Nothing
R/methods-xcmsRaw.R
In xcms: LC-MS and GC-MS Data Analysis
Defines functions
.rawMat
## All Methods for xcmsRaw should be here.
#' @include functions-xcmsRaw.R functions-utils.R
############################################################
## show
setMethod("show", "xcmsRaw", function(object) {
cat("An \"xcmsRaw\" object with", length(object@scantime),
"mass spectra\n\n")
if (length(object@scantime)>0) {
cat("Time range: ", paste(round(range(object@scantime), 1),
collapse = "-"),
" seconds (", paste(round(range(object@scantime)/60, 1),
collapse = "-"),
" minutes)\n", sep = "")
cat("Mass range:", paste(round(range(object@env$mz), 4),
collapse = "-"), "m/z\n")
cat("Intensity range:", paste(signif(range(object@env$intensity), 6),
collapse = "-"), "\n\n")
}
## summary MSn data
if (!is.null(object@msnLevel)) {
cat("MSn data on ", length(unique(object@msnPrecursorMz)), " mass(es)\n")
cat("\twith ", length(object@msnPrecursorMz)," MSn spectra\n")
}
cat("Profile method:", object@profmethod, "\n")
cat("Profile step: ")
if (is.null(object@env$profile))
cat("no profile data\n")
else {
profmz <- profMz(object)
cat(profStep(object), " m/z (", length(profmz), " grid points from ",
paste(object@mzrange, collapse = " to "), " m/z)\n", sep = "")
}
if (length(object@profparam)) {
cat("Profile parameters: ")
for (i in seq(along = object@profparam)) {
if (i != 1) cat(" ")
cat(names(object@profparam)[i], " = ", object@profparam[[i]], "\n",
sep = "")
}
}
memsize <- object.size(object)
for (key in ls(object@env))
memsize <- memsize + object.size(object@env[[key]])
cat("\nMemory usage:", signif(memsize/2^20, 3), "MB\n")
})
############################################################
## sortMz
setMethod("revMz", "xcmsRaw", function(object) {
for (i in 1:length(object@scanindex)) {
idx <- (object@scanindex[i]+1):min(object@scanindex[i+1],
length(object@env$mz), na.rm=TRUE)
object@env$mz[idx] <- rev(object@env$mz[idx])
object@env$intensity[idx] <- rev(object@env$intensity[idx])
}
})
############################################################
## sortMz
setMethod("sortMz", "xcmsRaw", function(object) {
for (i in 1:length(object@scanindex)) {
idx <- (object@scanindex[i]+1):min(object@scanindex[i+1],
length(object@env$mz), na.rm=TRUE)
ord <- order(object@env$mz[idx])
object@env$mz[idx] <- object@env$mz[idx[ord]]
object@env$intensity[idx] <- object@env$intensity[idx[ord]]
}
})
############################################################
## plotTIC
setMethod("plotTIC", "xcmsRaw", function(object, ident = FALSE, msident = FALSE) {
if (all(object@tic == 0))
points <- cbind(object@scantime, rawEIC(object,mzrange=range(object@env$mz))$intensity) else
points <- cbind(object@scantime, object@tic)
plot(points, type="l", main="TIC Chromatogram", xlab="Seconds",
ylab="Intensity")
if (ident) {
idx <- integer(0)
ticdev <- dev.cur()
if ((dev.cur()+1) %in% dev.list())
msdev <- dev.cur()+1
else
msdev <- integer(0)
while(length(id <- identify(points, labels = round(points[,1], 1), n = 1))) {
idx <- c(idx, id)
if (!length(msdev)) {
options("device")$device()
msdev <- dev.cur()
}
dev.set(msdev)
plotScan(object, id, ident = msident)
dev.set(ticdev)
}
return(idx)
}
invisible(points)
})
############################################################
## getScan
setMethod("getScan", "xcmsRaw", function(object, scan, mzrange = numeric()) {
if (scan < 0)
scan <- length(object@scantime) + 1 + scan
idx <- seq(object@scanindex[scan]+1, min(object@scanindex[scan+1],
length(object@env$mz), na.rm=TRUE))
if (length(mzrange) >= 2) {
mzrange <- range(mzrange)
idx <- idx[object@env$mz[idx] >= mzrange[1] & object@env$mz[idx] <= mzrange[2]]
}
points <- cbind(mz = object@env$mz[idx], intensity = object@env$intensity[idx])
invisible(points)
})
############################################################
## getSpec
setMethod("getSpec", "xcmsRaw", function(object, ...) {
## FIXME: unnecessary dependency on profile matrix?
sel <- profRange(object, ...)
scans <- list(length(sel$scanidx))
uniquemz <- numeric()
for (i in seq(along = sel$scanidx)) {
scans[[i]] <- getScan(object, sel$scanidx[i], sel$mzrange)
uniquemz <- unique(c(uniquemz, scans[[i]][,"mz"]))
}
uniquemz <- sort(uniquemz)
intmat <- matrix(nrow = length(uniquemz), ncol = length(sel$scanidx))
for (i in seq(along = sel$scanidx)) {
scan <- getScan(object, sel$scanidx[i], sel$mzrange)
intmat[,i] <- approx(scan, xout = uniquemz)$y
}
points <- cbind(mz = uniquemz, intensity = rowMeans(intmat))
invisible(points)
})
############################################################
## findPeaks.matchedFilter_orig
## We're keeping this one only for comparison in unit tests; this
## should be removed soon!
setGeneric("findPeaks.matchedFilter_orig", function(object, ...)
standardGeneric("findPeaks.matchedFilter_orig"))
setMethod("findPeaks.matchedFilter_orig", "xcmsRaw",
function(object, fwhm = 30, sigma = fwhm/2.3548, max = 5,
snthresh = 10, step = 0.1, steps = 2,
mzdiff = 0.8 - step*steps, index = FALSE, sleep = 0,
scanrange= numeric()) {
profFun <- match.profFun(object)
scanrange.old <- scanrange
## sanitize if too few or too many scanrange is given
if (length(scanrange) < 2)
scanrange <- c(1, length(object@scantime))
else
scanrange <- range(scanrange)
## restrict and sanitize scanrange to maximally cover all scans
scanrange[1] <- max(1,scanrange[1])
scanrange[2] <- min(length(object@scantime),scanrange[2])
## Mild warning if the actual scanrange doesn't match the scanrange argument
if (!(identical(scanrange.old,scanrange)) && (length(scanrange.old) >0)) {
cat("Warning: scanrange was adjusted to ",scanrange,"\n")
## Scanrange filtering
keepidx <- seq.int(1, length(object@scantime)) %in% seq.int(scanrange[1], scanrange[2])
object <- split(object, f=keepidx)[["TRUE"]]
}
### Create EIC buffer
mrange <- range(object@env$mz)
mass <- seq(floor(mrange[1]/step)*step, ceiling(mrange[2]/step)*step, by = step)
bufsize <- min(100, length(mass))
buf <- profFun(object@env$mz, object@env$intensity, object@scanindex,
bufsize, mass[1], mass[bufsize], TRUE, object@profparam)
bufMax <- profMaxIdxM(object@env$mz, object@env$intensity, object@scanindex,
bufsize, mass[1], mass[bufsize], TRUE,
object@profparam)
bufidx <- integer(length(mass))
idxrange <- c(1, bufsize)
bufidx[idxrange[1]:idxrange[2]] <- 1:bufsize
lookahead <- steps-1
lookbehind <- 1
scantime <- object@scantime
N <- nextn(length(scantime))
xrange <- range(scantime)
x <- c(0:(N/2), -(ceiling(N/2-1)):-1)*(xrange[2]-xrange[1])/(length(scantime)-1)
filt <- -attr(eval(deriv3(~ 1/(sigma*sqrt(2*pi))*exp(-x^2/(2*sigma^2)), "x")), "hessian")
filt <- filt/sqrt(sum(filt^2))
filt <- fft(filt, inverse = TRUE)/length(filt)
cnames <- c("mz", "mzmin", "mzmax", "rt", "rtmin", "rtmax", "into", "intf", "maxo", "maxf", "i", "sn")
rmat <- matrix(nrow = 2048, ncol = length(cnames))
num <- 0
for (i in seq(length = length(mass)-steps+1)) {
if (i %% 500 == 0) {
cat(round(mass[i]), ":", num, " ", sep = "")
flush.console()
}
### Update EIC buffer if necessary
if (bufidx[i+lookahead] == 0) {
bufidx[idxrange[1]:idxrange[2]] <- 0
idxrange <- c(max(1, i - lookbehind), min(bufsize+i-1-lookbehind, length(mass)))
bufidx[idxrange[1]:idxrange[2]] <- 1:(diff(idxrange)+1)
buf <- profFun(object@env$mz, object@env$intensity, object@scanindex,
diff(idxrange)+1, mass[idxrange[1]], mass[idxrange[2]],
TRUE, object@profparam)
bufMax <- profMaxIdxM(object@env$mz, object@env$intensity, object@scanindex,
diff(idxrange)+1, mass[idxrange[1]], mass[idxrange[2]],
TRUE, object@profparam)
}
ymat <- buf[bufidx[i:(i+steps-1)],,drop=FALSE]
ysums <- colMax(ymat)
yfilt <- filtfft(ysums, filt)
gmax <- max(yfilt)
for (j in seq(length = max)) {
maxy <- which.max(yfilt)
noise <- mean(ysums[ysums > 0])
##noise <- mean(yfilt[yfilt >= 0])
sn <- yfilt[maxy]/noise
if (yfilt[maxy] > 0 && yfilt[maxy] > snthresh*noise && ysums[maxy] > 0) {
peakrange <- descendZero(yfilt, maxy)
intmat <- ymat[,peakrange[1]:peakrange[2],drop=FALSE]
mzmat <- matrix(object@env$mz[bufMax[bufidx[i:(i+steps-1)],
peakrange[1]:peakrange[2]]],
nrow = steps)
which.intMax <- which.colMax(intmat)
mzmat <- mzmat[which.intMax]
if (all(is.na(mzmat))) {
yfilt[peakrange[1]:peakrange[2]] <- 0
next
}
mzrange <- range(mzmat, na.rm = TRUE)
massmean <- weighted.mean(mzmat, intmat[which.intMax], na.rm = TRUE)
## This case (the only non-na m/z had intensity 0) was reported
## by Gregory Alan Barding "binlin processing"
if(any(is.na(massmean))) {
massmean <- mean(mzmat, na.rm = TRUE)
}
pwid <- (scantime[peakrange[2]] - scantime[peakrange[1]])/(peakrange[2] - peakrange[1])
into <- pwid*sum(ysums[peakrange[1]:peakrange[2]])
intf <- pwid*sum(yfilt[peakrange[1]:peakrange[2]])
maxo <- max(ysums[peakrange[1]:peakrange[2]])
maxf <- yfilt[maxy]
if (sleep > 0) {
plot(scantime, yfilt, type = "l", main = paste(mass[i], "-", mass[i+1]), ylim=c(-gmax/3, gmax))
points(cbind(scantime, yfilt)[peakrange[1]:peakrange[2],], type = "l", col = "red")
points(scantime, colSums(ymat), type = "l", col = "blue", lty = "dashed")
abline(h = snthresh*noise, col = "red")
Sys.sleep(sleep)
}
yfilt[peakrange[1]:peakrange[2]] <- 0
num <- num + 1
### Double the size of the output matrix if it's full
if (num > nrow(rmat)) {
nrmat <- matrix(nrow = 2*nrow(rmat), ncol = ncol(rmat))
nrmat[seq(length = nrow(rmat)),] = rmat
rmat <- nrmat
}
rmat[num,] <- c(massmean, mzrange[1], mzrange[2], maxy, peakrange, into, intf, maxo, maxf, j, sn)
} else
break
}
}
cat("\n")
colnames(rmat) <- cnames
rmat <- rmat[seq(length = num),]
max <- max-1 + max*(steps-1) + max*ceiling(mzdiff/step)
if (index)
mzdiff <- mzdiff/step
else {
rmat[,"rt"] <- scantime[rmat[,"rt"]]
rmat[,"rtmin"] <- scantime[rmat[,"rtmin"]]
rmat[,"rtmax"] <- scantime[rmat[,"rtmax"]]
}
uorder <- order(rmat[,"into"], decreasing=TRUE)
uindex <- rectUnique(rmat[,c("mzmin","mzmax","rtmin","rtmax"),drop=FALSE],
uorder, mzdiff)
rmat <- rmat[uindex,,drop=FALSE]
invisible(new("xcmsPeaks", rmat))
})
############################################################
## findPeaks.matchedFilter
#' @title Peak detection in the chromatographic time domain
#'
#' @aliases findPeaks.matchedFilter
#'
#' @description Find peaks in the chromatographic time domain of the
#' profile matrix. For more details see
#' \code{\link{do_findChromPeaks_matchedFilter}}.
#'
#' @param object The \code{\linkS4class{xcmsRaw}} object on which peak detection
#' should be performed.
#'
#' @inheritParams findChromPeaks-matchedFilter
#'
#' @param step numeric(1) specifying the width of the bins/slices in m/z
#' dimension.
#'
#' @param sleep (DEPRECATED). The use of this parameter is highly discouraged,
#' as it could cause problems in parallel processing mode.
#'
#' @param scanrange Numeric vector defining the range of scans to which the
#' original \code{object} should be sub-setted before peak detection.
#'
#' @author Colin A. Smith
#'
#' @return A matrix, each row representing an intentified chromatographic peak,
#' with columns:
#' \describe{
#' \item{mz}{Intensity weighted mean of m/z values of the peak across
#' scans.}
#' \item{mzmin}{Minimum m/z of the peak.}
#' \item{mzmax}{Maximum m/z of the peak.}
#' \item{rt}{Retention time of the peak's midpoint.}
#' \item{rtmin}{Minimum retention time of the peak.}
#' \item{rtmax}{Maximum retention time of the peak.}
#' \item{into}{Integrated (original) intensity of the peak.}
#' \item{intf}{Integrated intensity of the filtered peak.}
#' \item{maxo}{Maximum intensity of the peak.}
#' \item{maxf}{Maximum intensity of the filtered peak.}
#' \item{i}{Rank of peak in merged EIC (\code{<= max}).}
#' \item{sn}{Signal to noise ratio of the peak.}
#' }
#'
#' @references
#' Colin A. Smith, Elizabeth J. Want, Grace O'Maille, Ruben Abagyan and
#' Gary Siuzdak. "XCMS: Processing Mass Spectrometry Data for Metabolite
#' Profiling Using Nonlinear Peak Alignment, Matching, and Identification"
#' \emph{Anal. Chem.} 2006, 78:779-787.
#' @family Old peak detection methods
#'
#' @seealso \code{\link{matchedFilter}} for the new user interface.
#' \code{\linkS4class{xcmsRaw}},
#' \code{\link{do_findChromPeaks_matchedFilter}} for the core function
#' performing the peak detection.
setMethod("findPeaks.matchedFilter", "xcmsRaw",
function(object, fwhm = 30, sigma = fwhm/2.3548, max = 5,
snthresh = 10, step = 0.1, steps = 2,
mzdiff = 0.8 - step*steps, index = FALSE, sleep = 0,
scanrange = numeric()) {
## Fix issue #63:
## Sub-set the xcmsRaw based on scanrange
if (length(scanrange) < 2) {
scanrange <- c(1, length(object@scantime))
} else {
scanrange <- range(scanrange)
}
if (min(scanrange) < 1 | max(scanrange) > length(object@scantime)) {
scanrange[1] <- max(1, scanrange[1])
scanrange[2] <- min(length(object@scantime), scanrange[2])
message("Provided scanrange was adjusted to ", scanrange)
}
object <- object[scanrange[1]:scanrange[2]]
scanrange <- c(1, length(object@scantime))
## ## Sub-set the xcmsRaw baesd on scanrange
## scanrange.old <- scanrange
## ## sanitize if too few or too many scanrange is given
## if (length(scanrange) < 2)
## scanrange <- c(1, length(object@scantime))
## else
## scanrange <- range(scanrange)
## ## restrict and sanitize scanrange to maximally cover all scans
## scanrange[1] <- max(1,scanrange[1])
## scanrange[2] <- min(length(object@scantime),scanrange[2])
## ## Mild warning if the actual scanrange doesn't match the scanrange
## ## argument
## if (!(identical(scanrange.old, scanrange)) &&
## (length(scanrange.old) > 0)) {
## cat("Warning: scanrange was adjusted to ",scanrange,"\n")
## ## Scanrange filtering
## keepidx <- seq.int(1, length(object@scantime))
## %in% seq.int(scanrange[1], scanrange[2])
## object <- split(object, f=keepidx)[["TRUE"]]
## }
## Determine the impute method:
imputeMeths <- c("none", "lin", "linbase", "intlin")
names(imputeMeths) <- c("bin", "binlin",
"binlinbase", "intlin")
profFun <- profMethod(object)
profFun <- match.arg(profFun, names(imputeMeths))
imputeMeth <- imputeMeths[profFun]
if (imputeMeth == "linbase") {
profp <- object@profparam
if (length(profp) == 0)
profp <- list()
## Determine the settings for this:
## o distance
## Define the distance argument; that's tricky, as it
## requires the bin_size, not the step.
mrange <- range(object@env$mz)
mass <- seq(floor(mrange[1]/step)*step,
ceiling(mrange[2]/step)*step, by = step)
mlength <- length(mass)
bin_size <- (mass[mlength] - mass[1]) / (mlength - 1)
rm(mass)
if (length(profp$basespace) > 0) {
if (!is.numeric(profp$basespace))
stop("Profile parameter 'basespace' has to be numeric!")
distance <- floor(profp$basespace[1] / bin_size)
} else {
distance <- floor(0.075 / bin_size)
}
## o baseValue
if (length(profp$baseleve) > 0) {
if (!is.numeric(profp$baselevel))
stop("Profile parameter 'baselevel' has to be numeric!")
baseValue <- profp$baselevel[1]
} else {
baseValue <- min(object@env$intensity) / 2
}
} else {
## For other methods these are not used anyway.
distance <- 0
baseValue <- 0
}
res <- do_findChromPeaks_matchedFilter(mz = object@env$mz,
int = object@env$intensity,
scantime = object@scantime,
valsPerSpect = diff(c(object@scanindex,
length(object@env$mz))),
binSize = step,
impute = imputeMeth,
baseValue = baseValue,
distance = distance,
fwhm = fwhm,
sigma = sigma,
max = max,
snthresh = snthresh,
steps = steps,
mzdiff = mzdiff,
index = index,
sleep = sleep
)
invisible(new("xcmsPeaks", res))
})
############################################################
## findPeaks.centWave
setMethod("findPeaks.centWave", "xcmsRaw", function(object, ppm=25,
peakwidth=c(20,50),
snthresh=10,
prefilter=c(3,100),
mzCenterFun="wMean",
integrate=1, mzdiff=-0.001,
fitgauss=FALSE,
scanrange = numeric(),
noise=0, ## noise.local=TRUE,
sleep=0,
verbose.columns=FALSE,
ROI.list=list(),
firstBaselineCheck=TRUE,
roiScales=NULL) {
if (!isCentroided(object))
warning("It looks like this file is in profile mode. centWave can",
" process only centroid mode data !\n")
## Fix issue #64:
## Sub-set the xcmsRaw based on scanrange
if (length(scanrange) < 2) {
scanrange <- c(1, length(object@scantime))
} else {
scanrange <- range(scanrange)
}
if (min(scanrange) < 1 | max(scanrange) > length(object@scantime)) {
scanrange[1] <- max(1, scanrange[1])
scanrange[2] <- min(length(object@scantime), scanrange[2])
message("Provided scanrange was adjusted to ", scanrange)
}
object <- object[scanrange[1]:scanrange[2]]
vps <- diff(c(object@scanindex, length(object@env$mz)))
res <- do_findChromPeaks_centWave(mz = object@env$mz,
int = object@env$intensity,
scantime = object@scantime,
valsPerSpect = vps,
ppm = ppm, peakwidth = peakwidth,
snthresh = snthresh,
prefilter = prefilter,
mzCenterFun = mzCenterFun,
integrate = integrate,
mzdiff = mzdiff, fitgauss = fitgauss,
noise = noise,
verboseColumns = verbose.columns,
roiList = ROI.list,
firstBaselineCheck = firstBaselineCheck,
roiScales = roiScales,
sleep = sleep
)
invisible(new("xcmsPeaks", res))
})
############################################################
## findPeaks.centWaveWithPredictedIsotopeROIs
## Performs first a centWave analysis and based on the identified peaks
## defines ROIs for a second centWave run to check for presence of
## predicted isotopes for the first peaks.
setMethod("findPeaks.centWaveWithPredictedIsotopeROIs", "xcmsRaw",
function(object, ppm = 25, peakwidth = c(20,50), snthresh = 10,
prefilter = c(3,100), mzCenterFun = "wMean", integrate = 1,
mzdiff = -0.001, fitgauss = FALSE, scanrange = numeric(),
noise = 0, sleep = 0, verbose.columns = FALSE,
ROI.list = list(), firstBaselineCheck = TRUE,
roiScales = NULL, snthreshIsoROIs = 6.25, maxcharge = 3,
maxiso = 5, mzIntervalExtension = TRUE) {
if (!isCentroided(object))
warning("It looks like this file is in profile mode. centWave",
" can process only centroid mode data !\n")
## Sub-set the xcmsRaw based on scanrange
if (length(scanrange) < 2) {
scanrange <- c(1, length(object@scantime))
} else {
scanrange <- range(scanrange)
}
if (min(scanrange) < 1 | max(scanrange) > length(object@scantime)) {
scanrange[1] <- max(1, scanrange[1])
scanrange[2] <- min(length(object@scantime), scanrange[2])
message("Provided scanrange was adjusted to ", scanrange)
}
object <- object[scanrange[1]:scanrange[2]]
vps <- diff(c(object@scanindex, length(object@env$mz)))
res <- do_findChromPeaks_centWaveWithPredIsoROIs(mz = object@env$mz,
int = object@env$intensity,
scantime = object@scantime,
valsPerSpect = vps,
ppm = ppm,
peakwidth = peakwidth,
snthresh = snthresh,
prefilter = prefilter,
mzCenterFun = mzCenterFun,
integrate = integrate,
mzdiff = mzdiff,
fitgauss = fitgauss,
noise = noise,
verboseColumns = verbose.columns,
roiList = ROI.list,
firstBaselineCheck = firstBaselineCheck,
roiScales = roiScales,
snthreshIsoROIs = snthreshIsoROIs,
maxCharge = maxcharge,
maxIso = maxiso,
mzIntervalExtension = mzIntervalExtension
)
invisible(new("xcmsPeaks", res))
})
setMethod("findPeaks.addPredictedIsotopeFeatures",
"xcmsRaw", function(object, ppm = 25, peakwidth = c(20,50),
prefilter = c(3,100), mzCenterFun = "wMean",
integrate = 1, mzdiff = -0.001, fitgauss = FALSE,
scanrange = numeric(), noise=0, ## noise.local=TRUE,
sleep = 0, verbose.columns = FALSE,
xcmsPeaks, snthresh = 6.25, maxcharge = 3,
maxiso = 5, mzIntervalExtension = TRUE) {
if (!isCentroided(object))
warning("It looks like this file is in profile mode. ",
"centWave works best for centroided data")
## Sub-set the xcmsRaw based on scanrange
if (length(scanrange) < 2) {
scanrange <- c(1, length(object@scantime))
} else {
scanrange <- range(scanrange)
}
if (min(scanrange) < 1 | max(scanrange) > length(object@scantime)) {
scanrange[1] <- max(1, scanrange[1])
scanrange[2] <- min(length(object@scantime), scanrange[2])
message("Provided scanrange was adjusted to ", scanrange)
}
object <- object[scanrange[1]:scanrange[2]]
if(class(xcmsPeaks) != "xcmsPeaks")
stop("Parameter 'xcmsPeaks' is not of class 'xcmsPeaks'")
vps <- diff(c(object@scanindex, length(object@env$mz)))
res <- do_findChromPeaks_addPredIsoROIs(
mz = object@env$mz, int = object@env$intensity,
scantime = object@scantime, valsPerSpect = vps,
ppm = ppm, peakwidth = peakwidth,
snthresh = snthresh, prefilter = prefilter,
mzCenterFun = mzCenterFun, integrate = integrate,
mzdiff = mzdiff, fitgauss = fitgauss, noise = noise,
verboseColumns = verbose.columns, peaks. = xcmsPeaks@.Data,
maxCharge = maxcharge, maxIso = maxiso,
mzIntervalExtension = mzIntervalExtension
)
invisible(new("xcmsPeaks", res))
})
############################################################
## findPeaks.MSW
#' @title Peak detection for single-spectrum non-chromatography MS data
#'
#' @aliases findPeaks.MSW
#'
#' @description This method performs peak detection in mass spectrometry
#' direct injection spectrum using a wavelet based algorithm.
#'
#' @details This is a wrapper around the peak picker in Bioconductor's
#' \code{MassSpecWavelet} package calling
#' \code{\link{peakDetectionCWT}} and
#' \code{\link{tuneInPeakInfo}} functions.
#'
#' @inheritParams findPeaks-MSW
#'
#' @inheritParams findChromPeaks-centWave
#'
#' @param object The \code{\linkS4class{xcmsRaw}} object on which peak
#' detection should be performed.
#'
#' @param verbose.columns Logical whether additional peak meta data columns
#' should be returned.
#'
#' @return
#' A matrix, each row representing an intentified peak, with columns:
#' \describe{
#' \item{mz}{m/z value of the peak at the centroid position.}
#' \item{mzmin}{Minimum m/z of the peak.}
#' \item{mzmax}{Maximum m/z of the peak.}
#' \item{rt}{Always \code{-1}.}
#' \item{rtmin}{Always \code{-1}.}
#' \item{rtmax}{Always \code{-1}.}
#' \item{into}{Integrated (original) intensity of the peak.}
#' \item{maxo}{Maximum intensity of the peak.}
#' \item{intf}{Always \code{NA}.}
#' \item{maxf}{Maximum MSW-filter response of the peak.}
#' \item{sn}{Signal to noise ratio.}
#' }
#'
#' @seealso \code{\link{MSW}} for the new user interface,
#' \code{\link{do_findPeaks_MSW}} for the downstream analysis
#' function or \code{\link{peakDetectionCWT}} from the
#' \code{MassSpecWavelet} for details on the algorithm and additionally
#' supported parameters.
#'
#' @author Joachim Kutzera, Steffen Neumann, Johannes Rainer
setMethod("findPeaks.MSW", "xcmsRaw",
function(object, snthresh=3, verbose.columns = FALSE, ...) {
if (length(object@scantime) > 1)
stop("MSW works only on single spectrum, direct injection",
" MS data, but 'object' has ", length(object@scantime),
" spectra")
res <- do_findPeaks_MSW(mz = object@env$mz,
int = object@env$intensity,
snthresh = snthresh,
verboseColumns = verbose.columns,
...)
invisible(new("xcmsPeaks", res))
})
############################################################
## findPeaks.MS1
setMethod("findPeaks.MS1", "xcmsRaw", function(object)
{
if (is.null(object@msnLevel)) {
stop("xcmsRaw contains no MS2 spectra\n")
}
## Select all MS2 scans, they have an MS1 parent defined
peakIndex <- object@msnLevel == 2
## (empty) return object
basenames <- c("mz","mzmin","mzmax",
"rt","rtmin","rtmax",
"into","maxo","sn")
peaklist <- matrix(-1, nrow = length(which(peakIndex)),
ncol = length(basenames))
colnames(peaklist) <- c(basenames)
## Assemble result
peaklist[,"mz"] <- object@msnPrecursorMz[peakIndex]
peaklist[,"mzmin"] <- object@msnPrecursorMz[peakIndex]
peaklist[,"mzmax"] <- object@msnPrecursorMz[peakIndex]
if (any(!is.na(object@msnPrecursorScan))&&any(object@msnPrecursorScan!=0)) {
peaklist[,"rt"] <- peaklist[,"rtmin"] <- peaklist[,"rtmax"] <- object@scantime[object@msnPrecursorScan[peakIndex]]
} else {
## This happened with ReAdW mzxml
cat("MS2 spectra without precursorScan references, using estimation")
## which object@Scantime are the biggest wich are smaller than the current object@msnRt[peaklist]?
ms1Rts<-rep(0,length(which(peakIndex)))
i<-1
for (a in which(peakIndex)){
ms1Rts[i] <- object@scantime[max(which(object@scantime<object@msnRt[a]))]
i<-i+1
}
peaklist[,"rt"] <- ms1Rts
peaklist[,"rtmin"] <- ms1Rts
peaklist[,"rtmax"] <- ms1Rts
}
if (any(object@msnPrecursorIntensity!=0)) {
peaklist[,"into"] <- peaklist[,"maxo"] <- peaklist[,"sn"] <- object@msnPrecursorIntensity[peakIndex]
} else {
## This happened with Agilent MzDataExport 1.0.98.2
warning("MS2 spectra without precursorIntensity, setting to zero")
peaklist[,"into"] <- peaklist[,"maxo"] <- peaklist[,"sn"] <- 0
}
cat('\n')
invisible(new("xcmsPeaks", peaklist))
})
############################################################
## findPeaks
setMethod("findPeaks", "xcmsRaw", function(object, method=getOption("BioC")$xcms$findPeaks.method,
...) {
method <- match.arg(method, getOption("BioC")$xcms$findPeaks.methods)
if (is.na(method))
stop("unknown method : ", method)
method <- paste("findPeaks", method, sep=".")
invisible(do.call(method, list(object, ...)))
})
setMethod("getPeaks", "xcmsRaw", function(object, peakrange, step = 0.1) {
if (useOriginalCode())
return(.getPeaks_orig(object, peakrange, step = step))
else
return(.getPeaks_new(object, peakrange, step = step))
})
############################################################
## plotPeaks
setMethod("plotPeaks", "xcmsRaw", function(object, peaks, figs, width = 200) {
if (missing(figs)) {
figs <- c(floor(sqrt(nrow(peaks))), ceiling(sqrt(nrow(peaks))))
if (prod(figs) < nrow(peaks))
figs <- rep(ceiling(sqrt(nrow(peaks))), 2)
}
mzi <- round((peaks[,c("mzmin","mzmax")]-object@mzrange[1])/profStep(object) + 1)
screens <- split.screen(figs)
on.exit(close.screen(all.screens = TRUE))
for (i in seq(length = min(nrow(peaks), prod(figs)))) {
screen(screens[i])
par(cex.main = 1, font.main = 1, mar = c(0, 0, 1, 0) + 0.1)
xlim <- c(-width/2, width/2) + peaks[i,"rt"]
##main <- paste(peaks[i,"i"], " ", round(peaks[i,"mz"]),
main <- paste(round(peaks[i,"mz"]),
" ", round(peaks[i,"rt"]), sep = "")
plot(object@scantime, colMax(object@env$profile[mzi[i,],,drop=FALSE]),
type = "l", xlim = xlim, ylim = c(0, peaks[i,"maxo"]), main = main,
xlab = "", ylab = "", xaxt = "n", yaxt = "n")
abline(v = peaks[i,c("rtmin","rtmax")], col = "grey")
}
})
############################################################
## getEIC
## Issue #74: implement an alternative (improved) getEIC method.
setMethod("getEIC", "xcmsRaw", function(object, mzrange, rtrange = NULL,
step = 0.1) {
profEIC(object, mzrange = mzrange, rtrange = rtrange, step = step)
})
############################################################
## rawMat
#' @description Extracts a matrix with columns time (retention time), mz and
#' intensity from an xcmsRaw object.
#'
#' @noRd
setMethod("rawMat", "xcmsRaw", function(object,
mzrange = numeric(),
rtrange = numeric(),
scanrange = numeric(),
log=FALSE) {
.rawMat(mz = object@env$mz, int = object@env$intensity,
scantime = object@scantime,
valsPerSpect = diff(c(object@scanindex, length(object@env$mz))),
mzrange = mzrange, rtrange = rtrange, scanrange = scanrange,
log = log)
})
## @jo TODO LLL replace that with an implementation in C.
## Note: this function silently drops retention times for which no intensity-mz
## pair was measured.
.rawMat <- function(mz, int, scantime, valsPerSpect, mzrange = numeric(),
rtrange = numeric(), scanrange = numeric(),
log = FALSE) {
if (length(rtrange) >= 2) {
rtrange <- range(rtrange)
## Fix for issue #267. rtrange outside scanrange causes scanrange
## being c(Inf, -Inf)
scns <- which((scantime >= rtrange[1]) & (scantime <= rtrange[2]))
if (!length(scns))
return(matrix(
nrow = 0, ncol = 3,
dimnames = list(character(), c("time", "mz", "intensity"))))
scanrange <- range(scns)
}
if (length(scanrange) < 2)
scanrange <- c(1, length(valsPerSpect))
else scanrange <- range(scanrange)
if (!all(is.finite(scanrange)))
stop("'scanrange' does not contain finite values")
if (!all(is.finite(mzrange)))
stop("'mzrange' does not contain finite values")
if (!all(is.finite(rtrange)))
stop("'rtrange' does not contain finite values")
if (scanrange[1] == 1)
startidx <- 1
else
startidx <- sum(valsPerSpect[1:(scanrange[1] - 1)]) + 1
endidx <- sum(valsPerSpect[1:scanrange[2]])
scans <- rep(scanrange[1]:scanrange[2],
valsPerSpect[scanrange[1]:scanrange[2]])
masses <- mz[startidx:endidx]
massidx <- 1:length(masses)
if (length(mzrange) >= 2) {
mzrange <- range(mzrange)
massidx <- massidx[(masses >= mzrange[1] & (masses <= mzrange[2]))]
}
int <- int[startidx:endidx][massidx]
if (log && (length(int) > 0))
int <- log(int + max(1 - min(int), 0))
cbind(time = scantime[scans[massidx]],
mz = masses[massidx],
intensity = int)
}
## .rawMat2 <- function(mz, int, scantime, valsPerSpect, mzrange = numeric(),
## rtrange = numeric(), scanrange = numeric,
## log = FALSE) {
## if (length(rtrange) >= 2) {
## rtrange <- range(rtrange)
## scanrange <- range(which((scantime >= rtrange[1]) &
## (scantime <= rtrange[2])))
## }
## if (length(scanrange) < 2)
## scanrange <- c(1, length(valsPerSpect))
## else scanrange <- range(scanrange)
## if (scanrange[1] == 1)
## startidx <- 1
## else
## startidx <- sum(valsPerSpect[1:(scanrange[1]-1)]) + 1
## endidx <- sum(valsPerSpect[1:scanrange[2]])
## scans <- rep(scanrange[1]:scanrange[2],
## valsPerSpect[scanrange[1]:scanrange[2]])
## masses <- mz[startidx:endidx]
## massidx <- 1:length(masses)
## if (length(mzrange) >= 2) {
## mzrange <- range(mzrange)
## massidx <- massidx[(masses >= mzrange[1] & (masses <= mzrange[2]))]
## }
## int <- int[startidx:endidx][massidx]
## if (log && (length(int) > 0))
## int <- log(int + max(1 - min(int), 0))
## cbind(time = scantime[scans[massidx]],
## mz = masses[massidx],
## intensity = int)
## }
############################################################
## plotRaw
setMethod("plotRaw", "xcmsRaw", function(object,
mzrange = numeric(),
rtrange = numeric(),
scanrange = numeric(),
log=FALSE,title='Raw Data' ) {
raw <- rawMat(object, mzrange, rtrange, scanrange, log)
if (nrow(raw) > 0) {
y <- raw[,"intensity"]
ylim <- range(y)
y <- y/ylim[2]
colorlut <- terrain.colors(16)
col <- colorlut[y*15+1]
plot(cbind(raw[,"time"], raw[,"mz"]), pch=20, cex=.5,
main = title, xlab="Seconds", ylab="m/z", col=col,
xlim=range(raw[,"time"]), ylim=range(raw[,"mz"]))
} else {
if (length(rtrange) >= 2) {
rtrange <- range(rtrange)
scanidx <- (object@scantime >= rtrange[1]) & (object@scantime <= rtrange[2])
scanrange <- c(match(TRUE, scanidx),
length(scanidx) - match(TRUE, rev(scanidx)))
} else if (length(scanrange) < 2)
scanrange <- c(1, length(object@scantime)) else
scanrange <- range(scanrange)
plot(c(NA,NA), main = title, xlab="Seconds", ylab="m/z",
xlim=c(object@scantime[scanrange[1]],object@scantime[scanrange[2]]), ylim=mzrange)
}
invisible(raw)
})
############################################################
## profMz
setMethod("profMz", "xcmsRaw", function(object) {
object@mzrange[1]+profStep(object)*(0:(dim(object@env$profile)[1]-1))
})
############################################################
## profMethods
setMethod("profMethod", "xcmsRaw", function(object) {
object@profmethod
})
setReplaceMethod("profMethod", "xcmsRaw", function(object, value) {
if (! (value %in% names(.profFunctions)))
stop("Invalid profile method")
if (length(object@env$mz) == 0) {
warning("MS1 scans empty. Skipping profile matrix creation.")
return(object)
}
have_profmethod <- object@profmethod
## Re-calculate the profile matrix if method differs
if (have_profmethod != value & profStep(object) > 0) {
object@env$profile <- profMat(object, method = value)
}
object@profmethod <- value
object
})
############################################################
## profStep
setMethod("profStep", "xcmsRaw", function(object) {
if (is.null(object@env$profile))
0
else
diff(object@mzrange)/(nrow(object@env$profile)-1)
})
## Update: related to issue #71
setReplaceMethod("profStep", "xcmsRaw", function(object, value) {
if (!is.numeric(value) && value < 0)
stop("'value' has to be a positive number!")
if (length(object@env$mz) == 0) {
warning("MS1 scans empty. Skipping profile matrix creation.")
return(object)
}
if (value == 0) {
if ("profile" %in% ls(object@env)) {
rm("profile", envir = object@env)
message("Removing profile matrix.")
}
return(object)
}
have_step <- profStep(object)
## Check if the value differs from step and only calculate if different.
if (have_step != value) {
## OK, now we're re-calculating
minmass <- round(min(object@env$mz) / value) * value
maxmass <- round(max(object@env$mz) / value) * value
object@mzrange <- c(minmass, maxmass)
## Fix for issue #98: to be in accordance with the "old" code we require
## that the number of rows of the profile matrix matches minmass to
## maxmass in steps of value
## To me that is somewhat problematic, as it means that the @mzrange does
## not correctly correspond to the range(object@env$mz)!
tmp <- seq(minmass, maxmass, by = value)
prf <- profMat(object, step = value)
object@env$profile <- prf[1:min(c(length(tmp), nrow(prf))), ]
}
return(object)
})
############################################################
## profStepPad
## The difference to the profStep? seems only to be the way how the range
## is calculated:
## profStep: minmass <- round(min(object@env$mz)/value)*value
## profStepPad: floor(mzrange(range(object@env$mz))[1])
setReplaceMethod("profStepPad", "xcmsRaw", function(object, value) {
if (!is.numeric(value) && value < 0)
stop("'value' has to be a positive number!")
if (length(object@env$mz) == 0) {
warning("MS1 scans empty. Skipping profile matrix creation.")
return(object)
}
if (value == 0) {
if ("profile" %in% ls(object@env)) {
rm("profile", envir = object@env)
message("Removing profile matrix.")
}
return(object)
}
mzr <- range(object@env$mz)
minmass <- floor(mzr[1])
maxmass <- ceiling(mzr[2])
object@mzrange <- c(minmass, maxmass)
object@env$profile <- profMat(object, step = value,
mzrange. = c(minmass, maxmass))
return(object)
})
############################################################
## profMedFilt
setMethod("profMedFilt", "xcmsRaw", function(object, massrad = 0, scanrad = 0) {
contdim <- dim(object@env$profile)
object@env$profile <- medianFilter(object@env$profile, massrad, scanrad)
})
############################################################
## profRange
setMethod("profRange", "xcmsRaw", function(object,
mzrange = numeric(),
rtrange = numeric(),
scanrange = numeric(), ...) {
if (length(object@env$profile)) {
contmass <- profMz(object)
if (length(mzrange) == 0) {
mzrange <- c(min(contmass), max(contmass))
} else if (length(mzrange) == 1) {
closemass <- contmass[which.min(abs(contmass-mzrange))]
mzrange <- c(closemass, closemass)
} else if (length(mzrange) > 2) {
mzrange <- c(min(mzrange), max(mzrange))
}
massidx <- which((contmass >= mzrange[1]) & (contmass <= mzrange[2]))
} else {
if (length(mzrange) == 0) {
mzrange <- range(object@env$mz)
} else {
mzrange <- c(min(mzrange), max(mzrange))
}
massidx <- integer()
}
if (mzrange[1] == mzrange[2])
masslab <- paste(mzrange[1], "m/z")
else
masslab <- paste(mzrange[1], "-", mzrange[2], " m/z", sep="")
if (length(rtrange) == 0) {
if (length(scanrange) == 0)
scanrange <- c(1, length(object@scanindex))
else if (length(scanrange) == 1)
scanrange <- c(scanrange, scanrange)
else if (length(scanrange) > 2)
scanrange <- c(max(1, min(scanrange)), min(max(scanrange), length(object@scantime)))
rtrange <- c(object@scantime[scanrange[1]], object@scantime[scanrange[2]])
} else if (length(rtrange) == 1) {
closetime <- object@scantime[which.min(abs(object@scantime-rtrange))]
rtrange <- c(closetime, closetime)
} else if (length(rtrange) > 2) {
rtrange <- c(min(rtrange), max(rtrange))
}
if (rtrange[1] == rtrange[2])
timelab <- paste(round(rtrange[1],1), "seconds")
else
timelab <- paste(round(rtrange[1],1), "-", round(rtrange[2],1), " seconds", sep="")
if (length(scanrange) == 0) {
scanidx <- which((object@scantime >= rtrange[1]) & (object@scantime <= rtrange[2]))
scanrange <- c(min(scanidx), max(scanidx))
} else {
scanidx <- scanrange[1]:scanrange[2]
}
if (scanrange[1] == scanrange[2])
scanlab <- paste("scan", scanrange[1])
else
scanlab <- paste("scans ", scanrange[1], "-", scanrange[2], sep="")
list(mzrange = mzrange, masslab = masslab, massidx = massidx,
scanrange = scanrange, scanlab = scanlab, scanidx = scanidx,
rtrange = rtrange, timelab = timelab)
})
############################################################
## rawEIC
setMethod("rawEIC", "xcmsRaw", function(object,
mzrange = numeric(),
rtrange = numeric(),
scanrange = numeric()) {
if (length(rtrange) >= 2) {
rtrange <- range(rtrange)
scanidx <- (object@scantime >= rtrange[1]) & (object@scantime <= rtrange[2])
scanrange <- c(match(TRUE, scanidx),
length(scanidx) - match(TRUE, rev(scanidx)) + 1)
} else if (length(scanrange) < 2)
scanrange <- c(1, length(object@scantime))
else
scanrange <- range(scanrange)
scanrange[1] <- max(1,scanrange[1])
scanrange[2] <- min(length(object@scantime), scanrange[2])
if (!is.double(object@env$mz))
object@env$mz <- as.double(object@env$mz)
if (!is.double(object@env$intensity))
object@env$intensity <- as.double(object@env$intensity)
if (!is.integer(object@scanindex))
object@scanindex <- as.integer(object@scanindex)
.Call("getEIC", object@env$mz, object@env$intensity, object@scanindex,
as.double(mzrange), as.integer(scanrange),
as.integer(length(object@scantime)), PACKAGE ='xcms' )
})
############################################################
## plotEIC
setMethod("plotEIC", "xcmsRaw", function(object,
mzrange = numeric(),
rtrange = numeric(),
scanrange = numeric(),
type="l", add=FALSE, ...) {
if(length(mzrange)==0)
mzrange <- range(object@env$mz)
if(length(rtrange)==0)
rtrange <- range(object@scantime)
EIC <- rawEIC(object,mzrange=mzrange, rtrange=rtrange, scanrange=scanrange)
points <- cbind(object@scantime[EIC$scan], EIC$intensity)
if(add){
points(points, type=type, ...)
}else{
plot(points, type=type, main=paste("Extracted Ion Chromatogram m/z ",mzrange[1]," - ",mzrange[2],sep=""), xlab="Seconds",
ylab="Intensity", xlim=rtrange, ...)
}
invisible(points)
})
############################################################
## rawMZ
setMethod("rawMZ", "xcmsRaw", function(object,
mzrange = numeric(),
rtrange = numeric(),
scanrange = numeric()) {
if (length(rtrange) >= 2) {
rtrange <- range(rtrange)
scanidx <- (object@scantime >= rtrange[1]) & (object@scantime <= rtrange[2])
scanrange <- c(match(TRUE, scanidx), length(scanidx) - match(TRUE, rev(scanidx)))
} else if (length(scanrange) < 2)
scanrange <- c(1, length(object@scantime))
else
scanrange <- range(scanrange)
scanrange[1] <- max(1,scanrange[1])
scanrange[2] <- min(length(object@scantime),scanrange[2])
if (!is.double(object@env$mz)) object@env$mz <- as.double(object@env$mz)
if (!is.double(object@env$intensity)) object@env$intensity <- as.double(object@env$intensity)
if (!is.integer(object@scanindex)) object@scanindex <- as.integer(object@scanindex)
.Call("getMZ",object@env$mz,object@env$intensity,object@scanindex,as.double(mzrange),as.integer(scanrange),as.integer(length(object@scantime)), PACKAGE ='xcms' )
})
############################################################
## findmzROI
setMethod("findmzROI", "xcmsRaw", function(object, mzrange=c(0.0,0.0), scanrange=c(1,length(object@scantime)),dev, minCentroids, prefilter=c(0,0), noise=0){
scanrange[1] <- max(1,scanrange[1])
scanrange[2] <- min(length(object@scantime),scanrange[2])
## mzrange not implemented yet
if (!is.double(object@env$mz)) object@env$mz <- as.double(object@env$mz)
if (!is.double(object@env$intensity)) object@env$intensity <- as.double(object@env$intensity)
if (!is.integer(object@scanindex)) object@scanindex <- as.integer(object@scanindex)
## .Call("findmzROI", object@env$mz,object@env$intensity,object@scanindex, as.double(mzrange),
## as.integer(scanrange), as.integer(length(object@scantime)),
## as.double(dev), as.integer(minCentroids), as.integer(prefilter), as.integer(noise), PACKAGE ='xcms' )
ROIs <- NULL
withRestarts(
tryCatch({
ROIs <- .Call("findmzROI",
object@env$mz,
object@env$intensity,
object@scanindex,
as.double(mzrange),
as.integer(scanrange),
as.integer(length(object@scantime)),
as.double(dev),
as.integer(minCentroids),
as.integer(prefilter),
as.integer(noise),
PACKAGE ='xcms' )
},
error=function(e) {if (grepl("m/z sort assumption violated !", e$message))
{invokeRestart("fixSort")} else {simpleError(e)}}),
fixSort = function() {
## Check and fix "m/z sort assumption violated !"
for(i in 1:length(object@scanindex)){
scan <- getScan(object, scan=i)
if(is.unsorted(scan[,"mz"])){
message("Scan ", i, " is unsorted. Fixing.")
o <- order(scan[,"mz"])
start <- object@scanindex[i] + 1
end <- start+nrow(scan) - 1
object@env$mz[start:end] <- scan[o, "mz"]
object@env$intensity[start:end] <- scan[o, "intensity"]
}
}
## Re-run now with fixed m/z order
ROIs <<- .Call("findmzROI",
object@env$mz,
object@env$intensity,
object@scanindex,
as.double(mzrange),
as.integer(scanrange),
as.integer(length(object@scantime)),
as.double(dev),
as.integer(minCentroids),
as.integer(prefilter),
as.integer(noise),
PACKAGE ='xcms' )
}
)
return(ROIs)
})
############################################################
## findKalmanROI
setMethod("findKalmanROI", "xcmsRaw", function(object, mzrange=c(0.0,0.0),
scanrange=c(1,length(object@scantime)),
minIntensity, minCentroids,
consecMissedLim, criticalVal,
ppm, segs, scanBack){
scanrange[1] <- max(1, scanrange[1])
scanrange[2] <- min(length(object@scantime), scanrange[2])
## Subset object by scanrange.
valsPS <- diff(c(object@scanindex, length(object@env$mz)))
do_findKalmanROI(mz = object@env$mz, int = object@env$intensity,
scantime = object@scantime,
valsPerSpect = valsPS, mzrange = mzrange,
scanrange = scanrange, minIntensity = minIntensity,
minCentroids = minCentroids,
consecMissedLim = consecMissedLim,
criticalVal = criticalVal, ppm = ppm, segs = segs,
scanBack = scanBack)
## .Call("massifquant", object@env$mz, object@env$intensity, object@scanindex,
## object@scantime, as.double(mzrange), as.integer(scanrange),
## as.integer(length(object@scantime)), as.double(minIntensity),
## as.integer(minCentroids),as.double(consecMissedLim), as.double(ppm),
## as.double(criticalVal), as.integer(segs), as.integer(scanBack),
## PACKAGE ='xcms' )
})
############################################################
## findPeaks.massifquant: Note the original code returned, if withWave = 1,
## a Peaks object otherwise a matrix!
setMethod("findPeaks.massifquant", "xcmsRaw", function(object,
ppm=10,
peakwidth = c(20,50),
snthresh = 10,
prefilter = c(3,100),
mzCenterFun = "wMean",
integrate = 1,
mzdiff = -0.001,
fitgauss = FALSE,
scanrange = numeric(),
noise = 0,
sleep = 0,
verbose.columns = FALSE,
criticalValue = 1.125,
consecMissedLimit = 2,
unions = 1,
checkBack = 0,
withWave = 0) {
if (sleep > 0)
cat("'sleep' argument is defunct and will be ignored.")
## Fix issue #61
## Sub-set the xcmsRaw based on scanrange
if (length(scanrange) < 2) {
scanrange <- c(1, length(object@scantime))
} else {
scanrange <- range(scanrange)
}
if (min(scanrange) < 1 | max(scanrange) > length(object@scantime)) {
scanrange[1] <- max(1, scanrange[1])
scanrange[2] <- min(length(object@scantime), scanrange[2])
message("Provided scanrange was adjusted to ", scanrange)
}
object <- object[scanrange[1]:scanrange[2]]
scanrange <- c(1, length(object@scantime))
## scanrange.old <- scanrange
## ## sanitize if too few or too many scanrange is given
## if (length(scanrange) < 2)
## scanrange <- c(1, length(object@scantime))
## else
## scanrange <- range(scanrange)
## ## restrict and sanitize scanrange to maximally cover all scans
## scanrange[1] <- max(1,scanrange[1])
## scanrange[2] <- min(length(object@scantime),scanrange[2])
## ## Mild warning if the actual scanrange doesn't match the scanrange
## ## argument
## if (!(identical(scanrange.old, scanrange)) &&
## (length(scanrange.old) > 0)) {
## cat("Warning: scanrange was adjusted to ",scanrange,"\n")
## ## Scanrange filtering
## keepidx <- seq.int(1, length(object@scantime)) %in% seq.int(scanrange[1], scanrange[2])
## object <- split(object, f=keepidx)[["TRUE"]]
## }
if (!isCentroided(object))
warning("It looks like this file is in profile mode.",
" Massifquant can process only centroid mode data !\n")
vps <- diff(c(object@scanindex, length(object@env$mz)))
res <- do_findChromPeaks_massifquant(mz = object@env$mz,
int = object@env$intensity,
scantime = object@scantime,
valsPerSpect = vps,
ppm = ppm, peakwidth = peakwidth,
snthresh = snthresh,
prefilter = prefilter,
mzCenterFun = mzCenterFun,
integrate = integrate,
mzdiff = mzdiff, fitgauss = fitgauss,
noise = noise,
verboseColumns = verbose.columns,
criticalValue = criticalValue,
consecMissedLimit = consecMissedLimit,
unions = unions, checkBack = checkBack,
withWave = as.logical(withWave))
invisible(new("xcmsPeaks", res))
})
############################################################
## findPeaks.massifquant: Note the original code returned, if withWave = 1,
## a Peaks object otherwise a matrix!
setMethod("findPeaks.massifquant", "xcmsRaw", function(object,
ppm=10,
peakwidth = c(20,50),
snthresh = 10,
prefilter = c(3,100),
mzCenterFun = "wMean",
integrate = 1,
mzdiff = -0.001,
fitgauss = FALSE,
scanrange = numeric(),
noise = 0,
sleep = 0,
verbose.columns = FALSE,
criticalValue = 1.125,
consecMissedLimit = 2,
unions = 1,
checkBack = 0,
withWave = 0) {
if (sleep > 0)
cat("'sleep' argument is defunct and will be ignored.")
## Fix issue #61
## Sub-set the xcmsRaw based on scanrange
if (length(scanrange) < 2) {
scanrange <- c(1, length(object@scantime))
} else {
scanrange <- range(scanrange)
}
if (min(scanrange) < 1 | max(scanrange) > length(object@scantime)) {
scanrange[1] <- max(1, scanrange[1])
scanrange[2] <- min(length(object@scantime), scanrange[2])
message("Provided scanrange was adjusted to ", scanrange)
}
object <- object[scanrange[1]:scanrange[2]]
scanrange <- c(1, length(object@scantime))
## scanrange.old <- scanrange
## ## sanitize if too few or too many scanrange is given
## if (length(scanrange) < 2)
## scanrange <- c(1, length(object@scantime))
## else
## scanrange <- range(scanrange)
## ## restrict and sanitize scanrange to maximally cover all scans
## scanrange[1] <- max(1,scanrange[1])
## scanrange[2] <- min(length(object@scantime),scanrange[2])
## ## Mild warning if the actual scanrange doesn't match the scanrange
## ## argument
## if (!(identical(scanrange.old, scanrange)) &&
## (length(scanrange.old) > 0)) {
## cat("Warning: scanrange was adjusted to ",scanrange,"\n")
## ## Scanrange filtering
## keepidx <- seq.int(1, length(object@scantime)) %in% seq.int(scanrange[1], scanrange[2])
## object <- split(object, f=keepidx)[["TRUE"]]
## }
if (!isCentroided(object))
warning("It looks like this file is in profile mode.",
" Massifquant can process only centroid mode data !\n")
vps <- diff(c(object@scanindex, length(object@env$mz)))
res <- do_findChromPeaks_massifquant(mz = object@env$mz,
int = object@env$intensity,
scantime = object@scantime,
valsPerSpect = vps,
ppm = ppm, peakwidth = peakwidth,
snthresh = snthresh,
prefilter = prefilter,
mzCenterFun = mzCenterFun,
integrate = integrate,
mzdiff = mzdiff, fitgauss = fitgauss,
noise = noise,
verboseColumns = verbose.columns,
criticalValue = criticalValue,
consecMissedLimit = consecMissedLimit,
unions = unions, checkBack = checkBack,
withWave = as.logical(withWave))
invisible(new("xcmsPeaks", res))
})
############################################################
## isCentroided
setMethod("isCentroided", "xcmsRaw", function(object){
if (length(getScan(object,length(object@scantime) / 2)) >2 ) {
quantile(diff(getScan(object,length(object@scantime) / 2)[,"mz"]),.25) > 0.025
} else {
TRUE
}
})
############################################################
## msnparent2ms
setMethod("msnparent2ms", "xcmsRaw", function(object) {
xr <- new("xcmsRaw")
xr@env$mz=object@msnPrecursorMz
xr@env$intensity=object@msnPrecursorIntensity
xr@scantime = object@msnRt
xr@scanindex = seq(1,length(object@msnRt))
xr@acquisitionNum = seq(1,length(object@msnRt))
xr@mzrange = range(object@msnPrecursorMz)
xr
})
############################################################
## msn2ms
setMethod("msn2ms", "xcmsRaw", function(object) {
object@tic <- rep(0, length(object@msnAcquisitionNum)) ##
object@scantime <- object@msnRt
object@acquisitionNum <- object@msnAcquisitionNum
object@scanindex <- object@msnScanindex
object@env$mz <- object@env$msnMz
object@env$intensity <- object@env$msnIntensity
invisible(object)
})
############################################################
## deepCopy
setMethod("deepCopy", "xcmsRaw", function(object) {
x <- object
x@env <- new.env(parent=.GlobalEnv)
for (variable in ls(object@env)) {
eval(parse(text=paste("x@env$",variable," <- object@env$",variable,sep="")))
}
invisible(x)
})
############################################################
## levelplot
## levelplot for xcmsRaw objects; contains code from the image method, but uses the levelplot
## from the lattice package.
setMethod("levelplot", "xcmsRaw", function(x, log=TRUE,
col.regions=colorRampPalette(brewer.pal(9, "YlOrRd"))(256), ...){
## some code taken from plotSurf...
sel <- profRange(x, ...)
zvals <- x@env$profile[sel$massidx, sel$scanidx]
if(log){
zvals <- log(zvals+max(c(-min(zvals), 1)))
}
## y axis is time
yvals <- x@scantime[sel$scanidx]
yrange <- range(yvals)
## y has to be sequentially increasing!
if(length(unique(yvals))!=length(yvals))
yvals <- 1:length(yvals)
## x is m/z
xvals <- profMz(x)[sel$massidx]
## that's much slower...
## grid <- expand.grid(x=xvals, y=yvals)
## grid <- cbind(grid, z=zvals)
## grid$z <- zvals
######
## now i have to match the x and y to the z.
## as.numeric of z returns values by column(!), i.e. the first nrow(z) correspond to
## the x of 1.
xvals <- rep(xvals, ncol(zvals))
yvals <- rep(yvals, each=nrow(zvals))
zvals <- as.numeric(zvals)
## get the file name
fileNpath <- x@filepath[1]
fileName <- unlist(strsplit(fileNpath, split=.Platform$file.sep))
fileName <- fileName[length(fileName)]
plt <- levelplot(zvals~xvals*yvals,
xlab="m/z", ylab="Time",
colorkey=list(height=1, width=0.7),
main=list(fileName, side=1, line=0.5), col.regions=col.regions)
## trellis.focus("legend", side="right", clipp.off=TRUE, highlight=FALSE)
## grid.text("Int.", 0.2, 0, hjust=0.5, vjust=1)
## trellis.unfocus()
plt
})
############################################################
## profinfo
setMethod("profinfo", "xcmsRaw", function(object) {
pinfo <- object@profparam
## fill with additional method and step.
pinfo$method <- profMethod(object)
pinfo$step <- profStep(object)
return(pinfo)
})
############################################################
## scanrange
setMethod("scanrange", "xcmsRaw", function(object) {
if(.hasSlot(object, "scanrange")){
srange <- object@scanrange
if(length(srange) == 0){
## for compatibility with the xcmsSet and xcmsRaw functions,
## which default the scanrange argument to NULL.
return(NULL)
}
return(srange)
}else{
warning("No slot scanrange available, consider updating the",
" object using the 'updateObject' method.")
return(NULL)
}
})
setReplaceMethod("scanrange", "xcmsRaw", function(object, value) {
if(.hasSlot(object, "scanrange")){
object@scanrange <- value
}else{
warning("Object has no slot scanrange, condider updating",
" the object using the 'updateObject' method.")
}
object
})
############################################################
## mslevel
setMethod("mslevel", "xcmsRaw", function(object){
if(.hasSlot(object, "mslevel")){
mlevel <- object@mslevel
if(length(mlevel) == 0){
## for compatibility with the xcmsSet and xcmsRaw functions,
## which default the mslevel argument to NULL.
return(NULL)
}
return(object@mslevel)
}else{
warning("No slot mslevel available, consider updating the",
" object using the 'updateObject' method.")
return(NULL)
}
})
setReplaceMethod("mslevel", "xcmsRaw", function(object, value){
if(.hasSlot(object, "mslevel")){
object@mslevel <- value
}else{
warning("Object has no slot mslevel, consider updating",
" the object using the 'updateObject' method.")
}
object
})
############################################################
## plotScan
setMethod("plotScan", "xcmsRaw", function(object, scan, mzrange = numeric(),
ident = FALSE)
{
if (scan<1 || scan>length(object@scanindex) ) {
warning("scan out of range")
return()
}
## handle last spectrum
if (scan == length(object@scanindex)) {
followingScanIndex <- length(object@env$mz)
} else {
followingScanIndex <- object@scanindex[scan+1]
}
## hendle empty spectra
if (object@scanindex[scan] == length(object@env$mz) ||
object@scanindex[scan] == followingScanIndex) {
warning("empty scan")
return()
}
idx <- (object@scanindex[scan]+1):min(followingScanIndex,
length(object@env$mz), na.rm=TRUE)
if (length(mzrange) >= 2) {
mzrange <- range(mzrange)
idx <- idx[object@env$mz[idx] >= mzrange[1] & object@env$mz[idx] <= mzrange[2]]
}
points <- cbind(object@env$mz[idx], object@env$intensity[idx])
title = paste("Mass Spectrum: ", round(object@scantime[scan], 1),
" seconds (scan ", scan, ")", sep = "")
plot(points, type="h", main = title, xlab="m/z", ylab="Intensity")
if (ident)
return(identify(points, labels = round(points[,1], 1)))
invisible(points)
})
############################################################
## plotSpec
setMethod("plotSpec", "xcmsRaw", function(object, ident = FALSE,
vline = numeric(0), ...) {
sel <- profRange(object, ...)
title = paste("Averaged Mass Spectrum: ", sel$timelab, " (",
sel$scanlab, ")", sep = "")
points <- cbind(profMz(object)[sel$massidx],
rowMeans(object@env$profile[sel$massidx,sel$scanidx,drop=FALSE]))
plot(points, type="l", main = title, xlab="m/z", ylab="Intensity")
if (length(vline))
abline(v = vline, col = "red")
if (ident)
return(identify(points, labels = round(points[,1], 1)))
invisible(points)
})
############################################################
## plotChrom
setMethod("plotChrom", "xcmsRaw", function(object, base = FALSE, ident = FALSE,
fitgauss = FALSE, vline = numeric(0), ...) {
sel <- profRange(object, ...)
if (base) {
title = paste("Base Peak Chromatogram: ", sel$masslab, sep = "")
pts <- cbind(object@scantime[sel$scanidx],
colMax(object@env$profile[sel$massidx,sel$scanidx,drop=FALSE]))
}
else {
title = paste("Averaged Ion Chromatogram: ", sel$masslab, sep = "")
pts <- cbind(object@scantime[sel$scanidx],
colMeans(object@env$profile[sel$massidx,sel$scanidx,drop=FALSE]))
}
plot(pts, type="l", main = title, xlab="Seconds", ylab="Intensity")
if (length(vline))
abline(v = vline, col = "red")
if (fitgauss) {
fit <- nls(y ~ SSgauss(x, mu, sigma, h), data.frame(x = pts[,1], y = pts[,2]))
points(pts[,1], fitted(fit), type = "l", col = "red", lwd = 2)
return(fit)
}
if (ident)
return(identify(pts, labels = round(pts[,1], 1)))
invisible(pts)
})
############################################################
## image
setMethod("image", "xcmsRaw", function(x, col = rainbow(256), ...) {
sel <- profRange(x, ...)
zlim <- log(range(x@env$intensity))
title <- paste("XC/MS Log Intensity Image (Profile Method: ",
x@profmethod, ")", sep = "")
if (zlim[1] < 0) {
zlim <- log(exp(zlim)+1)
image(profMz(x)[sel$massidx], x@scantime[sel$scanidx],
log(x@env$profile[sel$massidx, sel$scanidx]+1),
col = col, zlim = zlim, main = title, xlab="m/z", ylab="Seconds")
} else
image(profMz(x)[sel$massidx], x@scantime[sel$scanidx],
log(x@env$profile[sel$massidx, sel$scanidx]),
col = col, zlim = zlim, main = title, xlab="m/z", ylab="Seconds")
})
############################################################
## plotSurf
setMethod("plotSurf", "xcmsRaw", function(object, log = FALSE,
aspect = c(1, 1, .5), ...) {
require(rgl) || stop("Couldn't load package rgl")
sel <- profRange(object, ...)
y <- object@env$profile[sel$massidx, sel$scanidx]
if (log)
y <- log(y+max(1-min(y), 0))
ylim <- range(y)
x <- seq(0, aspect[1], length=length(sel$massidx))
z <- seq(0, aspect[2], length=length(sel$scanidx))
y <- y/ylim[2]*aspect[3]
colorlut <- terrain.colors(256)
col <- colorlut[y/aspect[3]*255+1]
rgl.clear("shapes")
rgl.clear("bbox")
rgl.surface(x, z, y, color = col, shininess = 128)
rgl.points(0, 0, 0, alpha = 0)
mztics <- pretty(sel$mzrange, n = 5*aspect[1])
rttics <- pretty(sel$rtrange, n = 5*aspect[2])
inttics <- pretty(c(0,ylim), n = 10*aspect[3])
inttics <- inttics[inttics > 0]
rgl.bbox(xat = (mztics - sel$mzrange[1])/diff(sel$mzrange)*aspect[1],
xlab = as.character(mztics),
yat = inttics/ylim[2]*aspect[3],
ylab = as.character(inttics),
zat = (rttics - sel$rtrange[1])/diff(sel$rtrange)*aspect[2],
zlab = as.character(rttics),
ylen = 0, alpha=0.5)
})
############################################################
## getMsnScan
setMethod("getMsnScan", "xcmsRaw", function(object, scan, mzrange = numeric()) {
if (scan < 0)
scan <- length(object@msnRt) + 1 + scan
idx <- seq(object@msnScanindex[scan]+1, min(object@msnScanindex[scan+1],
length(object@env$msnMz), na.rm=TRUE))
if (length(mzrange) >= 2) {
mzrange <- range(mzrange)
idx <- idx[object@env$msnMz[idx] >= mzrange[1] & object@env$msnMz[idx] <= mzrange[2]]
}
points <- cbind(mz = object@env$msnMz[idx], intensity = object@env$msnIntensity[idx])
invisible(points)
})
############################################################
## AutoLockMass
setMethod("AutoLockMass", "xcmsRaw", function(object) {
if(length(grep("xml|mzData|mzXML|mzML", object@filepath, ignore.case=TRUE)) >= 1){
tempFreq<-diff(which(diff(object@scantime) == 0))-1
idx <- which(tempFreq != floor(mean(tempFreq))) ## only needed for newer lockmass signal
if(is.nan(mean(tempFreq)) ){
dn<-density(diff(object@scantime))
lockMassScans <- quantile(dn$x, .75) ## hopefully always correct (?)
inx<-which(diff(object@scantime) >= lockMassScans) ## these seems to be some of the new files
return(inx)
}else if(all(tempFreq == mean(tempFreq)) ){
freqLock<-mean(tempFreq)
} else if(all(idx == which(tempFreq != floor(mean(tempFreq) )) )){
## for the newer mzML and mzXML not sure why the change?
## This means that there is only one gap :( ??
stop("This file is different from the normally seen files and requires special programming\n
This functionality has not been implemented yet\n ")
## these files seem to come either from newer MS units or/and msconvert ....
} else {
freqLock<-mean(tempFreq)
warning("\nLock mass frequency wasn't detected correctly", immediate.=TRUE)
}
if(diff(object@scantime[1:5])[1] == 0 ){
start<-1
} else{
start<-freqLock
}
return(makeacqNum(object, freqLock, start))
} else if(length(grep("cdf", object@filepath, ignore.case=TRUE)) >= 1){
## check to see if we have the X02.CDF files around
## These files should be the lock mass channel
file02<-list.files(gsub("01.CDF", "02.CDF", object@filepath), recursive=T)
if(length(file02)> 0){
xr<-xcmsRaw(file02)
lockMass<-sapply(xr@scantime, function(x, object){
which.min(abs(object@scantime - x))
}, object)
return(lockMass)
} else {
## we couldn't find the files so lets try to find them automatically
hr <- hist(diff(object@scantime), breaks=4, plot=FALSE)
if(length(hr$counts) > 2){
idx<-which(hr$counts == 0)
## could have something here about which way the plot is ie cor R is - or +
# if(cor(hr$mids, hr$counts) < 0){
inx<-which(diff(object@scantime) >= hr$mids[(max(idx))])
# } else {
# inx<-which()
# }
}else if(length(hr$counts) == 2){
inx<-which(diff(object@scantime) >= hr$mids[2])
} else {
stop("File appears to have been run without lock mass\n ")
}
if(length(inx) <= 1){
warning("\nLock mass frequency wasn't detected", immediate.=TRUE)
return(0)
}
## above we're looking for scantimes that are much longer than the normal scan times
tempFreq<-diff(inx)-1
if(all(tempFreq == median(tempFreq)) ){
freqLock<-median(tempFreq)
}else{
freqLock<-median(tempFreq)
warning("Lock mass frequency wasn't detected correctly\n", immediate.=TRUE)
}
if(inx[1] == 0 || inx[1] == 1){
start<-1
}else{
start<-freqLock
}
#return(inx)
return(makeacqNum(object, freqLock, start))
}
} else{
stop("Couldn't detect file type\n")
}
})
############################################################
## makeacqNum
setMethod("makeacqNum", "xcmsRaw", function(object, freq, start=1) {
freq<-freq+1 ##nessary for the start at +1 and others since 1st scan is +1
acqNum<-numeric()
fo<-seq(from=start, to=length(object@scanindex), by=freq)
for(i in fo){
acqNum<-c(acqNum, i,i+1)
}
return(acqNum)
})
############################################################
## stitch
setMethod("stitch", "xcmsRaw", function(object, lockMass) {
if(length(grep("xml|mzData", object@filepath, ignore.case=TRUE)) >= 1){
type<-stitch.xml
} else if(length(grep("cdf", object@filepath, ignore.case=TRUE)) >= 1){
## lets check to see if lockMass is one scan or two
if(any(diff(lockMass) == 0)){
type<-stitch.netCDF.new
}else {
type<-stitch.netCDF
}
} else{
stop("Unknown stitch method \n")
}
invisible(do.call(type, list(object, lockMass)))
})
############################################################
## stitch.xml
setMethod("stitch.xml", "xcmsRaw", function(object, lockMass) {
ob<-new("xcmsRaw")
ob@env$mz<-object@env$mz
ob@env$intensity<-object@env$intensity
ob@scanindex<-object@scanindex
ob@scantime<-object@scantime
ob@acquisitionNum<-1:length(ob@scanindex)
ob@filepath<-object@filepath
ob@mzrange<-range(ob@env$mz)
ob@profmethod<-object@profmethod
ob@tic<-object@tic
ob@profparam<-list()
## Array [x, y, z] with
## - x: mz and intensity
## - y: spectrum (1: max measurements within one of the spectra)
## - z: scans (1: number of spectra)
arr <- array(dim = c(2, max(diff(ob@scanindex)), length(ob@scanindex)))
if(lockMass[1] == 1){
lockMass<-lockMass[3:length(lockMass)]
}
## Remove the last lock mass if it is too close by the end
if ((lockMass[length(lockMass)] + 2) > length(ob@scanindex))
lockMass <- lockMass[1:(length(lockMass) - 1)]
## If the number of lockMass values is not even splitting them into a
## two-column matrix is not OK (causes also the first lockMass spectrum to
## be overwritten twice. That's to get rid of the warning in issue #173.
if (length(lockMass) %% 2)
lockMass <- c(lockMass, -99)
lockMass<-matrix(lockMass, ncol=2, byrow=TRUE)
## if((lockMass[nrow(lockMass),2]+2) > length(ob@scanindex)){
## lockMass<-lockMass[1:(nrow(lockMass)-1),]
## }
## We're looping from 1 to length - 1, thus we have to fill in the last
## scan later.
for(i in 1:(length(ob@scanindex)-1)){
if(any(i == lockMass[, 1])){
## Place mz and intensity values from the previous scan into the
## array and fill the rest with NA.
arr[1,,i] <-c(object@env$mz[(object@scanindex[(i-1)]+1):object@scanindex[i]],
rep(NA, (max(diff(object@scanindex))-
length((object@scanindex[(i-1)]+1):object@scanindex[i])) ))
arr[2,,i] <-c(object@env$intensity[(object@scanindex[(i-1)]+1):object@scanindex[i]],
rep(NA, (max(diff(object@scanindex)) -
length((object@scanindex[(i-1)]+1):object@scanindex[i])) ))
} else if(any(i == lockMass[, 2])){
## Place mz and intensity values from the next scan into the array.
arr[1,,i] <-c(object@env$mz[(object@scanindex[i+1]+1):object@scanindex[(i+2)]],
rep(NA, (max(diff(object@scanindex)) -
length((object@scanindex[i+1]+1):object@scanindex[(i+2)])) ))
arr[2,,i] <-c(object@env$intensity[(object@scanindex[i+1]+1):object@scanindex[(i+2)]],
rep(NA, (max(diff(object@scanindex)) -
length((object@scanindex[i+1]+1):object@scanindex[(i+2)])) ))
} else{
## Just fill with the actual values.
arr[1,,i] <-c(object@env$mz[(object@scanindex[i]+1):object@scanindex[i+1]],
rep(NA, (max(diff(object@scanindex))-
length((object@scanindex[i]+1):object@scanindex[i+1])) ))
arr[2,,i] <-c(object@env$intensity[(object@scanindex[i]+1):object@scanindex[i+1]],
rep(NA, (max(diff(object@scanindex)) -
length((object@scanindex[i]+1):object@scanindex[i+1])) ))
}
## mz is in 1; Intensity is in 2
##remake scanindex
if(i == 1){
ob@scanindex[i]<-as.integer(0)
}else if(i == length(ob@scanindex)-1){
ob@scanindex[i]<-as.integer(length(na.omit(arr[1,,(i-1)]))+ob@scanindex[(i-1)])
ob@scanindex[i+1]<-as.integer(length(na.omit(arr[1,,i]))+ob@scanindex[i])
# ob@scanindex[i+1]<-as.integer(length(ob@env$mz))
}else{
ob@scanindex[i]<-as.integer(length(na.omit(arr[1,,(i-1)]))+ob@scanindex[(i-1)])
}
}
## Fix for #173: fill also values for the last scan.
last_i <- length(ob@scanindex)
fetch_idx <- (object@scanindex[last_i] + 1):length(object@env$mz)
put_idx <- 1:length(fetch_idx)
arr[1, put_idx, length(ob@scanindex)] <- object@env$mz[fetch_idx]
arr[2, put_idx, length(ob@scanindex)] <- object@env$intensity[fetch_idx]
NAidx<-is.na(arr[1,,])
ob@env$mz<-as.numeric(arr[1,,][!NAidx])
ob@env$intensity<-as.numeric(arr[2,,][!NAidx])
ob<-remakeTIC(ob) ## remake TIC
return(ob)
})
############################################################
## stitch.netCDF
setMethod("stitch.netCDF", "xcmsRaw", function(object, lockMass) {
if(length(lockMass) == 0 | all(lockMass == 0)){
return(object)
}
ob<-new("xcmsRaw")
ob@filepath<-object@filepath
ob@mzrange<-range(ob@env$mz)
ob@profmethod<-object@profmethod
ob@profparam<-list()
arr<-array(dim=c(2,max(diff(object@scanindex)), (length(object@scanindex)+length(lockMass)) ))
ob@scanindex <- integer(length=length(arr[1,1,]))
ob@acquisitionNum<-1:length(ob@scanindex)
if(lockMass[1] == 1){
lockMass<-lockMass[3:length(lockMass)]
}
lockMass<-matrix(lockMass, ncol=2, byrow=TRUE)
if((lockMass[nrow(lockMass),2]+2) > length(ob@scanindex)){
lockMass<-lockMass[1:(nrow(lockMass)-1),]
} ## remove the last lock mass scan if it's at the end of the run
add<-0
arrMax<-length(arr[1,,1])
scanIx<-integer(length(arr[1,1,]))
for(i in 1:length(object@scanindex)){
# if((i+add) > length(object@scanindex)){
# break
# }
scan<-getScan(object, i)
arr[1,,i+add]<- c(scan[,"mz"], rep(NA, (arrMax-length(scan[,"mz"])) ))
arr[2,,i+add]<- c(scan[,"intensity"], rep(NA, (arrMax-length(scan[,"intensity"])) ))
scanIx[(i+add)+1]<- (scanIx[(i+add)])+nrow(scan)
##proably going to need a cut at the end of scanIx +1 problem
if(any(i == lockMass[,1])){
arr[1,,i+1+add]<- c(scan[,"mz"], rep(NA, (arrMax-length(scan[,"mz"])) ))
arr[2,,i+1+add]<- c(scan[,"intensity"], rep(NA, (arrMax-length(scan[,"intensity"])) ))
scanIx[(i+add)+2]<- (scanIx[1+i+add])+nrow(scan)
scan<-getScan(object, i+1)
arr[1,,i+2+add]<- c(scan[,"mz"], rep(NA, (arrMax-length(scan[,"mz"])) ))
arr[2,,i+2+add]<- c(scan[,"intensity"], rep(NA, (arrMax-length(scan[,"intensity"])) ))
scanIx[(i+add)+3]<- (scanIx[(i+2)+add])+nrow(scan)
add<-add+2
}
}
NAidx<-is.na(arr[1,,])
ob@env$mz<-as.numeric(arr[1,,][!NAidx])
ob@env$intensity<-as.numeric(arr[2,,][!NAidx])
##remake scanindex
# scanInx<- as.integer(apply(arr[1,,], 2, function(x){
# inx<-is.na(x)
# length(x[!inx]) ## need to add these length together
# }))
ob@scanindex<-as.integer(scanIx)
ob@scantime <- sapply(1:length(ob@scanindex), function(x, time){
time*x
}, mean(diff(object@scantime)))
ob<-remakeTIC(ob) ## remake TIC
return(ob)
})
############################################################
## stitch.netCDF.new
setMethod("stitch.netCDF.new", "xcmsRaw", function(object, lockMass) {
if(length(lockMass) == 0 | all(lockMass == 0)){
return(object)
}
ob<-new("xcmsRaw")
ob@filepath<-object@filepath
ob@mzrange<-range(ob@env$mz)
ob@profmethod<-object@profmethod
ob@profparam<-list()
arr<-array(dim=c(2,max(diff(object@scanindex)), (length(object@scanindex)+length(lockMass)) ))
ob@scanindex <- integer(length=length(arr[1,1,]))
ob@acquisitionNum<-1:length(ob@scanindex)
if(lockMass[1] == 1){
lockMass<-lockMass[2:length(lockMass)]
}
# lockMass<-matrix(lockMass, ncol=2, byrow=TRUE)
add<-0
arrMax<-length(arr[1,,1])
scanIx<-integer(length(arr[1,1,]))
for(i in 1:length(object@scanindex)){
scan<-getScan(object, i)
arr[1,,i+add]<- c(scan[,"mz"], rep(NA, (arrMax-length(scan[,"mz"])) ))
arr[2,,i+add]<- c(scan[,"intensity"], rep(NA, (arrMax-length(scan[,"intensity"])) ))
scanIx[(i+add)+1]<- (scanIx[(i+add)])+nrow(scan)
if(any(i == lockMass)){
arr[1,,i+1+add] <- c(scan[,"mz"], rep(NA, (arrMax-length(scan[,"mz"])) ))
arr[2,,i+1+add] <- c(scan[,"intensity"], rep(NA, (arrMax-length(scan[,"intensity"])) ))
scanIx[(i+add)+2]<- (scanIx[1+i+add])+nrow(scan)
add<-add+1
## for the moment lets be dirty and add the scan before
## upgrade later to 1/2 and 1/2 from each scan
}
}
NAidx<-is.na(arr[1,,])
ob@env$mz<-as.numeric(arr[1,,][!NAidx])
ob@env$intensity<-as.numeric(arr[2,,][!NAidx])
## above is to remove any NA buffers from the array
ob@scanindex<-as.integer(scanIx)
ob@scantime <- sapply(1:length(ob@scanindex), function(x, time){
time*x
}, mean(diff(object@scantime))) ## remake the scantime vector
ob<-remakeTIC(ob) ## remake TIC
return(ob)
})
############################################################
## [
## Subset by scan.
#' @title Subset an xcmsRaw object by scans
#'
#' @aliases subset-xcmsRaw
#'
#' @description Subset an \code{\linkS4class{xcmsRaw}} object by scans. The
#' returned \code{\linkS4class{xcmsRaw}} object contains values for all
#' scans specified with argument \code{i}. Note that the \code{scanrange}
#' slot of the returned \code{xcmsRaw} will be
#' \code{c(1, length(object@scantime))} and hence not \code{range(i)}.
#'
#' @details Only subsetting by scan index in increasing order or by a logical
#' vector are supported. If not ordered, argument \code{i} is sorted
#' automatically. Indices which are larger than the total number of scans
#' are discarded.
#'
#' @param x The \code{\linkS4class{xcmsRaw}} object that should be sub-setted.
#'
#' @param i Integer or logical vector specifying the scans/spectra to which
#' \code{x} should be sub-setted.
#'
#' @param j Not supported.
#'
#' @param drop Not supported.
#'
#' @return The sub-setted \code{\linkS4class{xcmsRaw}} object.
#'
#' @author Johannes Rainer
#'
#' @seealso \code{\link{split.xcmsRaw}}
#'
#' @examples
#' ## Load a test file
#' file <- system.file('cdf/KO/ko15.CDF', package = "faahKO")
#' xraw <- xcmsRaw(file, profstep = 0)
#' ## The number of scans/spectra:
#' length(xraw@scantime)
#'
#' ## Subset the object to scans with a scan time from 3500 to 4000.
#' xsub <- xraw[xraw@scantime >= 3500 & xraw@scantime <= 4000]
#' range(xsub@scantime)
#' ## The number of scans:
#' length(xsub@scantime)
#' ## The number of values of the subset:
#' length(xsub@env$mz)
setMethod("[", signature(x = "xcmsRaw",
i = "logicalOrNumeric",
j = "missing",
drop = "missing"),
function(x, i, j, drop) {
nScans <- length(x@scantime)
if (is.logical(i))
i <- which(i)
if (length(i) == 0)
return(new("xcmsRaw"))
## Ensure i is sorted.
if (is.unsorted(i)) {
warning("'i' has been sorted as subsetting supports",
" only sorted indices.")
i <- sort(i)
}
## Ensure that we're not supposed to return duplicated scans!
i <- unique(i)
## Check that i is within the number of scans
outsideRange <- !(i %in% 1:nScans)
if (any(outsideRange)) {
## Throw an error or just a warning?
iOut <- i[outsideRange]
i <- i[!outsideRange]
warning("Some of the indices in 'i' are larger than the",
" total number of scans which is ", nScans)
}
## If i is equal to the number of scans just return the object
if (all(1:nScans %in% i))
return(x)
have_profstep <- profStep(x)
valsPerSpect <- diff(c(x@scanindex, length(x@env$mz)))
## Subset:
## 1) scantime
x@scantime <- x@scantime[i]
## 2) scanindex
x@scanindex <- valueCount2ScanIndex(valsPerSpect[i])
## 3) @env$mz
newE <- new.env()
valsInScn <- rep(1:nScans, valsPerSpect) %in% i
newE$mz <- x@env$mz[valsInScn]
## 4) @env$intensity
newE$intensity <- x@env$intensity[valsInScn]
x@env <- newE
## 5) The remaining slots
x@tic <- x@tic[i]
if (length(x@polarity) == nScans)
x@polarity <- x@polarity[i]
if (length(x@acquisitionNum) == nScans)
x@acquisitionNum <- x@acquisitionNum[i]
x@mzrange <- range(x@env$mz)
scanrange(x) <- c(1, length(x@scantime))
## Profile matrix
if (have_profstep > 0) {
## Create the profile matrix.
## Call profStep<- that will also update the mzrange properly
profStep(x) <- have_profstep
}
## TODO: what with the MSn data?
return(x)
})
#' @title The profile matrix
#'
#' @aliases profile-matrix profMat profMat,xcmsRaw-method
#'
#' @description The \emph{profile} matrix is an n x m matrix, n (rows)
#' representing equally spaced m/z values (bins) and m (columns) the
#' retention time of the corresponding scans. Each cell contains the maximum
#' intensity measured for the specific scan and m/z values falling within
#' the m/z bin.
#'
#' The \code{profMat} method creates a new profile matrix or returns the
#' profile matrix within the object's \code{@env} slot, if available.
#' Settings for the profile matrix generation, such as \code{step} (the bin
#' size), \code{method} or additional settings are extracted from the
#' respective slots of the \code{\linkS4class{xcmsRaw}} object.
#' Alternatively it is possible to specify all of the settings as
#' additional parameters.
#'
#' @details Profile matrix generation methods:
#' \describe{
#' \item{bin}{The default profile matrix generation method that does a
#' simple binning, i.e. aggregating of intensity values falling within an
#' m/z bin.}
#' \item{binlin}{Binning followed by linear interpolation to impute missing
#' values. The value for m/z bins without a measured intensity are inferred
#' by a linear interpolation between neighboring bins with a measured
#' intensity.}
#' \item{binlinbase}{Binning followed by a linear interpolation to impute
#' values for empty elements (m/z bins) within a user-definable proximity to
#' non-empty elements while stetting the element's value to the
#' \code{baselevel} otherwise. See \code{impute = "linbase"} parameter of
#' \code{\link{imputeLinInterpol}} for more details.}
#' \item{intlin}{Set the elements' values to the integral of the linearly
#' interpolated data from plus to minus half the step size.}
#' }
#'
#' @note From \code{xcms} version 1.51.1 on only the \code{profMat} method
#' should be used to extract the profile matrix instead of the previously
#' default way to access it directly \emph{via} \code{object@env$profile}.
#'
#' @param object The \code{\linkS4class{xcmsRaw}} object.
#'
#' @param method The profile matrix generation method. Allowed are \code{"bin"},
#' \code{"binlin"}, \code{"binlinbase"} and \code{"intlin"}. See details
#' section for more information.
#'
#' @param step numeric(1) representing the m/z bin size.
#'
#' @param baselevel numeric(1) representing the base value to which
#' empty elements (i.e. m/z bins without a measured intensity) should be
#' set. Only considered if \code{method = "binlinbase"}. See
#' \code{baseValue} parameter of \code{\link{imputeLinInterpol}} for more
#' details.
#'
#' @param basespace numeric(1) representing the m/z length after
#' which the signal will drop to the base level. Linear interpolation will
#' be used between consecutive data points falling within
#' \code{2 * basespace} to each other. Only considered if
#' \code{method = "binlinbase"}. If not specified, it defaults to
#' \code{0.075}. Internally this parameter is translated into the
#' \code{distance} parameter of the \code{\link{imputeLinInterpol}}
#' function by \code{distance = floor(basespace / step)}. See
#' \code{distance} parameter of \code{\link{imputeLinInterpol}} for more
#' details.
#'
#' @param mzrange. Optional numeric(2) manually specifying the mz value range to
#' be used for binnind. If not provided, the whole mz value range is used.
#'
#' @seealso \code{\linkS4class{xcmsRaw}}, \code{\link{binYonX}} and
#' \code{\link{imputeLinInterpol}} for the employed binning and
#' missing value imputation methods, respectively.
#' \code{\link{profMat,XCMSnExp-method}} for the method on
#' \code{\link{XCMSnExp}} objects.
#'
#' @return \code{profMat} returns the profile matrix (rows representing scans,
#' columns equally spaced m/z values).
#'
#' @author Johannes Rainer
#'
#' @examples
#' file <- system.file('cdf/KO/ko15.CDF', package = "faahKO")
#' ## Load the data without generating the profile matrix (profstep = 0)
#' xraw <- xcmsRaw(file, profstep = 0)
#' ## Extract the profile matrix
#' profmat <- profMat(xraw, step = 0.3)
#' dim(profmat)
#' ## If not otherwise specified, the settings from the xraw object are used:
#' profinfo(xraw)
#' ## To extract a profile matrix with linear interpolation use
#' profmat <- profMat(xraw, step = 0.3, method = "binlin")
#' ## Alternatively, the profMethod of the xraw objects could be changed
#' profMethod(xraw) <- "binlin"
#' profmat_2 <- profMat(xraw, step = 0.3)
#' all.equal(profmat, profmat_2)
#'
#' @rdname profMat-xcmsSet
#'
#' @name profMat-xcmsSet
setMethod("profMat", signature(object = "xcmsRaw"), function(object, method,
step,
baselevel,
basespace,
mzrange.) {
## Call the .createProfileMatrix if the internal profile matrix is empty or
## if the settings differ from the internal settings.
pi <- profinfo(object)
if (missing(method))
method <- pi$method
if (missing(step))
step <- pi$step
if (missing(baselevel))
baselevel <- pi$baselevel
if (missing(basespace))
basespace <- pi$basespace
if (length(object@env$profile) > 0) {
## Check if the settings are the same...
have_method <- pi$method
have_step <- pi$step
have_basespace <- pi$basespace
have_baselevel <- pi$baselevel
if (!is.character(all.equal(list(method, step, basespace, baselevel),
list(have_method, have_step,
have_basespace, have_baselevel)))) {
return(object@env$profile)
}
}
if (missing(mzrange.)) {
mzrange. <- NULL
} else {
if (length(mzrange.) != 2 | !is.numeric(mzrange.))
stop("If provided, 'mzrange.' has to be a numeric of length 2!")
}
## Calculate the profile matrix
if (step <= 0)
stop("Can not calculate a profile matrix with step=",step, "! Either",
" provide a positive number with argument 'step' or use the",
" 'profStep' method to set the step parameter for the xcmsSet.")
message("Create profile matrix with method '", method,
"' and step ", step, " ... ", appendLF = FALSE)
res <- .createProfileMatrix(mz = object@env$mz, int = object@env$intensity,
valsPerSpect = diff(c(object@scanindex,
length(object@env$mz))),
method = method, step = step,
baselevel = baselevel, basespace = basespace,
mzrange. = mzrange.)
message("OK")
res
})
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Defines functions .rawMat
## All Methods for xcmsRaw should be here.
#' @include functions-xcmsRaw.R functions-utils.R
############################################################
## show
setMethod("show", "xcmsRaw", function(object) {
cat("An \"xcmsRaw\" object with", length(object@scantime),
"mass spectra\n\n")
if (length(object@scantime)>0) {
cat("Time range: ", paste(round(range(object@scantime), 1),
collapse = "-"),
" seconds (", paste(round(range(object@scantime)/60, 1),
collapse = "-"),
" minutes)\n", sep = "")
cat("Mass range:", paste(round(range(object@env$mz), 4),
collapse = "-"), "m/z\n")
cat("Intensity range:", paste(signif(range(object@env$intensity), 6),
collapse = "-"), "\n\n")
}
## summary MSn data
if (!is.null(object@msnLevel)) {
cat("MSn data on ", length(unique(object@msnPrecursorMz)), " mass(es)\n")
cat("\twith ", length(object@msnPrecursorMz)," MSn spectra\n")
}
cat("Profile method:", object@profmethod, "\n")
cat("Profile step: ")
if (is.null(object@env$profile))
cat("no profile data\n")
else {
profmz <- profMz(object)
cat(profStep(object), " m/z (", length(profmz), " grid points from ",
paste(object@mzrange, collapse = " to "), " m/z)\n", sep = "")
}
if (length(object@profparam)) {
cat("Profile parameters: ")
for (i in seq(along = object@profparam)) {
if (i != 1) cat(" ")
cat(names(object@profparam)[i], " = ", object@profparam[[i]], "\n",
sep = "")
}
}
memsize <- object.size(object)
for (key in ls(object@env))
memsize <- memsize + object.size(object@env[[key]])
cat("\nMemory usage:", signif(memsize/2^20, 3), "MB\n")
})
############################################################
## sortMz
setMethod("revMz", "xcmsRaw", function(object) {
for (i in 1:length(object@scanindex)) {
idx <- (object@scanindex[i]+1):min(object@scanindex[i+1],
length(object@env$mz), na.rm=TRUE)
object@env$mz[idx] <- rev(object@env$mz[idx])
object@env$intensity[idx] <- rev(object@env$intensity[idx])
}
})
############################################################
## sortMz
setMethod("sortMz", "xcmsRaw", function(object) {
for (i in 1:length(object@scanindex)) {
idx <- (object@scanindex[i]+1):min(object@scanindex[i+1],
length(object@env$mz), na.rm=TRUE)
ord <- order(object@env$mz[idx])
object@env$mz[idx] <- object@env$mz[idx[ord]]
object@env$intensity[idx] <- object@env$intensity[idx[ord]]
}
})
############################################################
## plotTIC
setMethod("plotTIC", "xcmsRaw", function(object, ident = FALSE, msident = FALSE) {
if (all(object@tic == 0))
points <- cbind(object@scantime, rawEIC(object,mzrange=range(object@env$mz))$intensity) else
points <- cbind(object@scantime, object@tic)
plot(points, type="l", main="TIC Chromatogram", xlab="Seconds",
ylab="Intensity")
if (ident) {
idx <- integer(0)
ticdev <- dev.cur()
if ((dev.cur()+1) %in% dev.list())
msdev <- dev.cur()+1
else
msdev <- integer(0)
while(length(id <- identify(points, labels = round(points[,1], 1), n = 1))) {
idx <- c(idx, id)
if (!length(msdev)) {
options("device")$device()
msdev <- dev.cur()
}
dev.set(msdev)
plotScan(object, id, ident = msident)
dev.set(ticdev)
}
return(idx)
}
invisible(points)
})
############################################################
## getScan
setMethod("getScan", "xcmsRaw", function(object, scan, mzrange = numeric()) {
if (scan < 0)
scan <- length(object@scantime) + 1 + scan
idx <- seq(object@scanindex[scan]+1, min(object@scanindex[scan+1],
length(object@env$mz), na.rm=TRUE))
if (length(mzrange) >= 2) {
mzrange <- range(mzrange)
idx <- idx[object@env$mz[idx] >= mzrange[1] & object@env$mz[idx] <= mzrange[2]]
}
points <- cbind(mz = object@env$mz[idx], intensity = object@env$intensity[idx])
invisible(points)
})
############################################################
## getSpec
setMethod("getSpec", "xcmsRaw", function(object, ...) {
## FIXME: unnecessary dependency on profile matrix?
sel <- profRange(object, ...)
scans <- list(length(sel$scanidx))
uniquemz <- numeric()
for (i in seq(along = sel$scanidx)) {
scans[[i]] <- getScan(object, sel$scanidx[i], sel$mzrange)
uniquemz <- unique(c(uniquemz, scans[[i]][,"mz"]))
}
uniquemz <- sort(uniquemz)
intmat <- matrix(nrow = length(uniquemz), ncol = length(sel$scanidx))
for (i in seq(along = sel$scanidx)) {
scan <- getScan(object, sel$scanidx[i], sel$mzrange)
intmat[,i] <- approx(scan, xout = uniquemz)$y
}
points <- cbind(mz = uniquemz, intensity = rowMeans(intmat))
invisible(points)
})
############################################################
## findPeaks.matchedFilter_orig
## We're keeping this one only for comparison in unit tests; this
## should be removed soon!
setGeneric("findPeaks.matchedFilter_orig", function(object, ...)
standardGeneric("findPeaks.matchedFilter_orig"))
setMethod("findPeaks.matchedFilter_orig", "xcmsRaw",
function(object, fwhm = 30, sigma = fwhm/2.3548, max = 5,
snthresh = 10, step = 0.1, steps = 2,
mzdiff = 0.8 - step*steps, index = FALSE, sleep = 0,
scanrange= numeric()) {
profFun <- match.profFun(object)
scanrange.old <- scanrange
## sanitize if too few or too many scanrange is given
if (length(scanrange) < 2)
scanrange <- c(1, length(object@scantime))
else
scanrange <- range(scanrange)
## restrict and sanitize scanrange to maximally cover all scans
scanrange[1] <- max(1,scanrange[1])
scanrange[2] <- min(length(object@scantime),scanrange[2])
## Mild warning if the actual scanrange doesn't match the scanrange argument
if (!(identical(scanrange.old,scanrange)) && (length(scanrange.old) >0)) {
cat("Warning: scanrange was adjusted to ",scanrange,"\n")
## Scanrange filtering
keepidx <- seq.int(1, length(object@scantime)) %in% seq.int(scanrange[1], scanrange[2])
object <- split(object, f=keepidx)[["TRUE"]]
}
### Create EIC buffer
mrange <- range(object@env$mz)
mass <- seq(floor(mrange[1]/step)*step, ceiling(mrange[2]/step)*step, by = step)
bufsize <- min(100, length(mass))
buf <- profFun(object@env$mz, object@env$intensity, object@scanindex,
bufsize, mass[1], mass[bufsize], TRUE, object@profparam)
bufMax <- profMaxIdxM(object@env$mz, object@env$intensity, object@scanindex,
bufsize, mass[1], mass[bufsize], TRUE,
object@profparam)
bufidx <- integer(length(mass))
idxrange <- c(1, bufsize)
bufidx[idxrange[1]:idxrange[2]] <- 1:bufsize
lookahead <- steps-1
lookbehind <- 1
scantime <- object@scantime
N <- nextn(length(scantime))
xrange <- range(scantime)
x <- c(0:(N/2), -(ceiling(N/2-1)):-1)*(xrange[2]-xrange[1])/(length(scantime)-1)
filt <- -attr(eval(deriv3(~ 1/(sigma*sqrt(2*pi))*exp(-x^2/(2*sigma^2)), "x")), "hessian")
filt <- filt/sqrt(sum(filt^2))
filt <- fft(filt, inverse = TRUE)/length(filt)
cnames <- c("mz", "mzmin", "mzmax", "rt", "rtmin", "rtmax", "into", "intf", "maxo", "maxf", "i", "sn")
rmat <- matrix(nrow = 2048, ncol = length(cnames))
num <- 0
for (i in seq(length = length(mass)-steps+1)) {
if (i %% 500 == 0) {
cat(round(mass[i]), ":", num, " ", sep = "")
flush.console()
}
### Update EIC buffer if necessary
if (bufidx[i+lookahead] == 0) {
bufidx[idxrange[1]:idxrange[2]] <- 0
idxrange <- c(max(1, i - lookbehind), min(bufsize+i-1-lookbehind, length(mass)))
bufidx[idxrange[1]:idxrange[2]] <- 1:(diff(idxrange)+1)
buf <- profFun(object@env$mz, object@env$intensity, object@scanindex,
diff(idxrange)+1, mass[idxrange[1]], mass[idxrange[2]],
TRUE, object@profparam)
bufMax <- profMaxIdxM(object@env$mz, object@env$intensity, object@scanindex,
diff(idxrange)+1, mass[idxrange[1]], mass[idxrange[2]],
TRUE, object@profparam)
}
ymat <- buf[bufidx[i:(i+steps-1)],,drop=FALSE]
ysums <- colMax(ymat)
yfilt <- filtfft(ysums, filt)
gmax <- max(yfilt)
for (j in seq(length = max)) {
maxy <- which.max(yfilt)
noise <- mean(ysums[ysums > 0])
##noise <- mean(yfilt[yfilt >= 0])
sn <- yfilt[maxy]/noise
if (yfilt[maxy] > 0 && yfilt[maxy] > snthresh*noise && ysums[maxy] > 0) {
peakrange <- descendZero(yfilt, maxy)
intmat <- ymat[,peakrange[1]:peakrange[2],drop=FALSE]
mzmat <- matrix(object@env$mz[bufMax[bufidx[i:(i+steps-1)],
peakrange[1]:peakrange[2]]],
nrow = steps)
which.intMax <- which.colMax(intmat)
mzmat <- mzmat[which.intMax]
if (all(is.na(mzmat))) {
yfilt[peakrange[1]:peakrange[2]] <- 0
next
}
mzrange <- range(mzmat, na.rm = TRUE)
massmean <- weighted.mean(mzmat, intmat[which.intMax], na.rm = TRUE)
## This case (the only non-na m/z had intensity 0) was reported
## by Gregory Alan Barding "binlin processing"
if(any(is.na(massmean))) {
massmean <- mean(mzmat, na.rm = TRUE)
}
pwid <- (scantime[peakrange[2]] - scantime[peakrange[1]])/(peakrange[2] - peakrange[1])
into <- pwid*sum(ysums[peakrange[1]:peakrange[2]])
intf <- pwid*sum(yfilt[peakrange[1]:peakrange[2]])
maxo <- max(ysums[peakrange[1]:peakrange[2]])
maxf <- yfilt[maxy]
if (sleep > 0) {
plot(scantime, yfilt, type = "l", main = paste(mass[i], "-", mass[i+1]), ylim=c(-gmax/3, gmax))
points(cbind(scantime, yfilt)[peakrange[1]:peakrange[2],], type = "l", col = "red")
points(scantime, colSums(ymat), type = "l", col = "blue", lty = "dashed")
abline(h = snthresh*noise, col = "red")
Sys.sleep(sleep)
}
yfilt[peakrange[1]:peakrange[2]] <- 0
num <- num + 1
### Double the size of the output matrix if it's full
if (num > nrow(rmat)) {
nrmat <- matrix(nrow = 2*nrow(rmat), ncol = ncol(rmat))
nrmat[seq(length = nrow(rmat)),] = rmat
rmat <- nrmat
}
rmat[num,] <- c(massmean, mzrange[1], mzrange[2], maxy, peakrange, into, intf, maxo, maxf, j, sn)
} else
break
}
}
cat("\n")
colnames(rmat) <- cnames
rmat <- rmat[seq(length = num),]
max <- max-1 + max*(steps-1) + max*ceiling(mzdiff/step)
if (index)
mzdiff <- mzdiff/step
else {
rmat[,"rt"] <- scantime[rmat[,"rt"]]
rmat[,"rtmin"] <- scantime[rmat[,"rtmin"]]
rmat[,"rtmax"] <- scantime[rmat[,"rtmax"]]
}
uorder <- order(rmat[,"into"], decreasing=TRUE)
uindex <- rectUnique(rmat[,c("mzmin","mzmax","rtmin","rtmax"),drop=FALSE],
uorder, mzdiff)
rmat <- rmat[uindex,,drop=FALSE]
invisible(new("xcmsPeaks", rmat))
})
############################################################
## findPeaks.matchedFilter
#' @title Peak detection in the chromatographic time domain
#'
#' @aliases findPeaks.matchedFilter
#'
#' @description Find peaks in the chromatographic time domain of the
#' profile matrix. For more details see
#' \code{\link{do_findChromPeaks_matchedFilter}}.
#'
#' @param object The \code{\linkS4class{xcmsRaw}} object on which peak detection
#' should be performed.
#'
#' @inheritParams findChromPeaks-matchedFilter
#'
#' @param step numeric(1) specifying the width of the bins/slices in m/z
#' dimension.
#'
#' @param sleep (DEPRECATED). The use of this parameter is highly discouraged,
#' as it could cause problems in parallel processing mode.
#'
#' @param scanrange Numeric vector defining the range of scans to which the
#' original \code{object} should be sub-setted before peak detection.
#'
#' @author Colin A. Smith
#'
#' @return A matrix, each row representing an intentified chromatographic peak,
#' with columns:
#' \describe{
#' \item{mz}{Intensity weighted mean of m/z values of the peak across
#' scans.}
#' \item{mzmin}{Minimum m/z of the peak.}
#' \item{mzmax}{Maximum m/z of the peak.}
#' \item{rt}{Retention time of the peak's midpoint.}
#' \item{rtmin}{Minimum retention time of the peak.}
#' \item{rtmax}{Maximum retention time of the peak.}
#' \item{into}{Integrated (original) intensity of the peak.}
#' \item{intf}{Integrated intensity of the filtered peak.}
#' \item{maxo}{Maximum intensity of the peak.}
#' \item{maxf}{Maximum intensity of the filtered peak.}
#' \item{i}{Rank of peak in merged EIC (\code{<= max}).}
#' \item{sn}{Signal to noise ratio of the peak.}
#' }
#'
#' @references
#' Colin A. Smith, Elizabeth J. Want, Grace O'Maille, Ruben Abagyan and
#' Gary Siuzdak. "XCMS: Processing Mass Spectrometry Data for Metabolite
#' Profiling Using Nonlinear Peak Alignment, Matching, and Identification"
#' \emph{Anal. Chem.} 2006, 78:779-787.
#' @family Old peak detection methods
#'
#' @seealso \code{\link{matchedFilter}} for the new user interface.
#' \code{\linkS4class{xcmsRaw}},
#' \code{\link{do_findChromPeaks_matchedFilter}} for the core function
#' performing the peak detection.
setMethod("findPeaks.matchedFilter", "xcmsRaw",
function(object, fwhm = 30, sigma = fwhm/2.3548, max = 5,
snthresh = 10, step = 0.1, steps = 2,
mzdiff = 0.8 - step*steps, index = FALSE, sleep = 0,
scanrange = numeric()) {
## Fix issue #63:
## Sub-set the xcmsRaw based on scanrange
if (length(scanrange) < 2) {
scanrange <- c(1, length(object@scantime))
} else {
scanrange <- range(scanrange)
}
if (min(scanrange) < 1 | max(scanrange) > length(object@scantime)) {
scanrange[1] <- max(1, scanrange[1])
scanrange[2] <- min(length(object@scantime), scanrange[2])
message("Provided scanrange was adjusted to ", scanrange)
}
object <- object[scanrange[1]:scanrange[2]]
scanrange <- c(1, length(object@scantime))
## ## Sub-set the xcmsRaw baesd on scanrange
## scanrange.old <- scanrange
## ## sanitize if too few or too many scanrange is given
## if (length(scanrange) < 2)
## scanrange <- c(1, length(object@scantime))
## else
## scanrange <- range(scanrange)
## ## restrict and sanitize scanrange to maximally cover all scans
## scanrange[1] <- max(1,scanrange[1])
## scanrange[2] <- min(length(object@scantime),scanrange[2])
## ## Mild warning if the actual scanrange doesn't match the scanrange
## ## argument
## if (!(identical(scanrange.old, scanrange)) &&
## (length(scanrange.old) > 0)) {
## cat("Warning: scanrange was adjusted to ",scanrange,"\n")
## ## Scanrange filtering
## keepidx <- seq.int(1, length(object@scantime))
## %in% seq.int(scanrange[1], scanrange[2])
## object <- split(object, f=keepidx)[["TRUE"]]
## }
## Determine the impute method:
imputeMeths <- c("none", "lin", "linbase", "intlin")
names(imputeMeths) <- c("bin", "binlin",
"binlinbase", "intlin")
profFun <- profMethod(object)
profFun <- match.arg(profFun, names(imputeMeths))
imputeMeth <- imputeMeths[profFun]
if (imputeMeth == "linbase") {
profp <- object@profparam
if (length(profp) == 0)
profp <- list()
## Determine the settings for this:
## o distance
## Define the distance argument; that's tricky, as it
## requires the bin_size, not the step.
mrange <- range(object@env$mz)
mass <- seq(floor(mrange[1]/step)*step,
ceiling(mrange[2]/step)*step, by = step)
mlength <- length(mass)
bin_size <- (mass[mlength] - mass[1]) / (mlength - 1)
rm(mass)
if (length(profp$basespace) > 0) {
if (!is.numeric(profp$basespace))
stop("Profile parameter 'basespace' has to be numeric!")
distance <- floor(profp$basespace[1] / bin_size)
} else {
distance <- floor(0.075 / bin_size)
}
## o baseValue
if (length(profp$baseleve) > 0) {
if (!is.numeric(profp$baselevel))
stop("Profile parameter 'baselevel' has to be numeric!")
baseValue <- profp$baselevel[1]
} else {
baseValue <- min(object@env$intensity) / 2
}
} else {
## For other methods these are not used anyway.
distance <- 0
baseValue <- 0
}
res <- do_findChromPeaks_matchedFilter(mz = object@env$mz,
int = object@env$intensity,
scantime = object@scantime,
valsPerSpect = diff(c(object@scanindex,
length(object@env$mz))),
binSize = step,
impute = imputeMeth,
baseValue = baseValue,
distance = distance,
fwhm = fwhm,
sigma = sigma,
max = max,
snthresh = snthresh,
steps = steps,
mzdiff = mzdiff,
index = index,
sleep = sleep
)
invisible(new("xcmsPeaks", res))
})
############################################################
## findPeaks.centWave
setMethod("findPeaks.centWave", "xcmsRaw", function(object, ppm=25,
peakwidth=c(20,50),
snthresh=10,
prefilter=c(3,100),
mzCenterFun="wMean",
integrate=1, mzdiff=-0.001,
fitgauss=FALSE,
scanrange = numeric(),
noise=0, ## noise.local=TRUE,
sleep=0,
verbose.columns=FALSE,
ROI.list=list(),
firstBaselineCheck=TRUE,
roiScales=NULL) {
if (!isCentroided(object))
warning("It looks like this file is in profile mode. centWave can",
" process only centroid mode data !\n")
## Fix issue #64:
## Sub-set the xcmsRaw based on scanrange
if (length(scanrange) < 2) {
scanrange <- c(1, length(object@scantime))
} else {
scanrange <- range(scanrange)
}
if (min(scanrange) < 1 | max(scanrange) > length(object@scantime)) {
scanrange[1] <- max(1, scanrange[1])
scanrange[2] <- min(length(object@scantime), scanrange[2])
message("Provided scanrange was adjusted to ", scanrange)
}
object <- object[scanrange[1]:scanrange[2]]
vps <- diff(c(object@scanindex, length(object@env$mz)))
res <- do_findChromPeaks_centWave(mz = object@env$mz,
int = object@env$intensity,
scantime = object@scantime,
valsPerSpect = vps,
ppm = ppm, peakwidth = peakwidth,
snthresh = snthresh,
prefilter = prefilter,
mzCenterFun = mzCenterFun,
integrate = integrate,
mzdiff = mzdiff, fitgauss = fitgauss,
noise = noise,
verboseColumns = verbose.columns,
roiList = ROI.list,
firstBaselineCheck = firstBaselineCheck,
roiScales = roiScales,
sleep = sleep
)
invisible(new("xcmsPeaks", res))
})
############################################################
## findPeaks.centWaveWithPredictedIsotopeROIs
## Performs first a centWave analysis and based on the identified peaks
## defines ROIs for a second centWave run to check for presence of
## predicted isotopes for the first peaks.
setMethod("findPeaks.centWaveWithPredictedIsotopeROIs", "xcmsRaw",
function(object, ppm = 25, peakwidth = c(20,50), snthresh = 10,
prefilter = c(3,100), mzCenterFun = "wMean", integrate = 1,
mzdiff = -0.001, fitgauss = FALSE, scanrange = numeric(),
noise = 0, sleep = 0, verbose.columns = FALSE,
ROI.list = list(), firstBaselineCheck = TRUE,
roiScales = NULL, snthreshIsoROIs = 6.25, maxcharge = 3,
maxiso = 5, mzIntervalExtension = TRUE) {
if (!isCentroided(object))
warning("It looks like this file is in profile mode. centWave",
" can process only centroid mode data !\n")
## Sub-set the xcmsRaw based on scanrange
if (length(scanrange) < 2) {
scanrange <- c(1, length(object@scantime))
} else {
scanrange <- range(scanrange)
}
if (min(scanrange) < 1 | max(scanrange) > length(object@scantime)) {
scanrange[1] <- max(1, scanrange[1])
scanrange[2] <- min(length(object@scantime), scanrange[2])
message("Provided scanrange was adjusted to ", scanrange)
}
object <- object[scanrange[1]:scanrange[2]]
vps <- diff(c(object@scanindex, length(object@env$mz)))
res <- do_findChromPeaks_centWaveWithPredIsoROIs(mz = object@env$mz,
int = object@env$intensity,
scantime = object@scantime,
valsPerSpect = vps,
ppm = ppm,
peakwidth = peakwidth,
snthresh = snthresh,
prefilter = prefilter,
mzCenterFun = mzCenterFun,
integrate = integrate,
mzdiff = mzdiff,
fitgauss = fitgauss,
noise = noise,
verboseColumns = verbose.columns,
roiList = ROI.list,
firstBaselineCheck = firstBaselineCheck,
roiScales = roiScales,
snthreshIsoROIs = snthreshIsoROIs,
maxCharge = maxcharge,
maxIso = maxiso,
mzIntervalExtension = mzIntervalExtension
)
invisible(new("xcmsPeaks", res))
})
setMethod("findPeaks.addPredictedIsotopeFeatures",
"xcmsRaw", function(object, ppm = 25, peakwidth = c(20,50),
prefilter = c(3,100), mzCenterFun = "wMean",
integrate = 1, mzdiff = -0.001, fitgauss = FALSE,
scanrange = numeric(), noise=0, ## noise.local=TRUE,
sleep = 0, verbose.columns = FALSE,
xcmsPeaks, snthresh = 6.25, maxcharge = 3,
maxiso = 5, mzIntervalExtension = TRUE) {
if (!isCentroided(object))
warning("It looks like this file is in profile mode. ",
"centWave works best for centroided data")
## Sub-set the xcmsRaw based on scanrange
if (length(scanrange) < 2) {
scanrange <- c(1, length(object@scantime))
} else {
scanrange <- range(scanrange)
}
if (min(scanrange) < 1 | max(scanrange) > length(object@scantime)) {
scanrange[1] <- max(1, scanrange[1])
scanrange[2] <- min(length(object@scantime), scanrange[2])
message("Provided scanrange was adjusted to ", scanrange)
}
object <- object[scanrange[1]:scanrange[2]]
if(class(xcmsPeaks) != "xcmsPeaks")
stop("Parameter 'xcmsPeaks' is not of class 'xcmsPeaks'")
vps <- diff(c(object@scanindex, length(object@env$mz)))
res <- do_findChromPeaks_addPredIsoROIs(
mz = object@env$mz, int = object@env$intensity,
scantime = object@scantime, valsPerSpect = vps,
ppm = ppm, peakwidth = peakwidth,
snthresh = snthresh, prefilter = prefilter,
mzCenterFun = mzCenterFun, integrate = integrate,
mzdiff = mzdiff, fitgauss = fitgauss, noise = noise,
verboseColumns = verbose.columns, peaks. = xcmsPeaks@.Data,
maxCharge = maxcharge, maxIso = maxiso,
mzIntervalExtension = mzIntervalExtension
)
invisible(new("xcmsPeaks", res))
})
############################################################
## findPeaks.MSW
#' @title Peak detection for single-spectrum non-chromatography MS data
#'
#' @aliases findPeaks.MSW
#'
#' @description This method performs peak detection in mass spectrometry
#' direct injection spectrum using a wavelet based algorithm.
#'
#' @details This is a wrapper around the peak picker in Bioconductor's
#' \code{MassSpecWavelet} package calling
#' \code{\link{peakDetectionCWT}} and
#' \code{\link{tuneInPeakInfo}} functions.
#'
#' @inheritParams findPeaks-MSW
#'
#' @inheritParams findChromPeaks-centWave
#'
#' @param object The \code{\linkS4class{xcmsRaw}} object on which peak
#' detection should be performed.
#'
#' @param verbose.columns Logical whether additional peak meta data columns
#' should be returned.
#'
#' @return
#' A matrix, each row representing an intentified peak, with columns:
#' \describe{
#' \item{mz}{m/z value of the peak at the centroid position.}
#' \item{mzmin}{Minimum m/z of the peak.}
#' \item{mzmax}{Maximum m/z of the peak.}
#' \item{rt}{Always \code{-1}.}
#' \item{rtmin}{Always \code{-1}.}
#' \item{rtmax}{Always \code{-1}.}
#' \item{into}{Integrated (original) intensity of the peak.}
#' \item{maxo}{Maximum intensity of the peak.}
#' \item{intf}{Always \code{NA}.}
#' \item{maxf}{Maximum MSW-filter response of the peak.}
#' \item{sn}{Signal to noise ratio.}
#' }
#'
#' @seealso \code{\link{MSW}} for the new user interface,
#' \code{\link{do_findPeaks_MSW}} for the downstream analysis
#' function or \code{\link{peakDetectionCWT}} from the
#' \code{MassSpecWavelet} for details on the algorithm and additionally
#' supported parameters.
#'
#' @author Joachim Kutzera, Steffen Neumann, Johannes Rainer
setMethod("findPeaks.MSW", "xcmsRaw",
function(object, snthresh=3, verbose.columns = FALSE, ...) {
if (length(object@scantime) > 1)
stop("MSW works only on single spectrum, direct injection",
" MS data, but 'object' has ", length(object@scantime),
" spectra")
res <- do_findPeaks_MSW(mz = object@env$mz,
int = object@env$intensity,
snthresh = snthresh,
verboseColumns = verbose.columns,
...)
invisible(new("xcmsPeaks", res))
})
############################################################
## findPeaks.MS1
setMethod("findPeaks.MS1", "xcmsRaw", function(object)
{
if (is.null(object@msnLevel)) {
stop("xcmsRaw contains no MS2 spectra\n")
}
## Select all MS2 scans, they have an MS1 parent defined
peakIndex <- object@msnLevel == 2
## (empty) return object
basenames <- c("mz","mzmin","mzmax",
"rt","rtmin","rtmax",
"into","maxo","sn")
peaklist <- matrix(-1, nrow = length(which(peakIndex)),
ncol = length(basenames))
colnames(peaklist) <- c(basenames)
## Assemble result
peaklist[,"mz"] <- object@msnPrecursorMz[peakIndex]
peaklist[,"mzmin"] <- object@msnPrecursorMz[peakIndex]
peaklist[,"mzmax"] <- object@msnPrecursorMz[peakIndex]
if (any(!is.na(object@msnPrecursorScan))&&any(object@msnPrecursorScan!=0)) {
peaklist[,"rt"] <- peaklist[,"rtmin"] <- peaklist[,"rtmax"] <- object@scantime[object@msnPrecursorScan[peakIndex]]
} else {
## This happened with ReAdW mzxml
cat("MS2 spectra without precursorScan references, using estimation")
## which object@Scantime are the biggest wich are smaller than the current object@msnRt[peaklist]?
ms1Rts<-rep(0,length(which(peakIndex)))
i<-1
for (a in which(peakIndex)){
ms1Rts[i] <- object@scantime[max(which(object@scantime<object@msnRt[a]))]
i<-i+1
}
peaklist[,"rt"] <- ms1Rts
peaklist[,"rtmin"] <- ms1Rts
peaklist[,"rtmax"] <- ms1Rts
}
if (any(object@msnPrecursorIntensity!=0)) {
peaklist[,"into"] <- peaklist[,"maxo"] <- peaklist[,"sn"] <- object@msnPrecursorIntensity[peakIndex]
} else {
## This happened with Agilent MzDataExport 1.0.98.2
warning("MS2 spectra without precursorIntensity, setting to zero")
peaklist[,"into"] <- peaklist[,"maxo"] <- peaklist[,"sn"] <- 0
}
cat('\n')
invisible(new("xcmsPeaks", peaklist))
})
############################################################
## findPeaks
setMethod("findPeaks", "xcmsRaw", function(object, method=getOption("BioC")$xcms$findPeaks.method,
...) {
method <- match.arg(method, getOption("BioC")$xcms$findPeaks.methods)
if (is.na(method))
stop("unknown method : ", method)
method <- paste("findPeaks", method, sep=".")
invisible(do.call(method, list(object, ...)))
})
setMethod("getPeaks", "xcmsRaw", function(object, peakrange, step = 0.1) {
if (useOriginalCode())
return(.getPeaks_orig(object, peakrange, step = step))
else
return(.getPeaks_new(object, peakrange, step = step))
})
############################################################
## plotPeaks
setMethod("plotPeaks", "xcmsRaw", function(object, peaks, figs, width = 200) {
if (missing(figs)) {
figs <- c(floor(sqrt(nrow(peaks))), ceiling(sqrt(nrow(peaks))))
if (prod(figs) < nrow(peaks))
figs <- rep(ceiling(sqrt(nrow(peaks))), 2)
}
mzi <- round((peaks[,c("mzmin","mzmax")]-object@mzrange[1])/profStep(object) + 1)
screens <- split.screen(figs)
on.exit(close.screen(all.screens = TRUE))
for (i in seq(length = min(nrow(peaks), prod(figs)))) {
screen(screens[i])
par(cex.main = 1, font.main = 1, mar = c(0, 0, 1, 0) + 0.1)
xlim <- c(-width/2, width/2) + peaks[i,"rt"]
##main <- paste(peaks[i,"i"], " ", round(peaks[i,"mz"]),
main <- paste(round(peaks[i,"mz"]),
" ", round(peaks[i,"rt"]), sep = "")
plot(object@scantime, colMax(object@env$profile[mzi[i,],,drop=FALSE]),
type = "l", xlim = xlim, ylim = c(0, peaks[i,"maxo"]), main = main,
xlab = "", ylab = "", xaxt = "n", yaxt = "n")
abline(v = peaks[i,c("rtmin","rtmax")], col = "grey")
}
})
############################################################
## getEIC
## Issue #74: implement an alternative (improved) getEIC method.
setMethod("getEIC", "xcmsRaw", function(object, mzrange, rtrange = NULL,
step = 0.1) {
profEIC(object, mzrange = mzrange, rtrange = rtrange, step = step)
})
############################################################
## rawMat
#' @description Extracts a matrix with columns time (retention time), mz and
#' intensity from an xcmsRaw object.
#'
#' @noRd
setMethod("rawMat", "xcmsRaw", function(object,
mzrange = numeric(),
rtrange = numeric(),
scanrange = numeric(),
log=FALSE) {
.rawMat(mz = object@env$mz, int = object@env$intensity,
scantime = object@scantime,
valsPerSpect = diff(c(object@scanindex, length(object@env$mz))),
mzrange = mzrange, rtrange = rtrange, scanrange = scanrange,
log = log)
})
## @jo TODO LLL replace that with an implementation in C.
## Note: this function silently drops retention times for which no intensity-mz
## pair was measured.
.rawMat <- function(mz, int, scantime, valsPerSpect, mzrange = numeric(),
rtrange = numeric(), scanrange = numeric(),
log = FALSE) {
if (length(rtrange) >= 2) {
rtrange <- range(rtrange)
## Fix for issue #267. rtrange outside scanrange causes scanrange
## being c(Inf, -Inf)
scns <- which((scantime >= rtrange[1]) & (scantime <= rtrange[2]))
if (!length(scns))
return(matrix(
nrow = 0, ncol = 3,
dimnames = list(character(), c("time", "mz", "intensity"))))
scanrange <- range(scns)
}
if (length(scanrange) < 2)
scanrange <- c(1, length(valsPerSpect))
else scanrange <- range(scanrange)
if (!all(is.finite(scanrange)))
stop("'scanrange' does not contain finite values")
if (!all(is.finite(mzrange)))
stop("'mzrange' does not contain finite values")
if (!all(is.finite(rtrange)))
stop("'rtrange' does not contain finite values")
if (scanrange[1] == 1)
startidx <- 1
else
startidx <- sum(valsPerSpect[1:(scanrange[1] - 1)]) + 1
endidx <- sum(valsPerSpect[1:scanrange[2]])
scans <- rep(scanrange[1]:scanrange[2],
valsPerSpect[scanrange[1]:scanrange[2]])
masses <- mz[startidx:endidx]
massidx <- 1:length(masses)
if (length(mzrange) >= 2) {
mzrange <- range(mzrange)
massidx <- massidx[(masses >= mzrange[1] & (masses <= mzrange[2]))]
}
int <- int[startidx:endidx][massidx]
if (log && (length(int) > 0))
int <- log(int + max(1 - min(int), 0))
cbind(time = scantime[scans[massidx]],
mz = masses[massidx],
intensity = int)
}
## .rawMat2 <- function(mz, int, scantime, valsPerSpect, mzrange = numeric(),
## rtrange = numeric(), scanrange = numeric,
## log = FALSE) {
## if (length(rtrange) >= 2) {
## rtrange <- range(rtrange)
## scanrange <- range(which((scantime >= rtrange[1]) &
## (scantime <= rtrange[2])))
## }
## if (length(scanrange) < 2)
## scanrange <- c(1, length(valsPerSpect))
## else scanrange <- range(scanrange)
## if (scanrange[1] == 1)
## startidx <- 1
## else
## startidx <- sum(valsPerSpect[1:(scanrange[1]-1)]) + 1
## endidx <- sum(valsPerSpect[1:scanrange[2]])
## scans <- rep(scanrange[1]:scanrange[2],
## valsPerSpect[scanrange[1]:scanrange[2]])
## masses <- mz[startidx:endidx]
## massidx <- 1:length(masses)
## if (length(mzrange) >= 2) {
## mzrange <- range(mzrange)
## massidx <- massidx[(masses >= mzrange[1] & (masses <= mzrange[2]))]
## }
## int <- int[startidx:endidx][massidx]
## if (log && (length(int) > 0))
## int <- log(int + max(1 - min(int), 0))
## cbind(time = scantime[scans[massidx]],
## mz = masses[massidx],
## intensity = int)
## }
############################################################
## plotRaw
setMethod("plotRaw", "xcmsRaw", function(object,
mzrange = numeric(),
rtrange = numeric(),
scanrange = numeric(),
log=FALSE,title='Raw Data' ) {
raw <- rawMat(object, mzrange, rtrange, scanrange, log)
if (nrow(raw) > 0) {
y <- raw[,"intensity"]
ylim <- range(y)
y <- y/ylim[2]
colorlut <- terrain.colors(16)
col <- colorlut[y*15+1]
plot(cbind(raw[,"time"], raw[,"mz"]), pch=20, cex=.5,
main = title, xlab="Seconds", ylab="m/z", col=col,
xlim=range(raw[,"time"]), ylim=range(raw[,"mz"]))
} else {
if (length(rtrange) >= 2) {
rtrange <- range(rtrange)
scanidx <- (object@scantime >= rtrange[1]) & (object@scantime <= rtrange[2])
scanrange <- c(match(TRUE, scanidx),
length(scanidx) - match(TRUE, rev(scanidx)))
} else if (length(scanrange) < 2)
scanrange <- c(1, length(object@scantime)) else
scanrange <- range(scanrange)
plot(c(NA,NA), main = title, xlab="Seconds", ylab="m/z",
xlim=c(object@scantime[scanrange[1]],object@scantime[scanrange[2]]), ylim=mzrange)
}
invisible(raw)
})
############################################################
## profMz
setMethod("profMz", "xcmsRaw", function(object) {
object@mzrange[1]+profStep(object)*(0:(dim(object@env$profile)[1]-1))
})
############################################################
## profMethods
setMethod("profMethod", "xcmsRaw", function(object) {
object@profmethod
})
setReplaceMethod("profMethod", "xcmsRaw", function(object, value) {
if (! (value %in% names(.profFunctions)))
stop("Invalid profile method")
if (length(object@env$mz) == 0) {
warning("MS1 scans empty. Skipping profile matrix creation.")
return(object)
}
have_profmethod <- object@profmethod
## Re-calculate the profile matrix if method differs
if (have_profmethod != value & profStep(object) > 0) {
object@env$profile <- profMat(object, method = value)
}
object@profmethod <- value
object
})
############################################################
## profStep
setMethod("profStep", "xcmsRaw", function(object) {
if (is.null(object@env$profile))
0
else
diff(object@mzrange)/(nrow(object@env$profile)-1)
})
## Update: related to issue #71
setReplaceMethod("profStep", "xcmsRaw", function(object, value) {
if (!is.numeric(value) && value < 0)
stop("'value' has to be a positive number!")
if (length(object@env$mz) == 0) {
warning("MS1 scans empty. Skipping profile matrix creation.")
return(object)
}
if (value == 0) {
if ("profile" %in% ls(object@env)) {
rm("profile", envir = object@env)
message("Removing profile matrix.")
}
return(object)
}
have_step <- profStep(object)
## Check if the value differs from step and only calculate if different.
if (have_step != value) {
## OK, now we're re-calculating
minmass <- round(min(object@env$mz) / value) * value
maxmass <- round(max(object@env$mz) / value) * value
object@mzrange <- c(minmass, maxmass)
## Fix for issue #98: to be in accordance with the "old" code we require
## that the number of rows of the profile matrix matches minmass to
## maxmass in steps of value
## To me that is somewhat problematic, as it means that the @mzrange does
## not correctly correspond to the range(object@env$mz)!
tmp <- seq(minmass, maxmass, by = value)
prf <- profMat(object, step = value)
object@env$profile <- prf[1:min(c(length(tmp), nrow(prf))), ]
}
return(object)
})
############################################################
## profStepPad
## The difference to the profStep? seems only to be the way how the range
## is calculated:
## profStep: minmass <- round(min(object@env$mz)/value)*value
## profStepPad: floor(mzrange(range(object@env$mz))[1])
setReplaceMethod("profStepPad", "xcmsRaw", function(object, value) {
if (!is.numeric(value) && value < 0)
stop("'value' has to be a positive number!")
if (length(object@env$mz) == 0) {
warning("MS1 scans empty. Skipping profile matrix creation.")
return(object)
}
if (value == 0) {
if ("profile" %in% ls(object@env)) {
rm("profile", envir = object@env)
message("Removing profile matrix.")
}
return(object)
}
mzr <- range(object@env$mz)
minmass <- floor(mzr[1])
maxmass <- ceiling(mzr[2])
object@mzrange <- c(minmass, maxmass)
object@env$profile <- profMat(object, step = value,
mzrange. = c(minmass, maxmass))
return(object)
})
############################################################
## profMedFilt
setMethod("profMedFilt", "xcmsRaw", function(object, massrad = 0, scanrad = 0) {
contdim <- dim(object@env$profile)
object@env$profile <- medianFilter(object@env$profile, massrad, scanrad)
})
############################################################
## profRange
setMethod("profRange", "xcmsRaw", function(object,
mzrange = numeric(),
rtrange = numeric(),
scanrange = numeric(), ...) {
if (length(object@env$profile)) {
contmass <- profMz(object)
if (length(mzrange) == 0) {
mzrange <- c(min(contmass), max(contmass))
} else if (length(mzrange) == 1) {
closemass <- contmass[which.min(abs(contmass-mzrange))]
mzrange <- c(closemass, closemass)
} else if (length(mzrange) > 2) {
mzrange <- c(min(mzrange), max(mzrange))
}
massidx <- which((contmass >= mzrange[1]) & (contmass <= mzrange[2]))
} else {
if (length(mzrange) == 0) {
mzrange <- range(object@env$mz)
} else {
mzrange <- c(min(mzrange), max(mzrange))
}
massidx <- integer()
}
if (mzrange[1] == mzrange[2])
masslab <- paste(mzrange[1], "m/z")
else
masslab <- paste(mzrange[1], "-", mzrange[2], " m/z", sep="")
if (length(rtrange) == 0) {
if (length(scanrange) == 0)
scanrange <- c(1, length(object@scanindex))
else if (length(scanrange) == 1)
scanrange <- c(scanrange, scanrange)
else if (length(scanrange) > 2)
scanrange <- c(max(1, min(scanrange)), min(max(scanrange), length(object@scantime)))
rtrange <- c(object@scantime[scanrange[1]], object@scantime[scanrange[2]])
} else if (length(rtrange) == 1) {
closetime <- object@scantime[which.min(abs(object@scantime-rtrange))]
rtrange <- c(closetime, closetime)
} else if (length(rtrange) > 2) {
rtrange <- c(min(rtrange), max(rtrange))
}
if (rtrange[1] == rtrange[2])
timelab <- paste(round(rtrange[1],1), "seconds")
else
timelab <- paste(round(rtrange[1],1), "-", round(rtrange[2],1), " seconds", sep="")
if (length(scanrange) == 0) {
scanidx <- which((object@scantime >= rtrange[1]) & (object@scantime <= rtrange[2]))
scanrange <- c(min(scanidx), max(scanidx))
} else {
scanidx <- scanrange[1]:scanrange[2]
}
if (scanrange[1] == scanrange[2])
scanlab <- paste("scan", scanrange[1])
else
scanlab <- paste("scans ", scanrange[1], "-", scanrange[2], sep="")
list(mzrange = mzrange, masslab = masslab, massidx = massidx,
scanrange = scanrange, scanlab = scanlab, scanidx = scanidx,
rtrange = rtrange, timelab = timelab)
})
############################################################
## rawEIC
setMethod("rawEIC", "xcmsRaw", function(object,
mzrange = numeric(),
rtrange = numeric(),
scanrange = numeric()) {
if (length(rtrange) >= 2) {
rtrange <- range(rtrange)
scanidx <- (object@scantime >= rtrange[1]) & (object@scantime <= rtrange[2])
scanrange <- c(match(TRUE, scanidx),
length(scanidx) - match(TRUE, rev(scanidx)) + 1)
} else if (length(scanrange) < 2)
scanrange <- c(1, length(object@scantime))
else
scanrange <- range(scanrange)
scanrange[1] <- max(1,scanrange[1])
scanrange[2] <- min(length(object@scantime), scanrange[2])
if (!is.double(object@env$mz))
object@env$mz <- as.double(object@env$mz)
if (!is.double(object@env$intensity))
object@env$intensity <- as.double(object@env$intensity)
if (!is.integer(object@scanindex))
object@scanindex <- as.integer(object@scanindex)
.Call("getEIC", object@env$mz, object@env$intensity, object@scanindex,
as.double(mzrange), as.integer(scanrange),
as.integer(length(object@scantime)), PACKAGE ='xcms' )
})
############################################################
## plotEIC
setMethod("plotEIC", "xcmsRaw", function(object,
mzrange = numeric(),
rtrange = numeric(),
scanrange = numeric(),
type="l", add=FALSE, ...) {
if(length(mzrange)==0)
mzrange <- range(object@env$mz)
if(length(rtrange)==0)
rtrange <- range(object@scantime)
EIC <- rawEIC(object,mzrange=mzrange, rtrange=rtrange, scanrange=scanrange)
points <- cbind(object@scantime[EIC$scan], EIC$intensity)
if(add){
points(points, type=type, ...)
}else{
plot(points, type=type, main=paste("Extracted Ion Chromatogram m/z ",mzrange[1]," - ",mzrange[2],sep=""), xlab="Seconds",
ylab="Intensity", xlim=rtrange, ...)
}
invisible(points)
})
############################################################
## rawMZ
setMethod("rawMZ", "xcmsRaw", function(object,
mzrange = numeric(),
rtrange = numeric(),
scanrange = numeric()) {
if (length(rtrange) >= 2) {
rtrange <- range(rtrange)
scanidx <- (object@scantime >= rtrange[1]) & (object@scantime <= rtrange[2])
scanrange <- c(match(TRUE, scanidx), length(scanidx) - match(TRUE, rev(scanidx)))
} else if (length(scanrange) < 2)
scanrange <- c(1, length(object@scantime))
else
scanrange <- range(scanrange)
scanrange[1] <- max(1,scanrange[1])
scanrange[2] <- min(length(object@scantime),scanrange[2])
if (!is.double(object@env$mz)) object@env$mz <- as.double(object@env$mz)
if (!is.double(object@env$intensity)) object@env$intensity <- as.double(object@env$intensity)
if (!is.integer(object@scanindex)) object@scanindex <- as.integer(object@scanindex)
.Call("getMZ",object@env$mz,object@env$intensity,object@scanindex,as.double(mzrange),as.integer(scanrange),as.integer(length(object@scantime)), PACKAGE ='xcms' )
})
############################################################
## findmzROI
setMethod("findmzROI", "xcmsRaw", function(object, mzrange=c(0.0,0.0), scanrange=c(1,length(object@scantime)),dev, minCentroids, prefilter=c(0,0), noise=0){
scanrange[1] <- max(1,scanrange[1])
scanrange[2] <- min(length(object@scantime),scanrange[2])
## mzrange not implemented yet
if (!is.double(object@env$mz)) object@env$mz <- as.double(object@env$mz)
if (!is.double(object@env$intensity)) object@env$intensity <- as.double(object@env$intensity)
if (!is.integer(object@scanindex)) object@scanindex <- as.integer(object@scanindex)
## .Call("findmzROI", object@env$mz,object@env$intensity,object@scanindex, as.double(mzrange),
## as.integer(scanrange), as.integer(length(object@scantime)),
## as.double(dev), as.integer(minCentroids), as.integer(prefilter), as.integer(noise), PACKAGE ='xcms' )
ROIs <- NULL
withRestarts(
tryCatch({
ROIs <- .Call("findmzROI",
object@env$mz,
object@env$intensity,
object@scanindex,
as.double(mzrange),
as.integer(scanrange),
as.integer(length(object@scantime)),
as.double(dev),
as.integer(minCentroids),
as.integer(prefilter),
as.integer(noise),
PACKAGE ='xcms' )
},
error=function(e) {if (grepl("m/z sort assumption violated !", e$message))
{invokeRestart("fixSort")} else {simpleError(e)}}),
fixSort = function() {
## Check and fix "m/z sort assumption violated !"
for(i in 1:length(object@scanindex)){
scan <- getScan(object, scan=i)
if(is.unsorted(scan[,"mz"])){
message("Scan ", i, " is unsorted. Fixing.")
o <- order(scan[,"mz"])
start <- object@scanindex[i] + 1
end <- start+nrow(scan) - 1
object@env$mz[start:end] <- scan[o, "mz"]
object@env$intensity[start:end] <- scan[o, "intensity"]
}
}
## Re-run now with fixed m/z order
ROIs <<- .Call("findmzROI",
object@env$mz,
object@env$intensity,
object@scanindex,
as.double(mzrange),
as.integer(scanrange),
as.integer(length(object@scantime)),
as.double(dev),
as.integer(minCentroids),
as.integer(prefilter),
as.integer(noise),
PACKAGE ='xcms' )
}
)
return(ROIs)
})
############################################################
## findKalmanROI
setMethod("findKalmanROI", "xcmsRaw", function(object, mzrange=c(0.0,0.0),
scanrange=c(1,length(object@scantime)),
minIntensity, minCentroids,
consecMissedLim, criticalVal,
ppm, segs, scanBack){
scanrange[1] <- max(1, scanrange[1])
scanrange[2] <- min(length(object@scantime), scanrange[2])
## Subset object by scanrange.
valsPS <- diff(c(object@scanindex, length(object@env$mz)))
do_findKalmanROI(mz = object@env$mz, int = object@env$intensity,
scantime = object@scantime,
valsPerSpect = valsPS, mzrange = mzrange,
scanrange = scanrange, minIntensity = minIntensity,
minCentroids = minCentroids,
consecMissedLim = consecMissedLim,
criticalVal = criticalVal, ppm = ppm, segs = segs,
scanBack = scanBack)
## .Call("massifquant", object@env$mz, object@env$intensity, object@scanindex,
## object@scantime, as.double(mzrange), as.integer(scanrange),
## as.integer(length(object@scantime)), as.double(minIntensity),
## as.integer(minCentroids),as.double(consecMissedLim), as.double(ppm),
## as.double(criticalVal), as.integer(segs), as.integer(scanBack),
## PACKAGE ='xcms' )
})
############################################################
## findPeaks.massifquant: Note the original code returned, if withWave = 1,
## a Peaks object otherwise a matrix!
setMethod("findPeaks.massifquant", "xcmsRaw", function(object,
ppm=10,
peakwidth = c(20,50),
snthresh = 10,
prefilter = c(3,100),
mzCenterFun = "wMean",
integrate = 1,
mzdiff = -0.001,
fitgauss = FALSE,
scanrange = numeric(),
noise = 0,
sleep = 0,
verbose.columns = FALSE,
criticalValue = 1.125,
consecMissedLimit = 2,
unions = 1,
checkBack = 0,
withWave = 0) {
if (sleep > 0)
cat("'sleep' argument is defunct and will be ignored.")
## Fix issue #61
## Sub-set the xcmsRaw based on scanrange
if (length(scanrange) < 2) {
scanrange <- c(1, length(object@scantime))
} else {
scanrange <- range(scanrange)
}
if (min(scanrange) < 1 | max(scanrange) > length(object@scantime)) {
scanrange[1] <- max(1, scanrange[1])
scanrange[2] <- min(length(object@scantime), scanrange[2])
message("Provided scanrange was adjusted to ", scanrange)
}
object <- object[scanrange[1]:scanrange[2]]
scanrange <- c(1, length(object@scantime))
## scanrange.old <- scanrange
## ## sanitize if too few or too many scanrange is given
## if (length(scanrange) < 2)
## scanrange <- c(1, length(object@scantime))
## else
## scanrange <- range(scanrange)
## ## restrict and sanitize scanrange to maximally cover all scans
## scanrange[1] <- max(1,scanrange[1])
## scanrange[2] <- min(length(object@scantime),scanrange[2])
## ## Mild warning if the actual scanrange doesn't match the scanrange
## ## argument
## if (!(identical(scanrange.old, scanrange)) &&
## (length(scanrange.old) > 0)) {
## cat("Warning: scanrange was adjusted to ",scanrange,"\n")
## ## Scanrange filtering
## keepidx <- seq.int(1, length(object@scantime)) %in% seq.int(scanrange[1], scanrange[2])
## object <- split(object, f=keepidx)[["TRUE"]]
## }
if (!isCentroided(object))
warning("It looks like this file is in profile mode.",
" Massifquant can process only centroid mode data !\n")
vps <- diff(c(object@scanindex, length(object@env$mz)))
res <- do_findChromPeaks_massifquant(mz = object@env$mz,
int = object@env$intensity,
scantime = object@scantime,
valsPerSpect = vps,
ppm = ppm, peakwidth = peakwidth,
snthresh = snthresh,
prefilter = prefilter,
mzCenterFun = mzCenterFun,
integrate = integrate,
mzdiff = mzdiff, fitgauss = fitgauss,
noise = noise,
verboseColumns = verbose.columns,
criticalValue = criticalValue,
consecMissedLimit = consecMissedLimit,
unions = unions, checkBack = checkBack,
withWave = as.logical(withWave))
invisible(new("xcmsPeaks", res))
})
############################################################
## findPeaks.massifquant: Note the original code returned, if withWave = 1,
## a Peaks object otherwise a matrix!
setMethod("findPeaks.massifquant", "xcmsRaw", function(object,
ppm=10,
peakwidth = c(20,50),
snthresh = 10,
prefilter = c(3,100),
mzCenterFun = "wMean",
integrate = 1,
mzdiff = -0.001,
fitgauss = FALSE,
scanrange = numeric(),
noise = 0,
sleep = 0,
verbose.columns = FALSE,
criticalValue = 1.125,
consecMissedLimit = 2,
unions = 1,
checkBack = 0,
withWave = 0) {
if (sleep > 0)
cat("'sleep' argument is defunct and will be ignored.")
## Fix issue #61
## Sub-set the xcmsRaw based on scanrange
if (length(scanrange) < 2) {
scanrange <- c(1, length(object@scantime))
} else {
scanrange <- range(scanrange)
}
if (min(scanrange) < 1 | max(scanrange) > length(object@scantime)) {
scanrange[1] <- max(1, scanrange[1])
scanrange[2] <- min(length(object@scantime), scanrange[2])
message("Provided scanrange was adjusted to ", scanrange)
}
object <- object[scanrange[1]:scanrange[2]]
scanrange <- c(1, length(object@scantime))
## scanrange.old <- scanrange
## ## sanitize if too few or too many scanrange is given
## if (length(scanrange) < 2)
## scanrange <- c(1, length(object@scantime))
## else
## scanrange <- range(scanrange)
## ## restrict and sanitize scanrange to maximally cover all scans
## scanrange[1] <- max(1,scanrange[1])
## scanrange[2] <- min(length(object@scantime),scanrange[2])
## ## Mild warning if the actual scanrange doesn't match the scanrange
## ## argument
## if (!(identical(scanrange.old, scanrange)) &&
## (length(scanrange.old) > 0)) {
## cat("Warning: scanrange was adjusted to ",scanrange,"\n")
## ## Scanrange filtering
## keepidx <- seq.int(1, length(object@scantime)) %in% seq.int(scanrange[1], scanrange[2])
## object <- split(object, f=keepidx)[["TRUE"]]
## }
if (!isCentroided(object))
warning("It looks like this file is in profile mode.",
" Massifquant can process only centroid mode data !\n")
vps <- diff(c(object@scanindex, length(object@env$mz)))
res <- do_findChromPeaks_massifquant(mz = object@env$mz,
int = object@env$intensity,
scantime = object@scantime,
valsPerSpect = vps,
ppm = ppm, peakwidth = peakwidth,
snthresh = snthresh,
prefilter = prefilter,
mzCenterFun = mzCenterFun,
integrate = integrate,
mzdiff = mzdiff, fitgauss = fitgauss,
noise = noise,
verboseColumns = verbose.columns,
criticalValue = criticalValue,
consecMissedLimit = consecMissedLimit,
unions = unions, checkBack = checkBack,
withWave = as.logical(withWave))
invisible(new("xcmsPeaks", res))
})
############################################################
## isCentroided
setMethod("isCentroided", "xcmsRaw", function(object){
if (length(getScan(object,length(object@scantime) / 2)) >2 ) {
quantile(diff(getScan(object,length(object@scantime) / 2)[,"mz"]),.25) > 0.025
} else {
TRUE
}
})
############################################################
## msnparent2ms
setMethod("msnparent2ms", "xcmsRaw", function(object) {
xr <- new("xcmsRaw")
xr@env$mz=object@msnPrecursorMz
xr@env$intensity=object@msnPrecursorIntensity
xr@scantime = object@msnRt
xr@scanindex = seq(1,length(object@msnRt))
xr@acquisitionNum = seq(1,length(object@msnRt))
xr@mzrange = range(object@msnPrecursorMz)
xr
})
############################################################
## msn2ms
setMethod("msn2ms", "xcmsRaw", function(object) {
object@tic <- rep(0, length(object@msnAcquisitionNum)) ##
object@scantime <- object@msnRt
object@acquisitionNum <- object@msnAcquisitionNum
object@scanindex <- object@msnScanindex
object@env$mz <- object@env$msnMz
object@env$intensity <- object@env$msnIntensity
invisible(object)
})
############################################################
## deepCopy
setMethod("deepCopy", "xcmsRaw", function(object) {
x <- object
x@env <- new.env(parent=.GlobalEnv)
for (variable in ls(object@env)) {
eval(parse(text=paste("x@env$",variable," <- object@env$",variable,sep="")))
}
invisible(x)
})
############################################################
## levelplot
## levelplot for xcmsRaw objects; contains code from the image method, but uses the levelplot
## from the lattice package.
setMethod("levelplot", "xcmsRaw", function(x, log=TRUE,
col.regions=colorRampPalette(brewer.pal(9, "YlOrRd"))(256), ...){
## some code taken from plotSurf...
sel <- profRange(x, ...)
zvals <- x@env$profile[sel$massidx, sel$scanidx]
if(log){
zvals <- log(zvals+max(c(-min(zvals), 1)))
}
## y axis is time
yvals <- x@scantime[sel$scanidx]
yrange <- range(yvals)
## y has to be sequentially increasing!
if(length(unique(yvals))!=length(yvals))
yvals <- 1:length(yvals)
## x is m/z
xvals <- profMz(x)[sel$massidx]
## that's much slower...
## grid <- expand.grid(x=xvals, y=yvals)
## grid <- cbind(grid, z=zvals)
## grid$z <- zvals
######
## now i have to match the x and y to the z.
## as.numeric of z returns values by column(!), i.e. the first nrow(z) correspond to
## the x of 1.
xvals <- rep(xvals, ncol(zvals))
yvals <- rep(yvals, each=nrow(zvals))
zvals <- as.numeric(zvals)
## get the file name
fileNpath <- x@filepath[1]
fileName <- unlist(strsplit(fileNpath, split=.Platform$file.sep))
fileName <- fileName[length(fileName)]
plt <- levelplot(zvals~xvals*yvals,
xlab="m/z", ylab="Time",
colorkey=list(height=1, width=0.7),
main=list(fileName, side=1, line=0.5), col.regions=col.regions)
## trellis.focus("legend", side="right", clipp.off=TRUE, highlight=FALSE)
## grid.text("Int.", 0.2, 0, hjust=0.5, vjust=1)
## trellis.unfocus()
plt
})
############################################################
## profinfo
setMethod("profinfo", "xcmsRaw", function(object) {
pinfo <- object@profparam
## fill with additional method and step.
pinfo$method <- profMethod(object)
pinfo$step <- profStep(object)
return(pinfo)
})
############################################################
## scanrange
setMethod("scanrange", "xcmsRaw", function(object) {
if(.hasSlot(object, "scanrange")){
srange <- object@scanrange
if(length(srange) == 0){
## for compatibility with the xcmsSet and xcmsRaw functions,
## which default the scanrange argument to NULL.
return(NULL)
}
return(srange)
}else{
warning("No slot scanrange available, consider updating the",
" object using the 'updateObject' method.")
return(NULL)
}
})
setReplaceMethod("scanrange", "xcmsRaw", function(object, value) {
if(.hasSlot(object, "scanrange")){
object@scanrange <- value
}else{
warning("Object has no slot scanrange, condider updating",
" the object using the 'updateObject' method.")
}
object
})
############################################################
## mslevel
setMethod("mslevel", "xcmsRaw", function(object){
if(.hasSlot(object, "mslevel")){
mlevel <- object@mslevel
if(length(mlevel) == 0){
## for compatibility with the xcmsSet and xcmsRaw functions,
## which default the mslevel argument to NULL.
return(NULL)
}
return(object@mslevel)
}else{
warning("No slot mslevel available, consider updating the",
" object using the 'updateObject' method.")
return(NULL)
}
})
setReplaceMethod("mslevel", "xcmsRaw", function(object, value){
if(.hasSlot(object, "mslevel")){
object@mslevel <- value
}else{
warning("Object has no slot mslevel, consider updating",
" the object using the 'updateObject' method.")
}
object
})
############################################################
## plotScan
setMethod("plotScan", "xcmsRaw", function(object, scan, mzrange = numeric(),
ident = FALSE)
{
if (scan<1 || scan>length(object@scanindex) ) {
warning("scan out of range")
return()
}
## handle last spectrum
if (scan == length(object@scanindex)) {
followingScanIndex <- length(object@env$mz)
} else {
followingScanIndex <- object@scanindex[scan+1]
}
## hendle empty spectra
if (object@scanindex[scan] == length(object@env$mz) ||
object@scanindex[scan] == followingScanIndex) {
warning("empty scan")
return()
}
idx <- (object@scanindex[scan]+1):min(followingScanIndex,
length(object@env$mz), na.rm=TRUE)
if (length(mzrange) >= 2) {
mzrange <- range(mzrange)
idx <- idx[object@env$mz[idx] >= mzrange[1] & object@env$mz[idx] <= mzrange[2]]
}
points <- cbind(object@env$mz[idx], object@env$intensity[idx])
title = paste("Mass Spectrum: ", round(object@scantime[scan], 1),
" seconds (scan ", scan, ")", sep = "")
plot(points, type="h", main = title, xlab="m/z", ylab="Intensity")
if (ident)
return(identify(points, labels = round(points[,1], 1)))
invisible(points)
})
############################################################
## plotSpec
setMethod("plotSpec", "xcmsRaw", function(object, ident = FALSE,
vline = numeric(0), ...) {
sel <- profRange(object, ...)
title = paste("Averaged Mass Spectrum: ", sel$timelab, " (",
sel$scanlab, ")", sep = "")
points <- cbind(profMz(object)[sel$massidx],
rowMeans(object@env$profile[sel$massidx,sel$scanidx,drop=FALSE]))
plot(points, type="l", main = title, xlab="m/z", ylab="Intensity")
if (length(vline))
abline(v = vline, col = "red")
if (ident)
return(identify(points, labels = round(points[,1], 1)))
invisible(points)
})
############################################################
## plotChrom
setMethod("plotChrom", "xcmsRaw", function(object, base = FALSE, ident = FALSE,
fitgauss = FALSE, vline = numeric(0), ...) {
sel <- profRange(object, ...)
if (base) {
title = paste("Base Peak Chromatogram: ", sel$masslab, sep = "")
pts <- cbind(object@scantime[sel$scanidx],
colMax(object@env$profile[sel$massidx,sel$scanidx,drop=FALSE]))
}
else {
title = paste("Averaged Ion Chromatogram: ", sel$masslab, sep = "")
pts <- cbind(object@scantime[sel$scanidx],
colMeans(object@env$profile[sel$massidx,sel$scanidx,drop=FALSE]))
}
plot(pts, type="l", main = title, xlab="Seconds", ylab="Intensity")
if (length(vline))
abline(v = vline, col = "red")
if (fitgauss) {
fit <- nls(y ~ SSgauss(x, mu, sigma, h), data.frame(x = pts[,1], y = pts[,2]))
points(pts[,1], fitted(fit), type = "l", col = "red", lwd = 2)
return(fit)
}
if (ident)
return(identify(pts, labels = round(pts[,1], 1)))
invisible(pts)
})
############################################################
## image
setMethod("image", "xcmsRaw", function(x, col = rainbow(256), ...) {
sel <- profRange(x, ...)
zlim <- log(range(x@env$intensity))
title <- paste("XC/MS Log Intensity Image (Profile Method: ",
x@profmethod, ")", sep = "")
if (zlim[1] < 0) {
zlim <- log(exp(zlim)+1)
image(profMz(x)[sel$massidx], x@scantime[sel$scanidx],
log(x@env$profile[sel$massidx, sel$scanidx]+1),
col = col, zlim = zlim, main = title, xlab="m/z", ylab="Seconds")
} else
image(profMz(x)[sel$massidx], x@scantime[sel$scanidx],
log(x@env$profile[sel$massidx, sel$scanidx]),
col = col, zlim = zlim, main = title, xlab="m/z", ylab="Seconds")
})
############################################################
## plotSurf
setMethod("plotSurf", "xcmsRaw", function(object, log = FALSE,
aspect = c(1, 1, .5), ...) {
require(rgl) || stop("Couldn't load package rgl")
sel <- profRange(object, ...)
y <- object@env$profile[sel$massidx, sel$scanidx]
if (log)
y <- log(y+max(1-min(y), 0))
ylim <- range(y)
x <- seq(0, aspect[1], length=length(sel$massidx))
z <- seq(0, aspect[2], length=length(sel$scanidx))
y <- y/ylim[2]*aspect[3]
colorlut <- terrain.colors(256)
col <- colorlut[y/aspect[3]*255+1]
rgl.clear("shapes")
rgl.clear("bbox")
rgl.surface(x, z, y, color = col, shininess = 128)
rgl.points(0, 0, 0, alpha = 0)
mztics <- pretty(sel$mzrange, n = 5*aspect[1])
rttics <- pretty(sel$rtrange, n = 5*aspect[2])
inttics <- pretty(c(0,ylim), n = 10*aspect[3])
inttics <- inttics[inttics > 0]
rgl.bbox(xat = (mztics - sel$mzrange[1])/diff(sel$mzrange)*aspect[1],
xlab = as.character(mztics),
yat = inttics/ylim[2]*aspect[3],
ylab = as.character(inttics),
zat = (rttics - sel$rtrange[1])/diff(sel$rtrange)*aspect[2],
zlab = as.character(rttics),
ylen = 0, alpha=0.5)
})
############################################################
## getMsnScan
setMethod("getMsnScan", "xcmsRaw", function(object, scan, mzrange = numeric()) {
if (scan < 0)
scan <- length(object@msnRt) + 1 + scan
idx <- seq(object@msnScanindex[scan]+1, min(object@msnScanindex[scan+1],
length(object@env$msnMz), na.rm=TRUE))
if (length(mzrange) >= 2) {
mzrange <- range(mzrange)
idx <- idx[object@env$msnMz[idx] >= mzrange[1] & object@env$msnMz[idx] <= mzrange[2]]
}
points <- cbind(mz = object@env$msnMz[idx], intensity = object@env$msnIntensity[idx])
invisible(points)
})
############################################################
## AutoLockMass
setMethod("AutoLockMass", "xcmsRaw", function(object) {
if(length(grep("xml|mzData|mzXML|mzML", object@filepath, ignore.case=TRUE)) >= 1){
tempFreq<-diff(which(diff(object@scantime) == 0))-1
idx <- which(tempFreq != floor(mean(tempFreq))) ## only needed for newer lockmass signal
if(is.nan(mean(tempFreq)) ){
dn<-density(diff(object@scantime))
lockMassScans <- quantile(dn$x, .75) ## hopefully always correct (?)
inx<-which(diff(object@scantime) >= lockMassScans) ## these seems to be some of the new files
return(inx)
}else if(all(tempFreq == mean(tempFreq)) ){
freqLock<-mean(tempFreq)
} else if(all(idx == which(tempFreq != floor(mean(tempFreq) )) )){
## for the newer mzML and mzXML not sure why the change?
## This means that there is only one gap :( ??
stop("This file is different from the normally seen files and requires special programming\n
This functionality has not been implemented yet\n ")
## these files seem to come either from newer MS units or/and msconvert ....
} else {
freqLock<-mean(tempFreq)
warning("\nLock mass frequency wasn't detected correctly", immediate.=TRUE)
}
if(diff(object@scantime[1:5])[1] == 0 ){
start<-1
} else{
start<-freqLock
}
return(makeacqNum(object, freqLock, start))
} else if(length(grep("cdf", object@filepath, ignore.case=TRUE)) >= 1){
## check to see if we have the X02.CDF files around
## These files should be the lock mass channel
file02<-list.files(gsub("01.CDF", "02.CDF", object@filepath), recursive=T)
if(length(file02)> 0){
xr<-xcmsRaw(file02)
lockMass<-sapply(xr@scantime, function(x, object){
which.min(abs(object@scantime - x))
}, object)
return(lockMass)
} else {
## we couldn't find the files so lets try to find them automatically
hr <- hist(diff(object@scantime), breaks=4, plot=FALSE)
if(length(hr$counts) > 2){
idx<-which(hr$counts == 0)
## could have something here about which way the plot is ie cor R is - or +
# if(cor(hr$mids, hr$counts) < 0){
inx<-which(diff(object@scantime) >= hr$mids[(max(idx))])
# } else {
# inx<-which()
# }
}else if(length(hr$counts) == 2){
inx<-which(diff(object@scantime) >= hr$mids[2])
} else {
stop("File appears to have been run without lock mass\n ")
}
if(length(inx) <= 1){
warning("\nLock mass frequency wasn't detected", immediate.=TRUE)
return(0)
}
## above we're looking for scantimes that are much longer than the normal scan times
tempFreq<-diff(inx)-1
if(all(tempFreq == median(tempFreq)) ){
freqLock<-median(tempFreq)
}else{
freqLock<-median(tempFreq)
warning("Lock mass frequency wasn't detected correctly\n", immediate.=TRUE)
}
if(inx[1] == 0 || inx[1] == 1){
start<-1
}else{
start<-freqLock
}
#return(inx)
return(makeacqNum(object, freqLock, start))
}
} else{
stop("Couldn't detect file type\n")
}
})
############################################################
## makeacqNum
setMethod("makeacqNum", "xcmsRaw", function(object, freq, start=1) {
freq<-freq+1 ##nessary for the start at +1 and others since 1st scan is +1
acqNum<-numeric()
fo<-seq(from=start, to=length(object@scanindex), by=freq)
for(i in fo){
acqNum<-c(acqNum, i,i+1)
}
return(acqNum)
})
############################################################
## stitch
setMethod("stitch", "xcmsRaw", function(object, lockMass) {
if(length(grep("xml|mzData", object@filepath, ignore.case=TRUE)) >= 1){
type<-stitch.xml
} else if(length(grep("cdf", object@filepath, ignore.case=TRUE)) >= 1){
## lets check to see if lockMass is one scan or two
if(any(diff(lockMass) == 0)){
type<-stitch.netCDF.new
}else {
type<-stitch.netCDF
}
} else{
stop("Unknown stitch method \n")
}
invisible(do.call(type, list(object, lockMass)))
})
############################################################
## stitch.xml
setMethod("stitch.xml", "xcmsRaw", function(object, lockMass) {
ob<-new("xcmsRaw")
ob@env$mz<-object@env$mz
ob@env$intensity<-object@env$intensity
ob@scanindex<-object@scanindex
ob@scantime<-object@scantime
ob@acquisitionNum<-1:length(ob@scanindex)
ob@filepath<-object@filepath
ob@mzrange<-range(ob@env$mz)
ob@profmethod<-object@profmethod
ob@tic<-object@tic
ob@profparam<-list()
## Array [x, y, z] with
## - x: mz and intensity
## - y: spectrum (1: max measurements within one of the spectra)
## - z: scans (1: number of spectra)
arr <- array(dim = c(2, max(diff(ob@scanindex)), length(ob@scanindex)))
if(lockMass[1] == 1){
lockMass<-lockMass[3:length(lockMass)]
}
## Remove the last lock mass if it is too close by the end
if ((lockMass[length(lockMass)] + 2) > length(ob@scanindex))
lockMass <- lockMass[1:(length(lockMass) - 1)]
## If the number of lockMass values is not even splitting them into a
## two-column matrix is not OK (causes also the first lockMass spectrum to
## be overwritten twice. That's to get rid of the warning in issue #173.
if (length(lockMass) %% 2)
lockMass <- c(lockMass, -99)
lockMass<-matrix(lockMass, ncol=2, byrow=TRUE)
## if((lockMass[nrow(lockMass),2]+2) > length(ob@scanindex)){
## lockMass<-lockMass[1:(nrow(lockMass)-1),]
## }
## We're looping from 1 to length - 1, thus we have to fill in the last
## scan later.
for(i in 1:(length(ob@scanindex)-1)){
if(any(i == lockMass[, 1])){
## Place mz and intensity values from the previous scan into the
## array and fill the rest with NA.
arr[1,,i] <-c(object@env$mz[(object@scanindex[(i-1)]+1):object@scanindex[i]],
rep(NA, (max(diff(object@scanindex))-
length((object@scanindex[(i-1)]+1):object@scanindex[i])) ))
arr[2,,i] <-c(object@env$intensity[(object@scanindex[(i-1)]+1):object@scanindex[i]],
rep(NA, (max(diff(object@scanindex)) -
length((object@scanindex[(i-1)]+1):object@scanindex[i])) ))
} else if(any(i == lockMass[, 2])){
## Place mz and intensity values from the next scan into the array.
arr[1,,i] <-c(object@env$mz[(object@scanindex[i+1]+1):object@scanindex[(i+2)]],
rep(NA, (max(diff(object@scanindex)) -
length((object@scanindex[i+1]+1):object@scanindex[(i+2)])) ))
arr[2,,i] <-c(object@env$intensity[(object@scanindex[i+1]+1):object@scanindex[(i+2)]],
rep(NA, (max(diff(object@scanindex)) -
length((object@scanindex[i+1]+1):object@scanindex[(i+2)])) ))
} else{
## Just fill with the actual values.
arr[1,,i] <-c(object@env$mz[(object@scanindex[i]+1):object@scanindex[i+1]],
rep(NA, (max(diff(object@scanindex))-
length((object@scanindex[i]+1):object@scanindex[i+1])) ))
arr[2,,i] <-c(object@env$intensity[(object@scanindex[i]+1):object@scanindex[i+1]],
rep(NA, (max(diff(object@scanindex)) -
length((object@scanindex[i]+1):object@scanindex[i+1])) ))
}
## mz is in 1; Intensity is in 2
##remake scanindex
if(i == 1){
ob@scanindex[i]<-as.integer(0)
}else if(i == length(ob@scanindex)-1){
ob@scanindex[i]<-as.integer(length(na.omit(arr[1,,(i-1)]))+ob@scanindex[(i-1)])
ob@scanindex[i+1]<-as.integer(length(na.omit(arr[1,,i]))+ob@scanindex[i])
# ob@scanindex[i+1]<-as.integer(length(ob@env$mz))
}else{
ob@scanindex[i]<-as.integer(length(na.omit(arr[1,,(i-1)]))+ob@scanindex[(i-1)])
}
}
## Fix for #173: fill also values for the last scan.
last_i <- length(ob@scanindex)
fetch_idx <- (object@scanindex[last_i] + 1):length(object@env$mz)
put_idx <- 1:length(fetch_idx)
arr[1, put_idx, length(ob@scanindex)] <- object@env$mz[fetch_idx]
arr[2, put_idx, length(ob@scanindex)] <- object@env$intensity[fetch_idx]
NAidx<-is.na(arr[1,,])
ob@env$mz<-as.numeric(arr[1,,][!NAidx])
ob@env$intensity<-as.numeric(arr[2,,][!NAidx])
ob<-remakeTIC(ob) ## remake TIC
return(ob)
})
############################################################
## stitch.netCDF
setMethod("stitch.netCDF", "xcmsRaw", function(object, lockMass) {
if(length(lockMass) == 0 | all(lockMass == 0)){
return(object)
}
ob<-new("xcmsRaw")
ob@filepath<-object@filepath
ob@mzrange<-range(ob@env$mz)
ob@profmethod<-object@profmethod
ob@profparam<-list()
arr<-array(dim=c(2,max(diff(object@scanindex)), (length(object@scanindex)+length(lockMass)) ))
ob@scanindex <- integer(length=length(arr[1,1,]))
ob@acquisitionNum<-1:length(ob@scanindex)
if(lockMass[1] == 1){
lockMass<-lockMass[3:length(lockMass)]
}
lockMass<-matrix(lockMass, ncol=2, byrow=TRUE)
if((lockMass[nrow(lockMass),2]+2) > length(ob@scanindex)){
lockMass<-lockMass[1:(nrow(lockMass)-1),]
} ## remove the last lock mass scan if it's at the end of the run
add<-0
arrMax<-length(arr[1,,1])
scanIx<-integer(length(arr[1,1,]))
for(i in 1:length(object@scanindex)){
# if((i+add) > length(object@scanindex)){
# break
# }
scan<-getScan(object, i)
arr[1,,i+add]<- c(scan[,"mz"], rep(NA, (arrMax-length(scan[,"mz"])) ))
arr[2,,i+add]<- c(scan[,"intensity"], rep(NA, (arrMax-length(scan[,"intensity"])) ))
scanIx[(i+add)+1]<- (scanIx[(i+add)])+nrow(scan)
##proably going to need a cut at the end of scanIx +1 problem
if(any(i == lockMass[,1])){
arr[1,,i+1+add]<- c(scan[,"mz"], rep(NA, (arrMax-length(scan[,"mz"])) ))
arr[2,,i+1+add]<- c(scan[,"intensity"], rep(NA, (arrMax-length(scan[,"intensity"])) ))
scanIx[(i+add)+2]<- (scanIx[1+i+add])+nrow(scan)
scan<-getScan(object, i+1)
arr[1,,i+2+add]<- c(scan[,"mz"], rep(NA, (arrMax-length(scan[,"mz"])) ))
arr[2,,i+2+add]<- c(scan[,"intensity"], rep(NA, (arrMax-length(scan[,"intensity"])) ))
scanIx[(i+add)+3]<- (scanIx[(i+2)+add])+nrow(scan)
add<-add+2
}
}
NAidx<-is.na(arr[1,,])
ob@env$mz<-as.numeric(arr[1,,][!NAidx])
ob@env$intensity<-as.numeric(arr[2,,][!NAidx])
##remake scanindex
# scanInx<- as.integer(apply(arr[1,,], 2, function(x){
# inx<-is.na(x)
# length(x[!inx]) ## need to add these length together
# }))
ob@scanindex<-as.integer(scanIx)
ob@scantime <- sapply(1:length(ob@scanindex), function(x, time){
time*x
}, mean(diff(object@scantime)))
ob<-remakeTIC(ob) ## remake TIC
return(ob)
})
############################################################
## stitch.netCDF.new
setMethod("stitch.netCDF.new", "xcmsRaw", function(object, lockMass) {
if(length(lockMass) == 0 | all(lockMass == 0)){
return(object)
}
ob<-new("xcmsRaw")
ob@filepath<-object@filepath
ob@mzrange<-range(ob@env$mz)
ob@profmethod<-object@profmethod
ob@profparam<-list()
arr<-array(dim=c(2,max(diff(object@scanindex)), (length(object@scanindex)+length(lockMass)) ))
ob@scanindex <- integer(length=length(arr[1,1,]))
ob@acquisitionNum<-1:length(ob@scanindex)
if(lockMass[1] == 1){
lockMass<-lockMass[2:length(lockMass)]
}
# lockMass<-matrix(lockMass, ncol=2, byrow=TRUE)
add<-0
arrMax<-length(arr[1,,1])
scanIx<-integer(length(arr[1,1,]))
for(i in 1:length(object@scanindex)){
scan<-getScan(object, i)
arr[1,,i+add]<- c(scan[,"mz"], rep(NA, (arrMax-length(scan[,"mz"])) ))
arr[2,,i+add]<- c(scan[,"intensity"], rep(NA, (arrMax-length(scan[,"intensity"])) ))
scanIx[(i+add)+1]<- (scanIx[(i+add)])+nrow(scan)
if(any(i == lockMass)){
arr[1,,i+1+add] <- c(scan[,"mz"], rep(NA, (arrMax-length(scan[,"mz"])) ))
arr[2,,i+1+add] <- c(scan[,"intensity"], rep(NA, (arrMax-length(scan[,"intensity"])) ))
scanIx[(i+add)+2]<- (scanIx[1+i+add])+nrow(scan)
add<-add+1
## for the moment lets be dirty and add the scan before
## upgrade later to 1/2 and 1/2 from each scan
}
}
NAidx<-is.na(arr[1,,])
ob@env$mz<-as.numeric(arr[1,,][!NAidx])
ob@env$intensity<-as.numeric(arr[2,,][!NAidx])
## above is to remove any NA buffers from the array
ob@scanindex<-as.integer(scanIx)
ob@scantime <- sapply(1:length(ob@scanindex), function(x, time){
time*x
}, mean(diff(object@scantime))) ## remake the scantime vector
ob<-remakeTIC(ob) ## remake TIC
return(ob)
})
############################################################
## [
## Subset by scan.
#' @title Subset an xcmsRaw object by scans
#'
#' @aliases subset-xcmsRaw
#'
#' @description Subset an \code{\linkS4class{xcmsRaw}} object by scans. The
#' returned \code{\linkS4class{xcmsRaw}} object contains values for all
#' scans specified with argument \code{i}. Note that the \code{scanrange}
#' slot of the returned \code{xcmsRaw} will be
#' \code{c(1, length(object@scantime))} and hence not \code{range(i)}.
#'
#' @details Only subsetting by scan index in increasing order or by a logical
#' vector are supported. If not ordered, argument \code{i} is sorted
#' automatically. Indices which are larger than the total number of scans
#' are discarded.
#'
#' @param x The \code{\linkS4class{xcmsRaw}} object that should be sub-setted.
#'
#' @param i Integer or logical vector specifying the scans/spectra to which
#' \code{x} should be sub-setted.
#'
#' @param j Not supported.
#'
#' @param drop Not supported.
#'
#' @return The sub-setted \code{\linkS4class{xcmsRaw}} object.
#'
#' @author Johannes Rainer
#'
#' @seealso \code{\link{split.xcmsRaw}}
#'
#' @examples
#' ## Load a test file
#' file <- system.file('cdf/KO/ko15.CDF', package = "faahKO")
#' xraw <- xcmsRaw(file, profstep = 0)
#' ## The number of scans/spectra:
#' length(xraw@scantime)
#'
#' ## Subset the object to scans with a scan time from 3500 to 4000.
#' xsub <- xraw[xraw@scantime >= 3500 & xraw@scantime <= 4000]
#' range(xsub@scantime)
#' ## The number of scans:
#' length(xsub@scantime)
#' ## The number of values of the subset:
#' length(xsub@env$mz)
setMethod("[", signature(x = "xcmsRaw",
i = "logicalOrNumeric",
j = "missing",
drop = "missing"),
function(x, i, j, drop) {
nScans <- length(x@scantime)
if (is.logical(i))
i <- which(i)
if (length(i) == 0)
return(new("xcmsRaw"))
## Ensure i is sorted.
if (is.unsorted(i)) {
warning("'i' has been sorted as subsetting supports",
" only sorted indices.")
i <- sort(i)
}
## Ensure that we're not supposed to return duplicated scans!
i <- unique(i)
## Check that i is within the number of scans
outsideRange <- !(i %in% 1:nScans)
if (any(outsideRange)) {
## Throw an error or just a warning?
iOut <- i[outsideRange]
i <- i[!outsideRange]
warning("Some of the indices in 'i' are larger than the",
" total number of scans which is ", nScans)
}
## If i is equal to the number of scans just return the object
if (all(1:nScans %in% i))
return(x)
have_profstep <- profStep(x)
valsPerSpect <- diff(c(x@scanindex, length(x@env$mz)))
## Subset:
## 1) scantime
x@scantime <- x@scantime[i]
## 2) scanindex
x@scanindex <- valueCount2ScanIndex(valsPerSpect[i])
## 3) @env$mz
newE <- new.env()
valsInScn <- rep(1:nScans, valsPerSpect) %in% i
newE$mz <- x@env$mz[valsInScn]
## 4) @env$intensity
newE$intensity <- x@env$intensity[valsInScn]
x@env <- newE
## 5) The remaining slots
x@tic <- x@tic[i]
if (length(x@polarity) == nScans)
x@polarity <- x@polarity[i]
if (length(x@acquisitionNum) == nScans)
x@acquisitionNum <- x@acquisitionNum[i]
x@mzrange <- range(x@env$mz)
scanrange(x) <- c(1, length(x@scantime))
## Profile matrix
if (have_profstep > 0) {
## Create the profile matrix.
## Call profStep<- that will also update the mzrange properly
profStep(x) <- have_profstep
}
## TODO: what with the MSn data?
return(x)
})
#' @title The profile matrix
#'
#' @aliases profile-matrix profMat profMat,xcmsRaw-method
#'
#' @description The \emph{profile} matrix is an n x m matrix, n (rows)
#' representing equally spaced m/z values (bins) and m (columns) the
#' retention time of the corresponding scans. Each cell contains the maximum
#' intensity measured for the specific scan and m/z values falling within
#' the m/z bin.
#'
#' The \code{profMat} method creates a new profile matrix or returns the
#' profile matrix within the object's \code{@env} slot, if available.
#' Settings for the profile matrix generation, such as \code{step} (the bin
#' size), \code{method} or additional settings are extracted from the
#' respective slots of the \code{\linkS4class{xcmsRaw}} object.
#' Alternatively it is possible to specify all of the settings as
#' additional parameters.
#'
#' @details Profile matrix generation methods:
#' \describe{
#' \item{bin}{The default profile matrix generation method that does a
#' simple binning, i.e. aggregating of intensity values falling within an
#' m/z bin.}
#' \item{binlin}{Binning followed by linear interpolation to impute missing
#' values. The value for m/z bins without a measured intensity are inferred
#' by a linear interpolation between neighboring bins with a measured
#' intensity.}
#' \item{binlinbase}{Binning followed by a linear interpolation to impute
#' values for empty elements (m/z bins) within a user-definable proximity to
#' non-empty elements while stetting the element's value to the
#' \code{baselevel} otherwise. See \code{impute = "linbase"} parameter of
#' \code{\link{imputeLinInterpol}} for more details.}
#' \item{intlin}{Set the elements' values to the integral of the linearly
#' interpolated data from plus to minus half the step size.}
#' }
#'
#' @note From \code{xcms} version 1.51.1 on only the \code{profMat} method
#' should be used to extract the profile matrix instead of the previously
#' default way to access it directly \emph{via} \code{object@env$profile}.
#'
#' @param object The \code{\linkS4class{xcmsRaw}} object.
#'
#' @param method The profile matrix generation method. Allowed are \code{"bin"},
#' \code{"binlin"}, \code{"binlinbase"} and \code{"intlin"}. See details
#' section for more information.
#'
#' @param step numeric(1) representing the m/z bin size.
#'
#' @param baselevel numeric(1) representing the base value to which
#' empty elements (i.e. m/z bins without a measured intensity) should be
#' set. Only considered if \code{method = "binlinbase"}. See
#' \code{baseValue} parameter of \code{\link{imputeLinInterpol}} for more
#' details.
#'
#' @param basespace numeric(1) representing the m/z length after
#' which the signal will drop to the base level. Linear interpolation will
#' be used between consecutive data points falling within
#' \code{2 * basespace} to each other. Only considered if
#' \code{method = "binlinbase"}. If not specified, it defaults to
#' \code{0.075}. Internally this parameter is translated into the
#' \code{distance} parameter of the \code{\link{imputeLinInterpol}}
#' function by \code{distance = floor(basespace / step)}. See
#' \code{distance} parameter of \code{\link{imputeLinInterpol}} for more
#' details.
#'
#' @param mzrange. Optional numeric(2) manually specifying the mz value range to
#' be used for binnind. If not provided, the whole mz value range is used.
#'
#' @seealso \code{\linkS4class{xcmsRaw}}, \code{\link{binYonX}} and
#' \code{\link{imputeLinInterpol}} for the employed binning and
#' missing value imputation methods, respectively.
#' \code{\link{profMat,XCMSnExp-method}} for the method on
#' \code{\link{XCMSnExp}} objects.
#'
#' @return \code{profMat} returns the profile matrix (rows representing scans,
#' columns equally spaced m/z values).
#'
#' @author Johannes Rainer
#'
#' @examples
#' file <- system.file('cdf/KO/ko15.CDF', package = "faahKO")
#' ## Load the data without generating the profile matrix (profstep = 0)
#' xraw <- xcmsRaw(file, profstep = 0)
#' ## Extract the profile matrix
#' profmat <- profMat(xraw, step = 0.3)
#' dim(profmat)
#' ## If not otherwise specified, the settings from the xraw object are used:
#' profinfo(xraw)
#' ## To extract a profile matrix with linear interpolation use
#' profmat <- profMat(xraw, step = 0.3, method = "binlin")
#' ## Alternatively, the profMethod of the xraw objects could be changed
#' profMethod(xraw) <- "binlin"
#' profmat_2 <- profMat(xraw, step = 0.3)
#' all.equal(profmat, profmat_2)
#'
#' @rdname profMat-xcmsSet
#'
#' @name profMat-xcmsSet
setMethod("profMat", signature(object = "xcmsRaw"), function(object, method,
step,
baselevel,
basespace,
mzrange.) {
## Call the .createProfileMatrix if the internal profile matrix is empty or
## if the settings differ from the internal settings.
pi <- profinfo(object)
if (missing(method))
method <- pi$method
if (missing(step))
step <- pi$step
if (missing(baselevel))
baselevel <- pi$baselevel
if (missing(basespace))
basespace <- pi$basespace
if (length(object@env$profile) > 0) {
## Check if the settings are the same...
have_method <- pi$method
have_step <- pi$step
have_basespace <- pi$basespace
have_baselevel <- pi$baselevel
if (!is.character(all.equal(list(method, step, basespace, baselevel),
list(have_method, have_step,
have_basespace, have_baselevel)))) {
return(object@env$profile)
}
}
if (missing(mzrange.)) {
mzrange. <- NULL
} else {
if (length(mzrange.) != 2 | !is.numeric(mzrange.))
stop("If provided, 'mzrange.' has to be a numeric of length 2!")
}
## Calculate the profile matrix
if (step <= 0)
stop("Can not calculate a profile matrix with step=",step, "! Either",
" provide a positive number with argument 'step' or use the",
" 'profStep' method to set the step parameter for the xcmsSet.")
message("Create profile matrix with method '", method,
"' and step ", step, " ... ", appendLF = FALSE)
res <- .createProfileMatrix(mz = object@env$mz, int = object@env$intensity,
valsPerSpect = diff(c(object@scanindex,
length(object@env$mz))),
method = method, step = step,
baselevel = baselevel, basespace = basespace,
mzrange. = mzrange.)
message("OK")
res
})
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