,
Endre Laczko [ctb]| Title: | cosmiq - COmbining Single Masses Into Quantities |
|---|---|
| Description: | cosmiq is a tool for the preprocessing of liquid- or gas - chromatography mass spectrometry (LCMS/GCMS) data with a focus on metabolomics or lipidomics applications. To improve the detection of low abundant signals, cosmiq generates master maps of the mZ/RT space from all acquired runs before a peak detection algorithm is applied. The result is a more robust identification and quantification of low-intensity MS signals compared to conventional approaches where peak picking is performed in each LCMS/GCMS file separately. The cosmiq package builds on the xcmsSet object structure and can be therefore integrated well with the package xcms as an alternative preprocessing step. |
| Authors: | David Fischer [aut, cre],
Christian Panse [aut] ,
Endre Laczko [ctb] |
| Maintainer: | David Fischer <[email protected]> |
| License: | GPL-3 |
| Version: | 1.41.0 |
| Built: | 2024-10-31 03:51:38 UTC |
| Source: | https://github.com/bioc/cosmiq |
combine_spectra imports each raw file using xcmsRaw and
assigns each ion to a previously defined mass vector, which is created using
bin size parameter mzbin. This process is repeated for each raw file.
combine_spectra(xs, mzbin=0.003, linear=FALSE, continuum=FALSE)combine_spectra(xs, mzbin=0.003, linear=FALSE, continuum=FALSE)
xs |
xcmsSet object |
mzbin |
Bin size for the generation of mass vector |
linear |
logical. If TRUE, linear vector will be generated with
|
continuum |
boolean flag. default value is FALSE. |
This processing step calculates a combined mass spectrum. Mass spectra not only from all scans of a single LCMS run alone are combined but from all acquired datasets. As a result, signal to noise ratio increases for each additional LCMS run.
David Fischer 2013
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 xs <- new("xcmsSet") xs@filepaths <- my.input.files 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')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 xs <- new("xcmsSet") xs@filepaths <- my.input.files 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')
This is the main wrapper function for the package cosmiq. Every
processing step of cosmiq will be calculated during this function,
including mass spectra combination, detection of relevant masses, generation
and combination of extracted ion chromatograms, detection of chromatographic
peaks, Localisation and quantification of detected peaks.
combine_spectra imports each raw file using xcmsRaw and
assigns each ion to a previously defined mass vector, which is created using
bin size parameter mzbin. This process is repeated for each raw file.
cosmiq (files, RTinfo=TRUE, mzbin=0.003, center=0, linear=FALSE, profStep=1, retcorrect=TRUE, continuum=FALSE, rtcombine=c(0,0), scales=c(1:10), SNR.Th=10, SNR.area=20, RTscales=c(1:10, seq(12,32,2)), RTSNR.Th=20, RTSNR.area=20, mintr=0.5)cosmiq (files, RTinfo=TRUE, mzbin=0.003, center=0, linear=FALSE, profStep=1, retcorrect=TRUE, continuum=FALSE, rtcombine=c(0,0), scales=c(1:10), SNR.Th=10, SNR.area=20, RTscales=c(1:10, seq(12,32,2)), RTSNR.Th=20, RTSNR.area=20, mintr=0.5)
files |
String vector containing exact location of each file |
RTinfo |
Logical indicating whether retention time should be used or not. If FALSE, the quantitative information will be retrieved as a sum of ion intensities whithin a retention time window. This retention time window |
rtcombine |
Numerical, with two entries. If no retention time
information is to be used, |
mzbin |
Bin size for the generation of mass vector |
center |
Indicates number of file which will be used for retention time alignment. If zero, file will be automatically selected based on maximum summed ion intensity of the combined mass spectrum. |
linear |
logical. If TRUE, linear vector will be generated
with |
profStep |
step size (in m/z) to use for profile generation from the raw data files. |
retcorrect |
Logical, should retention time correction be
used? |
continuum |
Logical, is continuum data used? |
scales |
Peak Detection of Mass Peaks in the combined mass spectrum: scales for continuous wavelet transformation (CWT). |
SNR.Th |
Peak Detection of Mass Peaks in the combined mass spectrum: Signal to noise ratio threshold |
SNR.area |
Peak Detection of Mass Peaks in the combined mass
spectrum: Area around the peak for noise determination. Indicates
number of surrounding peaks on the first CWT scale. |
RTscales |
Peak Detection of Chromatographic peaks on the
combined extracted ion chromatogram: scales for continuous wavelet
transformation (CWT), see also the |
RTSNR.Th |
Peak Detection of Chromatographic peaks on the combined extracted ion chromatogram: Signal to noise ratio threshold |
RTSNR.area |
Peak Detection of Chromatographic peaks on the
combined extracted ion chromatogram: Area around the peak for noise
determination. Indicates number of surrounding peaks on the first
CWT scale. |
mintr |
Minimal peak width intensity treshold (in percentage), for which two overlapping peaks are considered as separated. The default calue is 0.5. |
Current data processing tools focus on a peak detection strategy where initially each LCMS run is treated independently from each other. Subsequent data processing for the alignment of different samples is then calculated on reduced peak tables. This function involves the merging of all LCMS datasets of a given experiment as a first step of raw data processing. The merged LCMS dataset contains an overlay and the sum of ion intensities from all LCMS runs. Peak detection is then performed only on this merged dataset and quantification of the signals in each sample is guided by the peak location in the merged data.
See also xcms::retcor.obiwarp and cosmiq::create_datamatrix which
executes each step of the cosmiq function.
For running examples please read the package vignette
or ?cosmiq::create_datamatrix.
Christan Panse, David Fischer 2014
## Not run: # the following lines consume 2m17.877s # on a Intel(R) Xeon(R) CPU X5650 @ 2.67GHz library(faahKO) cdfpath <- file.path(find.package("faahKO"), "cdf") my.input.files <- dir(c(paste(cdfpath, "WT", sep='/'), paste(cdfpath, "KO", sep='/')), full.names=TRUE) x <- cosmiq(files=my.input.files, mzbin=0.25, SNR.Th=0, linear=TRUE) image(t(x$eicmatrix^(1/4)), col=rev(gray(1:20/20)), xlab='rt', ylab='m/z', axes=FALSE ) head(xcms::peakTable(x$xcmsSet)) ## End(Not run)## Not run: # the following lines consume 2m17.877s # on a Intel(R) Xeon(R) CPU X5650 @ 2.67GHz library(faahKO) cdfpath <- file.path(find.package("faahKO"), "cdf") my.input.files <- dir(c(paste(cdfpath, "WT", sep='/'), paste(cdfpath, "KO", sep='/')), full.names=TRUE) x <- cosmiq(files=my.input.files, mzbin=0.25, SNR.Th=0, linear=TRUE) image(t(x$eicmatrix^(1/4)), col=rev(gray(1:20/20)), xlab='rt', ylab='m/z', axes=FALSE ) head(xcms::peakTable(x$xcmsSet)) ## End(Not run)
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.
create_datamatrix(xs,rxy)create_datamatrix(xs,rxy)
xs |
xcmsSet object |
rxy |
matrix containing mz and RT boundaries for each detected peak |
With the information about their position in the combined datasets, each individual mz/RT feature is located in the raw data.
David Fischer 2013
## 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)## 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)
for each selected mass window, eicmatrix calculates EICs of
every raw file and combines them together. It is recommended to
correct the retention time first using retcor.obiwarp
eicmatrix(xs, xy, center)eicmatrix(xs, xy, center)
xs |
xcmsSet object |
xy |
table including mz location parameters |
center |
file number which is used as a template for retention time correction |
For each detected mass, an extracted ion chromatogram (EIC) is calculated. In order to determine the elution time for each detected mass, the EICs of every mass are combined between all acquired runs.
Make sure that xcms-retention time correction (centwave) was applied to the dataset. The output will be a matrix of EIC's
David Fischer 2013
## Not run: # see package vignette section # 'Generation and combination of extracted ion chromatograms' xs<-create_datamatrix(xs=xs, rxy=rxy) ## End(Not run)## Not run: # see package vignette section # 'Generation and combination of extracted ion chromatograms' xs<-create_datamatrix(xs=xs, rxy=rxy) ## End(Not run)
peakdetection uses Continuous wavelet transformation
(CWT) to determine optimal peak location. A modified algorithm of Du
et al. (2006) is used to localize peak positions.
peakdetection(scales, y, x, SNR.Th, SNR.area, mintr)peakdetection(scales, y, x, SNR.Th, SNR.area, mintr)
scales |
vector with the scales to perform CWT |
y |
vector of ion intensities |
x |
vector of mz bins |
SNR.Th |
Signal-to-noise threshold |
SNR.area |
Window size for noise estimation |
mintr |
Minimal peak width intensity treshold (in percentage), for which two overlapping peaks are considered as separated. default is set to 0.5. |
A peak detection algorithm based on continuous wavelet transformation (CWT) is used for this step (modified from Du et al., 2006). Peak detection based on CWT has the advantage that a sliding scale of wavelets instead of a single filter function with fixed wavelength is used. This allows for a flexible and automatic approximation of the peak width. As a result it is possible to locate both narrow and broad peaks within a given dynamic range.
David Fischer 2013
Du, P., Kibbe, W. A., & Lin, S. M. (2006). Improved peak detection in mass spectrum by incorporating continuous wavelet transform-based pattern matching. Bioinformatics, 22(17), 2059-2065. doi:10.1093/bioinformatics/btl355
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 xs <- new("xcmsSet") xs@filepaths <- my.input.files op<-par(mfrow=c(3,1)) x<-combine_spectra(xs=xs, mzbin=0.25, linear=TRUE, continuum=FALSE) plot(x$mz, x$intensity, type='h', main='original', xlab='m/Z', ylab='ion intensity') xy <- peakdetection(x=x$mz, y=x$intensity, scales=1:10, SNR.Th=1.0, SNR.area=20, mintr=0.5) id.peakcenter<-xy[,4] plot(x$mz[id.peakcenter], x$intensity[id.peakcenter], type='h', main='filtered') plot(x$mz, x$intensity, type='l', xlim=c(400,450), main='zoom', log='y', xlab='m/Z', ylab='ion intensity (log scale)') points(x$mz[id.peakcenter], x$intensity[id.peakcenter],col='red', type='h')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 xs <- new("xcmsSet") xs@filepaths <- my.input.files op<-par(mfrow=c(3,1)) x<-combine_spectra(xs=xs, mzbin=0.25, linear=TRUE, continuum=FALSE) plot(x$mz, x$intensity, type='h', main='original', xlab='m/Z', ylab='ion intensity') xy <- peakdetection(x=x$mz, y=x$intensity, scales=1:10, SNR.Th=1.0, SNR.area=20, mintr=0.5) id.peakcenter<-xy[,4] plot(x$mz[id.peakcenter], x$intensity[id.peakcenter], type='h', main='filtered') plot(x$mz, x$intensity, type='l', xlim=c(400,450), main='zoom', log='y', xlab='m/Z', ylab='ion intensity (log scale)') points(x$mz[id.peakcenter], x$intensity[id.peakcenter],col='red', type='h')
quantify_combined calculates a data table of mz intensities
from a sum of ions within a selected mz window and from all MS scans.
quantify_combined(xs,xy, rtcombine)quantify_combined(xs,xy, rtcombine)
xs |
xcmsSet object |
xy |
table including mz location parameters |
rtcombine |
Numerical, with two entries. If no retention
time information is to be used, |
This function is only used, when no retention time information should be used, for example with direct infusion experiments.
David Fischer 2013
## Not run: # see package vignette ## End(Not run)## Not run: # see package vignette ## End(Not run)
For each EIC in the EIC matrix, retention_time localizes
chromatographic peaks using peakdetection.
retention_time(xy, xs, eicmatrix, RTscales, RTSNR.Th, RTSNR.area, mintr)retention_time(xy, xs, eicmatrix, RTscales, RTSNR.Th, RTSNR.area, mintr)
xy |
table including mz location parameters |
xs |
xcmsSet object |
eicmatrix |
Matrix containing extracted ion chromatograms (EICs) |
RTscales |
Peak Detection of Chromatographic peaks on the
combined extracted ion chromatogram: scales for continuous wavelet
transformation (CWT), see also the |
RTSNR.Th |
Peak Detection of Chromatographic peaks on the combined extracted ion chromatogram: Signal to noise ratio threshold |
RTSNR.area |
Peak Detection of Chromatographic peaks on the
combined extracted ion chromatogram: Area around the peak for noise
determination. Indicates number of surrounding peaks on the first
CWT scale. |
mintr |
Minimal peak width intensity treshold (in
percentage), for which two overlapping peaks are considered as
separated ( |
Based on the combined extracted ion chromatograms, there is another peak detection step to be performed. The same algorithm as described for the peak picking of mass signals (continuous wavelet transformation) is also used for peak picking in the retention time domain.
David Fischer 2013
## Not run: # see package vignette ## End(Not run)## Not run: # see package vignette ## End(Not run)