cosmiq (version 1.6.0)

create_datamatrix: Quantifying mz/RT intensities using peak locations from master map

Description

create_datamatrix identifies mz/RT peak boundaries in each raw file using the information from a master mass spectrum and master EIC. For each mz/RT location, the peak volume is calculated and stored in a report table.

Usage

create_datamatrix(xs,rxy)

Arguments

xs

xcmsSet object

rxy

matrix containing mz and RT boundaries for each detected peak

Details

With the information about their position in the combined datasets, each individual mz/RT feature is located in the raw data.

Examples

    ## Not run: 
#     cdfpath <- file.path(find.package("faahKO"), "cdf")
#     my.input.files <- dir(c(paste(cdfpath, "WT", sep='/'),
#         paste(cdfpath, "KO", sep='/')), full.names=TRUE)
# 
#     #
#     # create xcmsSet object
#     # todo
#     xs <- new("xcmsSet")
#     # consider only two files!!!
#     xs@filepaths <- my.input.files[1:2]
# 
#     class<-as.data.frame(c(rep("KO",2),rep("WT", 0)))
#     rownames(class)<-basename(my.input.files[1:2])
#     xs@phenoData<-class
# 
#     x<-combine_spectra(xs=xs, mzbin=0.25,
#         linear=TRUE, continuum=FALSE)
# 
#     plot(x$mz, x$intensity, type='l',
#         xlab='m/Z', ylab='ion intensity')
# 
#     xy <- peakdetection(x=x$mz, y=x$intensity, scales=1:10,
#     SNR.Th=0.0, SNR.area=20, mintr=0.5)
# 
#     id.peakcenter<-xy[,4]
# 
#     plot(x$mz, x$intensity, type='l',
#         xlim=c(440,460),
#             xlab='m/Z', ylab='ion intensity')
# 
#     points(x$mz[id.peakcenter], x$intensity[id.peakcenter],
#         col='red', type='h')
# 
# 
#     # create dummy object
#     xs@peaks<-matrix(c(rep(1, length(my.input.files) * 6),
#         1:length(my.input.files)), ncol=7)
# 
#     colnames(xs@peaks) <- c("mz", "mzmin", "mzmax", "rt",
#         "rtmin", "rtmax", "sample")
# 
#     xs<-xcms::retcor(xs, method="obiwarp", profStep=1,
#         distFunc="cor", center=1)
# 
# 
# 
#     eicmat<-eicmatrix(xs=xs, xy=xy, center=1)
# 
#     #  process a reduced mz range for a better package build performance
#     (eicmat.mz.range<-range(which(475 < xy[,1] & xy[,1] < 485)))
# 
#     eicmat.filter <- eicmat[eicmat.mz.range[1]:eicmat.mz.range[2],]
#     xy.filter <- xy[eicmat.mz.range[1]:eicmat.mz.range[2],]
# 
#     #
#     # determine the new range and plot the mz versus RT map
#     (rt.range <- range(as.double(colnames(eicmat.filter))))
#     (mz.range<-range(as.double(row.names(eicmat.filter))))
# 
#     image(log(t(eicmat.filter))/log(2), col=rev(gray(1:20/20)),
#         xlab='rt [in seconds]', ylab='m/z', axes=FALSE,
#         main='overlay of 12 samples using faahKO')
# 
#     axis(1, seq(0,1, length=6), round(seq(rt.range[1], rt.range[2], length=6)))
#     axis(2, seq(0,1, length=4), seq(mz.range[1], mz.range[2], length=4))
# 
#     #
#     # determine the chromatographic peaks
#     rxy<-retention_time(xs=xs, RTscales=c(1:10, seq(12,32, by=2)),
#         xy=xy.filter,
#         eicmatrix=eicmat.filter,
#         RTSNR.Th=120, RTSNR.area=20)
# 
#     rxy.rt <- (rxy[,4] - rt.range[1])/diff(rt.range)
#     rxy.mz <- (rxy[,1] - mz.range[1])/diff(mz.range)
# 
#     points(rxy.rt, rxy.mz, pch="X", lwd=2, col="red")
# 
# 
#     xs<-create_datamatrix(xs=xs, rxy=rxy)
# 
#     peaktable <- xcms::peakTable(xs)
# 
#     head(peaktable)
#     ## End(Not run)