R/peaksDataset.R
In rromoli/flagme: Analysis of Metabolomics GC/MS Data
Defines functions
addChromaTOFPeaks
addAMDISPeaks
peaksDataset
Documented in
addAMDISPeaks
addChromaTOFPeaks
peaksDataset
#' Data Structure for raw GCMS data and peak detection results
#'
#' Store the raw data and optionally, information regarding signal peaks for a
#' number of GCMS runs
#'
#'
#' peaksDataset is a hold-all data structure of the raw and peak detection
#' data.
#'
#' @title peaksDataset-show
#' @param object an object of class \code{\link{peakDataset}}
#' @return \code{peaksDataset} object
#' @author Mark Robinson
#' @export
#' @noRd
setMethod("show","peaksDataset",
function(object) {
cat("An object of class \"",class(object),"\"\n",sep="")
cat(length(object@rawdata), "samples:",names(object@rawdata),"\n",sep=" ")
cat(length(object@mz), "m/z bins - range: (",range(object@mz),")\n",sep=" ")
cat("scans:",sapply(object@rawdata,ncol),"\n",sep=" ")
cat("peaks:",sapply(object@peaksdata,ncol),"\n",sep=" ")
})
#' Data Structure for raw GCMS data and peak detection results
#'
#' Store the raw data and optionally, information regarding signal peaks for a
#' number of GCMS runs
#'
#'
#' peaksDataset is a hold-all data structure of the raw and peak detection
#' data.
#'
#' @aliases peaksDataset-class peaksDataset-show peaksDataset-plot peaksDataset
#' show,peaksDataset-method plot,peaksDataset-method
#' plot,peaksDataset,ANY-method
#' @param fns character vector, filenames of raw data in CDF format.
#' @param verbose logical, if \code{TRUE} then iteration progress information
#' is output.
#' @param mz vector giving bins of raw data table.
#' @param rtDivide number giving the amount to divide the retention times by.
#' @param rtrange retention time range to limit data to (must be \code{numeric}
#' vector of length 2)
#' @return
#'
#' \code{peaksDataset} object
#' @author Mark Robinson
#' @references
#'
#' Mark D Robinson (2008). Methods for the analysis of gas chromatography -
#' mass spectrometry data \emph{PhD dissertation} University of Melbourne.
#' @keywords classes
#' @examples
#'
#' 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
#' pd<-peaksDataset(cdfFiles[1:2],mz=seq(50,550),rtrange=c(7.5,8.5))
#' show(pd)
#'
#' @export peaksDataset
peaksDataset <- function(fns=dir(, "[Cc][Dd][Ff]"), verbose=TRUE,
mz=seq(50, 550), rtDivide=60, rtrange=NULL){
rawdata <- vector("list", length(fns))
rawrt <- vector("list", length(fns))
for(i in 1:length(fns)){
if(verbose)
cat(" Reading ", fns[i], "\n")
a <- xcmsRaw(fns[i])
D1 <- cbind.data.frame(mz=a@mzrange[1]:a@mzrange[2],
a@env$profile)
D2 <- data.frame(mz=mz)
if(a@mzrange[1]-range(mz)[1] < 3 & a@mzrange[2]-range(mz)[2] <
3){
mrg <- merge(D1, D2, by='mz', all=TRUE)
}else{
mrg <- merge(D1, D2, by='mz', all=FALSE)
}# not a very clean approach. The problem is in the raw m/z;
# the possible cause is the centroid approx. diring the
# acquisition of the cgromatogram se all=TRUE funziona con
# mzrange= 50-550; se all=FALSE funziona con mzrange= 100-400
mrg[is.na(mrg)] <- 0
rownames(mrg) <- 1:nrow(mrg)
colnames(mrg) <- 1:ncol(mrg)
a@env$profile <- data.matrix(mrg)
a@mzrange <- range(mz) ## allows mzrange param without errors
if(is.null(mz))
mz <- seq(a@mzrange[1], a@mzrange[2])
this.mz <- seq(a@mzrange[1], a@mzrange[2])
rawrt[[i]] <- a@scantime/rtDivide
nc <- length(rawrt[[i]])
if(length(rtrange) == 2){
w <- which(rawrt[[i]] >= rtrange[1] & rawrt[[i]] <= rtrange[2])
rawrt[[i]] <- rawrt[[i]][w]
}else{
w <- seq(1, nc)
}
rawdata[[i]] <- a@env$profile[, w]
rm(a)
if(length(this.mz) != length(mz)){
d <- matrix(0, nrow=length(mz), ncol=length(w))
d[match(this.mz, mz), ] <- rawdata[[i]]
rawdata[[i]] <- d
}
}
gc()
nm <- lapply(fns, function(u){
sp <- strsplit(u, split="/")[[1]]
sp[length(sp)]
})
nm <- sub(".CDF", "", nm)
names(rawdata) <- names(rawrt) <- nm
new("peaksDataset", rawdata=rawdata,
rawrt=rawrt, mz=mz, files=fns)
## new("peaksDataset",
## rawdata = lapply(rawdata, function(x){ as.matrix.csc(x) }),
## rawrt = rawrt,
## mz = mz,
## files = fns)
}
#' Plotting functions for GCMS data objects
#'
#' Store the raw data and optionally, information regarding signal peaks for a
#' number of GCMS runs
#'
#' Each TIC is scale to the maximum value (as specified by the
#' \code{how.near} and \code{max.near} values). The many parameters gives
#' considerable flexibility of how the TICs can be visualized.
#'
##' @param object a \code{peaksDataset} object.
##' @param runs set of run indices to plot
##' @param mzind set of mass-to-charge indices to sum over (default, all)
##' @param mind matrix of aligned indices
##' @param plotSampleLabels logical, whether to display sample labels
##' @param calcGlobalMax logical, whether to calculate an overall maximum
##' for scaling
##' @param peakCex character expansion factor for peak labels
##' @param plotPeaks logical, whether to plot hashes for each peak
##' @param plotPeakBoundaries logical, whether to display peak boundaries
##' @param plotPeakLabels logical, whether to display peak labels
##' @param plotMergedPeakLabels logical, whether to display 'merged' peak
##' labels
##' @param mlwd line width of lines indicating the alignment
##' @param usePeaks logical, whether to plot alignment of peaks
##' (otherwise, scans)
##' @param plotAcrossRuns logical, whether to plot across peaks when
##' unmatched peak is given
##' @param overlap logical, whether to plot TIC/XICs overlapping
##' @param rtrange vector of length 2 giving start and end of the X-axis
##' @param cols vector of colours (same length as the length of runs)
##' @param thin when \code{usePeaks=FALSE}, plot the alignment lines
##' every \code{thin} values
##' @param max.near where to look for maximum
##' @param how.near how far away from \code{max.near} to look
##' @param scale.up a constant factor to scale the TICs
##' @param ... further arguments passed to the \code{plot}
##' @return plot the chromatograms
##' @author Mark Robinson
##' @seealso \code{\link{peaksDataset}}
##' @references Mark D Robinson (2008). Methods for the analysis of gas
##' chromatography - mass spectrometry data \emph{PhD dissertation} University
##' of Melbourne.
##' @keywords classes
##' @importFrom graphics lines axis plot points text par
##' @examples
##'
##' 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
##' pd <- peaksDataset(cdfFiles[1:3], mz=seq(50,550), rtrange=c(7.5,8.5))
##'
##' ## image plot
##' plotChrom(pd, rtrange = c(7.5,8.5), plotPeaks = TRUE,
##' plotPeakLabels = TRUE)
##' @export
setMethod("plotChrom","peaksDataset",
function(object, runs = 1:length(object@rawdata),
mzind = 1:nrow(object@rawdata[[1]]),
mind = NULL, plotSampleLabels = TRUE,
calcGlobalMax = FALSE, peakCex = 0.8,
plotPeaks = TRUE, plotPeakBoundaries = FALSE,
plotPeakLabels = FALSE,
plotMergedPeakLabels = TRUE, mlwd = 3,
usePeaks = TRUE, plotAcrossRuns = FALSE,
overlap = F, rtrange = NULL, cols = NULL, thin = 1,
max.near = median(object@rawrt[[1]]), how.near = 50,
scale.up = 1, ...){
## only specific one of ma or runs
if(is.null(rtrange))
rtrange <- c(min(object@rawrt[[1]]), max(object@rawrt[[1]]))
if(is.factor(cols))
cols <- c("black", "green", "blue", "red", "grey", "orange")[cols]
if(is.null(cols))
cols <- rep(c("black", "green", "blue", "red"),
each = length(object@rawdata)/4)
if(length(cols) != length(runs))
cols <- rep("black", length(runs))
if(usePeaks){
datalist <- object@peaksdata
rtlist <- object@peaksrt
}else{
datalist <- object@rawdata
rtlist <- object@rawrt
}
if(calcGlobalMax){
mx <- 0
for(i in 1:length(runs)) {
ind <- runs[i]
mxind <- which(abs(object@rawrt[[ind]]-max.near)<how.near)
b <- colSums(matrix(object@rawdata[[ind]][mzind,],
nrow = length(mzind)))
if(mx < max(b[mxind]))
mx <- max(b[mxind])
}
}
if(length(object@peaksdata) != length(object@rawdata))
plotPeaks <- FALSE
for(i in 1:length(runs)){
ind <- runs[i]
b <- colSums(matrix(object@rawdata[[ind]][mzind,],
nrow = length(mzind)))
if(!calcGlobalMax){
mxind <- which(abs(object@rawrt[[ind]]-max.near) < how.near)
mx <- max(b[mxind])
}
b <- b / mx*scale.up
if(!overlap){
if(i == 1){
plot(object@rawrt[[ind]], b, type = "l", xlab = "Retention Time",
ylab = "", xlim = rtrange,
ylim = c(-.5,length(runs)), col = cols[i], xaxs = "i",
axes = FALSE, ...)
axis(1)
}else{
lines(object@rawrt[[ind]], b+i-1, col = cols[i], ...)
}
if(plotSampleLabels)
text(rtrange[1]+.1*diff(rtrange), i-.5, names(object@rawdata)[ind],
col = "black")
} else {
if(i == 1) {
plot(object@rawrt[[ind]], b, type = "l", xlab = "Retention Time",
ylab = "", xlim = rtrange,
ylim = c(0,1), col = cols[i], xaxs = "i", axes = FALSE)
axis(1)
} else {
lines(object@rawrt[[ind]], b, col = cols[i])
}
}
if(plotPeaks & !overlap){
rt <- rtlist[[ind]]; d <- min(diff(rt))*.4
for(j in 1:length(rt)) {
if(rt[j] > rtrange[1] & rt[j] < rtrange[2]) {
if (plotPeakBoundaries) {
st <- object@rawrt[[ind]][ object@peaksind.start[[ind]][j] ]
en <- object@rawrt[[ind]][ object@peaksind.end[[ind]][j] ]
## cat(ind,st,en,rt[j],"\n")
if(j%%2==0)
{
lines(c(st,st,rt[j],rt[j],rt[j],en,en),
c(i-1+.025,i-1,i-1,i-1+.3,i-1,i-1,i-1+.025), col = "blue")
}
else
{
lines(c(st,st,rt[j],rt[j],rt[j],en,en),
c(i-1+.025,i-1,i-1,i-1+.3,i-1,i-1,i-1+.025)-.025, col = "red")
## lines(c(rt[j],rt[j]),c(i-1,i-1+.3))
}
} else {
lines(c(rt[j]-d,rt[j]+d), c(i-1,i-1))
lines(c(rt[j],rt[j]), c(i-1,i-1+.3))
}
}
}
if(plotPeakLabels)
text(rt, i-1+.15, labels = 1:length(rt), adj = 0, cex = peakCex)
}
}
if(!is.null(mind) & !overlap) {
rws <- as.integer(seq(1, nrow(mind), by = thin))
for(i in rws) {
if( !is.na(mind[i,1]) & plotMergedPeakLabels )
text(rtlist[[runs[1]]][mind[i,1]], 0, srt = 90, i, col = "blue",
pos = 1, cex = peakCex*.8, adj = 1)
for(j in 1:(ncol(mind)-1)) {
ind1 <- mind[i,j]
ind2 <- mind[i,j+1]
if(!is.na(ind1) & !is.na(ind2)) {
rt1 <- rtlist[[runs[j]]][ind1]
rt2 <- rtlist[[runs[j+1]]][ind2]
## cat(ind1,ind2,runs[j],runs[j+1],rt1,rtrange[1],rt2,rtrange[2],"\n")
if((rt1 > rtrange[1] | rt2 > rtrange[1]) &
(rt1 < rtrange[2] | rt2 < rtrange[2])){
if(j > 1)
lines(c(rt1, rt2), c(j+.4-1, j+.95-1), lwd = mlwd, lty = 1,
col = "darkgrey")
else
lines(c(rt1,rt2), c(j+.4-1,j+.95-1), lwd = mlwd, lty = 1,
col = "darkgrey")
}
}
}
if (plotAcrossRuns) {
if (ncol(mind) <= 2) next
for(j in 1:(ncol(mind)-2)) {
ind1 <- mind[i,j]
ind2 <- mind[i,j+1]
ind3 <- mind[i,j+2]
if(!is.na(ind1) & is.na(ind2) & !is.na(ind3)) {
rt1 <- rtlist[[runs[j]]][ind1]
rt3 <- rtlist[[runs[j+2]]][ind3]
## cat(rt1,rtrange[1],rt3,rtrange[2],"\n")
if( (rt1 > rtrange[1] | rt3 > rtrange[1]) &
(rt1 < rtrange[2] | rt3 < rtrange[2]))
lines(c(rt1,rt3), c(j+.4-1,j+.95), lwd = mlwd, lty = 1,
col = "green")
}
}
if (ncol(mind) <= 3) next
for(j in 1:(ncol(mind)-3)) {
ind1 <- mind[i,j]
ind2 <- mind[i,j+1]
ind3 <- mind[i,j+2]
ind4 <- mind[i,j+3]
if(!is.na(ind1) & is.na(ind2) & is.na(ind3) & !is.na(ind4)) {
rt1<-rtlist[[runs[j]]][ind1]
rt4<-rtlist[[runs[j+3]]][ind4]
## cat(rt1,rtrange[1],rt4,rtrange[2],"\n")
if( (rt1 > rtrange[1] | rt4 > rtrange[1]) &
(rt1 < rtrange[2] | rt4 < rtrange[2]))
lines(c(rt1,rt4), c(j+.4-1,j+1.95), lwd = mlwd, lty = 1,
col = "red")
}
}
}
}
}
})
#' Add AMDIS peak detection results
#'
#' Reads ASCII ELU-format files (output from AMDIS) and attaches them to an
#' already created \code{peaksDataset} object
#'
#'
#' Repeated calls to \code{parseELU} to add peak detection results to the
#' original \code{peaksDataset} object.
#'
#' @param object a \code{peaksDataset} object.
#' @param fns character vector of same length as \code{object@rawdata} (user
#' ensures the order matches)
#' @param verbose whether to give verbose output, default \code{TRUE}
#' @param ... arguments passed on to \code{parseELU}
#' @return
#'
#' \code{peaksDataset} object
#' @author Mark Robinson
#' @seealso \code{\link{parseELU}}, \code{\link{peaksDataset}}
#' @references
#'
#' Mark D Robinson (2008). Methods for the analysis of gas chromatography -
#' mass spectrometry data \emph{PhD dissertation} University of Melbourne.
#' @keywords manip
#' @examples
#'
#' # need access to CDF (raw data) and ELU files
#' require(gcspikelite)
#' gcmsPath<-paste(find.package("gcspikelite"),"data",sep="/")
#'
#' # full paths to file names
#' cdfFiles<-dir(gcmsPath,"CDF",full=TRUE)
#' eluFiles<-dir(gcmsPath,"ELU",full=TRUE)
#'
#' # create a 'peaksDataset' object and add AMDIS peaks to it
#' pd<-peaksDataset(cdfFiles[1],mz=seq(50,550),rtrange=c(7.5,8.5))
#' pd<-addAMDISPeaks(pd,eluFiles[1])
#'
#' @export addAMDISPeaks
addAMDISPeaks<-function(object,fns=dir(,"[Eu][Ll][Uu]"),verbose=TRUE,...) {
if(length(fns)!=length(object@rawdata))
stop("Number of files must be the same as the number of runs (and must match).")
mn<-1e6; mx<--1
for(i in 1:length(object@rawrt)) {
rng<-range(object@rawrt[[i]])
if(mn > rng[1]) mn<-rng[1]
if(mx < rng[2]) mx<-rng[2]
}
if (verbose)
cat("Reading retention time range:",c(mn,mx),"\n")
for(i in 1:length(object@files)) {
if (verbose)
cat("Reading",fns[i],"...")
csin<-parseELU(fns[i],mz=object@mz,rtrange=c(mn,mx),...)
thisind<-rep(NA,nrow(csin$tab))
thisind.st<-rep(NA,nrow(csin$tab))
thisind.en<-rep(NA,nrow(csin$tab))
thisrt<-rep(NA,nrow(csin$tab))
thisdata<-matrix(0,nrow=length(object@mz),ncol=nrow(csin$tab))
for(j in 1:length(thisind)) {
thisind[j]<-which.min(abs(object@rawrt[[i]]-csin$tab[j,2]))
thisind.st[j]<-csin$tab[j,3]
thisind.en[j]<-csin$tab[j,4]
thisrt[j]<-object@rawrt[[i]][thisind[j]]
take<-which(csin$peaks[,j]>0)
#thisdata[take,j]<-sqrt(object@rawdata[[i]][take,thisind[j]])
thisdata[take,j]<-object@rawdata[[i]][take,thisind[j]]
}
if (verbose)
cat(" Done.\n")
object@peaksind[[i]]<-thisind
thisind.st[thisind.st==0]<-1
object@peaksind.start[[i]]<-thisind.st
object@peaksind.end[[i]]<-thisind.en
object@peaksrt[[i]]<-thisrt
object@peaksdata[[i]]<-thisdata
}
names(object@peaksdata)<-names(object@peaksrt)<-names(object@peaksind)<-names(object@rawdata)
new("peaksDataset",object)
}
#' Add ChromaTOF peak detection results
#'
#' Reads ASCII tab-delimited format files (output from ChromaTOF) and attaches
#' them to an already created \code{peaksDataset} object
#'
#'
#' Repeated calls to \code{parseChromaTOF} to add peak detection results to the
#' original \code{peaksDataset} object.
#'
#' @param object a \code{peaksDataset} object.
#' @param fns character vector of same length as \code{object@rawdata} (user
#' ensures the order matches)
#' @param rtDivide number giving the amount to divide the retention times by.
#' @param verbose whether to give verbose output, default \code{TRUE}
#' @param ... arguments passed on to \code{parseChromaTOF}
#' @return
#'
#' \code{peaksDataset} object
#' @author Mark Robinson
#' @seealso \code{\link{parseChromaTOF}}, \code{\link{peaksDataset}}
#' @references
#'
#' Mark D Robinson (2008). Methods for the analysis of gas chromatography -
#' mass spectrometry data \emph{PhD dissertation} University of Melbourne.
#' @keywords manip
#' @examples
#'
#' # need access to CDF (raw data) and ChromaTOF files
#' require(gcspikelite)
#' gcmsPath<-paste(find.package("gcspikelite"),"data",sep="/")
#'
#' # full paths to file names
#' cdfFiles<-dir(gcmsPath,"CDF",full=TRUE)
#' # [not run] cTofFiles<-dir(gcmsPath,"txt",full=TRUE)
#'
#' # create a 'peaksDataset' object and add ChromaTOF peaks to it
#' pd<-peaksDataset(cdfFiles[1],mz=seq(50,550),rtrange=c(7.5,8.5))
#' # [not run] pd<-addChromTOFPeaks(pd,...)
#'
#' @export addChromaTOFPeaks
addChromaTOFPeaks<-function(object,fns=dir(,"[Tt][Xx][Tx]"),rtDivide=60,verbose=TRUE,...) {
if(length(fns)!=length(object@rawdata))
stop("Number of files must be the same as the number of runs (and must match).")
mn<-1e6; mx<--1
for(i in 1:length(object@rawrt)) {
rng<-range(object@rawrt[[i]])
if(mn > rng[1]) mn<-rng[1]
if(mx < rng[2]) mx<-rng[2]
}
if (verbose)
cat("Reading retention time range:",c(mn,mx),"\n")
for(i in 1:length(object@files)) {
if (verbose)
cat("Reading",fns[i],"...")
csin<-parseChromaTOF(fns[i],mz=object@mz,rtrange=c(mn,mx),rtDivide=rtDivide,...)
thisrt<-csin$tab$rt
thisind<-rep(NA,length(thisrt))
thisdata<-matrix(0,nrow=length(object@mz),ncol=nrow(csin$tab))
for(j in 1:length(thisind)) {
thisind[j]<-which.min(abs(object@rawrt[[i]]-csin$tab$rt[j]/rtDivide))
thisrt[j]<-object@rawrt[[i]][thisind[j]]
take<-which(csin$peaks[,j]>0)
#thisdata[take,j]<-sqrt(object@rawdata[[i]][take,thisind[j]])
thisdata[take,j]<-object@rawdata[[i]][take,thisind[j]]
}
if (verbose)
cat(" Done.\n")
object@peaksind[[i]]<-thisind
object@peaksrt[[i]]<-thisrt
object@peaksdata[[i]]<-thisdata
}
names(object@peaksdata)<-names(object@peaksrt)<-names(object@peaksind)<-names(object@rawdata)
new("peaksDataset",object)
}
#' Plot of images of GCMS data
#'
#' Image plots (i.e. 2D heatmaps) of raw GCMS profile data
#'
#' For \code{peakDataset} objects, each TIC is scale to the maximum value (as
#' specified by the \code{how.near} and \code{max.near} values). The many
#' parameters gives considerable flexibility of how the TICs can be visualized.
#'
#' For \code{peakAlignment} objects, the similarity matrix is plotted and
#' optionally, the set of matching peaks. \code{clusterAlignment} objects are
#' just a collection of all pairwise \code{peakAlignment} objects.
#'
#' @name plotImage
#' @aliases plotImage plotImage,peaksDataset-method
#' @param object a \code{peaksDataset} object
#' @param run index of the run to plot an image for
#' @param rtrange vector of length 2 giving start and end of the X-axis
#' (retention time)
#' @param main main title (auto-constructed if not specified)
#' @param mzrange vector of length 2 giving start and end of the Y-axis
#' (mass-to-charge ratio)
#' @param SCALE function called to scale the data (default: \code{log2})
#' @param ... further arguments passed to the \code{image} command
#' @author Mark Robinson
#' @seealso \code{\link{plot}}, \code{\link{peaksDataset}}
#' @references
#'
#' Mark D Robinson (2008). Methods for the analysis of gas chromatography -
#' mass spectrometry data \emph{PhD dissertation} University of Melbourne.
#' @keywords classes
#' @examples
#'
#' 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
#' pd<-peaksDataset(cdfFiles[1],mz=seq(50,550),rtrange=c(7.5,8.5))
#'
#' # image plot
#' plotImage(pd,run=1,rtrange=c(7.5,8.5),main="")
#' @importFrom gplots colorpanel
#' @importFrom graphics image
#' @export
setMethod("plotImage","peaksDataset",
function(object, run = 1, rtrange = c(11,13), main = NULL, mzrange = c(50,200), SCALE = log2, ...){
mz <- object@mz
for(i in run){
rt <- object@rawrt[[i]]
d <- object@rawdata[[i]]
cols <- colorpanel(50, "green", "blue", "red")
rind <- which(rt > rtrange[1]&rt<rtrange[2])
mind <- which(mz>mzrange[1]&mz<mzrange[2])
ff <- t(d[mind,rind])
ff[ff == 0]<-min(ff[ff>0])
if(is.null(main))
tit <- object@files[i]
else
tit <- main
## cat("min=",min(SCALE(ff),na.rm=TRUE)," max=",max(SCALE(ff),na.rm=T),"\n")
image(rt[rind],mz[mind], SCALE(ff), col = cols, xlab = "retention time",
ylab = "m/z", xlim = rtrange, main = tit, ...)
#abline(h=c(72.5,73.5,146.5,147.5),col="white",lty=3)
#if (!is.null(pts)) {
# points(pts[,1],pts[,2],pch=19)
# matplot(t(pts[,8:9]),t(pts[,c(2,2)]),type="l",col="black",lty=1,add=TRUE)
#}
}
return(invisible(ff))
})
rromoli/flagme documentation built on Feb. 10, 2023, 12:59 a.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Defines functions addChromaTOFPeaks addAMDISPeaks peaksDataset
Documented in addAMDISPeaks addChromaTOFPeaks peaksDataset
#' Data Structure for raw GCMS data and peak detection results
#'
#' Store the raw data and optionally, information regarding signal peaks for a
#' number of GCMS runs
#'
#'
#' peaksDataset is a hold-all data structure of the raw and peak detection
#' data.
#'
#' @title peaksDataset-show
#' @param object an object of class \code{\link{peakDataset}}
#' @return \code{peaksDataset} object
#' @author Mark Robinson
#' @export
#' @noRd
setMethod("show","peaksDataset",
function(object) {
cat("An object of class \"",class(object),"\"\n",sep="")
cat(length(object@rawdata), "samples:",names(object@rawdata),"\n",sep=" ")
cat(length(object@mz), "m/z bins - range: (",range(object@mz),")\n",sep=" ")
cat("scans:",sapply(object@rawdata,ncol),"\n",sep=" ")
cat("peaks:",sapply(object@peaksdata,ncol),"\n",sep=" ")
})
#' Data Structure for raw GCMS data and peak detection results
#'
#' Store the raw data and optionally, information regarding signal peaks for a
#' number of GCMS runs
#'
#'
#' peaksDataset is a hold-all data structure of the raw and peak detection
#' data.
#'
#' @aliases peaksDataset-class peaksDataset-show peaksDataset-plot peaksDataset
#' show,peaksDataset-method plot,peaksDataset-method
#' plot,peaksDataset,ANY-method
#' @param fns character vector, filenames of raw data in CDF format.
#' @param verbose logical, if \code{TRUE} then iteration progress information
#' is output.
#' @param mz vector giving bins of raw data table.
#' @param rtDivide number giving the amount to divide the retention times by.
#' @param rtrange retention time range to limit data to (must be \code{numeric}
#' vector of length 2)
#' @return
#'
#' \code{peaksDataset} object
#' @author Mark Robinson
#' @references
#'
#' Mark D Robinson (2008). Methods for the analysis of gas chromatography -
#' mass spectrometry data \emph{PhD dissertation} University of Melbourne.
#' @keywords classes
#' @examples
#'
#' 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
#' pd<-peaksDataset(cdfFiles[1:2],mz=seq(50,550),rtrange=c(7.5,8.5))
#' show(pd)
#'
#' @export peaksDataset
peaksDataset <- function(fns=dir(, "[Cc][Dd][Ff]"), verbose=TRUE,
mz=seq(50, 550), rtDivide=60, rtrange=NULL){
rawdata <- vector("list", length(fns))
rawrt <- vector("list", length(fns))
for(i in 1:length(fns)){
if(verbose)
cat(" Reading ", fns[i], "\n")
a <- xcmsRaw(fns[i])
D1 <- cbind.data.frame(mz=a@mzrange[1]:a@mzrange[2],
a@env$profile)
D2 <- data.frame(mz=mz)
if(a@mzrange[1]-range(mz)[1] < 3 & a@mzrange[2]-range(mz)[2] <
3){
mrg <- merge(D1, D2, by='mz', all=TRUE)
}else{
mrg <- merge(D1, D2, by='mz', all=FALSE)
}# not a very clean approach. The problem is in the raw m/z;
# the possible cause is the centroid approx. diring the
# acquisition of the cgromatogram se all=TRUE funziona con
# mzrange= 50-550; se all=FALSE funziona con mzrange= 100-400
mrg[is.na(mrg)] <- 0
rownames(mrg) <- 1:nrow(mrg)
colnames(mrg) <- 1:ncol(mrg)
a@env$profile <- data.matrix(mrg)
a@mzrange <- range(mz) ## allows mzrange param without errors
if(is.null(mz))
mz <- seq(a@mzrange[1], a@mzrange[2])
this.mz <- seq(a@mzrange[1], a@mzrange[2])
rawrt[[i]] <- a@scantime/rtDivide
nc <- length(rawrt[[i]])
if(length(rtrange) == 2){
w <- which(rawrt[[i]] >= rtrange[1] & rawrt[[i]] <= rtrange[2])
rawrt[[i]] <- rawrt[[i]][w]
}else{
w <- seq(1, nc)
}
rawdata[[i]] <- a@env$profile[, w]
rm(a)
if(length(this.mz) != length(mz)){
d <- matrix(0, nrow=length(mz), ncol=length(w))
d[match(this.mz, mz), ] <- rawdata[[i]]
rawdata[[i]] <- d
}
}
gc()
nm <- lapply(fns, function(u){
sp <- strsplit(u, split="/")[[1]]
sp[length(sp)]
})
nm <- sub(".CDF", "", nm)
names(rawdata) <- names(rawrt) <- nm
new("peaksDataset", rawdata=rawdata,
rawrt=rawrt, mz=mz, files=fns)
## new("peaksDataset",
## rawdata = lapply(rawdata, function(x){ as.matrix.csc(x) }),
## rawrt = rawrt,
## mz = mz,
## files = fns)
}
#' Plotting functions for GCMS data objects
#'
#' Store the raw data and optionally, information regarding signal peaks for a
#' number of GCMS runs
#'
#' Each TIC is scale to the maximum value (as specified by the
#' \code{how.near} and \code{max.near} values). The many parameters gives
#' considerable flexibility of how the TICs can be visualized.
#'
##' @param object a \code{peaksDataset} object.
##' @param runs set of run indices to plot
##' @param mzind set of mass-to-charge indices to sum over (default, all)
##' @param mind matrix of aligned indices
##' @param plotSampleLabels logical, whether to display sample labels
##' @param calcGlobalMax logical, whether to calculate an overall maximum
##' for scaling
##' @param peakCex character expansion factor for peak labels
##' @param plotPeaks logical, whether to plot hashes for each peak
##' @param plotPeakBoundaries logical, whether to display peak boundaries
##' @param plotPeakLabels logical, whether to display peak labels
##' @param plotMergedPeakLabels logical, whether to display 'merged' peak
##' labels
##' @param mlwd line width of lines indicating the alignment
##' @param usePeaks logical, whether to plot alignment of peaks
##' (otherwise, scans)
##' @param plotAcrossRuns logical, whether to plot across peaks when
##' unmatched peak is given
##' @param overlap logical, whether to plot TIC/XICs overlapping
##' @param rtrange vector of length 2 giving start and end of the X-axis
##' @param cols vector of colours (same length as the length of runs)
##' @param thin when \code{usePeaks=FALSE}, plot the alignment lines
##' every \code{thin} values
##' @param max.near where to look for maximum
##' @param how.near how far away from \code{max.near} to look
##' @param scale.up a constant factor to scale the TICs
##' @param ... further arguments passed to the \code{plot}
##' @return plot the chromatograms
##' @author Mark Robinson
##' @seealso \code{\link{peaksDataset}}
##' @references Mark D Robinson (2008). Methods for the analysis of gas
##' chromatography - mass spectrometry data \emph{PhD dissertation} University
##' of Melbourne.
##' @keywords classes
##' @importFrom graphics lines axis plot points text par
##' @examples
##'
##' 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
##' pd <- peaksDataset(cdfFiles[1:3], mz=seq(50,550), rtrange=c(7.5,8.5))
##'
##' ## image plot
##' plotChrom(pd, rtrange = c(7.5,8.5), plotPeaks = TRUE,
##' plotPeakLabels = TRUE)
##' @export
setMethod("plotChrom","peaksDataset",
function(object, runs = 1:length(object@rawdata),
mzind = 1:nrow(object@rawdata[[1]]),
mind = NULL, plotSampleLabels = TRUE,
calcGlobalMax = FALSE, peakCex = 0.8,
plotPeaks = TRUE, plotPeakBoundaries = FALSE,
plotPeakLabels = FALSE,
plotMergedPeakLabels = TRUE, mlwd = 3,
usePeaks = TRUE, plotAcrossRuns = FALSE,
overlap = F, rtrange = NULL, cols = NULL, thin = 1,
max.near = median(object@rawrt[[1]]), how.near = 50,
scale.up = 1, ...){
## only specific one of ma or runs
if(is.null(rtrange))
rtrange <- c(min(object@rawrt[[1]]), max(object@rawrt[[1]]))
if(is.factor(cols))
cols <- c("black", "green", "blue", "red", "grey", "orange")[cols]
if(is.null(cols))
cols <- rep(c("black", "green", "blue", "red"),
each = length(object@rawdata)/4)
if(length(cols) != length(runs))
cols <- rep("black", length(runs))
if(usePeaks){
datalist <- object@peaksdata
rtlist <- object@peaksrt
}else{
datalist <- object@rawdata
rtlist <- object@rawrt
}
if(calcGlobalMax){
mx <- 0
for(i in 1:length(runs)) {
ind <- runs[i]
mxind <- which(abs(object@rawrt[[ind]]-max.near)<how.near)
b <- colSums(matrix(object@rawdata[[ind]][mzind,],
nrow = length(mzind)))
if(mx < max(b[mxind]))
mx <- max(b[mxind])
}
}
if(length(object@peaksdata) != length(object@rawdata))
plotPeaks <- FALSE
for(i in 1:length(runs)){
ind <- runs[i]
b <- colSums(matrix(object@rawdata[[ind]][mzind,],
nrow = length(mzind)))
if(!calcGlobalMax){
mxind <- which(abs(object@rawrt[[ind]]-max.near) < how.near)
mx <- max(b[mxind])
}
b <- b / mx*scale.up
if(!overlap){
if(i == 1){
plot(object@rawrt[[ind]], b, type = "l", xlab = "Retention Time",
ylab = "", xlim = rtrange,
ylim = c(-.5,length(runs)), col = cols[i], xaxs = "i",
axes = FALSE, ...)
axis(1)
}else{
lines(object@rawrt[[ind]], b+i-1, col = cols[i], ...)
}
if(plotSampleLabels)
text(rtrange[1]+.1*diff(rtrange), i-.5, names(object@rawdata)[ind],
col = "black")
} else {
if(i == 1) {
plot(object@rawrt[[ind]], b, type = "l", xlab = "Retention Time",
ylab = "", xlim = rtrange,
ylim = c(0,1), col = cols[i], xaxs = "i", axes = FALSE)
axis(1)
} else {
lines(object@rawrt[[ind]], b, col = cols[i])
}
}
if(plotPeaks & !overlap){
rt <- rtlist[[ind]]; d <- min(diff(rt))*.4
for(j in 1:length(rt)) {
if(rt[j] > rtrange[1] & rt[j] < rtrange[2]) {
if (plotPeakBoundaries) {
st <- object@rawrt[[ind]][ object@peaksind.start[[ind]][j] ]
en <- object@rawrt[[ind]][ object@peaksind.end[[ind]][j] ]
## cat(ind,st,en,rt[j],"\n")
if(j%%2==0)
{
lines(c(st,st,rt[j],rt[j],rt[j],en,en),
c(i-1+.025,i-1,i-1,i-1+.3,i-1,i-1,i-1+.025), col = "blue")
}
else
{
lines(c(st,st,rt[j],rt[j],rt[j],en,en),
c(i-1+.025,i-1,i-1,i-1+.3,i-1,i-1,i-1+.025)-.025, col = "red")
## lines(c(rt[j],rt[j]),c(i-1,i-1+.3))
}
} else {
lines(c(rt[j]-d,rt[j]+d), c(i-1,i-1))
lines(c(rt[j],rt[j]), c(i-1,i-1+.3))
}
}
}
if(plotPeakLabels)
text(rt, i-1+.15, labels = 1:length(rt), adj = 0, cex = peakCex)
}
}
if(!is.null(mind) & !overlap) {
rws <- as.integer(seq(1, nrow(mind), by = thin))
for(i in rws) {
if( !is.na(mind[i,1]) & plotMergedPeakLabels )
text(rtlist[[runs[1]]][mind[i,1]], 0, srt = 90, i, col = "blue",
pos = 1, cex = peakCex*.8, adj = 1)
for(j in 1:(ncol(mind)-1)) {
ind1 <- mind[i,j]
ind2 <- mind[i,j+1]
if(!is.na(ind1) & !is.na(ind2)) {
rt1 <- rtlist[[runs[j]]][ind1]
rt2 <- rtlist[[runs[j+1]]][ind2]
## cat(ind1,ind2,runs[j],runs[j+1],rt1,rtrange[1],rt2,rtrange[2],"\n")
if((rt1 > rtrange[1] | rt2 > rtrange[1]) &
(rt1 < rtrange[2] | rt2 < rtrange[2])){
if(j > 1)
lines(c(rt1, rt2), c(j+.4-1, j+.95-1), lwd = mlwd, lty = 1,
col = "darkgrey")
else
lines(c(rt1,rt2), c(j+.4-1,j+.95-1), lwd = mlwd, lty = 1,
col = "darkgrey")
}
}
}
if (plotAcrossRuns) {
if (ncol(mind) <= 2) next
for(j in 1:(ncol(mind)-2)) {
ind1 <- mind[i,j]
ind2 <- mind[i,j+1]
ind3 <- mind[i,j+2]
if(!is.na(ind1) & is.na(ind2) & !is.na(ind3)) {
rt1 <- rtlist[[runs[j]]][ind1]
rt3 <- rtlist[[runs[j+2]]][ind3]
## cat(rt1,rtrange[1],rt3,rtrange[2],"\n")
if( (rt1 > rtrange[1] | rt3 > rtrange[1]) &
(rt1 < rtrange[2] | rt3 < rtrange[2]))
lines(c(rt1,rt3), c(j+.4-1,j+.95), lwd = mlwd, lty = 1,
col = "green")
}
}
if (ncol(mind) <= 3) next
for(j in 1:(ncol(mind)-3)) {
ind1 <- mind[i,j]
ind2 <- mind[i,j+1]
ind3 <- mind[i,j+2]
ind4 <- mind[i,j+3]
if(!is.na(ind1) & is.na(ind2) & is.na(ind3) & !is.na(ind4)) {
rt1<-rtlist[[runs[j]]][ind1]
rt4<-rtlist[[runs[j+3]]][ind4]
## cat(rt1,rtrange[1],rt4,rtrange[2],"\n")
if( (rt1 > rtrange[1] | rt4 > rtrange[1]) &
(rt1 < rtrange[2] | rt4 < rtrange[2]))
lines(c(rt1,rt4), c(j+.4-1,j+1.95), lwd = mlwd, lty = 1,
col = "red")
}
}
}
}
}
})
#' Add AMDIS peak detection results
#'
#' Reads ASCII ELU-format files (output from AMDIS) and attaches them to an
#' already created \code{peaksDataset} object
#'
#'
#' Repeated calls to \code{parseELU} to add peak detection results to the
#' original \code{peaksDataset} object.
#'
#' @param object a \code{peaksDataset} object.
#' @param fns character vector of same length as \code{object@rawdata} (user
#' ensures the order matches)
#' @param verbose whether to give verbose output, default \code{TRUE}
#' @param ... arguments passed on to \code{parseELU}
#' @return
#'
#' \code{peaksDataset} object
#' @author Mark Robinson
#' @seealso \code{\link{parseELU}}, \code{\link{peaksDataset}}
#' @references
#'
#' Mark D Robinson (2008). Methods for the analysis of gas chromatography -
#' mass spectrometry data \emph{PhD dissertation} University of Melbourne.
#' @keywords manip
#' @examples
#'
#' # need access to CDF (raw data) and ELU files
#' require(gcspikelite)
#' gcmsPath<-paste(find.package("gcspikelite"),"data",sep="/")
#'
#' # full paths to file names
#' cdfFiles<-dir(gcmsPath,"CDF",full=TRUE)
#' eluFiles<-dir(gcmsPath,"ELU",full=TRUE)
#'
#' # create a 'peaksDataset' object and add AMDIS peaks to it
#' pd<-peaksDataset(cdfFiles[1],mz=seq(50,550),rtrange=c(7.5,8.5))
#' pd<-addAMDISPeaks(pd,eluFiles[1])
#'
#' @export addAMDISPeaks
addAMDISPeaks<-function(object,fns=dir(,"[Eu][Ll][Uu]"),verbose=TRUE,...) {
if(length(fns)!=length(object@rawdata))
stop("Number of files must be the same as the number of runs (and must match).")
mn<-1e6; mx<--1
for(i in 1:length(object@rawrt)) {
rng<-range(object@rawrt[[i]])
if(mn > rng[1]) mn<-rng[1]
if(mx < rng[2]) mx<-rng[2]
}
if (verbose)
cat("Reading retention time range:",c(mn,mx),"\n")
for(i in 1:length(object@files)) {
if (verbose)
cat("Reading",fns[i],"...")
csin<-parseELU(fns[i],mz=object@mz,rtrange=c(mn,mx),...)
thisind<-rep(NA,nrow(csin$tab))
thisind.st<-rep(NA,nrow(csin$tab))
thisind.en<-rep(NA,nrow(csin$tab))
thisrt<-rep(NA,nrow(csin$tab))
thisdata<-matrix(0,nrow=length(object@mz),ncol=nrow(csin$tab))
for(j in 1:length(thisind)) {
thisind[j]<-which.min(abs(object@rawrt[[i]]-csin$tab[j,2]))
thisind.st[j]<-csin$tab[j,3]
thisind.en[j]<-csin$tab[j,4]
thisrt[j]<-object@rawrt[[i]][thisind[j]]
take<-which(csin$peaks[,j]>0)
#thisdata[take,j]<-sqrt(object@rawdata[[i]][take,thisind[j]])
thisdata[take,j]<-object@rawdata[[i]][take,thisind[j]]
}
if (verbose)
cat(" Done.\n")
object@peaksind[[i]]<-thisind
thisind.st[thisind.st==0]<-1
object@peaksind.start[[i]]<-thisind.st
object@peaksind.end[[i]]<-thisind.en
object@peaksrt[[i]]<-thisrt
object@peaksdata[[i]]<-thisdata
}
names(object@peaksdata)<-names(object@peaksrt)<-names(object@peaksind)<-names(object@rawdata)
new("peaksDataset",object)
}
#' Add ChromaTOF peak detection results
#'
#' Reads ASCII tab-delimited format files (output from ChromaTOF) and attaches
#' them to an already created \code{peaksDataset} object
#'
#'
#' Repeated calls to \code{parseChromaTOF} to add peak detection results to the
#' original \code{peaksDataset} object.
#'
#' @param object a \code{peaksDataset} object.
#' @param fns character vector of same length as \code{object@rawdata} (user
#' ensures the order matches)
#' @param rtDivide number giving the amount to divide the retention times by.
#' @param verbose whether to give verbose output, default \code{TRUE}
#' @param ... arguments passed on to \code{parseChromaTOF}
#' @return
#'
#' \code{peaksDataset} object
#' @author Mark Robinson
#' @seealso \code{\link{parseChromaTOF}}, \code{\link{peaksDataset}}
#' @references
#'
#' Mark D Robinson (2008). Methods for the analysis of gas chromatography -
#' mass spectrometry data \emph{PhD dissertation} University of Melbourne.
#' @keywords manip
#' @examples
#'
#' # need access to CDF (raw data) and ChromaTOF files
#' require(gcspikelite)
#' gcmsPath<-paste(find.package("gcspikelite"),"data",sep="/")
#'
#' # full paths to file names
#' cdfFiles<-dir(gcmsPath,"CDF",full=TRUE)
#' # [not run] cTofFiles<-dir(gcmsPath,"txt",full=TRUE)
#'
#' # create a 'peaksDataset' object and add ChromaTOF peaks to it
#' pd<-peaksDataset(cdfFiles[1],mz=seq(50,550),rtrange=c(7.5,8.5))
#' # [not run] pd<-addChromTOFPeaks(pd,...)
#'
#' @export addChromaTOFPeaks
addChromaTOFPeaks<-function(object,fns=dir(,"[Tt][Xx][Tx]"),rtDivide=60,verbose=TRUE,...) {
if(length(fns)!=length(object@rawdata))
stop("Number of files must be the same as the number of runs (and must match).")
mn<-1e6; mx<--1
for(i in 1:length(object@rawrt)) {
rng<-range(object@rawrt[[i]])
if(mn > rng[1]) mn<-rng[1]
if(mx < rng[2]) mx<-rng[2]
}
if (verbose)
cat("Reading retention time range:",c(mn,mx),"\n")
for(i in 1:length(object@files)) {
if (verbose)
cat("Reading",fns[i],"...")
csin<-parseChromaTOF(fns[i],mz=object@mz,rtrange=c(mn,mx),rtDivide=rtDivide,...)
thisrt<-csin$tab$rt
thisind<-rep(NA,length(thisrt))
thisdata<-matrix(0,nrow=length(object@mz),ncol=nrow(csin$tab))
for(j in 1:length(thisind)) {
thisind[j]<-which.min(abs(object@rawrt[[i]]-csin$tab$rt[j]/rtDivide))
thisrt[j]<-object@rawrt[[i]][thisind[j]]
take<-which(csin$peaks[,j]>0)
#thisdata[take,j]<-sqrt(object@rawdata[[i]][take,thisind[j]])
thisdata[take,j]<-object@rawdata[[i]][take,thisind[j]]
}
if (verbose)
cat(" Done.\n")
object@peaksind[[i]]<-thisind
object@peaksrt[[i]]<-thisrt
object@peaksdata[[i]]<-thisdata
}
names(object@peaksdata)<-names(object@peaksrt)<-names(object@peaksind)<-names(object@rawdata)
new("peaksDataset",object)
}
#' Plot of images of GCMS data
#'
#' Image plots (i.e. 2D heatmaps) of raw GCMS profile data
#'
#' For \code{peakDataset} objects, each TIC is scale to the maximum value (as
#' specified by the \code{how.near} and \code{max.near} values). The many
#' parameters gives considerable flexibility of how the TICs can be visualized.
#'
#' For \code{peakAlignment} objects, the similarity matrix is plotted and
#' optionally, the set of matching peaks. \code{clusterAlignment} objects are
#' just a collection of all pairwise \code{peakAlignment} objects.
#'
#' @name plotImage
#' @aliases plotImage plotImage,peaksDataset-method
#' @param object a \code{peaksDataset} object
#' @param run index of the run to plot an image for
#' @param rtrange vector of length 2 giving start and end of the X-axis
#' (retention time)
#' @param main main title (auto-constructed if not specified)
#' @param mzrange vector of length 2 giving start and end of the Y-axis
#' (mass-to-charge ratio)
#' @param SCALE function called to scale the data (default: \code{log2})
#' @param ... further arguments passed to the \code{image} command
#' @author Mark Robinson
#' @seealso \code{\link{plot}}, \code{\link{peaksDataset}}
#' @references
#'
#' Mark D Robinson (2008). Methods for the analysis of gas chromatography -
#' mass spectrometry data \emph{PhD dissertation} University of Melbourne.
#' @keywords classes
#' @examples
#'
#' 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
#' pd<-peaksDataset(cdfFiles[1],mz=seq(50,550),rtrange=c(7.5,8.5))
#'
#' # image plot
#' plotImage(pd,run=1,rtrange=c(7.5,8.5),main="")
#' @importFrom gplots colorpanel
#' @importFrom graphics image
#' @export
setMethod("plotImage","peaksDataset",
function(object, run = 1, rtrange = c(11,13), main = NULL, mzrange = c(50,200), SCALE = log2, ...){
mz <- object@mz
for(i in run){
rt <- object@rawrt[[i]]
d <- object@rawdata[[i]]
cols <- colorpanel(50, "green", "blue", "red")
rind <- which(rt > rtrange[1]&rt<rtrange[2])
mind <- which(mz>mzrange[1]&mz<mzrange[2])
ff <- t(d[mind,rind])
ff[ff == 0]<-min(ff[ff>0])
if(is.null(main))
tit <- object@files[i]
else
tit <- main
## cat("min=",min(SCALE(ff),na.rm=TRUE)," max=",max(SCALE(ff),na.rm=T),"\n")
image(rt[rind],mz[mind], SCALE(ff), col = cols, xlab = "retention time",
ylab = "m/z", xlim = rtrange, main = tit, ...)
#abline(h=c(72.5,73.5,146.5,147.5),col="white",lty=3)
#if (!is.null(pts)) {
# points(pts[,1],pts[,2],pch=19)
# matplot(t(pts[,8:9]),t(pts[,c(2,2)]),type="l",col="black",lty=1,add=TRUE)
#}
}
return(invisible(ff))
})
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Embedding an R snippet on your website
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.