 4)) expect_equal(sPQN, calibrateIntensity(s, method="TIC", range=c(2, 4))) }) MALDIquant/tests/testthat/test_morphologicalFilter-functions.R0000644000175000017500000000110412624130232025647 0ustar sebastiansebastiancontext("morphological filters") x <- list(c(8, 6, 2, 4, 5, 7, 1, 10), c(15, 8, 4, 11, 10, 1, 13, 12, 6, 9, 5, 3, 2, 7, 14)) e <- list(c(rep(2:1, each=4)), c(rep(4, 3), rep(1, 5), 5, 3, rep(2, 5))) d <- list(c(rep(8, 3), rep(7, 2), rep(10, 3)), c(rep(15, 3), 11, rep(13, 5), 12, rep(9, 2), rep(14, 3))) test_that(".erosion", { for (i in seq(along=x)) { expect_equal(.erosion(x[[i]], halfWindowSize=2), e[[i]]) } }) test_that(".dilation", { for (i in seq(along=x)) { expect_equal(.dilation(x[[i]], halfWindowSize=2), d[[i]]) } }) MALDIquant/tests/testthat/test_totalIonCurrent-methods.R0000644000175000017500000000054612624130232024442 0ustar sebastiansebastiancontext("totalIonCurrent") s <- createMassSpectrum(mass=1:10, intensity=1:10) i <- createMassSpectrum(mass=1:2, intensity=rep.int(.Machine$integer.max, 2)) e <- createMassSpectrum(mass=1:10, intensity=rep(0, 10)) test_that("totalIonCurrent", { expect_equal(totalIonCurrent(s), 49.5) expect_equal(totalIonCurrent(i), as.double(.Machine$integer.max)) }) MALDIquant/tests/testthat/test_warpingFunctions-functions.R0000644000175000017500000000141712624130232025211 0ustar sebastiansebastiancontext("warpingFunctions") test_that(".warpingFunctionLowess", { al <- function(x) { l <- lowess(x=x, y=x^2) return(approxfun(l$x, l$y)) } expect_equal(MALDIquant:::.warpingFunctionLowess(1:10, (1:10)^2)(1:10), al(1:10)(1:10)) }) test_that(".warpingFunctionLinear", { expect_equal(MALDIquant:::.warpingFunctionLinear(1:10, rep(1, 10))(1:10), rep(1, 10)) expect_equal(MALDIquant:::.warpingFunctionLinear(1:10, 1:10)(1:10), 1:10) }) test_that(".warpingFunctionQuadratic", { expect_equal(MALDIquant:::.warpingFunctionQuadratic(1:10, (1:10)^2)(1:10), (1:10)^2) }) test_that(".warpingFunctionCubic", { expect_equal(MALDIquant:::.warpingFunctionCubic(1:10, (1:10)^3)(1:10), (1:10)^3) }) MALDIquant/tests/testthat/test_doByLabel-functions.R0000644000175000017500000000475712624130232023520 0ustar sebastiansebastiancontext(".doByLabels") s <- createMassSpectrum(mass=1:5, intensity=1:5) test_that(".doByLabels throws errors", { expect_error(MALDIquant:::.doByLabels(l=list(x=1, y=1), labels=as.factor(c("a", "b")), FUN=sum), "no list of MALDIquant::AbstractMassObject objects") expect_error(MALDIquant:::.doByLabels(l=list(s, s), labels=as.factor("a"), FUN=sum), "For each item in .*l.* there must be a label in .*labels.*") expect_error(MALDIquant:::.doByLabels(l=list(s, s), labels=as.factor(c("a", "b")))) }) test_that(".doByLabels runs a function for group labels", { expect_identical(MALDIquant:::.doByLabels(l=list(s, s), FUN=function(x){ return(1) }), 1) expect_identical(unname(MALDIquant:::.doByLabels(l=list(s, s), labels=as.factor(c("a", "b")), FUN=function(x)1)), c(1, 1)) l <- list(s, s, s, s) l[[1]]@metaData$file <- 1 l[[2]]@metaData$file <- 2 l[[3]]@metaData$file <- 10 l[[4]]@metaData$file <- 11 ## preserve order m <- MALDIquant:::.doByLabels(l=l, ## error because of wrong alphabetical order ## always add levels #labels=as.factor(paste("s", c(1, 2, 10, 11))), labels=factor(paste("s", c(1, 2, 10, 11)), levels=paste("s", c(1, 2, 10, 11))), FUN=function(x)x) expect_equal(c(1:2, 10:11), unname(sapply(m, function(x)metaData(x)$file))) ## bug #19; order changes if length of output is smaller than input m <- MALDIquant:::.doByLabels(l=l, ## results in factor(c(2, 2, 1, 1), levels=2:1) labels=rep(2:1, each=2), FUN=function(x)x[[1]]) expect_equal(c(1, 10), unname(sapply(m, function(x)metaData(x)$file))) ## respect order of factor m <- MALDIquant:::.doByLabels(l=l, ## results in factor(c(2, 2, 1, 1), levels=1:2) labels=factor(rep(2:1, each=2)), FUN=function(x)x[[1]]) expect_equal(c(10, 1), unname(sapply(m, function(x)metaData(x)$file))) ## see https://github.com/sgibb/MALDIquant/issues/1 expect_equal(unname(MALDIquant:::.doByLabels(l, 1:4, function(x)x[[1]])), l) expect_equal(unname(MALDIquant:::.doByLabels(l, rep(1, 4), function(x)x)), l) }) MALDIquant/tests/testthat/test_isRegular-methods.R0000644000175000017500000000100512624130232023232 0ustar sebastiansebastiancontext("isRegular") test_that("isRegular", { expect_false(isRegular(createMassSpectrum(double(), double()))) expect_false(isRegular(createMassSpectrum(double(1), double(1)))) expect_false(isRegular(createMassSpectrum(double(2), double(2)))) expect_true(isRegular(createMassSpectrum(double(3), double(3)))) expect_true(isRegular(createMassSpectrum(1:10, 1:10))) expect_false(isRegular(createMassSpectrum((1:10)[-8], 1:9))) expect_true(isRegular(createMassSpectrum((1:10)[-8], 1:9), threshold=0.15)) }) MALDIquant/tests/testthat/test_deprecated-functions.R0000644000175000017500000000256712672203466023775 0ustar sebastiansebastiancontext(".deprecated") test_that(".deprecated", { expect_error(.deprecated("0.1", "foo", "bar"), "foobar") version <- packageVersion("MALDIquant") version <- paste(version$major, version$minor - 1L, sep=".") expect_warning(.deprecated(version, "foobar"), "foobar") expect_message(.deprecated(packageVersion("MALDIquant"), "foobar", "foobar")) }) test_that(".deprecatedFunction", { foobar <- function() { .deprecatedFunction("0.1", new="y") } expect_error(foobar(), "\"foobar\" is deprecated..*Use .*y.* instead. See help(.*y*).") expect_error(.deprecatedFunction("0.1", old="foobar", "\"foobar\" is deprecated.")) expect_error(.deprecatedFunction("0.1", old="foo", new="bar", "\"foo\" is deprecated.\nUse \"bar\" instead. See help(\"bar\").")) }) test_that(".deprecatedArgument", { foobar <- function(x, y) { .deprecatedArgument("0.1", old="x", new="y") } expect_error(foobar(1L, 1L), "Argument .*x.* is deprecated..*Use .*y.* instead. See help(.*foobar.*).") foobar <- function(x, y) { .deprecatedArgument("0.1", old="x") } expect_error(foobar(1L, 1L), "Argument \"x\" is deprecated.") foobar <- function(x, y) { .deprecatedArgument("0.1", old="x", new="y", help="barfoo") } expect_error(foobar(1L, 1L), "Argument .*x.* is deprecated..*Use .*y.* instead. See help(.*barfoo.*).") }) MALDIquant/tests/testthat/test_grouper-functions.R0000644000175000017500000000457312624130232023342 0ustar sebastiansebastiancontext("grouper") test_that("grouperStrict", { expect_true(is.na(MALDIquant:::.grouperStrict(mass=c(1, 2), intensities=c(1, 2), samples=c(1, 1), tolerance=0.01))) expect_true(is.na(MALDIquant:::.grouperStrict(mass=c(1, 2, 2.1, 2.01), intensities=rep(1, 4), samples=1:4, tolerance=0.01))) expect_true(MALDIquant:::.grouperStrict(mass=c(2, 2.02, 2.01), intensities=rep(1, 3), samples=1:4, tolerance=0.01) == 2.01) }) test_that("grouperRelaxed", { expect_true(is.na(MALDIquant:::.grouperRelaxed(mass=c(1, 2, 2.1, 2.01), intensities=rep(1, 4), samples=1:4, tolerance=0.01))) expect_equal(MALDIquant:::.grouperRelaxed(mass=c(2.01, 2.03, 2.04, 2.01), intensities=c(2, 1, 1, 1), samples=c(1, 1, 2, 3), tolerance=0.1), c(2.02, 2.03, 2.02, 2.02)) expect_equal(MALDIquant:::.grouperRelaxed(mass=c(1.009, 1.01, 1, 1.03), intensities=c(2, 1, 1, 1), samples=c(1, 1, 2, 3), tolerance=0.1), c(1.013, 1.01, 1.013, 1.013)) }) test_that("grouperRelaxedHighestAtReference", { expect_identical(MALDIquant:::.grouperRelaxedHighestAtReference( mass=1:5, intensities=rep(1, 5), samples=2:6, tolerance=0.01), 0L) expect_true(is.na(MALDIquant:::.grouperRelaxedHighestAtReference( mass=1:5, intensities=rep(1, 5), samples=c(1, 1:4), tolerance=0.01))) expect_equal(MALDIquant:::.grouperRelaxedHighestAtReference( mass=c(2.01, 2.03, 2.04, 2.01), intensities=c(1, 2, 1, 1), samples=c(1, 2, 2, 3), tolerance=0.1), c(2.01, 2.01, 0, 2.01)) }) MALDIquant/tests/testthat/test_mass-methods.R0000644000175000017500000000066312624130232022251 0ustar sebastiansebastiancontext("mass") s <- createMassSpectrum(mass=1:10, intensity=11:20) test_that("mass", { expect_identical(mass(s), 1:10) expect_identical(mass(s)[1:3], 1:3) }) test_that("mass<- throws errors", { expect_error(mass(s) <- LETTERS[1:10]) expect_error(mass(s) <- 1, "have to be equal") }) test_that("mass<-", { mass(s) <- 11:20 expect_equal(mass(s), 11:20) mass(s)[5:10] <- 5:10 expect_equal(mass(s), c(11:14, 5:10)) }) MALDIquant/tests/testthat/test_as.matrix-functions.R0000644000175000017500000000270312624130232023556 0ustar sebastiansebastiancontext(".as.matrix") p <- list(createMassPeaks(mass=1:4, intensity=11:14), createMassPeaks(mass=2:5, intensity=22:25)) s <- list(createMassSpectrum(mass=1:5, intensity=11:15), createMassSpectrum(mass=1:5, intensity=21:25)) mp <- matrix(c(11:14, NA_real_, NA_real_, 22:25), byrow=TRUE, ncol=5, nrow=2, dimnames=list(NULL, 1:5)) ms <- matrix(as.double(c(11:15, 21:25)), byrow=TRUE, ncol=5, nrow=2, dimnames=list(NULL, 1:5)) mb <- matrix(c(rep(1L, 4), 0L, 0L, rep(1L, 4)), byrow=TRUE, ncol=5, nrow=2, dimnames=list(NULL, 1:5)) attr(mp, "mass") <- attr(ms, "mass") <- attr(mb, "mass") <- 1:5 test_that(".as.matrix.MassObjectsList throws errors", { expect_error(MALDIquant:::.as.matrix.MassObjectList(p[[1]]), "no list of MALDIquant::AbstractMassObject objects") expect_error(MALDIquant:::.as.matrix.MassObjectList(list()), "no list of MALDIquant::AbstractMassObject objects") expect_error(MALDIquant:::.as.matrix.MassObjectList(list(p, mp)), "no list of MALDIquant::AbstractMassObject objects") }) test_that(".as.binary.matrix", { expect_error(MALDIquant:::.as.binary.matrix(p[[1]])) }) test_that(".as.matrix.MassObjectsList", { expect_identical(MALDIquant:::.as.matrix.MassObjectList(p), mp) expect_identical(MALDIquant:::.as.matrix.MassObjectList(s), ms) }) test_that(".as.binary.matrix", { expect_identical(MALDIquant:::.as.binary.matrix(mp), mb) }) MALDIquant/tests/testthat/test_removeBaseline-methods.R0000644000175000017500000000414612624130232024246 0ustar sebastiansebastiancontext("removeBaseline") s <- createMassSpectrum(mass=1:20, intensity=rep(10:1, 2)) e <- createMassSpectrum(double(), double()) test_that("removeBaseline throws errors", { expect_error(removeBaseline(s, method="foobar"), ".*arg.* should be one of .*SNIP.*, .*ConvexHull.*, .*median.*") }) test_that("removeBaseline shows warnings", { expect_warning(estimateBaseline(e), "empty") }) test_that("removeBaseline returns an empty spectrum if input is empty", { expect_identical(suppressWarnings(removeBaseline(e)), e) }) test_that("removeBaseline works with ConvexHull", { expect_equal(removeBaseline(s, method="ConvexHull"), createMassSpectrum(mass=1:20, intensity=c(rep(0, 10), 9:0))) }) test_that("removeBaseline works with median", { ## halfWindowSize expect_error(removeBaseline(s, method="median", halfWindowSize=0), "too small") expect_error(removeBaseline(s, method="median"), "too large") expect_equal(removeBaseline(s, method="median", halfWindowSize=2), createMassSpectrum(mass=1:20, intensity=c(rep(0, 8), -1:-2, 2:1, rep(0, 8)))) }) test_that("removeBaseline works with SNIP", { expect_equal(removeBaseline(s, iterations=2), createMassSpectrum(mass=1:20, intensity=c(rep(0, 10), 7.5, 5, 2.5, rep(0, 7)))) expect_equal(removeBaseline(s, method="SNIP", iterations=2), createMassSpectrum(mass=1:20, intensity=c(rep(0, 10), 7.5, 5, 2.5, rep(0, 7)))) }) test_that("removeBaseline works with list of MassSpectrum objects", { expect_error(removeBaseline(list(x=1, y=1)), "no list of MALDIquant::MassSpectrum objects") expect_error(removeBaseline(list(s, createMassPeaks(1, 1)), "no list of MALDIquant::MassSpectrum objects")) r <- createMassSpectrum(mass=1:20, intensity=c(rep(0, 10), 7.5, 5, 2.5, rep(0, 7))) expect_equal(removeBaseline(list(s, s), iterations=2), list(r, r)) }) MALDIquant/tests/testthat/test_estimateBaseline-methods.R0000644000175000017500000000714712624130232024570 0ustar sebastiansebastiancontext("estimateBaseline") s <- createMassSpectrum(mass=1:20, intensity=rep(10:1, 2)) test_that("estimateBaseline throws errors", { expect_error(estimateBaseline(s, method="foobar"), ".*arg.* should be one of .*SNIP.*, .*ConvexHull.*, .*median.*") }) test_that("estimateBaseline shows warnings", { expect_warning(estimateBaseline( createMassSpectrum(mass=double(), intensity=double()), "empty")) }) test_that("estimateBaselineConvexHull", { b <- c(10:1, rep(1, 10)) m <- cbind(mass=mass(s), intensity=b) expect_identical(MALDIquant:::.estimateBaselineConvexHull(mass(s), intensity(s)), b) ## user method expect_identical(estimateBaseline(s, method="ConvexHull"), m) }) test_that("estimateBaselineMedian", { b <- c(10:4, rep(c(3, 8), each=3), 7:1) b1 <- c(10:3, rep(c(2, 9), each=2), 8:1) m <- cbind(mass=mass(s), intensity=b) expect_identical(MALDIquant:::.estimateBaselineMedian(mass(s), intensity(s), 1), b1) expect_identical(MALDIquant:::.estimateBaselineMedian(mass(s), intensity(s), 2), b) ## user method expect_identical(estimateBaseline(s, method="median", halfWindowSize=2), m) ## halfWindowSize expect_error(MALDIquant:::.estimateBaselineMedian(mass(s), intensity(s), 0), "too small") expect_error(MALDIquant:::.estimateBaselineMedian(mass(s), intensity(s), 20), "too large") }) test_that("estimateBaselineSnip", { b <- list(decreasing=list(b=c(10:1, c(2.5, 4.0, 5.5), 7:1), b100=c(10:1, rep(c(1.25, 1.5, 1.75, 1.375, 1), times=2))), increasing=list(b=c(10:1, 5:4, 5.5, 7:1), b100=c(10:1, 3.75, 3.375, 3:1, 2.5, 4:1))) ## test default decreasing argument expect_identical(MALDIquant:::.estimateBaselineSnip(mass(s), intensity(s), 2), b$decreasing$b) for (j in seq(along=b)) { d <- names(b)[j] == "decreasing" expect_identical(MALDIquant:::.estimateBaselineSnip(mass(s), intensity(s), 2, decreasing=d), b[[j]]$b) expect_equal(MALDIquant:::.estimateBaselineSnip(mass(s), intensity(s), decreasing=d), b[[j]]$b100) ## user method m <- cbind(mass=mass(s), intensity=b[[j]]$b) m100 <- cbind(mass=mass(s), intensity=b[[j]]$b100) expect_identical(estimateBaseline(s, method="SNIP", iterations=2, decreasing=d), m) expect_identical(estimateBaseline(s, iterations=2, decreasing=d), m) expect_equal(estimateBaseline(s, decreasing=d), m100) } }) test_that("estimateBaselineTopHat", { b <- c(rep(8, 3), 7:1, rep(6, 5), 5:1) b1 <- c(rep(9, 2), 8:1, rep(8, 3), 7:1) m <- cbind(mass=mass(s), intensity=b) expect_identical(MALDIquant:::.estimateBaselineTopHat(mass(s), intensity(s), 1), b1) expect_identical(MALDIquant:::.estimateBaselineTopHat(mass(s), intensity(s), 2), b) ## user method expect_identical(estimateBaseline(s, method="TopHat", halfWindowSize=2), m) ## halfWindowSize expect_error(MALDIquant:::.estimateBaselineTopHat(mass(s), intensity(s), 0), "too small") expect_error(MALDIquant:::.estimateBaselineTopHat(mass(s), intensity(s), 20), "too large") }) MALDIquant/tests/testthat/test_as-methods.R0000644000175000017500000000042312624130232021703 0ustar sebastiansebastiancontext("as") test_that("as", { p <- createMassPeaks(mass=1:10, intensity=11:20, snr=rep(1, 10), metaData=list(file="foo")) s <- createMassSpectrum(mass=1:10, intensity=11:20, metaData=list(file="foo")) expect_equal(as(p, "MassSpectrum"), s) }) MALDIquant/tests/testthat/test_detectPeaks-methods.R0000644000175000017500000000141212624130232023533 0ustar sebastiansebastiancontext("detectPeaks") s <- createMassSpectrum(mass=1:5, intensity=c(1, 2, 1, 2, 1)) p <- createMassPeaks(c(2, 4), c(2, 2), c(Inf, Inf)) test_that("detectPeaks", { expect_equal(detectPeaks(s, halfWindowSize=1), p) }) test_that("detectPeaks shows warnings", { expect_warning(detectPeaks(createMassSpectrum(mass=double(), intensity=double()), "empty")) }) test_that("detectPeaks works with list of MassSpectrum objects", { expect_error(detectPeaks(list(x=1, y=1)), "no list of MALDIquant::MassSpectrum objects") expect_error(detectPeaks(list(s, createMassPeaks(1, 1)), "no list of MALDIquant::MassSpectrum objects")) expect_equal(detectPeaks(list(s, s), halfWindowSize=1), list(p, p)) }) MALDIquant/tests/testthat/test_constructor-functions.R0000644000175000017500000000376412624130232024245 0ustar sebastiansebastiancontext("constructors") meta <- list(name="test", file="test_constructor-functions.R") test_that("a MassSpectrum object is constructed", { expect_error(createMassSpectrum(mass=1:5, intensity=1:10), "have to be equal") expect_error(createMassSpectrum(mass=1:10, intensity=1:5), "have to be equal") expect_error(createMassSpectrum(mass=1:10, intensity=1:10, metaData=1:10), "invalid object for slot \"metaData\"") expect_error(createMassSpectrum(mass=LETTERS[1:5], intensity=1:5), "invalid object for slot \"mass\"") expect_error(createMassSpectrum(mass=1:5, intensity=LETTERS[1:5]), "invalid object for slot \"intensity\"") expect_warning(createMassSpectrum(mass=-(1:5), intensity=1:5), "Negative mass values found.") expect_warning(createMassSpectrum(mass=1:5, intensity=-(1:5)), "Negative intensity values found.") expect_equal(createMassSpectrum(mass=1:10, intensity=1:10), new("MassSpectrum", mass=1:10, intensity=1:10, metaData=list())) expect_equal(createMassSpectrum(1:10, 1:10, meta), new("MassSpectrum", mass=1:10, intensity=1:10, metaData=meta)) }) test_that("a MassPeaks object is constructed", { expect_error(createMassPeaks(mass=1:5, intensity=1:10), "have to be equal") expect_error(createMassPeaks(mass=1:10, intensity=1:5), "have to be equal") expect_error(createMassPeaks(mass=1:5, intensity=1:5, snr=1:2), "have to be equal") expect_error(createMassPeaks(mass=1:10, intensity=1:10, metaData=1:10), "invalid object for slot \"metaData\"") expect_equal(createMassPeaks(mass=1:10, intensity=1:10), new("MassPeaks", mass=1:10, intensity=1:10, snr=as.double(rep(NA, 10)), metaData=list())) expect_equal(createMassPeaks(1:10, 1:10, snr=1:10, meta), new("MassPeaks", mass=1:10, intensity=1:10, snr=1:10, metaData=meta)) }) MALDIquant/tests/testthat/test_trim-methods.R0000644000175000017500000000165112672203466022274 0ustar sebastiansebastiancontext("trim") s <- createMassSpectrum(mass=1:10, intensity=11:20) test_that("trim throws errors", { expect_error(trim(s, range=1:10), "has to be a vector of length 2") expect_error(trim(s, range=1), "has to be a vector of length 2") expect_error(trim(c(s, createMassSpectrum(mass=21:30, intensity=1:10))), "No overlap") }) test_that("trim throws warnings", { expect_warning(trim(s, range=c(20, 30)), "No data points left") }) test_that("trim", { expect_equal(trim(s, c(2, 9)), createMassSpectrum(mass=2:9, intensity=12:19)) }) test_that("trim,list throws errors", { expect_error(trim(list(x=1, y=2)), "no list of MALDIquant::AbstractMassObject objects") }) test_that("trim works with list of AbstractMassObject objects", { r <- createMassSpectrum(mass=2:9, intensity=12:19) expect_equal(trim(list(s, s), c(2, 9)), list(r, r)) expect_equal(trim(list(s, r)), list(r, r)) }) MALDIquant/tests/testthat/test_labelPeaks-methods.R0000644000175000017500000000114112672203466023356 0ustar sebastiansebastiancontext("labelPeaks") test_that("labelPeaks", { pdf(NULL) on.exit(dev.off()) plot.new() p <- createMassPeaks(1:5, 1:5) expect_error(labelPeaks(p, index=-1), "No valid .*index.* nor .*mass.* given.") expect_error(labelPeaks(p, index=1:10), "No valid .*index.* nor .*mass.* given.") expect_error(labelPeaks(p, index=1, labels=LETTERS[1:5]), "Lenghts of .*index.*/.*mass.* and .*labels.* have to be equal") expect_error(labelPeaks(p, srt=45, avoidOverlap=TRUE), ".*avoidOverlap = TRUE.* and .*srt != x \\* 90.* is not supported.") }) MALDIquant/NEWS0000644000175000017500000006300713027254751014174 0ustar sebastiansebastianRELEASE HISTORY OF THE "MALDIquant" PACKAGE =========================================== CHANGES IN MALDIquant VERSION 1.16.1 [2016-12-23]: -------------------------------------------------- BUGFIXES * `match.closest`: fix results for `x >= max(table)`. CHANGES IN MALDIquant VERSION 1.16 [2016-12-13]: ------------------------------------------------ NEW FEATURES * `match.closest` allows relaxed matching. INTERNAL * The former hidden `.which.closest` is renamed into the exported `match.closest` and gains a "tolerance" and "nomatch" argument to allow relaxed matching. * `.pseudoCluster` use the new `match.closest` (could result in slightly different tolerance values). CHANGES IN MALDIquant VERSION 1.15 [2016-06-25]: ------------------------------------------------ NEW FEATURES * Add `monoisotopicPeaks` to find monoisotopic peaks in `MassPeaks` objects. IMPROVEMENTS * `plot`: x-axis label is "m/z" now (was "mass" before). Thanks to Robert Winkler for reporting of the sometimes misleading label. * Add `mz` and `mz<-` (same as `mass`/`mass<-`) to follow `ProtGenerics` guidelines and for consistency. BUGFIXES * `labelPeaks` didn't respect "srt=c(90, 180, 270, 360)" if "avoidOverlap=TRUE". Please note that "srt %% 90 != 0" in combination with "avoidOverlap=TRUE" is still not supported! INTERNAL * Throw a warning if `plotMsiSlice` was called on a interactive device with multiple centers (the warning was not thrown correctly before). * Throw an error if `trim,list` was called on a generic list instead of a list of AbstractMassObjects. * Remove textLabelRect-functions.R and move its content to calculateLabelPositions-functions.R. CHANGES IN MALDIquant VERSION 1.14 [2015-11-18]: ------------------------------------------------ IMPROVEMENTS * `?estimateBaseline`: clarify that "decreasing=TRUE" is the default for the SNIP baseline estimation algorithm (`estimateBaseline(..., method="SNIP")`). BUGFIXES * MALDIquant depends on R 3.2.0 now. Due to a bug in R (PR#15707, fixed in R 3.2.0) the ... argument was not interpreted in nested function calls. This results in failures of `calibratyIntensity` on R < 3.2.0. REMOVED ARGUMENTS/FUNCTIONS * `plotImsSlice`: (was defunct since 1.13) use `plotMsiSlice` instead. It has slightly different arguments; see `?plotMsiSlice` for details. INTERNAL CHANGES: * `.as.binary.matrix` now always returns an integer matrix. CHANGES IN MALDIquant VERSION 1.13 [2015-09-16]: ------------------------------------------------ IMPROVEMENTS * `calibratyIntensity` gains a "range" argument for "median" and "PQN" as well; in 1.12 just "TIC" was supported. * Extend example section in `?MALDIquant-parallel` manual page. * Add hint about _relative_ tolerance in manual pages `?alignSpectra`, `?binPeaks`, `?determineWarpingFunctions` and `?referencePeaks`; Thanks to Damien Portevin for reporting of the ambiguous phrase. MODIFICATIONS * Rewrite DESCRIPTION and explain acronyms to follow the latest version of the CRAN Repository Policy. DEFUNCT ARGUMENTS/FUNCTIONS * `plotImsSlice`: use `plotMsiSlice` instead. It has slightly different arguments; see `?plotMsiSlice` for details. BUGFIXES * `calibrateIntensity`: don't apply a scaling factor that equals zero anymore (happens in rare cases for method="median"). It throws a warning instead and leaves the spectrum untouched; fixes #51. * `.plotMsiSlice` just used the first center if "combine=TRUE"; affected functions: `plotMsiSlice`. * Add `smoothIntensity` to the `?MALDIquant-parallel` manual page. Thanks to Leiming Yu for reporting the missing entry. INTERNAL CHANGES * Importing non-base functions/methods from recommended R packages (graphics, stats, ...) as required by R-devel. * Turn `.calculateLabelPositions`, `.testLabelOverlap`, `.labelOverlap` into normal functions (were methods before). * `msiSlices`: use NA_real_ instead of logical NA. CHANGES IN MALDIquant VERSION 1.12 [2015-06-11]: ------------------------------------------------ NEW FEATURES * Add `as` for MassPeaks to convert them into MassSpectrum objects. * Add `coordinates` methods for AbstractMassObjects and list objects to access/set coordinates for MSI datasets. * Add `msiSlices` functions to create slices of a MSI dataset. * `calibratyIntensity(object, method="TIC")`: gains a "range" argument allowing to calibrate spectra by TIC on a specific mass range; closes #27. * Add parallel support on unix-based platforms using the parallel package. The following functions/methods supports the "mc.cores" argument (default is 1): `trim`, `transformIntensity`, `removeBaseline`, `calibrateIntensity`, `detectPeaks`, `alignSpectra`, `averageMassSpectra`, `mergeMassPeaks`; Please read also `?"MALDIquant-parallel"`. IMPROVEMENTS * `intensityMatrix`: rewrite to improve execution time. * `trim`: rewrite to improve execution time. MODIFICATIONS * Change generics of `mass` and `intensity` from `function(object)` to `function(object, ...)` to provide the same generics as Bioconductor's ProtGenerics package. * `intensityMatrix`: gains an attribute "mass" that stores the unique mass. DEPRECATED ARGUMENTS/FUNCTIONS * `plotImsSlice`: use `plotMsiSlice` instead. It has slightly different arguments; see `?plotMsiSlice` for details. REMOVED ARGUMENTS/FUNCTIONS * `intensityMatrix` for MassSpectrum objects (was defunct since 1.10). BUGFIXES * Fix vignette call in `?MALDIquant-package`. INTERNAL CHANGES * The return value of the internal `.as.matrix.MassObjectList` function gains an attribute "mass" that stores the unique mass (conversions of the colnames are not needed anymore); affected functions: `intensityMatrix` * Remove some useless `cbind` calls in `.estimateNoise*` and `.estimateBaseline`; no user visible changes; affected functions: `estimateNoise`, `estimateBaseline`, `detectPeaks` * Remove superfluous arguments in internal `.calibrateIntensitySimple`. * Avoid double check for negative intensity values in `transformIntensity`. CHANGES IN MALDIquant VERSION 1.11 [2014-08-11]: ------------------------------------------------ NEW FEATURES * Add `plotImsSlice`: a function to plot Imaging Mass Spectrometry data. MODIFICATIONS * Rename the vignette to MALDIquant-intro.pdf. * Move the content of the manual pages of `.savitzkyGolay` and `.movingAverage` to `smoothIntensity` and remove the old ones. * Move the content of the manual pages of `.estimateBaseline{Snip,TopHat,ConvexHull,Median}` to `estimateBaseline` and remove the old ones. * Move the content of the manual pages of `.estimateNoise{Mad,SuperSmoother}` to `estimateNoise` and remove the old ones. CHANGES IN MALDIquant VERSION 1.10 [2014-04-11]: ------------------------------------------------ IMPROVEMENTS * Add a general vignette. * `createMassSpectrum`/`createMassPeaks`: a warning is shown if negative intensity values are found. * `.transformIntensity`: a warning is shown if negative intensity values are generated and set them to zero; affected functions: `transformIntensity`, `smoothIntensity`, `calibrateIntensity`. * `determineWarpingFunctions`: a warning is shown if the "reference" contains less than ten peaks; affected functions: `determineWarpingFunctions`, `alignSpectra` * `trim`: a warning is shown if the resulting MassSpectrum/MassPeaks object does not contain any data points. MODIFICATIONS * Use `table` instead of `any(length(fiedler2009subset[[1]]) == sapply(...` in demo/workflow.R. DEFUNCT ARGUMENTS/FUNCTIONS * `intensityMatrix` for MassSpectrum objects. REMOVED ARGUMENTS/FUNCTIONS * `isMassObject`/`isMassObjectList`: use `isMassSpectrum`, `isMassPeaks`, `isMassSpectrumList` or `isMassPeaksList` instead. * `totalIonCurrent,MassPeaks-method`: TIC of peaks is meaningless. * `totalIonCurrent<-,MassSpectrum-method`: use `calibrateIntensity(..., method="TIC")` instead to calibrate/normalize MassSpectrum objects. * `standardizeTotalIonCurrent`: use `calibrateIntensity(..., method="TIC")` instead to calibrate/normalize MassSpectrum objects. * `calibrate`: use `calibrateIntensity(..., method="TIC")` instead to calibrate/normalize MassSpectrum objects. * `ltrim`/`rtrim`: use `trim(..., range=c(min, max))` instead. * `determineWarpingFunctions`, argument "warpingFunction": use "method" argument instead. * `mergeMassPeaks`, argument "fun": use "method" argument instead. * `mergeMassSpectra`: use `averageMassSpectra`. * `savitzkyGolay`: use `smoothIntensity(..., method="SavitzkyGolay")` instead. * `movingAverage`: use `smoothIntensity(..., method="MovingAverage")` instead. * `transformIntensity`, argument "fun": use "method" argument instead. * `removeBaseline`, argument "fun": use "method" argument instead. * `detectPeaks`, argument "fun": use "method" argument instead. BUGFIXES * `.which.closest`: check limits to avoid missing matches in some rare edge cases; affected functions: `labelPeaks`. * Add missing reference "Andrew 1979" to the `.estimateBaselineConvexHull` manual page. INTERNAL CHANGES * `estimateNoise`: move body of `estimateNoise,MassSpectrum` to internal `.estimateNoise`. * Move tests into tests/testthat/ to adapt to testthat 0.8 and new CRAN policy; closes #44. * `.localMaximaR`: removed. It was replaced by a C version of `.localMaxima` in 1.5. * `.snipR`: removed. It was replaced by a C version of `.estimateBaselineSnip` in 0.2. * `.lowerConvexHullR`: removed. It was replaced by a C version of `.estimateBaselineConvexHull` in 0.3. * `.topHatR`: removed. It was never used and replaced by a C version of `.estimateBaselineTopHat` in 1.6. CHANGES IN MALDIquant VERSION 1.9 [2014-01-14]: ----------------------------------------------- NEW FEATURES * Add `alignSpectra`: a wrapper around `detectPeaks`, `determineWarpingFunctions` and `warpMassSpectra` to provide an easier workflow. IMPROVEMENTS * `filterPeaks`: introduce "mergeWhitelists" argument to additionally keep peaks that occur at least in one group. The default behaviour does not changed. Please see the man page of `filterPeaks` for details. * `intensityMatrix`: add "spectra" argument to allow interpolation of missing peaks if corresponding spectra is given. * Replace some `lapply` calls by `mapply` to allow different "halfWindowSize" arguments etc.; affected methods: `smoothIntensity`, `removeBaseline`, `detectPeaks`. MODIFICATIONS * `filterPeaks`: "minFrequency" > 1 and "minNumber" > n is not set to 1 or n anymore; only a warning about "empty peak whitelists" is given; see also issue #26. * `filterPeaks`: replace warning about too large "minFrequency" or "minNumber" by a warning about "empty peak whitelists". * `intensityMatrix`: rename first argument to "peaks" * demo/workflow.R: introduce `alignSpectra`. * demo/warping.R: adapt to current preferred workflow. * demo/peaks.R: add smoothing and a few new plots. DEPRECATED ARGUMENTS/FUNCTIONS * `intensityMatrix` for MassSpectrum objects. DEFUNCT ARGUMENTS/FUNCTIONS * `isMassObject`/`isMassObjectList`: use `isMassSpectrum`, `isMassPeaks`, `isMassSpectrumList` or `isMassPeaksList` instead. * `totalIonCurrent,MassPeaks-method`: TIC of peaks is meaningless. * `totalIonCurrent<-,MassSpectrum-method`: use `calibrateIntensity(..., method="TIC")` instead to calibrate/normalize MassSpectrum objects. * `standardizeTotalIonCurrent`: use `calibrateIntensity(..., method="TIC")` instead to calibrate/normalize MassSpectrum objects. * `calibrate`: use `calibrateIntensity(..., method="TIC")` instead to calibrate/normalize MassSpectrum objects. * `ltrim`/`rtrim`: use `trim(..., range=c(min, max))` instead. * `determineWarpingFunctions`, argument "warpingFunction": use "method" argument instead. * `mergeMassPeaks`, argument "fun": use "method" argument instead. * `mergeMassSpectra`: use `averageMassSpectra`. * `savitzkyGolay`: use `smoothIntensity(..., method="SavitzkyGolay")` instead. * `movingAverage`: use `smoothIntensity(..., method="MovingAverage")` instead. * `transformIntensity`, argument "fun": use "method" argument instead. * `removeBaseline`, argument "fun": use "method" argument instead. * `detectPeaks`, argument "fun": use "method" argument instead. BUGFIXES * `plot`: fix subtitle for averaged spectra. INTERNAL CHANGES * Add `.as.matrix.MassObjectList` to avoid internal dependencies on `intensityMatrix`; affected functions: `filterPeaks`, `referencePeaks`. * Add `.as.binary.matrix`. * Remove man/MALDIquant-internal.Rd. * Argument "representation" of `setClass` is deprecated since R 3.0.0; use "slots" instead. affected classes: AbstractMassObject, MassPeaks, MassSpectrum. CHANGES IN MALDIquant VERSION 1.8 [2013-09-08]: ----------------------------------------------- NEW FEATURES * Add `smoothIntensity`: to apply smoothing filters. Supports "SavitzkyGolay" and "MovingAverage". `savitzkyGolay` and `movingAverage` are now marked as deprecated. * Add `calibrateIntensity`: to calibrate/normalize intensity values. Supports "TIC", "median" and "PQN" calibration/normalization. It replaces three similar functions with different names: `totalIonCurrent<-`, `standardizeTotalIonCurrent`, `calibrate` (these three are marked as deprecated and will be removed in future releases). * Add `averageMassSpectra`: supports "mean", "median" and "sum" aggregation of spectra. It replaces `mergeMassSpectra`. * Add C implementation of colMedians for faster averaging/merging of MassSpectrum/MassPeaks objects. * Add LongVector support; now MALDIquant depends on R >= 3.0.0. MODIFICATIONS * MassSpectrum/MassPeaks class: change representation of slots mass, intensity, snr to "numeric" (was "vector" before). Should not affect anything. Maybe you have to reimport raw data and resave RData files. * `trim`: replace "minMass" and "maxMass" arguments by new "range" argument. * `transformIntensity`: add "method" argument (supports "sqrt", "log", "log2", "log10"). "fun" argument is deprecated. * `estimateBaseline`/`removeBaseline`: rename argument method="Median" to method="median". * `determineWarpingFunctions`: add "method" argument (supports "lowess", "linear", "quadratic", "cubic"); "warpingFunctions" argument is deprecated. * `mergeMassPeaks`: add "method" (supports "mean", "median", "sum") argument; "fun" argument is deprecated. DEPRECATED ARGUMENTS/FUNCTIONS * `isMassObject`/`isMassObjectList`: use `isMassSpectrum`, `isMassPeaks`, `isMassSpectrumList` or `isMassPeaksList` instead. * `totalIonCurrent,MassPeaks-method`: TIC of peaks is meaningless. * `totalIonCurrent<-,MassSpectrum-method`: use `calibrateIntensity(..., method="TIC")` instead to calibrate/normalize MassSpectrum objects. * `standardizeTotalIonCurrent`: use `calibrateIntensity(..., method="TIC")` instead to calibrate/normalize MassSpectrum objects. * `calibrate`: use `calibrateIntensity(..., method="TIC")` instead to calibrate/normalize MassSpectrum objects. * `ltrim`/`rtrim`: use `trim(..., range=c(min, max))` instead. * `determineWarpingFunctions`, argument "warpingFunction": use "method" argument instead. * `mergeMassPeaks`, argument "fun": use "method" argument instead. * `mergeMassSpectra`: use `averageMassSpectra`. * `savitzkyGolay`: use `smoothIntensity(..., method="SavitzkyGolay")` instead. * `movingAverage`: use `smoothIntensity(..., method="MovingAverage")` instead. * `transformIntensity`, argument "fun": use "method" argument instead. * `removeBaseline`, argument "fun": use "method" argument instead. * `detectPeaks`, argument "fun": use "method" argument instead. REMOVED FUNCTIONS * `iplot`: because it throws NOTEs on CRAN's R CMD check and the most important function `grDevices::getGraphicEvents` behaves different on Unix/Windows platform (some keys don't working) and contains some nasty bugs, e.g.: https://bugs.r-project.org/bugzilla3/show_bug.cgi?id=15208 Also the "zoom" R package provides similar functionality. BUGFIXES * `totalIonCurrent`: fix calculation by estimating the integral; fixes #29. affected functions: `totalIonCurrent`, `calibrateIntensity(..., method="TIC")`. * `filterPeaks`: reduce minNumber to length(l) to avoid complete removal of peaks; fixes #26. * src/snip.c: fix some segmention faults because of a missing PROTECT for "duplicate" call. affected functions: `estimateBasline/removeBaseline(..., method="SNIP")` - Add `import("methods")` to NAMESPACE (fixes #36). INTERNAL CHANGES * Remove internal `imputeMass`. * Remove internal `.replaceNonEmptyMassObjects`. * `approxfun`: returns NA for empty MassSpectrum objects. * `[` (subset method): little speed up by calling `which` to evaluate logical expressions only once. * Use integer values where possible. * Modify C written functions to support LongVectors; closes #13; now MALDIquant depends on R >= 3.0.0. CHANGES IN MALDIquant VERSION 1.7 [2013-05-23]: ----------------------------------------------- NEW FEATURES * Add `isRegular` method to test MassSpectrum objects for regular increasing mass values/intervals; closes #23. * Add `savitzkyGolay`, Savitzky-Golay Smoothing Filter. * Add memory usage to `show` output. * `iplot`: add horizontal distance measurement (using left mouse clicks). * `filterPeaks`: add "minNumber" argument to allow an absolute threshold for filtering; closes #21. * demo/workflow.R: add raw data check. IMPROVEMENTS * demo/workflow.R: replace moving average smoother by Savitzky-Golay-Filter. * `.estimateBaselineSnip`: support decreasing/increasing clipping window; controlled by new argument "decreasing"; use a decreasing window per default to get a smoother baseline; (use decreasing=FALSE to get the old behaviour). * `isEmpty`: treat spectra with only zeros as intensity values as empty ones. * `movingAverage`: left/right extrema now calculated for 0:windowSize and (n-windowSize+1):n (before they were set to NA). BUGFIXES * `filterPeaks`: doesn't round (floor) minFrequency argument anymore; led to unexpected results; fixes #22. * `mergeMassSpectra`: empty MassSpectrum objects are ignored now (but if all objects are empty `mergeMassSpectra` will return an empty MassSpectrum object, too); fixes #20. * `.mergeMetaData`: fix merging of lists with different lengths; fixes #17. affected functions: `mergeMassPeaks`, `mergeMassSpectra`. * `.doByLabels: preserve order of non-factor labels; fixes #19; affected functions: `filterPeaks`, `mergeMassPeaks`, `mergeMassSpectra`. * `totalIonCurrent`/`isEmpty`: fix integer overflow; fixes #25. * `totalIonCurrent<-`: fix handling of empty spectra; fixes #15. * Remove LICENSE file. INTERNAL CHANGES * Replace all `paste(..., sep="")` calls by `paste0`; now MALDIquant depends on R >= 2.15. * Replace all `.C` interfaces by `.Call`. CHANGES IN MALDIquant VERSION 1.6 [2013-03-01]: ----------------------------------------------- NEW FEATURES * Add interactive plot method: `iplot`. * Add TopHat baseline estimation: `estimateBaseline(..., method="TopHat")`/ `removeBaseline(..., method="TopHat")`. IMPROVEMENTS * Include tests in inst/tests. BUGFIXES * `.which.closest`: rounding .5 to the next highest integer (before: to the next lowest one); affected functions: `labelPeaks`. * `.doByLabels`: fix an error that occurred if all labels were unique (fixes #1); affected functions: `filterPeaks`, `mergeMassPeaks`, `mergeMassSpectra`. * `estimateBaseline(..., method="Median")`: do not allow a window size > n (fixes #4). CHANGES IN MALDIquant VERSION 1.5 [2012-12-06]: ----------------------------------------------- NEW FEATURES * Add `movingAverage`. * `labelPeaks`: add labels argument. IMPROVEMENTS * `detectPeaks`: only throw an error if windowSize > length (windowSize == length is no error anymore). * `.doByLabels`: drop unused levels of argument "labels"; affected functions: `filterPeaks`, `mergeMassPeaks`, `mergeMassSpectra`. * Rewrite `localMaxima` in C to reduce running time and memory consumption. CHANGES IN MALDIquant VERSION 1.4 [2012-10-30]: ----------------------------------------------- NEW FEATURES * Add slot "snr" to MassPeaks. * Add `.estimateNoiseMad` (same implementation as old `estimateNoise` method). * Add `.estimateNoiseSuperSmoother`. * `estimateNoise`: add method argument, change return value to two column matrix. * `binPeaks`/`referencePeaks`: add argument method=c("strict", "relaxed"); "relaxed" allows multiple peaks of the same spectrum in a bin. IMPROVEMENTS * `labelPeaks`: remove tolerance argument (now always select the closest one). * `mergeMassPeaks`/`mergeMassSpectra`: merge metaData, too. * `detectPeaks`: new example for a custom noise estimation function. * `calibrate`: add ... to generic definition of `calibrate`. * Change demo licences to public domain. INTERNAL CHANGES * `detectPeaks`: change internal snr handling. * `show`: rewrite (OO design). CHANGES IN MALDIquant VERSION 1.3 [2012-09-16]: ----------------------------------------------- NEW FEATURES * Add `trim` and `[` methods. * `labelPeaks`: add avoidOverlap argument to avoid overlapping of peak labels. IMPROVEMENTS * `detectPeaks`: add error message if windowSize >= length. * `determineWarpingFunctions`: add error message if it could not match any peak to reference peaks. MODIFICATIONS * `labelPeaks`: change default value for verticalOffset argument. * `labelPeaks`: remove family argument. BUGFIXES * Fix `isMassSpectrumList`. GENERAL * Update DESCRIPTION file to use Authors@R and depends on R>=2.14 now. * Add CITATION file. CHANGES IN MALDIquant VERSION 1.2 [2012-07-18]: ----------------------------------------------- BUG FIXES * `determineWarpingFunctions`: stop with an error if reference MassPeaks object is empty. * `.doByLabel`: preserve list order (affects `filterPeaks`). INTERNAL CHANGES * Replace some `sapply` calls by `lapply`. CHANGES IN MALDIquant VERSION 1.1 [2012-05-06]: ----------------------------------------------- NEW FEATURES * Add `totalIonCurrent` method (replacement for `calibrate` function on MassSpectrum objects). * Add methods accepting lists as arguments: `transformIntensity`, `removeBaseline`, `detectPeaks`. MODIFICATIONS * `detectPeaks`: remove localMaxima argument. * `detectPeaks`: replace noise argument by fun. * `removeBaseline`: replace baseline argument by fun. * `labelPeaks`: use par("usr") to calculate default verticalOffset. * `labelPeaks`: add absoluteVerticalPos argument. * `plot`: change "sub" for merged spectra/peaks. * `intensityMatrix`: remove argument replaceByNa. * `determineWarpingFunctions`, `warpMassSpectra`, `warpMassPeaks` now require lists as arguments and return a list. * `calibrate`: works only on matrices now. * Replace all `lapply` calls by new methods in demonstration scripts. * Allow only numeric values for `intensity<-` and `mass<-`. * Rename `findLocalMaxima` to `.findLocalMaxima` and hide by NAMESPACE. * Rename dataset spectra in fiedler2009subset. * Remove peaks dataset. CHANGES IN MALDIquant VERSION 1.0 [2012-03-28]: ----------------------------------------------- NEW FEATURES * Add demonstration scripts. * Add functions: `binPeaks`, `determineWarpingFunctions`, `filterPeaks`, `isMassObject`, `isMassPeaks`, `isMassSpectrum`, `mergeMassPeaks`, `mergeMassSpectra`, `referencePeaks`, `warpMassPeaks`, `warpMassSpectra`. MODIFICATIONS * Change NAMESPACE. * Hide `imputeMass` method because it fails on Bruker Daltonics' CompassXport 32bit output. * Add \keyword{internal} to estimateBasline*-functions.Rd. * `labelPeaks`: rename massTolerance argument to tolerance. * Replace dataset sA1/pA1 by spectra/peaks (containing 16 example spectra). CHANGES IN MALDIquant VERSION 0.5 [2012-01-18]: ----------------------------------------------- NEW FEATURES * Add `imputeMass` method. IMPROVEMENTS * Rewrite intensityMatrix function (little speed improvement). CHANGES IN MALDIquant VERSION 0.4 [2011-08-04]: ----------------------------------------------- NEW FEATURES * Add functions: `isMassObjectList`, `isMassPeaksList`, `isMassSpectrumList`, `findEmptyMassObjects`, `removeEmptyMassObjects`, `intensityMatrix` and `calibrate` (only TIC supported up to now). * Add mass and massTolerance argument to `labelPeaks`. IMPROVEMENTS * Change default value for verticalOffset argument in `labelPeaks`. * Add pA1 dataset. MODIFICATIONS * Rename AbstractMassSpectrumData to AbstractMassObject. CHANGES IN MALDIquant VERSION 0.3 [2011-05-28]: ----------------------------------------------- NEW FEATURES * Add `transformIntensity`, `estimateNoise`, `findLocalMaxima`, `detectPeaks` and `points` method. * Add `createMassPeaks` constructor. IMPROVEMENTS * Replace `.estimateBaselineMovingEstimator` by `.estimateBaselineMedian` (uses `runmed` instead of an own slow `movingEstimator` function). MODIFICATIONS * Rewrite `labelPeaks` (some arguments changed). * Rename SingleSpectrum class to MassSpectrum. * Rename SinglePeakList class to MassPeaks. * Remove fdrtool dependency and rewrite `.estimateBaselineConvexHull` in C. * Move importing functions to the following R-packages: readBrukerFlexData, readMzXmlData. BUGFIXES * change `.C(..., DUP=F)` to `.C(..., DUP=T)` in `.estimateBaselineSnip` to avoid changes of global variables. CHANGES IN MALDIquant VERSION 0.2 [2011-03-29]: ----------------------------------------------- * Add baseline correction algorithm SNIP (implemented in C) [default]. * Add MovingEstimator and ConvexHull baseline estimation algorithm. * Add `as.matrix`, `intensity`, `length`, `lines`, `mass` and `metaData` methods. * Add basic SinglePeakList class, adopt `plot` and `lines`, add `labelPeaks` (still useless). CHANGES IN MALDIquant VERSION 0.1 [2011-02-22]: ----------------------------------------------- * First public release. * Up to now only importing and plotting of a single mass spectrum are supported. 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See the * GNU General Public License for more details. * * You should have received a copy of the GNU General Public License * along with MALDIquant. If not, see */ /* SNIP algorithm based on: * C.G. Ryan, E. Clayton, W.L. Griffin, S.H. Sie, and D.R. Cousens. * "Snip, a statistics-sensitive background treatment for the quantitative * analysis of pixe spectra in geoscience applications." * Nuclear Instruments and Methods in Physics Research Section B: * Beam Interactions with Materials and Atoms, 34(3):396-402, 1988. * ISSN 0168-583X. doi:10.1016/0168-583X(88)90063-8. * URL http://www.sciencedirect.com/science/article/B6TJN-46YSYTJ-30/2/e0d015ceb8ea8a7bc0702a857a19750b * * decreasing clipping window adapted from: * M. Morhac. 2009. * "An algorithm for determination of peak regions and baseline elimination in * spectroscopic data." * Nuclear Instruments and Methods in Physics Research Section A: * Accelerators, Spectrometers, Detectors and Associated Equipment, 600(2), 478-487. * ISSN 0168-9002. doi:10.1016/S0168-9002(97)01023-1. * URL http://www.sciencedirect.com/science/article/pii/S0168900297010231 */ #include "MALDIquant.h" #include #include /* y = array of double values * iterations = max iteration steps * decreasing = use a decreasing clipping window? */ SEXP C_snip(SEXP y, SEXP iterations, SEXP decreasing) { SEXP dup, output; R_xlen_t n, i, j, k; int d; double a, b; PROTECT(dup=duplicate(y)); PROTECT(y=coerceVector(dup, REALSXP)); n=XLENGTH(y); d=asInteger(decreasing); PROTECT(output=allocVector(REALSXP, n)); double* xo=REAL(output); double* xy=REAL(y); k=asInteger(iterations); /* code duplication to use faster ++i/--i instead of i+=step */ if (d) { for (i=k; i>0; --i) { for (j=i; j * * This file is part of MALDIquant for R and related languages. * * MALDIquant is free software: you can redistribute it and/or modify * it under the terms of the GNU General Public License as published by * the Free Software Foundation, either version 3 of the License, or * (at your option) any later version. * * MALDIquant is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU General Public License for more details. * * You should have received a copy of the GNU General Public License * along with MALDIquant. If not, see */ #ifndef MALDIQUANT_H #define MALDIQUANT_H #include #include SEXP C_colMedians(SEXP x, SEXP na_rm); SEXP C_snip(SEXP y, SEXP iterations, SEXP decreasing); SEXP C_lowerConvexHull(SEXP x, SEXP y); SEXP C_dilation(SEXP y, SEXP s); SEXP C_erosion(SEXP y, SEXP s); SEXP C_localMaxima(SEXP y, SEXP s); #endif /* end of MALDIQUANT_H */ MALDIquant/src/localMaxima.c0000644000175000017500000000344013035216066016646 0ustar sebastiansebastian/* Copyright 2012-2013 Sebastian Gibb * * * This file is part of MALDIquant for R and related languages. * * MALDIquant is free software: you can redistribute it and/or modify * it under the terms of the GNU General Public License as published by * the Free Software Foundation, either version 3 of the License, or * (at your option) any later version. * * MALDIquant is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU General Public License for more details. * * You should have received a copy of the GNU General Public License * along with MALDIquant. If not, see */ #include "MALDIquant.h" #include #include R_xlen_t windowMaxIdx(double* x, R_xlen_t start, R_xlen_t end) { R_xlen_t i, m=start; for (i=start+1; i<=end; ++i) { if (x[m] < x[i]) { m=i; } } return(m); } /* y = array of double values * s = half window size */ SEXP C_localMaxima(SEXP y, SEXP s) { SEXP output; R_xlen_t n, q, m, windowSize, i, l, mid; PROTECT(y=coerceVector(y, REALSXP)); n=XLENGTH(y); PROTECT(output=allocVector(LGLSXP, n)); double* xy=REAL(y); int* xo=LOGICAL(output); memset(xo, 0, n*sizeof(int)); q=asInteger(s); windowSize=q*2; m=windowMaxIdx(xy, 0, windowSize); xo[m]=m==q; /* i == rhs; l == lhs; mid == middle pos */ for (i=windowSize+1, l=i-windowSize, mid=(l+i)/2; i xy[m]) { m=i; } if (m == mid) { xo[m]=1; } } UNPROTECT(2); return(output); } MALDIquant/src/colMedians.c0000644000175000017500000000464013035216066016500 0ustar sebastiansebastian/* Copyright 2013 Sebastian Gibb * * * This file is part of MALDIquant for R and related languages. * * MALDIquant is free software: you can redistribute it and/or modify * it under the terms of the GNU General Public License as published by * the Free Software Foundation, either version 3 of the License, or * (at your option) any later version. * * MALDIquant is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU General Public License for more details. * * You should have received a copy of the GNU General Public License * along with MALDIquant. If not, see */ #include "MALDIquant.h" #include #include /* x = matrix of double values * na_rm = remove NA? * based on src/library/stats/R/median.R */ SEXP C_colMedians(SEXP x, SEXP na_rm) { SEXP dims, column, output; R_xlen_t ncol, nrow, ir, ic, icr, half; int narm, isEven; double v; PROTECT(x=coerceVector(x, REALSXP)); PROTECT(dims=getAttrib(x, R_DimSymbol)); /* TODO: Replace by R_xlen_t in R 3.0.0. * TODO: Maybe this will cause problems because we need Long Vector support. */ nrow=INTEGER(dims)[0]; ncol=INTEGER(dims)[1]; narm=asInteger(na_rm); PROTECT(output=allocVector(REALSXP, ncol)); PROTECT(column=allocVector(REALSXP, nrow)); double* xx=REAL(x); double* xo=REAL(output); double* xc=REAL(column); for (ic=0; ic * * This file is part of MALDIquant for R and related languages. * * MALDIquant is free software: you can redistribute it and/or modify * it under the terms of the GNU General Public License as published by * the Free Software Foundation, either version 3 of the License, or * (at your option) any later version. * * MALDIquant is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU General Public License for more details. * * You should have received a copy of the GNU General Public License * along with MALDIquant. If not, see */ /* Lower Convex Hull Algorithm based on: * A. M. Andrew, "Another Efficient Algorithm for Convex Hulls in Two * Dimensions", Info. Proc. Letters 9, 216-219 (1979). * only calculate lower hull (we don't need the upper one) */ #include "MALDIquant.h" #include #include /* build cross product of 2 vectors and compare to zero * returns true for P2(x2, y2) left of P0->P1 */ double left(double x0, double y0, double x1, double y1, double x2, double y2) { return(((x1-x0)*(y2-y0) - (x2-x0)*(y1-y0)) > 0); } /* x = array of double values * y = array of double values * length = length of y * output = array of double values (new y values) */ SEXP C_lowerConvexHull(SEXP x, SEXP y) { SEXP output; /* TODO: replace by R_xlen_t in R 3.0.0 */ R_xlen_t n, i, j, k=0; int* nodes; double m, c; PROTECT(x=coerceVector(x, REALSXP)); PROTECT(y=coerceVector(y, REALSXP)); n=XLENGTH(x); PROTECT(output=allocVector(REALSXP, n)); /* TODO: replace by R_xlen_t in R 3.0.0 */ /* allocate vector - error handling is done by R */ nodes=(int*) Calloc((size_t) n, int); double* xx=REAL(x); double* xy=REAL(y); double* xo=REAL(output); /* find lower convex hull */ for (i=0; i 1 && !left(xx[nodes[k-2]], xy[nodes[k-2]], xx[nodes[k-1]], xy[nodes[k-1]], xx[i], xy[i])) { k-=1; } nodes[k]=i; k+=1; } /* build linear function y=mx+c to calculate values between nodes */ for (i=0; i * * This file is part of MALDIquant for R and related languages. * * MALDIquant is free software: you can redistribute it and/or modify * it under the terms of the GNU General Public License as published by * the Free Software Foundation, either version 3 of the License, or * (at your option) any later version. * * MALDIquant is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU General Public License for more details. * * You should have received a copy of the GNU General Public License * along with MALDIquant. If not, see */ #include "MALDIquant.h" #include #include /* Dilation/Erosion is based on the vHGW Algorithm * (needs only 3 comparison per element): * * M. van Herk. "A Fast Algorithm for Local Minimum and Maximum Filters on * Rectangular and Octagonal Kernels." * Pattern Recognition Letters 13.7 (1992): 517-521. * * J. Y. Gil and M. Werman. "Computing 2-Dimensional Min, Median and Max * Filters." IEEE Transactions (1996): 504-507. * * Gil and Kimmel (GK) decrease the number of comparisons to nearly 1.5: * * J. Y. Gil and R. Kimmel. "Efficient Dilation, Erosion, Opening, and Closing * Algorithms." Pattern Analysis and Machine Intelligence, * IEEE Transactions on 24.12 (2002): 1606-1617. * * The implementation of the GK algorithm is much more complex and needs pointer * arithmetic, "goto" or multiple for-loops (increasing number of comparision). * The speed improvement is marginal and does not matter for our purposes. */ /* y = input vector * s = half window size (half filter length) */ SEXP C_dilation(SEXP y, SEXP s) { SEXP f, g, h, output; R_xlen_t n, fn, k, q, i, r, j, gi, hi; PROTECT(y=coerceVector(y, REALSXP)); n=XLENGTH(y); q=asInteger(s); k=2*q+1; /* add q (== halfWindowSize) values left/right * increase n to make n%windowSize == 0 */ fn=n+2*q+(k-(n%k)); PROTECT(f=allocVector(REALSXP, fn)); PROTECT(g=allocVector(REALSXP, fn)); PROTECT(h=allocVector(REALSXP, fn)); PROTECT(output=allocVector(REALSXP, n)); double* xy=REAL(y); double* xf=REAL(f); double* xg=REAL(g); double* xh=REAL(h); double* xo=REAL(output); memcpy(xf+q, xy, n*sizeof(double)); /* init left extrema */ for (i=0; i xf[gi]) { xg[gi]=xf[gi]; } else { xg[gi]=xg[gi-1]; } if (xh[hi+1] > xf[hi]) { xh[hi]=xf[hi]; } else { xh[hi]=xh[hi+1]; } } } /* merging */ for (i=0, gi=k-1, hi=0; i xh[hi]) { xo[i]=xh[hi]; } else { xo[i]=xg[gi]; } } UNPROTECT(5); return(output); } MALDIquant/src/init.c0000644000175000017500000000272613035216066015370 0ustar sebastiansebastian/* Copyright 2013 Sebastian Gibb * * * This file is part of MALDIquant for R and related languages. * * MALDIquant is free software: you can redistribute it and/or modify * it under the terms of the GNU General Public License as published by * the Free Software Foundation, either version 3 of the License, or * (at your option) any later version. * * MALDIquant is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU General Public License for more details. * * You should have received a copy of the GNU General Public License * along with MALDIquant. If not, see */ #include "MALDIquant.h" #include #include #include static const R_CallMethodDef callMethods[] = { {"C_colMedians", (DL_FUNC) &C_colMedians, 2}, {"C_snip", (DL_FUNC) &C_snip, 3}, {"C_lowerConvexHull", (DL_FUNC) &C_lowerConvexHull, 2}, {"C_dilation", (DL_FUNC) &C_dilation, 2}, {"C_erosion", (DL_FUNC) &C_erosion, 2}, {"C_localMaxima", (DL_FUNC) &C_localMaxima, 2}, { NULL, NULL, 0 } /* mark last element */ }; void #ifdef HAVE_VISIBILITY_ATTRIBUTE __attribute__ ((visibility ("default"))) #endif R_init_MALDIquant(DllInfo *info) { /* no .C, .Fortran, or .External routines => NULL */ R_registerRoutines(info, NULL, callMethods, NULL, NULL); }