| plotImage {flagme} | R Documentation |
Image plots (i.e. 2D heatmaps) of raw GCMS profile data
plotImage(object,run=1,rtrange=c(11,13),main=NULL,mzrange=c(50,200),SCALE=log2,...)
object |
a peaksDataset object |
run |
index of the run to plot an image for |
rtrange |
vector of length 2 giving start and end of the X-axis (retention time) |
main |
main title (auto-constructed if not specified) |
mzrange |
vector of length 2 giving start and end of the Y-axis (mass-to-charge ratio) |
SCALE |
function called to scale the data (default: log2) |
... |
further arguments passed to the 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],mz=seq(50,550),rtrange=c(7.5,8.5))
# image plot
plotImage(pd,run=1,rtrange=c(7.5,8.5),main="")