| progressiveAlignment-class {flagme} | R Documentation |
Performs a progressive peak alignment (clustalw style) of multiple GCMS peak lists
progressiveAlignment(pD,cA,D=1000,gap=.5,verbose=TRUE,usePeaks=TRUE,df=30,compress=TRUE)
pD |
a peaksDataset object |
cA |
a clusterAlignment object |
D |
retention time penalty |
gap |
gap parameter |
verbose |
logical, whether to print information |
usePeaks |
logical, whether to use peaks (if TRUE) or the full 2D profile alignment (if FALSE) |
df |
distance from diagonal to calculate similarity |
compress |
logical, whether to store the similarity matrices in sparse form |
The progressive peak alignment we implemented here for multiple GCMS peak lists is analogous to how clustalw takes a set of pairwise sequence alignments and progressively builds a multiple alignment. More details can be found in the reference below.
progressiveAlignment object
Mark Robinson
Mark D Robinson (2008). Methods for the analysis of gas chromatography - mass spectrometry data PhD dissertation University of Melbourne.
peaksDataset, multipleAlignment
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])
ca<-clusterAlignment(pd, gap = .5,D=.05,df=30)
pa<-progressiveAlignment(pd, ca, gap = .6, D=.1,df=30)