Group peaks from different samples together

Group peaks together across samples by creating a master peak list and assigning corresponding peaks from all samples. It is inspired by the alignment algorithm of mzMine. For further details check http://mzmine.sourceforge.net/ and

Katajamaa M, Miettinen J, Oresic M: MZmine: Toolbox for processing and visualization of mass spectrometry based molecular profile data. Bioinformatics (Oxford, England) 2006, 22:634?636.

Currently, there is no equivalent to minfrac or minsamp.

Methods

object = "xcmsSet"

group(object, mzVsRTbalance=10, mzCheck=0.2, rtCheck=15, kNN=10)

Arguments

object

the xcmsSet object

mzVsRTbalance

Multiplicator for mz value before calculating the (euclidean) distance between two peaks.

mzCheck

Maximum tolerated distance for mz.

rtCheck

Maximum tolerated distance for RT.

kNN

Number of nearest Neighbours to check

Value

An xcmsSet object with peak group assignments and statistics.

See also

xcmsSet-class, group.density and group.mzClust

Examples

if (FALSE) library(xcms)
    library(faahKO)
    ## These files do not have this problem to correct for
    ## but just for an example
    cdfpath <- system.file("cdf", package = "faahKO")
    cdffiles <- list.files(cdfpath, recursive = TRUE, full.names = TRUE)

    xset<-xcmsSet(cdffiles)

    gxset<-group(xset, method="nearest")
#> Processing sample number 8 ... 
#> OK
#> Processing sample number 1 ... 
#> OK
#> Processing sample number 7 ... 
#> OK
#> Processing sample number 3 ... 
#> OK
#> Processing sample number 12 ... 
#> OK
#> Processing sample number 9 ... 
#> OK
#> Processing sample number 6 ... 
#> OK
#> Processing sample number 5 ... 
#> OK
#> Processing sample number 4 ... 
#> OK
#> Processing sample number 11 ... 
#> OK
#> Processing sample number 10 ... 
#> OK
    nrow(gxset@groups) == 1096 ## the number of features before minFrac
#> [1] FALSE

    post.minFrac<-function(object, minFrac=0.5){
        ix.minFrac<-sapply(1:length(unique(sampclass(object))),
                           function(x, object, mf){
                               meta<-groups(object)
                               minFrac.idx<-numeric(length=nrow(meta))
                               idx<-which(
                                   meta[,levels(sampclass(object))[x]] >=
                                   mf*length(which(levels(sampclass(object))[x]
                                                   == sampclass(object)) ))
                               minFrac.idx[idx]<-1
                               return(minFrac.idx)
                           }, object, minFrac)
        ix.minFrac<-as.logical(apply(ix.minFrac, 1, sum))
        ix<-which(ix.minFrac == TRUE)
        return(ix)
    }

    ## using the above function we can get a post processing minFrac
    idx<-post.minFrac(gxset)

    gxset.post<-gxset ## copy the xcmsSet object
    gxset.post@groupidx<-gxset@groupidx[idx]
    gxset.post@groups<-gxset@groups[idx,]

    nrow(gxset.post@groups) == 465 ## number of features after minFrac
#> [1] FALSE