flagme (version 1.28.0)

progressiveAlignment-class: Data Structure for progressive alignment of many GCMS samples

Description

Performs a progressive peak alignment (clustalw style) of multiple GCMS peak lists

Usage

progressiveAlignment(pD,cA,D=1000,gap=.5,verbose=TRUE,usePeaks=TRUE,df=30,compress=TRUE)

Arguments

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

Value

progressiveAlignment object

Details

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.

References

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

See Also

peaksDataset, multipleAlignment

Examples

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)