group.nearest {xcms} | R Documentation

## Group peaks from different samples together

### Description

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`.

### 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.

### Methods

object = "xcmsSet"

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

### See Also

`xcmsSet-class`, `group.density` and `group.mzClust`

### Examples

    ## Not run: 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")
        nrow(gxset@groups) == 1096 ## the number of features before minFrac

        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

    ## End(Not run)

[Package xcms version 3.9.1 Index]