multipleAlignment-class {flagme} | R Documentation |
Store the raw data and optionally, information regarding signal peaks for a number of GCMS runs
multipleAlignment(pd,group,bw.gap=0.8,wn.gap=0.6,bw.D=.20,wn.D=.05,filterMin=3,lite=FALSE,usePeaks=TRUE,df=50,verbose=TRUE,timeAdjust=FALSE,doImpute=FALSE)
pd |
a peaksDataset object |
group |
factor variable of experiment groups, used to guide the alignment algorithm |
bw.gap |
gap parameter for "between" alignments |
wn.gap |
gap parameter for "within" alignments |
bw.D |
distance penalty for "between" alignments |
wn.D |
distance penalty for "within" alignments |
filterMin |
minimum number of peaks within a merged peak to be kept in the analysis |
lite |
logical, whether to keep "between" alignment details (default, FALSE ) |
usePeaks |
logical, whether to use peaks (if TRUE ) or the full 2D profile alignment (if FALSE ) |
df |
distance from diagonal to calculate similarity |
verbose |
logical, whether to print information |
timeAdjust |
logical, whether to use the full 2D profile data to estimate retention time drifts (Note: time required) |
doImpute |
logical, whether to impute the location of unmatched peaks |
multipleAlignment is the data structure giving the result of an alignment across several GCMS runs.
Multiple alignments are done progressively. First, all samples with the same tg$Group
label with be aligned (denoted a "within" alignment). Second, each group will be summarized into a pseudo-data set, essentially a spectrum and retention time for each matched peak of the within-alignment. Third, these "merged peaks" are aligned in the same progressive manner, here called a "between" alignment.
multipleAlignment
object
Mark Robinson
Mark D Robinson (2008). Methods for the analysis of gas chromatography - mass spectrometry data PhD dissertation University of Melbourne.
peaksDataset
, betweenAlignment
, 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]) # multiple alignment ma<-multipleAlignment(pd,c(1,1),wn.gap=0.5,wn.D=.05,bw.gap=0.6,bw.D=.2,usePeaks=TRUE,filterMin=1,df=50,verbose=TRUE)