| compress {flagme} | R Documentation |
Many of the peaks are not similar. So, the set of pairwise similarity matrices can be compressed.
compress(object,verbose=TRUE,...) decompress(object,verbose=TRUE,...)
object |
a peaksAlignment, peaksAlignment or peaksAlignment object to be compressed |
verbose |
logical, whether to print out information |
... |
further arguments |
Using sparse matrix representations, a significant compression can be achieved. Here, we use the matrix.csc class of the SpareM package.
an object of the same type as the input object
Mark Robinson
Mark D Robinson (2008). Methods for the analysis of gas chromatography - mass spectrometry data PhD dissertation University of Melbourne.
peaksAlignment, clusterAlignment, progressiveAlignment
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, peak detection results
pd<-peaksDataset(cdfFiles[1:2],mz=seq(50,550),rtrange=c(7.5,8.5))
pd<-addAMDISPeaks(pd,eluFiles[1:2])
# pairwise alignment (it is compressed by default)
ca<-clusterAlignment(pd, usePeaks = TRUE, df = 20)
object.size(ca)
# decompress
ca<-decompress(ca)
object.size(ca)