plot.peaksDataset: Plotting functions for GCMS data objects

Description

Store the raw data and optionally, information regarding signal peaks for a number of GCMS runs

Usage

.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,...)

Arguments

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

Details

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.

References

Mark D Robinson (2008). Methods for the analysis of gas chromatography - mass spectrometry data PhD dissertation University of Melbourne.

Examples

Run this code

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)