plot.peaksDataset {flagme} | R Documentation |
Store the raw data and optionally, information regarding signal peaks for a number of GCMS runs
.plotpD(object,runs=1:length(object@rawdata),mzind=1:nrow(object@rawdata[[1]]), mind=NULL,plotSampleLabels=TRUE,calcGlobalMax=FALSE,peakCex = 0.8,plotPeaks=TRUE, plotPeakBoundaries=FALSE,plotPeakLabels=FALSE,plotMergedPeakLabels=TRUE,mlwd=3, usePeaks=TRUE,plotAcrossRuns=FALSE,overlap=F,rtrange=NULL,cols=NULL,thin=1, max.near=median(object@rawrt[[1]]),how.near=50,scale.up=1,...) .plotpA(object,xlab="Peaks - run 1",ylab="Peaks - run 2",plotMatches=TRUE,matchPch=19,matchLwd=3, matchCex=.5,matchCol="black",col=colorpanel(50,"black","blue","white"), breaks=seq(0,1,length=51),...) .plotcA(object,alignment=1,...)
object |
a peaksDataset , peaksAlignment or clusterAlignment object. |
runs |
for peaksDataset only: set of run indices to plot |
mzind |
for peaksDataset only: set of mass-to-charge indices to sum over (default, all) |
mind |
for peaksDataset only: matrix of aligned indices |
plotSampleLabels |
for peaksDataset only: logical, whether to display sample labels |
calcGlobalMax |
for peaksDataset only: logical, whether to calculate an overall maximum for scaling |
peakCex |
character expansion factor for peak labels |
plotPeaks |
for peaksDataset only: logical, whether to plot hashes for each peak |
plotPeakBoundaries |
for peaksDataset only: logical, whether to display peak boundaries |
plotPeakLabels |
for peaksDataset only: logical, whether to display peak labels |
plotMergedPeakLabels |
for peaksDataset only: logical, whether to display 'merged' peak labels |
mlwd |
for peaksDataset only: line width of lines indicating the alignment |
usePeaks |
for peaksDataset only: logical, whether to plot alignment of peaks (otherwise, scans) |
plotAcrossRuns |
for peaksDataset only: logical, whether to plot across peaks when unmatched peak is given |
overlap |
for peaksDataset only: logical, whether to plot TIC/XICs overlapping |
rtrange |
for peaksDataset only: vector of length 2 giving start and end of the X-axis |
cols |
for peaksDataset only: vector of colours (same length as the length of runs) |
thin |
for peaksDataset only: when usePeaks=FALSE , plot the alignment lines every thin values |
max.near |
for peaksDataset only: where to look for maximum |
how.near |
for peaksDataset only: how far away from max.near to look |
scale.up |
for peaksDataset only: a constant factor to scale the TICs |
plotMatches |
for peaksDataset only: logical, whether to plot matches |
xlab |
for peaksAlignment and clusterAlignment only: x-axis label |
ylab |
for peaksAlignment and clusterAlignment only: y-axis label |
matchPch |
for peaksAlignment and clusterAlignment only: match plotting character |
matchLwd |
for peaksAlignment and clusterAlignment only: match line width |
matchCex |
for peaksAlignment and clusterAlignment only: match character expansion factor |
matchCol |
for peaksAlignment and clusterAlignment only: match colour |
col |
for peaksAlignment and clusterAlignment only: vector of colours for colourscale |
breaks |
for peaksAlignment and clusterAlignment only: vector of breaks for colourscale |
alignment |
for peaksAlignment and clusterAlignment only: the set of alignments to plot |
... |
further arguments passed to the plot or image command |
For peakDataset
objects, each TIC is scale to the maximum value (as specified by the how.near
and max.near
values). The many parameters gives considerable flexibility of how the TICs can be visualized.
For peakAlignment
objects, the similarity matrix is plotted and optionally, the set of matching peaks. clusterAlignment
objects are just a collection of all pairwise peakAlignment
objects.
Mark Robinson
Mark D Robinson (2008). Methods for the analysis of gas chromatography - mass spectrometry data PhD dissertation University of Melbourne.
require(gcspikelite) # paths and files gcmsPath<-paste(.find.package("gcspikelite"),"data",sep="/") cdfFiles<-dir(gcmsPath,"CDF",full=TRUE) eluFiles<-dir(gcmsPath,"ELU",full=TRUE) # read data pd<-peaksDataset(cdfFiles[1:3],mz=seq(50,550),rtrange=c(7.5,8.5)) # image plot plot(pd,rtrange=c(7.5,8.5),plotPeaks=TRUE,plotPeakLabels=TRUE)