Nothing
R/methods-xcmsSet.R
In xcms: LC-MS and GC-MS Data Analysis
Defines functions
.retcor.peakgroups_orig
## All methods for xcmsSet should go here.
#' @include functions-xcmsSet.R
############################################################
## show
setMethod("show", "xcmsSet", function(object) {
cat("An \"xcmsSet\" object with", nrow(object@phenoData), "samples\n\n")
if (nrow(object@peaks)) {
cat("Time range: ", paste(round(range(object@peaks[,"rt"]), 1),
collapse = "-"),
" seconds (", paste(round(range(object@peaks[,"rt"])/60, 1),
collapse = "-"),
" minutes)\n", sep = "")
cat("Mass range:", paste(round(range(object@peaks[,"mz"], na.rm = TRUE), 4),
collapse = "-"), "m/z\n")
cat("Peaks:", nrow(object@peaks), "(about",
round(nrow(object@peaks)/nrow(object@phenoData)), "per sample)\n")
cat("Peak Groups:", nrow(object@groups), "\n")
} else
cat("Peaks: 0\n")
cat("Sample classes:", paste(levels(sampclass(object)), collapse = ", "), "\n\n")
## Processing info.
## Feature detection errors.
cat("Feature detection:\n")
if(.hasSlot(object, "mslevel")){
MSn <- mslevel(object)
if(is.null(MSn))
MSn <- 1
cat(paste0(" o Peak picking performed on MS", MSn, ".\n"))
}
if(.hasSlot(object, "scanrange")){
if(!is.null(scanrange(object))){
cat(" o Scan range limited to ", scanrange(object)[1], "-",
scanrange(object)[2], "\n")
}
}
errs <- .getProcessErrors(object, PROCSTEP = .PROCSTEP.PEAK.DETECTION)
if (length(errs) > 0) {
cat(" o Detection errors: ", length(errs), " files failed.\n",
" Use method 'showError' to list the error(s).\n\n", sep ="")
}
if (length(object@profinfo)) {
cat("Profile settings: ")
for (i in seq(along = object@profinfo)) {
if (i != 1) cat(" ")
cat(names(object@profinfo)[i], " = ", object@profinfo[[i]],
"\n", sep = "")
}
cat("\n")
}
memsize <- object.size(object)
cat("Memory usage:", signif(memsize/2^20, 3), "MB\n")
})
#' @description This method updates an \emph{old} \code{\linkS4class{xcmsSet}}
#' object to the latest definition.
#'
#' @title Update an \code{\linkS4class{xcmsSet}} object
#'
#' @param object The \code{\linkS4class{xcmsSet}} object to update.
#'
#' @param ... Optional additional arguments. Currently ignored.
#'
#' @param verbose Currently ignored.
#'
#' @return An updated \code{\linkS4class{xcmsSet}} containing all data from
#' the input object.
#'
#' @author Johannes Rainer
setMethod("updateObject", "xcmsSet", function(object, ..., verbose = FALSE) {
## Create a new empty xcmsSet and start filling it with the slot
## values (if present.)
## Define the default values.
pks <- matrix(nrow = 0, ncol = 0)
grps <- matrix(nrow = 0, ncol = 0)
grpidx <- list()
flld <- integer(0)
phnD <- data.frame()
theRt <- list()
flpth <- character(0)
prfnf <- vector("list")
datCorr <- integer(0)
pol <- character(0)
prgInfo <- list()
msl <- numeric(0)
scnr <- numeric(0)
prgCb <- function(progress) NULL
procH <- list()
## Now replace the values with the slots... if present.
if (.hasSlot(object, "peaks"))
pks <- object@peaks
if (.hasSlot(object, "groups"))
grps <- object@groups
if (.hasSlot(object, "groupidx"))
grpidx <- object@groupidx
if (.hasSlot(object, "filles"))
flld <- object@filles
if (.hasSlot(object, "phenoData"))
phnD <- object@phenoData
if (.hasSlot(object, "rt"))
theRt <- object@rt
if (.hasSlot(object, "filepaths"))
flpth <- object@filepaths
if (.hasSlot(object, "profinfo"))
prfnf <- object@profinfo
if (.hasSlot(object, "dataCorrection"))
datCorr <- object@dataCorrection
if (.hasSlot(object, "polarity"))
pol <- object@polarity
if (.hasSlot(object, "polarity"))
prgInfo <- object@progressInfo
if (.hasSlot(object, "mslevel"))
msl <- object@mslevel
if (.hasSlot(object, "scanrange"))
scnr <- object@scanrange
if (.hasSlot(object, "progressCallback"))
prgCb <- object@progressCallback
if (.hasSlot(object, ".processHistory"))
procH <- object@.processHistory
## Generate the new object.
newXs <- new("xcmsSet",
peaks = pks,
groups = grps,
groupidx = grpidx,
filled = flld,
phenoData = phnD,
rt = theRt,
filepaths = flpth,
profinfo = prfnf,
dataCorrection = datCorr,
polarity = pol,
progressInfo = prgInfo,
mslevel = msl,
scanrange = scnr,
progressCallback = prgCb,
.processHistory = procH
)
return(newXs)
})
############################################################
## peaks
setMethod("peaks", "xcmsSet", function(object) object@peaks)
setReplaceMethod("peaks", "xcmsSet", function(object, value) {
object@peaks <- value
object
})
############################################################
## groups
setMethod("groups", "xcmsSet", function(object) object@groups)
setReplaceMethod("groups", "xcmsSet", function(object, value) {
object@groups <- value
object
})
############################################################
## groupidx
setMethod("groupidx", "xcmsSet", function(object) object@groupidx)
setReplaceMethod("groupidx", "xcmsSet", function(object, value) {
object@groupidx <- value
object
})
############################################################
## sampnames
setMethod("sampnames", "xcmsSet", function(object) rownames(object@phenoData))
setReplaceMethod("sampnames", "xcmsSet", function(object, value) {
if (length(object@phenoData)==0) {
object@phenoData <- data.frame(class=rep("dummy", length(value)))
}
rownames(object@phenoData) <- value
object
})
############################################################
## sampclass
setMethod("sampclass", "xcmsSet", function(object) {
if (ncol(object@phenoData) >0) {
if(any(colnames(object@phenoData)=="class")){
sclass <- object$class
## in any rate: transform class to a character vector
## and generate a new factor on that with the levels
## being in the order of the first occurrence of the
## elements (i.e. no alphanumeric ordering).
sclass <- as.character(sclass)
sclass <- factor(sclass, levels=unique(sclass))
return(sclass)
}
interaction(object@phenoData, drop=TRUE)
} else {
factor()
}
})
setReplaceMethod("sampclass", "xcmsSet", function(object, value) {
## if we're submitting a data.frame, we're using interaction on that.
if(class(value)=="data.frame"){
message("Setting the class labels as the interaction of the data.frame columns.")
value <- as.character(interaction(value, drop=TRUE))
}
if (!is.factor(value))
value <- factor(value, unique(value))
object@phenoData$class <- value
object
})
############################################################
## phenoData
setMethod("phenoData", "xcmsSet", function(object) object@phenoData)
setReplaceMethod("phenoData", "xcmsSet", function(object, value) {
if (is.matrix(value))
value <- as.data.frame(value)
## if (is.data.frame(value) && !("class" %in% colnames(value)))
## value[,"class"] <- interaction(value)
## else
if (!is.data.frame(value))
value <- data.frame(class = value)
object@phenoData <- value
object
})
############################################################
## filepaths
setMethod("filepaths", "xcmsSet", function(object) object@filepaths)
setReplaceMethod("filepaths", "xcmsSet", function(object, value) {
object@filepaths <- value
object
})
############################################################
## profinfo
setMethod("profinfo", "xcmsSet", function(object) object@profinfo)
setReplaceMethod("profinfo", "xcmsSet", function(object, value) {
object@profinfo <- value
object
})
############################################################
## calibrate
setMethod("calibrate", "xcmsSet", function(object, calibrants,
method = "linear",
mzabs = 0.0001, mzppm = 5,
neighbours = 3, plotres = FALSE) {
nsamp = length(unique(object@peaks[,"sample"]))
match.arg(method, c("shift", "linear", "edgeshift"))
if (is.list(calibrants))
if (length(calibrants) != nsamp)
stop("Error: Number of masslists differs with number of samples")
## Loop over samples, estimate calibration and apply it.
for (s in 1:nsamp) {
peaklist = object@peaks[which(object@peaks[,"sample"] == s), ]
if (is.list(calibrants)) {
masslist <- calibrants[s]
} else {
masslist <- calibrants
}
## Check that peaks are ordered by mz value (issue #200)
if (is.unsorted(peaklist[, "mz"]))
warning("Peaks in sample ", s, " are not sorted by mz value")
masses <- matchpeaks(peaklist, masslist, mzabs, mzppm, neighbours)
if (length(masses) == 0) {
warning("No masses close enough!\n")
next
}
if (nrow(masses) == 1 & method != "shift") {
warning("Sample ", s, ": only one peak found, falling back to ",
"method = 'shift'")
method = "shift"
}
## Estimate the adjustment.
params <- estimate(masses, method)
mzu <- peaklist[, "mz"]
mposs <- masses[, "pos"]
mdiffs <- masses[, "dif"]
a <- params[1]
b <- params[2]
## cat("a=",a,"b=",b)
if (method != "edgeshift"){
mzu <- mzu - (a * mzu + b)
} else {
## Different adjustment for peaks below the smallest and peaks above
## the largest mz
mzu[c(1:(min(mposs) - 1))] <- mzu[c(1:(min(mposs) - 1))] -
(a * mzu[min(mposs)] + b)
mzu[c((min(mposs)):(max(mposs)))] <-
mzu[c((min(mposs)):(max(mposs)))] -
(a * mzu[c((min(mposs)):(max(mposs)))] + b)
mzu[c((max(mposs)+1):length(mzu))] <-
mzu[c((max(mposs)+1):length(mzu))] - (a * mzu[max(mposs)] + b)
}
peaklist[,"mz"] <- mzu
object@peaks[which(object@peaks[,"sample"] == s), ] <- peaklist
}
if (plotres) {
plot(mzu[mposs],mdiffs, xlim=c(min(mzu),max(mzu)))
if (method!="edgeshift") {
abline(b,a)
} else {
lines(c(min(mzu), mzu[min(mposs)]),
c(a * mzu[min(mposs)] + b,a * mzu[min(mposs)] + b))
lines(c(mzu[min(mposs)], mzu[max(mposs)]),
c(a * mzu[min(mposs)] + b,a * mzu[max(mposs)] + b))
lines(c(mzu[max(mposs)], max(mzu)),
c(a * mzu[max(mposs)] + b,a * mzu[max(mposs)] + b))
}
}
invisible(object)
})
############################################################
## groupnames
setMethod("groupnames", "xcmsSet", function(object, mzdec = 0, rtdec = 0,
template = NULL) {
if ( nrow(object@groups)<1 || length(object@groupidx) <1) {
stop("No group information. Use group().")
}
if (!missing(template)) {
tempsplit <- strsplit(template[1], "[T_]")
tempsplit <- strsplit(unlist(tempsplit), "\\.")
if (length(tempsplit[[1]]) > 1)
mzdec <- nchar(tempsplit[[1]][2])
else
mzdec <- 0
if (length(tempsplit[[2]]) > 1)
rtdec <- nchar(tempsplit[[2]][2])
else
rtdec <- 0
}
mzfmt <- paste("%.", mzdec, "f", sep = "")
rtfmt <- paste("%.", rtdec, "f", sep = "")
gnames <- paste("M", sprintf(mzfmt, groups(object)[,"mzmed"]), "T",
sprintf(rtfmt, groups(object)[,"rtmed"]), sep = "")
if (any(dup <- duplicated(gnames)))
for (dupname in unique(gnames[dup])) {
dupidx <- which(gnames == dupname)
gnames[dupidx] <- paste(gnames[dupidx], seq(along = dupidx), sep = "_")
}
gnames
})
############################################################
## group.density
setMethod("group.density", "xcmsSet", function(object, bw = 30, minfrac = 0.5,
minsamp = 1, mzwid = 0.25,
max = 50, sleep = 0) {
## Using now the do_groupChromPeaks_density function:
res <- do_groupChromPeaks_density(peaks(object),
sampleGroups = sampclass(object),
bw = bw,
minFraction = minfrac,
minSamples = minsamp,
binSize = mzwid,
maxFeatures = max,
sleep = sleep)
groups(object) <- as.matrix(res[, -match("peakidx", colnames(res))])
groupidx(object) <- res$peakidx
object
})
############################################################
## group.mzClust
setMethod("group.mzClust", "xcmsSet", function(object,
mzppm = 20,
mzabs = 0,
minsamp = 1,
minfrac=0.5) {
res <- do_groupPeaks_mzClust(peaks = peaks(object),
sampleGroups = sampclass(object),
ppm = mzppm,
absMz = mzabs,
minFraction = minfrac,
minSamples = minsamp)
groups(object) <- res$featureDefinitions
groupidx(object) <- res$peakIndex
object
})
############################################################
## group.nearest
setMethod("group.nearest", "xcmsSet", function(object, mzVsRTbalance=10,
mzCheck=0.2, rtCheck=15, kNN=10) {
res <- do_groupChromPeaks_nearest(peaks = peaks(object),
sampleGroups = sampclass(object),
mzVsRtBalance = mzVsRTbalance,
absMz = mzCheck,
absRt = rtCheck,
kNN = kNN)
groups(object) <- res$featureDefinitions
groupidx(object) <- res$peakIndex
invisible(object)
})
############################################################
## group
setMethod("group", "xcmsSet", function(object, method=getOption("BioC")$xcms$group.method,
...) {
method <- match.arg(method, getOption("BioC")$xcms$group.methods)
if (is.na(method))
stop("unknown method : ", method)
method <- paste("group", method, sep=".")
invisible(do.call(method, alist(object, ...)))
})
############################################################
## groupval
setMethod("groupval", "xcmsSet", function(object, method = c("medret", "maxint"),
value = "index", intensity = "into") {
if ( nrow(object@groups)<1 || length(object@groupidx) <1) {
stop("xcmsSet is not been group()ed.")
}
method <- match.arg(method)
peakmat <- peaks(object)
groupmat <- groups(object)
groupindex <- groupidx(object)
sampnum <- seq(length = length(sampnames(object)))
retcol <- match("rt", colnames(peakmat))
intcol <- match(intensity, colnames(peakmat))
sampcol <- match("sample", colnames(peakmat))
values <- matrix(nrow = length(groupindex), ncol = length(sampnum))
if (method == "medret") {
for (i in seq(along = groupindex)) {
gidx <- groupindex[[i]][order(abs(peakmat[groupindex[[i]],retcol] - median(peakmat[groupindex[[i]],retcol])))]
values[i,] <- gidx[match(sampnum, peakmat[gidx,sampcol])]
}
} else {
for (i in seq(along = groupindex)) {
gidx <- groupindex[[i]][order(peakmat[groupindex[[i]],intcol], decreasing = TRUE)]
values[i,] <- gidx[match(sampnum, peakmat[gidx,sampcol])]
}
}
if (value != "index") {
values <- peakmat[values,value]
dim(values) <- c(length(groupindex), length(sampnum))
}
colnames(values) <- sampnames(object)
rownames(values) <- paste(round(groupmat[,"mzmed"],1), round(groupmat[,"rtmed"]), sep = "/")
values
})
############################################################
## retcor
setMethod("retcor", "xcmsSet", function(object, method=getOption("BioC")$xcms$retcor.method,
...) {
## Backward compatibility for old "methods"
if (method == "linear" || method == "loess") {
args <- list(...)
if (any(names(args) == "smooth"))
warning("Provided argument 'smooth' will be replaced with the ",
"value of 'method', i.e. with ", method)
args$smooth <- method
## Overwriting eventually provided smooth parameter.
return(invisible(do.call(retcor.peakgroups, c(list(object), args))))
}
method <- match.arg(method, getOption("BioC")$xcms$retcor.methods)
if (is.na(method))
stop("unknown method : ", method)
method <- paste("retcor", method, sep=".")
invisible(do.call(method, alist(object, ...)))
})
############################################################
## retcor.peakgroups
setMethod("retcor.peakgroups", "xcmsSet", function(object, missing = 1, extra = 1,
smooth = c("loess", "linear"),
span = .2,
family = c("gaussian", "symmetric"),
plottype = c("none", "deviation", "mdevden"),
col = NULL, ty = NULL) {
peakmat <- peaks(object)
groupmat <- groups(object)
if (length(groupmat) == 0)
stop("No group information found")
samples <- sampnames(object)
classlabel <- as.vector(unclass(sampclass(object)))
n <- length(samples)
corpeaks <- peakmat
smooth <- match.arg(smooth)
plottype <- match.arg(plottype)
family <- match.arg(family)
if (length(object@rt) == 2)
rtcor <- object@rt$corrected
else {
fnames <- filepaths(object)
rtcor <- vector("list", length(fnames))
for (i in seq(along = fnames)) {
xraw <- xcmsRaw(fnames[i])
rtcor[[i]] <- xraw@scantime
}
object@rt <- list(raw = rtcor, corrected = rtcor)
}
minFr <- (n - missing) / n
res <- do_adjustRtime_peakGroups(peaks = peakmat,
peakIndex = object@groupidx,
rtime = rtcor,
minFraction = minFr,
extraPeaks = extra,
smooth = smooth,
span = span,
family = family)
rtdevsmo <- vector("list", n)
for (i in 1:n) {
rtdevsmo[[i]] <- rtcor[[i]] - res[[i]]
}
## rtdevsmo <- mapply(FUN = function(a, b) {
## return(a - b)
## }, rtcor, res)
if (plottype == "mdevden") {
split.screen(matrix(c(0, 1, .3, 1, 0, 1, 0, .3), ncol = 4, byrow = TRUE))
screen(1)
par(mar = c(0, 4.1, 4.1, 2), xaxt = "n")
}
if (plottype %in% c("deviation", "mdevden")) {
## Need also the 'rt' matrix:
rt <- .getPeakGroupsRtMatrix(peakmat, object@groupidx, seq_len(n),
missing, extra)
rtdev <- rt - apply(rt, 1, median, na.rm = TRUE)
## define the colors and line types and returns a list of
## mypal, col and ty. Uses the original code if no colors are
## submitted. Supports manually selected colors (e.g. in hex)
vals <- defineColAndTy(col, ty, classlabel)
col <- vals$col
mypal <- vals$mypal
ty <- vals$ty
rtrange <- range(do.call(c, rtcor))
devrange <- range(do.call(c, rtdevsmo))
plot(0, 0, type="n", xlim = rtrange, ylim = devrange,
main = "Retention Time Deviation vs. Retention Time",
xlab = "Retention Time", ylab = "Retention Time Deviation")
legend(rtrange[2], devrange[2], samples, col = mypal[col], lty = ty,
pch = ceiling(1:n/length(mypal)), xjust = 1)
for (i in 1:n) {
points(data.frame(rt = rt[,i], rtdev = rtdev[,i]),
col = mypal[col[i]], pch = ty[i], type = "p")
points(rtcor[[i]], rtdevsmo[[i]], type="l", col = mypal[col[i]],
lty = ty[i])
}
}
if (plottype == "mdevden") {
screen(2)
par(mar = c(5.1, 4.1, 0, 2), yaxt = "n")
allden <- density(peakmat[,"rt"], bw = diff(rtrange)/200,
from = rtrange[1], to = rtrange[2])[c("x","y")]
corden <- density(rt, bw = diff(rtrange)/200, from = rtrange[1],
to = rtrange[2], na.rm = TRUE)[c("x","y")]
allden$y <- allden$y / sum(allden$y)
corden$y <- corden$y / sum(corden$y)
maxden <- max(allden$y, corden$y)
plot(c(0,0), xlim = rtrange, ylim = c(0, maxden), type = "n",
main = "", xlab = "Retention Time", ylab = "Peak Density")
points(allden, type = "l", col = 1)
points(corden, type = "l", col = 2)
abline(h = 0, col = "grey")
legend(rtrange[2], maxden, c("All", "Correction"), col = 1:2,
lty = c(1,1), xjust = 1)
close.screen(all.screens = TRUE)
}
for (i in 1:n) {
cfun <- stepfun(rtcor[[i]][-1] - diff(rtcor[[i]]) / 2,
rtcor[[i]] - rtdevsmo[[i]])
rtcor[[i]] <- rtcor[[i]] - rtdevsmo[[i]]
sidx <- which(corpeaks[,"sample"] == i)
corpeaks[sidx, c("rt", "rtmin", "rtmax")] <-
cfun(corpeaks[sidx, c("rt", "rtmin", "rtmax")])
}
object@rt$corrected <- rtcor
peaks(object) <- corpeaks
groups(object) <- matrix(nrow = 0, ncol = 0)
groupidx(object) <- list()
invisible(object)
})
## The original code!
.retcor.peakgroups_orig <- function(object, missing = 1, extra = 1,
smooth = c("loess", "linear"), span = .2,
family = c("gaussian", "symmetric"),
plottype = c("none", "deviation", "mdevden"),
col = NULL, ty = NULL) {
peakmat <- peaks(object)
groupmat <- groups(object)
if (length(groupmat) == 0)
stop("No group information found")
samples <- sampnames(object)
classlabel <- as.vector(unclass(sampclass(object)))
n <- length(samples)
corpeaks <- peakmat
smooth <- match.arg(smooth)
plottype <- match.arg(plottype)
family <- match.arg(family)
if (length(object@rt) == 2)
rtcor <- object@rt$corrected
else {
fnames <- filepaths(object)
rtcor <- vector("list", length(fnames))
for (i in seq(along = fnames)) {
xraw <- xcmsRaw(fnames[i])
rtcor[[i]] <- xraw@scantime
}
object@rt <- list(raw = rtcor, corrected = rtcor)
}
nsamp <- rowSums(groupmat[,match("npeaks", colnames(groupmat))+unique(classlabel),drop=FALSE])
idx <- which(nsamp >= n-missing & groupmat[,"npeaks"] <= nsamp + extra)
if (length(idx) == 0)
stop("No peak groups found for retention time correction")
## Ordering the peaks by the rtmed might not represent the ordering
## of the below selected "representative" peak for each peak.
## See issue #110
## idx <- idx[order(groupmat[idx,"rtmed"])]
rt <- groupval(object, "maxint", "rt")[idx,, drop=FALSE]
## And now order them by median retention time: issue #110
rt <- rt[order(rowMedians(rt, na.rm = TRUE)), , drop = FALSE]
cat("Retention Time Correction Groups:", nrow(rt), "\n")
rtdev <- rt - apply(rt, 1, median, na.rm = TRUE)
if (smooth == "loess") {
mingroups <- min(colSums(!is.na(rt)))
if (mingroups < 4) {
smooth <- "linear"
warning("Too few peak groups, reverting to linear method")
} else if (mingroups*span < 4) {
span <- 4/mingroups
warning("Span too small, resetting to ", round(span, 2))
}
}
rtdevsmo <- vector("list", n)
## Code for checking to see if retention time correction is overcorrecting
rtdevrange <- range(rtdev, na.rm = TRUE)
warn.overcorrect <- FALSE
for (i in 1:n) {
pts <- na.omit(data.frame(rt = rt[,i], rtdev = rtdev[,i]))
if (smooth == "loess") {
lo <- suppressWarnings(loess(rtdev ~ rt, pts, span = span, degree = 1, family = family))
rtdevsmo[[i]] <- xcms:::na.flatfill(predict(lo, data.frame(rt = rtcor[[i]])))
### Remove singularities from the loess function
rtdevsmo[[i]][abs(rtdevsmo[[i]]) > quantile(abs(rtdevsmo[[i]]), 0.9)*2] <- NA
if (length(naidx <- which(is.na(rtdevsmo[[i]]))))
rtdevsmo[[i]][naidx] <- suppressWarnings(approx(na.omit(data.frame(rtcor[[i]], rtdevsmo[[i]])),
xout = rtcor[[i]][naidx], rule = 2)$y)
while (length(decidx <- which(diff(rtcor[[i]] - rtdevsmo[[i]]) < 0))) {
d <- diff(rtcor[[i]] - rtdevsmo[[i]])[tail(decidx, 1)]
rtdevsmo[[i]][tail(decidx, 1)] <- rtdevsmo[[i]][tail(decidx, 1)] - d
if (abs(d) <= 1e-06)
break;
}
rtdevsmorange <- range(rtdevsmo[[i]])
if (any(rtdevsmorange/rtdevrange > 2)) warn.overcorrect <- TRUE
} else {
if (nrow(pts) < 2) {
stop("Not enough ``well behaved'' peak groups even for linear smoothing of retention times")
}
fit <- lsfit(pts$rt, pts$rtdev)
rtdevsmo[[i]] <- rtcor[[i]] * fit$coef[2] + fit$coef[1]
ptsrange <- range(pts$rt)
minidx <- rtcor[[i]] < ptsrange[1]
maxidx <- rtcor[[i]] > ptsrange[2]
rtdevsmo[[i]][minidx] <- rtdevsmo[[i]][head(which(!minidx), n = 1)]
rtdevsmo[[i]][maxidx] <- rtdevsmo[[i]][tail(which(!maxidx), n = 1)]
}
}
if (warn.overcorrect) {
warning(paste("Fitted retention time deviation curves exceed points by more than 2x.",
"This is dangerous and the algorithm is probably overcorrecting your data.",
"Consider increasing the span parameter or switching to the linear smoothing method.",
sep = "\n"))
}
if (plottype == "mdevden") {
split.screen(matrix(c(0, 1, .3, 1, 0, 1, 0, .3), ncol = 4, byrow = TRUE))
screen(1)
par(mar = c(0, 4.1, 4.1, 2), xaxt = "n")
}
if (plottype %in% c("deviation", "mdevden")) {
## define the colors and line types and returns a list of
## mypal, col and ty. Uses the original code if no colors are
## submitted. Supports manually selected colors (e.g. in hex)
vals <- defineColAndTy(col, ty, classlabel)
col <- vals$col
mypal <- vals$mypal
ty <- vals$ty
rtrange <- range(do.call(c, rtcor))
devrange <- range(do.call(c, rtdevsmo))
plot(0, 0, type="n", xlim = rtrange, ylim = devrange,
main = "Retention Time Deviation vs. Retention Time",
xlab = "Retention Time", ylab = "Retention Time Deviation")
legend(rtrange[2], devrange[2], samples, col = mypal[col], lty = ty,
pch = ceiling(1:n/length(mypal)), xjust = 1)
for (i in 1:n) {
points(data.frame(rt = rt[,i], rtdev = rtdev[,i]), col = mypal[col[i]], pch = ty[i], type="p")
points(rtcor[[i]], rtdevsmo[[i]], type="l", col = mypal[col[i]], lty = ty[i])
}
}
if (plottype == "mdevden") {
screen(2)
par(mar = c(5.1, 4.1, 0, 2), yaxt = "n")
allden <- density(peakmat[,"rt"], bw = diff(rtrange)/200,
from = rtrange[1], to = rtrange[2])[c("x","y")]
corden <- density(rt, bw = diff(rtrange)/200, from = rtrange[1],
to = rtrange[2], na.rm = TRUE)[c("x","y")]
allden$y <- allden$y / sum(allden$y)
corden$y <- corden$y / sum(corden$y)
maxden <- max(allden$y, corden$y)
plot(c(0,0), xlim = rtrange, ylim = c(0, maxden), type = "n", main = "", xlab = "Retention Time", ylab = "Peak Density")
points(allden, type = "l", col = 1)
points(corden, type = "l", col = 2)
abline(h = 0, col = "grey")
legend(rtrange[2], maxden, c("All", "Correction"), col = 1:2, lty = c(1,1), xjust = 1)
close.screen(all.screens = TRUE)
}
for (i in 1:n) {
cfun <- stepfun(rtcor[[i]][-1] - diff(rtcor[[i]])/2, rtcor[[i]] - rtdevsmo[[i]])
rtcor[[i]] <- rtcor[[i]] - rtdevsmo[[i]]
sidx <- which(corpeaks[,"sample"] == i)
corpeaks[sidx, c("rt", "rtmin", "rtmax")] <- cfun(corpeaks[sidx, c("rt", "rtmin", "rtmax")])
}
object@rt$corrected <- rtcor
peaks(object) <- corpeaks
groups(object) <- matrix(nrow = 0, ncol = 0)
groupidx(object) <- list()
invisible(object)
}
############################################################
## retcor.obiwarp
setMethod("retcor.obiwarp", "xcmsSet", function(object, plottype = c("none", "deviation"),
profStep=1, center=NULL,
col = NULL, ty = NULL,
response=1, distFunc="cor_opt",
gapInit=NULL, gapExtend=NULL,
factorDiag=2, factorGap=1,
localAlignment=0, initPenalty=0) {
if (is.null(gapInit)) {
if (distFunc=="cor") {gapInit=0.3}
if (distFunc=="cor_opt") {gapInit=0.3}
if (distFunc=="cov") {gapInit=0.0}
if (distFunc=="euc") {gapInit=0.9}
if (distFunc=="prd") {gapInit=0.0}
}
if (is.null(gapExtend)) {
if (distFunc=="cor") {gapExtend=2.4}
if (distFunc=="cor_opt") {gapExtend=2.4}
if (distFunc=="cov") {gapExtend= 11.7}
if (distFunc=="euc") {gapExtend= 1.8}
if (distFunc=="prd") {gapExtend= 7.8}
}
peakmat <- peaks(object)
samples <- sampnames(object)
classlabel <- as.vector(unclass(sampclass(object)))
N <- length(samples)
corpeaks <- peakmat
plottype <- match.arg(plottype)
if (length(object@rt) == 2) {
rtcor <- object@rt$corrected
} else {
fnames <- filepaths(object)
rtcor <- vector("list", length(fnames))
for (i in seq(along = fnames)) {
xraw <- xcmsRaw(fnames[i])
rtcor[[i]] <- xraw@scantime
}
object@rt <- list(raw = rtcor, corrected = rtcor)
}
rtimecor <- vector("list", N)
rtdevsmo <- vector("list", N)
plength <- rep(0, N)
if (missing(center)) {
for(i in 1:N){
plength[i] <- length(which(peakmat[,"sample"]==i))
}
center <- which.max(plength)
}
cat("center sample: ", samples[center], "\nProcessing: ")
idx <- which(seq(1,N) != center)
obj1 <- xcmsRaw(object@filepaths[center], profmethod="bin", profstep=0)
## Scanrange checking: fix for issue #194
## Check if we've got the scanrange slot.
scanrange <- NULL
if (.hasSlot(object, "scanrange")) {
if (length(object@scanrange) == 2)
scanrange <- object@scanrange
} else {
if(length(obj1@scantime) != length(object@rt$raw[[center]])){
## This is in case the xcmsSet was read using a scanrange, i.e. if
## the data was read in with defining a scan range, then we would
## have a mismatch here. This code essentially ensures that the
## retention time of the raw object would match the retention time
## present in the xcmsSet.
## This was before the days in which @scanrange was added as a slot
## to xcmsSet.
##figure out the scan time range
scantime.start <- object@rt$raw[[center]][1]
scantime.end <- object@rt$raw[[center]][length(object@rt$raw[[center]])]
scanrange.start <- which.min(abs(obj1@scantime - scantime.start))
scanrange.end <- which.min(abs(obj1@scantime - scantime.end))
scanrange <- c(scanrange.start, scanrange.end)
}
}
## Subset the object if scanrange not NULL
if (!is.null(scanrange))
obj1 <- obj1[scanrange[1]:scanrange[2]]
## ## added t automatically find the correct scan range from the xcmsSet object
## if(length(obj1@scantime) != length(object@rt$raw[[center]])){
## ## This is in case the xcmsSet was read using a scanrange, i.e. if
## ## the data was read in with defining a scan range, then we would have a
## ## mismatch here. This code essentially ensures that the retention time
## ## of the raw object would match the retention time present in the xcmsSet.
## ## This was before the days in which @scanrange was added as a slot to
## ## xcmsSet.
## ##figure out the scan time range
## scantime.start <-object@rt$raw[[center]][1]
## scantime.end <-object@rt$raw[[center]][length(object@rt$raw[[center]])]
## scanrange.start <-which.min(abs(obj1@scantime - scantime.start))
## scanrange.end <-which.min(abs(obj1@scantime - scantime.end))
## scanrange<-c(scanrange.start, scanrange.end)
## obj1 <- xcmsRaw(object@filepaths[center], profmethod="bin",
## profstep=0, scanrange=scanrange)
## } else{
## scanrange<-NULL
## }
for (si in 1:length(idx)) {
s <- idx[si]
cat(samples[s], " ")
##
## Might be better to just get the profile matrix from the center object
## outside of the for loop and then modifying a internal variable within
## the loop - avoids creation of two profile matrices in each iteration.
profStepPad(obj1) <- profStep ## (re-)generate profile matrix, since it
## might have been modified during previous iteration
if(is.null(scanrange)){
obj2 <- xcmsRaw(object@filepaths[s], profmethod="bin", profstep=0)
} else{
obj2 <- xcmsRaw(object@filepaths[s], profmethod="bin", profstep=0,
scanrange = scanrange)
}
profStepPad(obj2) <- profStep ## generate profile matrix
mzmin <- min(obj1@mzrange[1], obj2@mzrange[1])
mzmax <- max(obj1@mzrange[2], obj2@mzrange[2])
mz <- seq(mzmin,mzmax, by=profStep)
mz <- as.double(mz)
mzval <- length(mz)
scantime1 <- obj1@scantime
scantime2 <- obj2@scantime
## median difference between spectras' scan times.
mstdiff <- median(c(diff(scantime1), diff(scantime2)))
rtup1 <- c(1:length(scantime1))
rtup2 <- c(1:length(scantime2))
mst1 <- which(diff(scantime1)>5*mstdiff)[1]
if(!is.na(mst1)) {
rtup1 <- which(rtup1<=mst1)
cat("Found gaps: cut scantime-vector at ", scantime1[mst1],"seconds", "\n")
}
mst2 <- which(diff(scantime2)>5*mstdiff)[1]
if(!is.na(mst2)) {
rtup2 <- which(rtup2<=mst2)
cat("Found gaps: cut scantime-vector at ", scantime2[mst2],"seconds", "\n")
}
scantime1 <- scantime1[rtup1]
scantime2 <- scantime2[rtup2]
## Drift of measured scan times - expected to be largest at the end.
rtmaxdiff <- abs(diff(c(scantime1[length(scantime1)],
scantime2[length(scantime2)])))
## If the drift is larger than the threshold, cut the matrix up to the
## max allowed difference.
if(rtmaxdiff>(5*mstdiff)){
rtmax <- min(scantime1[length(scantime1)],
scantime2[length(scantime2)])
rtup1 <- which(scantime1<=rtmax)
rtup2 <- which(scantime2<=rtmax)
}
scantime1 <- scantime1[rtup1]
scantime2 <- scantime2[rtup2]
valscantime1 <- length(scantime1)
valscantime2 <- length(scantime2)
## Restrict the profile matrix to columns 1:valscantime
if(length(obj1@scantime)>valscantime1) {
obj1@env$profile <- obj1@env$profile[,-c((valscantime1+1):length(obj1@scantime))]
}
if(length(obj2@scantime)>valscantime2) {
obj2@env$profile <- obj2@env$profile[,-c((valscantime2+1):length(obj2@scantime))]
}
## Now ensure that the nrow of the profile matrix matches.
## Add empty rows at the beginning
if(mzmin < obj1@mzrange[1]) {
## The profile matrices should not get larger than mz!
max_missing_rows <- mzval - nrow(obj1@env$profile)
low_mz <- seq(mzmin, obj1@mzrange[1], profStep)
## keep all mz bins that are smaller than mzrange, but ensure that
## we're not adding more rows than needed.
seqlen <- min(sum(low_mz < obj1@mzrange[1]), max_missing_rows)
## seqlen <- length(seq(mzmin, obj1@mzrange[1], profStep)) - 1
x <- matrix(0, seqlen, dim(obj1@env$profile)[2])
obj1@env$profile <- rbind(x, obj1@env$profile)
}
## Add emtpy rows at the end.
if(mzmax > obj1@mzrange[2]){
max_missing_rows <- mzval - nrow(obj1@env$profile)
high_mz <- seq(obj1@mzrange[2], mzmax, profStep)
seqlen <- min(sum(high_mz > obj1@mzrange[2]), max_missing_rows)
## seqlen <- length(seq(obj1@mzrange[2], mzmax, profStep)) - 1
x <- matrix(0, seqlen, dim(obj1@env$profile)[2])
obj1@env$profile <- rbind(obj1@env$profile, x)
}
if(mzmin < obj2@mzrange[1]){
max_missing_rows <- mzval - nrow(obj2@env$profile)
low_mz <- seq(mzmin, obj2@mzrange[1], profStep)
seqlen <- min(sum(low_mz < obj2@mzrange[1]), max_missing_rows)
## seqlen <- length(seq(mzmin, obj2@mzrange[1], profStep))-1
x <- matrix(0, seqlen, dim(obj2@env$profile)[2])
obj2@env$profile <- rbind(x, obj2@env$profile)
}
if(mzmax > obj2@mzrange[2]){
max_missing_rows <- mzval - nrow(obj2@env$profile)
high_mz <- seq(obj2@mzrange[2], mzmax, profStep)
seqlen <- min(sum(high_mz > obj2@mzrange[2]), max_missing_rows)
## seqlen <- length(seq(obj2@mzrange[2], mzmax, profStep)) - 1
x <- matrix(0, seqlen, dim(obj2@env$profile)[2])
obj2@env$profile <- rbind(obj2@env$profile, x)
}
## OK, now that the matrices are "aligned" extract the intensities
intensity1 <- obj1@env$profile
intensity2 <- obj2@env$profile
## Final check to ensure that our expansion of profile matrix rows was
## correct.
if ((mzval * valscantime1 != length(intensity1)) ||
(mzval * valscantime2 != length(intensity2)))
stop("Dimensions of profile matrices do not match !\n")
## Would it be possible to supply non-binned data too???
rtimecor[[s]] <-.Call("R_set_from_xcms",
valscantime1,scantime1,mzval,mz,intensity1,
valscantime2,scantime2,mzval,mz,intensity2,
response, distFunc,
gapInit, gapExtend,
factorDiag, factorGap,
localAlignment, initPenalty)
## Hm, silently add the raw retention times if we cut the retention time
## vector above - would merit at least a warning I believe.
if(length(obj2@scantime) > valscantime2) {
object@rt$corrected[[s]] <- c(rtimecor[[s]],
obj2@scantime[(max(rtup2)+1):length(obj2@scantime)])
} else {
object@rt$corrected[[s]] <- rtimecor[[s]]
}
## Why are we rounding here, but NOT in the retcor.peakgroups?
## -> issue #122
## The point is we're using the un-rounded adjusted rt for the rt, BUT
## use the rounded values for the adjustment of the peak rts.
rtdevsmo[[s]] <- round(rtcor[[s]]-object@rt$corrected[[s]],2)
rm(obj2)
gc()
## updateProgressInfo
object@progressInfo$retcor.obiwarp <- si / length(idx)
xcms:::progressInfoUpdate(object)
}
cat("\n")
## Why are we rounding here, but NOT in the retcor.peakgroups?
## -> issue #122
rtdevsmo[[center]] <- round(rtcor[[center]] - object@rt$corrected[[center]], 2)
if (plottype == "deviation") {
## define the colors and line types and returns a list of
## mypal, col and ty. Uses the original code if no colors are
## submitted. Supports manually selected colors (e.g. in hex)
vals <- defineColAndTy(col, ty, classlabel)
col <- vals$col
mypal <- vals$mypal
ty <- vals$ty
rtrange <- range(do.call("c", rtcor))
devrange <- range(do.call("c", rtdevsmo))
layout(matrix(c(1, 2),ncol=2, byrow=F),widths=c(1,0.3))
par(mar=c(4,4,2,0))
plot(0, 0, type="n", xlim = rtrange, ylim = devrange,
main = "Retention Time Deviation vs. Retention Time",
xlab = "Retention Time", ylab = "Retention Time Deviation")
for (i in 1:N) {
points(rtcor[[i]], rtdevsmo[[i]], type="l", col = mypal[col[i]], lty = ty[i])
}
plot.new() ; par(mar= c(2, 0, 2, 0))
plot.window(c(0,1), c(0,1))
legend(0,1.04, basename(samples), col = mypal[col], lty = ty)
}
for (i in 1:N) {
cfun <- stepfun(rtcor[[i]][-1] - diff(rtcor[[i]])/2, rtcor[[i]] - rtdevsmo[[i]])
rtcor[[i]] <- rtcor[[i]] - rtdevsmo[[i]]
sidx <- which(corpeaks[,"sample"] == i)
corpeaks[sidx, c("rt", "rtmin", "rtmax")] <- cfun(corpeaks[sidx, c("rt", "rtmin", "rtmax")])
}
peaks(object) <- corpeaks
groups(object) <- matrix(nrow = 0, ncol = 0)
groupidx(object) <- list()
invisible(object)
})
############################################################
## plotrt
setMethod("plotrt", "xcmsSet", function(object, col = NULL, ty = NULL, leg = TRUE, densplit = FALSE) {
samples <- sampnames(object)
classlabel <- as.vector(unclass(sampclass(object)))
n <- length(samples)
rtuncor <- object@rt$raw
rtcor <- object@rt$corrected
## define the colors and line types and returns a list of
## mypal, col and ty. Uses the original code if no colors are
## submitted. Supports manually selected colors (e.g. in hex)
vals <- defineColAndTy(col, ty, classlabel)
col <- vals$col
mypal <- vals$mypal
ty <- vals$ty
rtdevsmo <- vector("list", n)
for (i in 1:n)
rtdevsmo[[i]] <- rtuncor[[i]] - rtcor[[i]]
rtrange <- range(do.call(c, rtuncor))
devrange <- range(do.call(c, rtdevsmo))
if (densplit) {
split.screen(matrix(c(0, 1, .3, 1, 0, 1, 0, .3), ncol = 4, byrow = TRUE))
screen(1)
par(mar = c(0, 4.1, 4.1, 2), xaxt = "n")
}
plot(0, 0, type="n", xlim = rtrange, ylim = devrange, main = "Retention Time Deviation vs. Retention Time", xlab = "Retention Time", ylab = "Retention Time Deviation")
if (leg)
legend(rtrange[2], devrange[2], samples, col = mypal[col], lty = ty, pch = ceiling(1:n/length(mypal)), xjust = 1)
for (i in 1:n)
points(rtuncor[[i]], rtdevsmo[[i]], type="l", col = mypal[col[i]], lty = ty[i])
if (densplit) {
screen(2)
par(mar = c(5.1, 4.1, 0, 2), yaxt = "n")
allden <- density(object@peaks[,"rt"], bw = diff(rtrange)/200,
from = rtrange[1], to = rtrange[2])[c("x","y")]
plot(allden, xlim = rtrange, type = "l", main = "", xlab = "Retention Time", ylab = "Peak Density")
abline(h = 0, col = "grey")
close.screen(all.screens = TRUE)
}
})
############################################################
## fillPeaks.chrom
## New version using BiocParallel instead of nSlaves and manual setup.
setMethod("fillPeaks.chrom", "xcmsSet", function(object, nSlaves = 0,
expand.mz = 1,expand.rt = 1,
BPPARAM = bpparam()) {
## development mockup:
if (FALSE) {
library(xcms)
library(faahKO)
object <- group(faahko)
gf <- fillPeaks(object)
pkgEnv = getNamespace("xcms")
attach(pkgEnv)
}
if (!is.null(nSlaves)) {
if (nSlaves > 0) {
message("Use of argument 'nSlaves' is deprecated,",
" please use 'BPPARAM' instead.")
options(mc.cores = nSlaves)
}
}
peakmat <- peaks(object)
groupmat <- groups(object)
if (length(groupmat) == 0)
stop("No group information found")
files <- filepaths(object)
samp <- sampnames(object)
classlabel <- as.vector(unclass(sampclass(object)))
prof <- profinfo(object)
rtcor <- object@rt$corrected
## Remove groups that overlap with more "well-behaved" groups
sampclasscols <- seq(match("npeaks", colnames(groupmat))+1,
length.out=length(levels(sampclass(object))))
numsamp <- rowSums(groupmat[ , sampclasscols, drop=FALSE])
uorder <- order(-numsamp, groupmat[,"npeaks"])
uindex <- rectUnique(groupmat[,c("mzmin","mzmax","rtmin","rtmax"),drop=FALSE],
uorder)
groupmat <- groupmat[uindex,]
groupindex <- groupidx(object)[uindex]
gvals <- groupval(object)[uindex,]
peakrange <- matrix(nrow = nrow(gvals), ncol = 4)
colnames(peakrange) <- c("mzmin","mzmax","rtmin","rtmax")
mzmin <- peakmat[gvals,"mzmin"]
dim(mzmin) <- c(nrow(gvals), ncol(gvals))
peakrange[,"mzmin"] <- apply(mzmin, 1, median, na.rm = TRUE)
mzmax <- peakmat[gvals,"mzmax"]
dim(mzmax) <- c(nrow(gvals), ncol(gvals))
peakrange[,"mzmax"] <- apply(mzmax, 1, median, na.rm = TRUE)
retmin <- peakmat[gvals,"rtmin"]
dim(retmin) <- c(nrow(gvals), ncol(gvals))
peakrange[,"rtmin"] <- apply(retmin, 1, median, na.rm = TRUE)
retmax <- peakmat[gvals,"rtmax"]
dim(retmax) <- c(nrow(gvals), ncol(gvals))
peakrange[,"rtmax"] <- apply(retmax, 1, median, na.rm = TRUE)
lastpeak <- nrow(peakmat)
lastpeakOrig <- lastpeak
## peakmat <- rbind(peakmat, matrix(nrow = sum(is.na(gvals)), ncol = ncol(peakmat)))
cnames <- colnames(object@peaks)
## Making gvals environment so that when it is repeated for each file it only uses the memory one time
gvals_env <- new.env(parent=baseenv())
assign("gvals", gvals, envir = gvals_env)
ft <- cbind(file=files,id=1:length(files))
argList <- apply(ft,1,function(x) {
## Add only those samples which actually have NA in them
if (!any(is.na(gvals[,as.numeric(x["id"])]))) {
## nothing to do.
list()
} else {
list(file=x["file"],id=as.numeric(x["id"]),
params=list(method="chrom",
gvals=gvals_env,
prof=prof,
dataCorrection=object@dataCorrection,
polarity=object@polarity,
rtcor=object@rt$corrected[[as.numeric(x["id"])]],
peakrange=peakrange,
expand.mz=expand.mz,
expand.rt=expand.rt))
}
})
nonemptyIdx <- (sapply(argList, length) > 0)
if (!any(nonemptyIdx)) {
## Nothing to do
return(invisible(object))
}
argList <- argList[nonemptyIdx]
## Use BiocParallel for parallel computing.
newpeakslist <- bplapply(argList, fillPeaksChromPar, BPPARAM = BPPARAM)
o <- order(sapply(newpeakslist, function(x) x$myID))
newpeaks <- do.call(rbind, lapply(newpeakslist[o], function(x) x$newpeaks))
## Make sure colnames are compatible
newpeaks <- newpeaks[, match(cnames, colnames(newpeaks)), drop = FALSE]
colnames(newpeaks) <- cnames
peakmat <- rbind(peakmat, newpeaks)
for (i in seq(along = files)) {
naidx <- which(is.na(gvals[,i]))
for (j in seq(along = naidx))
groupindex[[naidx[j]]] <- c(groupindex[[naidx[j]]], lastpeak+j)
lastpeak <- lastpeak + length(naidx)
}
peaks(object) <- peakmat
object@filled <- seq((lastpeakOrig+1),nrow(peakmat))
groups(object) <- groupmat
groupidx(object) <- groupindex
invisible(object)
})
############################################################
## fillPeaks.MSW
setMethod("fillPeaks.MSW", "xcmsSet", function(object, mrange=c(0,0), sample=NULL) {
peakmat <- peaks(object)
groupmat <- groups(object)
if (length(groupmat) == 0)
stop("No group information found")
files <- filepaths(object)
samp <- sampnames(object)
classlabel <- as.vector(unclass(sampclass(object)))
prof <- profinfo(object)
rtcor <- object@rt$corrected
## Remove groups that overlap with more "well-behaved" groups
numsamp <- rowSums(groupmat[,(match("npeaks", colnames(groupmat))+1):ncol(groupmat),drop=FALSE])
uorder <- order(-numsamp, groupmat[,"npeaks"])
uindex <- rectUnique(groupmat[,c("mzmin","mzmax","rtmin","rtmax"),drop=FALSE],
uorder)
groupmat <- groupmat[uindex,]
groupindex <- groupidx(object)[uindex]
gvals <- groupval(object)[uindex,]
peakrange <- matrix(nrow = nrow(gvals), ncol = 4)
colnames(peakrange) <- c("mzmin","mzmax","rtmin","rtmax")
mzmin <- peakmat[gvals,"mzmin"]
dim(mzmin) <- c(nrow(gvals), ncol(gvals))
peakrange[,"mzmin"] <- apply(mzmin, 1, min, na.rm = TRUE)
mzmax <- peakmat[gvals,"mzmax"]
dim(mzmax) <- c(nrow(gvals), ncol(gvals))
peakrange[,"mzmax"] <- apply(mzmax, 1, min, na.rm = TRUE)
retmin <- peakmat[gvals,"rtmin"]
dim(retmin) <- c(nrow(gvals), ncol(gvals))
peakrange[,"rtmin"] <- apply(retmin, 1, median, na.rm = TRUE)
retmax <- peakmat[gvals,"rtmax"]
dim(retmax) <- c(nrow(gvals), ncol(gvals))
peakrange[,"rtmax"] <- apply(retmax, 1, median, na.rm = TRUE)
lastpeak <- nrow(peakmat)
peakmat <- rbind(peakmat, matrix(nrow = sum(is.na(gvals)), ncol = ncol(peakmat)))
cnames <- colnames(object@peaks)
for (i in seq(along = files)) {
cat(samp[i], "")
flush.console()
naidx <- which(is.na(gvals[,i]))
newpeaks <- matrix(nrow=length(naidx), ncol=9)
nppos<-0 ## line position in the newpeaks matrix
if (length(naidx)) {
lcraw <- xcmsRaw(files[i], profmethod = prof$method, profstep = 0)
ngs <- as.vector(naidx)
for (g in ngs)
{
nppos <- nppos+1
## Get the index of the mz value(s) closest to the mzmed of the
## group; could eventually be more than one.
mzpos <- which(abs(lcraw@env$mz - groupmat[g,"mzmed"]) ==
min(abs(lcraw@env$mz - groupmat[g,"mzmed"])))
## Get the index of the maximum intensity for the above mz index.
mmzpos <- mzpos[which(lcraw@env$intensity[mzpos] ==
max(lcraw@env$intensity[mzpos]))]
## Eventually increase the range around the mzmed.
mmzr <- seq((mmzpos-mrange[1]),(mmzpos+mrange[2]))
## maxo is the maximum signal for mz values that are closest to
## the mzmed of the feature.
maxo <- max(lcraw@env$intensity[mmzr])
## this is the new one, summing the scale-range
## calculating scale, adding intensities in this scale
medMZmin <- median(peakmat[groupindex[[g]],"mzmin"])
medMZmax <- median(peakmat[groupindex[[g]],"mzmax"])
## mz values to be considered: from median mzmin of all peaks in
## the current peak group/feature to median mzmax.
## Might eventually be easier to just check for $mz >= medMZmin
minMzpos <- min(which(abs(lcraw@env$mz - medMZmin) ==
min(abs(lcraw@env$mz - medMZmin))))
maxMzpos <- max(which(abs(lcraw@env$mz - medMZmax) ==
min(abs(lcraw@env$mz - medMZmax))))
## into: the sum of intensities in this range.
into = sum(lcraw@env$intensity[minMzpos:maxMzpos])
newpeaks[nppos,] <- c(groupmat[g,"mzmed"],medMZmin,medMZmax,-1,-1,-1,into,maxo,i)
}
colnames(newpeaks) <- c("mz","mzmin","mzmax","rt","rtmin","rtmax","into","maxo","sample")
rm(lcraw)
gc()
newcols <-colnames(newpeaks)[colnames(newpeaks) %in% cnames]
peakmat[lastpeak+seq(along = naidx),newcols] <- newpeaks[,newcols]
for (i in seq(along = naidx))
groupindex[[naidx[i]]] <- c(groupindex[[naidx[i]]], lastpeak+i)
lastpeak <- lastpeak + length(naidx)
}
}
cat("\n")
peaks(object) <- peakmat
object@filled <- seq((lastpeak+1),nrow(peakmat))
groups(object) <- groupmat
groupidx(object) <- groupindex
invisible(object)
})
############################################################
## fillPeaks
setMethod("fillPeaks", "xcmsSet", function(object, method=getOption("BioC")$xcms$fillPeaks.method,...) {
method <- match.arg(method, getOption("BioC")$xcms$fillPeaks.methods)
if (is.na(method))
stop("unknown method : ", method)
method <- paste("fillPeaks", method, sep=".")
invisible(do.call(method, alist(object, ...)))
})
############################################################
## getEIC
setMethod("getEIC", "xcmsSet", function(object, mzrange, rtrange = 200,
groupidx, sampleidx = sampnames(object),
rt = c("corrected", "raw")) {
files <- filepaths(object)
grp <- groups(object)
samp <- sampnames(object)
prof <- profinfo(object)
rt <- match.arg(rt)
if (is.numeric(sampleidx))
sampleidx <- sampnames(object)[sampleidx]
sampidx <- match(sampleidx, sampnames(object))
if (!missing(groupidx)) {
if (is.numeric(groupidx))
groupidx <- groupnames(object)[unique(as.integer(groupidx))]
grpidx <- match(groupidx, groupnames(object, template = groupidx))
}
if (missing(mzrange)) {
if (missing(groupidx))
stop("No m/z range or groups specified")
if (any(is.na(groupval(object, value = "mz"))))
warning(
"`NA` values in xcmsSet. Use fillPeaks() on the object to fill",
"-in missing peak values. Note however that this will also ",
"insert intensities of 0 for peaks that can not be filled in.")
mzmin <- apply(groupval(object, value = "mzmin"), 1, min, na.rm = TRUE)
mzmax <- apply(groupval(object, value = "mzmax"), 1, max, na.rm = TRUE)
mzrange <- matrix(c(mzmin[grpidx], mzmax[grpidx]), ncol = 2)
## if (any(is.na(groupval(object, value = "mz"))))
## stop('Please use fillPeaks() to fill up NA values !')
## mzmin <- -rowMax(-groupval(object, value = "mzmin"))
## mzmax <- rowMax(groupval(object, value = "mzmax"))
## mzrange <- matrix(c(mzmin[grpidx], mzmax[grpidx]), ncol = 2)
} else if (all(c("mzmin","mzmax") %in% colnames(mzrange)))
mzrange <- mzrange[,c("mzmin", "mzmax"),drop=FALSE]
else if (is.null(dim(mzrange)))
stop("mzrange must be a matrix")
colnames(mzrange) <- c("mzmin", "mzmax")
if (length(rtrange) == 1) {
if (missing(groupidx))
rtrange <- matrix(rep(range(object@rt[[rt]][sampidx]), nrow(mzrange)),
ncol = 2, byrow = TRUE)
else {
rtrange <- retexp(grp[grpidx,c("rtmin","rtmax"),drop=FALSE], rtrange)
}
} else if (is.null(dim(rtrange)))
stop("rtrange must be a matrix or single number")
colnames(rtrange) <- c("rtmin", "rtmax")
## Ensure that we've got corrected retention time if requested.
if (is.null(object@rt[[rt]]))
stop(rt, " retention times not present in 'object'!")
## Ensure that the defined retention time range is within the rtrange of the
## object: we're using the max minimal rt of all files and the min maximal rt
rtrs <- lapply(object@rt[[rt]], range)
rtr <- c(max(unlist(lapply(rtrs, "[", 1))),
min(unlist(lapply(rtrs, "[", 2))))
## Check if we've got a range which is completely off:
if (any(rtrange[, "rtmin"] >= rtr[2] | rtrange[, "rtmax"] <= rtr[1])) {
outs <- which(rtrange[, "rtmin"] >= rtr[2] |
rtrange[, "rtmax"] <= rtr[1])
stop(length(outs), " of the specified 'rtrange' are completely outside ",
"of the retention time range of 'object' which is (", rtr[1], ", ",
rtr[2], "). The first was: (", rtrange[outs[1], "rtmin"], ", ",
rtrange[outs[1], "rtmax"], "!")
}
lower_rt_outside <- rtrange[, "rtmin"] < rtr[1]
upper_rt_outside <- rtrange[, "rtmax"] > rtr[2]
if (any(lower_rt_outside) | any(upper_rt_outside)) {
## Silently fix these ranges.
rtrange[lower_rt_outside, "rtmin"] <- rtr[1]
rtrange[upper_rt_outside, "rtmax"] <- rtr[2]
}
if (missing(groupidx))
gnames <- character(0)
else
gnames <- groupidx
eic <- vector("list", length(sampleidx))
names(eic) <- sampleidx
for (i in seq(along = sampidx)) {
## cat(sampleidx[i], "")
flush.console()
## getXcmsRaw takes care of rt correction, susetting to scanrage and other
## stuff.
lcraw <- getXcmsRaw(object, sampleidx = sampidx[i], rt=rt)
currenteic <- xcms::getEIC(lcraw, mzrange, rtrange, step = prof$step)
eic[[i]] <- currenteic@eic[[1]]
rm(lcraw)
gc()
}
## cat("\n")
invisible(new("xcmsEIC", eic = eic, mzrange = mzrange, rtrange = rtrange,
rt = rt, groupnames = gnames))
})
############################################################
## peakTable
setMethod("peakTable", "xcmsSet", function(object, filebase = character(), ...) {
if (length(sampnames(object)) == 1) {
return(object@peaks)
}
if (nrow(object@groups) < 1) {
stop ('First argument must be an xcmsSet with group information or contain only one sample.')
}
groupmat <- groups(object)
if (! "value" %in% names(list(...))) {
ts <- data.frame(cbind(groupmat,groupval(object, value="into", ...)), row.names = NULL)
} else {
ts <- data.frame(cbind(groupmat,groupval(object, ...)), row.names = NULL)
}
cnames <- colnames(ts)
if (cnames[1] == 'mzmed') {
cnames[1] <- 'mz'
} else {
stop ('mzmed column missing')
}
if (cnames[4] == 'rtmed') {
cnames[4] <- 'rt'
} else {
stop ('mzmed column missing')
}
colnames(ts) <- cnames
if (length(filebase))
write.table(ts, paste(filebase, ".tsv", sep = ""), quote = FALSE, sep = "\t", col.names = NA)
ts
})
############################################################
## diffreport
setMethod("diffreport", "xcmsSet", function(object,
class1 = levels(sampclass(object))[1],
class2 = levels(sampclass(object))[2],
filebase = character(),
eicmax = 0, eicwidth = 200,
sortpval = TRUE,
classeic = c(class1,class2),
value = c("into","maxo","intb"),
metlin = FALSE,
h = 480, w = 640, mzdec=2,
missing = numeric(), ...) {
if ( nrow(object@groups)<1 || length(object@groupidx) <1) {
stop("No group information. Use group().")
}
if (!is.numeric(w) || !is.numeric(h))
stop("'h' and 'w' have to be numeric")
if (!requireNamespace("multtest", quietly = TRUE))
stop("The use of 'diffreport' requires package 'multtest'. Please ",
"install with 'Biobase::install(\"multtest\")'")
value <- match.arg(value)
groupmat <- groups(object)
if (length(groupmat) == 0)
stop("No group information found")
samples <- sampnames(object)
n <- length(samples)
classlabel <- sampclass(object)
classlabel <- levels(classlabel)[as.vector(unclass(classlabel))]
values <- groupval(object, "medret", value=value)
indecies <- groupval(object, "medret", value = "index")
if (!all(c(class1,class2) %in% classlabel))
stop("Incorrect Class Labels")
## c1 and c2 are column indices of class1 and class2 resp.
c1 <- which(classlabel %in% class1)
c2 <- which(classlabel %in% class2)
ceic <- which(classlabel %in% classeic)
if (length(intersect(c1, c2)) > 0)
stop("Intersecting Classes")
## Optionally replace NA values with the value provided with missing
if (length(missing)) {
if (is.numeric(missing)) {
## handles NA, Inf and -Inf
values[, c(c1, c2)][!is.finite(values[, c(c1, c2)])] <- missing[1]
} else
stop("'missing' should be numeric")
}
## Check against missing Values
if (any(is.na(values[, c(c1, c2)])))
warning("`NA` values in xcmsSet. Use fillPeaks() on the object to fill",
"-in missing peak values. Note however that this will also ",
"insert intensities of 0 for peaks that can not be filled in.")
mean1 <- rowMeans(values[,c1,drop=FALSE], na.rm = TRUE)
mean2 <- rowMeans(values[,c2,drop=FALSE], na.rm = TRUE)
## Calculate fold change.
## For foldchange <1 set fold to 1/fold
## See tstat to check which was higher
fold <- mean2 / mean1
fold[!is.na(fold) & fold < 1] <- 1/fold[!is.na(fold) & fold < 1]
testval <- values[,c(c1,c2)]
## Replace eventual infinite values with NA (CAMERA issue #33)
testval[is.infinite(testval)] <- NA
testclab <- c(rep(0,length(c1)),rep(1,length(c2)))
if (min(length(c1), length(c2)) >= 2) {
tstat <- multtest::mt.teststat(testval, testclab, ...)
pvalue <- pval(testval, testclab, tstat)
} else {
message("Too few samples per class, skipping t-test.")
tstat <- pvalue <- rep(NA,nrow(testval))
}
stat <- data.frame(fold = fold, tstat = tstat, pvalue = pvalue)
if (length(levels(sampclass(object))) >2) {
pvalAnova<-c()
for(i in 1:nrow(values)){
var<-as.numeric(values[i,])
ano<-summary(aov(var ~ sampclass(object)) )
pvalAnova<-append(pvalAnova, unlist(ano)["Pr(>F)1"])
}
stat<-cbind(stat, anova= pvalAnova)
}
if (metlin) {
neutralmass <- groupmat[,"mzmed"] + ifelse(metlin < 0, 1, -1)
metlin <- abs(metlin)
digits <- ceiling(-log10(metlin))+1
metlinurl <-
paste("http://metlin.scripps.edu/simple_search_result.php?mass_min=",
round(neutralmass - metlin, digits), "&mass_max=",
round(neutralmass + metlin, digits), sep="")
values <- cbind(metlin = metlinurl, values)
}
twosamp <- cbind(name = groupnames(object), stat, groupmat, values)
if (sortpval) {
tsidx <- order(twosamp[,"pvalue"])
twosamp <- twosamp[tsidx,]
rownames(twosamp) <- 1:nrow(twosamp)
values<-values[tsidx,]
} else
tsidx <- 1:nrow(values)
if (length(filebase))
write.table(twosamp, paste(filebase, ".tsv", sep = ""), quote = FALSE, sep = "\t", col.names = NA)
if (eicmax > 0) {
if (length(unique(peaks(object)[,"rt"])) > 1) {
## This looks like "normal" LC data
eicmax <- min(eicmax, length(tsidx))
eics <- getEIC(object, rtrange = eicwidth*1.1, sampleidx = ceic,
groupidx = tsidx[seq(length = eicmax)])
if (length(filebase)) {
eicdir <- paste(filebase, "_eic", sep="")
boxdir <- paste(filebase, "_box", sep="")
dir.create(eicdir)
dir.create(boxdir)
if (capabilities("png")){
xcmsBoxPlot(values[seq(length = eicmax),],
sampclass(object), dirpath=boxdir, pic="png",
width=w, height=h)
png(file.path(eicdir, "%003d.png"), width = w, height = h,
units = "px")
} else {
xcmsBoxPlot(values[seq(length = eicmax),],
sampclass(object), dirpath=boxdir, pic="pdf",
width=w, height=h)
pdf(file.path(eicdir, "%003d.pdf"), width = w/72,
height = h/72, onefile = FALSE)
}
}
plot(eics, object, rtrange = eicwidth, mzdec=mzdec)
if (length(filebase))
dev.off()
} else {
## This looks like a direct-infusion single spectrum
if (length(filebase)) {
specdir <- paste(filebase, "_spec", sep="")
dir.create(specdir)
if (capabilities("png")){
png(file.path(specdir, "%003d.png"), width = w, height = h)
}else{
pdf(file.path(specdir, "%003d.pdf"), width = w/72,
height = h/72, onefile = FALSE)
}
}
plotSpecWindow(object, gidxs = tsidx[seq(length = eicmax)], borderwidth=1)
if (length(filebase))
dev.off()
}
}
invisible(twosamp)
})
############################################################
## progressCallback
setMethod("progressCallback", "xcmsSet", function(object) object@progressCallback)
setReplaceMethod("progressCallback", "xcmsSet", function(object, value) {
object@progressCallback <- value
object
})
############################################################
## progressInfoUpdate
setMethod("progressInfoUpdate", "xcmsSet", function(object)
do.call(object@progressCallback, list(progress=object@progressInfo))
)
############################################################
## $
## eSet/ExpressionSet like methods to access columns in @phenoData
setMethod("$", "xcmsSet", function(x, name) {
eval(substitute(phenoData(x)$NAME_ARG, list(NAME_ARG=name)))
})
setReplaceMethod("$", "xcmsSet", function(x, name, value) {
phenoData(x)[[name]] = value
x
})
############################################################
## getXcmsRaw
## read the raw data for a xset.
## sampleidx: argument which allows to specify which file from the xcmsSet
## should be read, if length > 1 a list of xcmsRaw is returned
## Note: if scanrange is submitted here I have to subset the xcmsRaw objects
## AFTER having filled in adjusted retention times etc.
setMethod("getXcmsRaw", "xcmsSet", function(object, sampleidx = 1,
profmethod = profMethod(object),
profstep = profStep(object),
profparam = profinfo(object),
mslevel = NULL,
scanrange = NULL,
rt = c("corrected", "raw"),
BPPARAM = bpparam()) {
if (is.numeric(sampleidx))
sampleidx <- sampnames(object)[sampleidx]
sampidx <- match(sampleidx, sampnames(object)) ## numeric
if (length(sampidx) == 0)
stop("submitted value for sampleidx outside of the",
" available files!")
fn <- filepaths(object)[sampidx]
rt <- match.arg(rt)
if (rt == "corrected" & !any(names(object@rt) == "corrected")) {
message("No RT correction has been performed, thus returning",
" raw retention times.")
rt <- "raw"
}
if (missing(mslevel)) {
msl <- mslevel(object)
} else {
msl <- mslevel
}
## if (missing(scanrange)) {
## srange <- scanrange(object)
## } else {
## srange <- scanrange
## }
## If scanrange is NULL we don't have to do subset and can skip some stuff.
## If scanrange is provided we don't want to subset in xcmsRaw but subset
## at the very end using []
srange <- NULL ## ensures we're reading all data.
if (is.null(scanrange) | length(scanrange) < 2) {
srange <- scanrange(object)
} else {
scanrange <- range(scanrange)
}
## include MSn?
includeMsn <- FALSE
if (!is.null(msl)) {
if(msl > 1)
includeMsn <- TRUE
}
ret <- bplapply(as.list(fn), FUN = function(z) {
raw <- xcmsRaw(z, profmethod = profmethod,
profstep = profstep, mslevel = msl,
scanrange = srange,
includeMSn = includeMsn)
return(raw)
}, BPPARAM=BPPARAM)
## do corrections etc.
for(i in 1:length(ret)){
if(length(object@dataCorrection) > 1){
if(object@dataCorrection[[sampidx[i]]] == 1){
ret[[i]] <- stitch(ret[[i]], AutoLockMass(ret[[i]]))
message(paste0("Applying lock Waters mass correction",
" to ", basename(fn[i])))
}
}
if(rt == "corrected"){
## check if there is any need to apply correction...
## This includes fix for the bug reported by Aleksandr (issue 44)
if(all(object@rt$corrected[[sampidx[i]]] ==
object@rt$raw[[sampidx[i]]])){
message("No need to perform retention time correction,",
" raw and corrected rt are identical for ",
basename(fn[i]), ".")
ret[[i]]@scantime <- object@rt$raw[[sampidx[i]]]
}else{
message(paste0("Applying retention time correction",
" to ", basename(fn[i]), "."))
ret[[i]]@scantime <- object@rt$corrected[[sampidx[i]]]
}
}
## Finally doing the sub-setting...
if (length(scanrange) > 1) {
## Doing the sub-setting here ensures that the scantime, profile
## matrix and everything matches the scanrange.
ret[[i]] <- ret[[i]][scanrange[1]:scanrange[2]]
}
}
## what's missing?
## + consider calibration(s)?
## + what with polarity?
if(length(ret)==1)
return(ret[[1]])
return(ret)
})
############################################################
## levelplot
setMethod("levelplot", "xcmsSet",
function(x, log=TRUE, sampleidx=1,
col.regions=colorRampPalette(brewer.pal(9, "YlOrRd"))(256),
highlight.peaks=FALSE, highlight.col="#377EB880",
rt="raw", ...){
if (is.numeric(sampleidx))
sampleidx <- sampnames(x)[sampleidx]
sampidx <- match(sampleidx, sampnames(x))
if(length(sampidx) > 1)
stop("A levelplot can only be created for one file at a time!")
xraw <- getXcmsRaw(x, sampleidx=sampidx, rt=rt)
if(highlight.peaks){
pks <- peaks(x)
pks <- pks[pks[, "sample"]==sampidx, ]
## call the levelplot AND specify the panel...
## some code taken from plotSurf...
sel <- profRange(xraw, ...)
zvals <- xraw@env$profile[sel$massidx, sel$scanidx]
if(log){
zvals <- log(zvals+max(c(-min(zvals), 1)))
}
## y axis is time
yvals <- xraw@scantime[sel$scanidx]
yrange <- range(yvals)
## x is m/z
xvals <- profMz(xraw)[sel$massidx]
## 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
fileName <- sampnames(x)[sampidx]
plt <- levelplot(zvals~xvals*yvals,
panel=function(...){
panel.levelplot(...)
panel.rect(xleft=pks[, "mzmin"], xright=pks[, "mzmax"],
ybottom=pks[, "rtmin"], ytop=pks[, "rtmax"],
border=highlight.col)
},
xlab="m/z", ylab="Time",
colorkey=list(height=1, width=0.7),
main=list(fileName, side=1, line=0.5), col.regions=col.regions)
## plt <- plt + layer(panel.rect(xleft=pks[, "mzmin"], xright=pks[, "mzmax"],
## ybottom=pks[, "rtmin"], ytop=pks[, "rtmax"],
## border="#377EB820")
## )
}else{
plt <- levelplot(xraw, col.regions = col.regions, log=log)
}
plt
})
############################################################
## mslevel
## getter methods for the slots mslevel and scanrange of the xcmsSet object.
setMethod("mslevel", "xcmsSet", function(object){
## for xcmsSet objects that don't have (yet) the slot...
if(!.hasSlot(object, "mslevel")){
message("No slot mslevel available, consider updating the",
" object with the 'updateObject' method.")
return(NULL)
}else{
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(mlevel)
}
})
setReplaceMethod("mslevel", "xcmsSet", function(object, value){
if(.hasSlot(object, "mslevel")){
object@mslevel <- value
}else{
message("Object has no slot mslevel, consider updating",
" the object using 'updateObject'.")
}
object
})
############################################################
## scanrange
setMethod("scanrange", "xcmsSet", function(object){
if(!.hasSlot(object, "scanrange")){
message("No slot scanrange available, consider updating the",
" object with the 'updateObject' method.")
return(NULL)
}else{
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)
}
})
setReplaceMethod("scanrange", "xcmsSet", function(object, value){
if(.hasSlot(object, "scanrange")){
object@scanrange <- value
}else{
message("Object has no slot scanrange, consider updating",
" the object with the 'updateObject' method.")
}
object
})
############################################################
## profMethod
## getter methods for the prof method and step.
setMethod("profMethod", "xcmsSet", function(object) {
return(profinfo(object)$method)
})
setMethod("profStep", "xcmsSet", function(object) {
return(profinfo(object)$step)
})
## sub setting an xcmsSet object...
setMethod("[", "xcmsSet", function(x, i, j, ..., drop = FALSE) {
if (missing(drop))
drop <- FALSE
if (missing(i) && missing(j)) {
if (length(list(...))!=0)
stop("specify samples to subset; use '",
substitute(x), "$", names(list(...))[[1]],
"' to access phenoData variables")
return(x)
}
## Update the xcmsSet
x <- updateObject(x)
## don't allow i, but allow j to be: numeric or logical. If
## it's a character vector <- has to fit to sampnames(x)
if (!missing(i))
stop("Subsetting to rows is not supported!")
if (missing(j)) {
j <- 1:length(sampnames(x))
} else {
if (is.character(j)) {
## check if these match to the sampnames.
matches <- match(j, sampnames(x))
if(length(matches)!=length(j))
stop("All provided sample names have to match the",
" sample names in the xcmsSet!")
j <- matches
}
if (is.logical(j)) {
if (length(j) != length(sampnames(x)))
stop("If j is a logical its length has to match",
" the number of samples in the xcmsSet!")
j <- which(j)
}
if (!length(j)) {
tmp <- new("xcmsSet")
cn <- c("rtmin", "rtmax", "rt", "mzmin", "mzmax", "mz",
"into", "maxo", "sample")
pks <- matrix(ncol = length(cn), nrow = 0, dimnames = list(c(), cn))
tmp@peaks <- pks
return(tmp)
}
}
if (class(j) == "numeric")
j <- as.integer(j)
if(!is.integer(j))
stop("j has to be an integer vector specifying the",
" index of the samples for which the data has to be extracted")
## check if j is within the range of 1:length(sampnames)
if(any(!j %in% (1:length(sampnames(x)))))
stop("j has to be a numeric with values between 1 and ",
length(sampnames(x)), "!")
## OK, j is now an integer vector...
## "copy" the xcmsSet, that way we keep parameters mslevel, scanrange,
## profinfo, polarity.
xsub <- x
## first of all, subset the phenoData
phenoData(xsub) <- droplevels(phenoData(xsub)[j,, drop=FALSE])
## then the file paths
filepaths(xsub) <- filepaths(x)[j]
## now starting to subset data:
## 1) @rt$raw, @rt$corrected
xsub@rt$raw <- x@rt$raw[j]
xsub@rt$corrected <- x@rt$corrected[j]
## 2) @peaks
keep.peaks <- x@peaks
rownames(keep.peaks) <- as.character(1:nrow(keep.peaks))
## subsetting the peaks. Since we want to also allow reverse ordering we have to
## do it a little more complicated...
keep.peaks <- split(data.frame(keep.peaks), f=keep.peaks[, "sample"])
keep.peaks <- keep.peaks[as.character(j)]
names(keep.peaks) <- NULL
keep.peaks <- as.matrix(do.call(rbind, keep.peaks))
##keep.peaks <- keep.peaks[as.character(keep.peaks[, "sample"]) %in% as.character(j), ]
## have to replace the sample index.
newsample <- numeric(nrow(keep.peaks))
for(idx in 1:length(j)){
newsample[keep.peaks[, "sample"]==j[idx]] <- idx
}
keep.peaks[, "sample"] <- newsample
xsub@peaks <- keep.peaks
rownames(xsub@peaks) <- NULL
## 3) groupidx if present. subset this to indices of peaks which we will keep.
## will use the rownames of the keep.peaks matrix for that (represents the
## original index).
if(length(x@groupidx) > 0){
keep.groupidx <- lapply(x@groupidx, function(z){
newidx <- match(as.character(z), rownames(keep.peaks))
return(newidx[!is.na(newidx)])
## that way I just return the indices as they are!
##return(z[as.character(z) %in% rownames(keep.peaks)])
})
xsub@groupidx <- keep.groupidx
## 4) groups have to be re-calculated.
keep.groups <- x@groups
keep.groups <- keep.groups[, -(which(colnames(keep.groups)=="npeaks"):ncol(keep.groups))]
sampclasses <- sampclass(xsub)
peakCounts <- matrix(ncol=length(levels(sampclasses)), nrow=nrow(keep.groups), 0)
colnames(peakCounts) <- levels(sampclasses)
## loop throught the peakcounts
for(idx in 1:nrow(keep.groups)){
groupidx <- keep.groupidx[[idx]]
if(length(groupidx) == 0)
next
tab <- keep.peaks[groupidx, "sample"]
tab <- table(sampclasses[tab])
peakCounts[idx, names(tab)] <- as.numeric(tab)
}
keep.groups <- cbind(keep.groups, npeaks=rowSums(peakCounts), peakCounts)
xsub@groups <- keep.groups
}
## 5) filled
if(length(x@filled) > 0){
xsub@filled <- (1:nrow(keep.peaks))[rownames(keep.peaks) %in%
as.character(x@filled)]
}
## 6) dataCorrection
if(length(x@dataCorrection)>0)
xsub@dataCorrection <- x@dataCorrection[j]
## 7) processHistory: we want to make sure that the ordering of the objects stays
procHist <- .getProcessHistory(x, fileIndex = j)
procHist <- lapply(procHist, updateFileIndex, old = j, new = 1:length(j))
xsub@.processHistory <- procHist
OK <- .validProcessHistory(xsub)
if (!is.logical(OK))
stop(OK)
return(xsub)
})
############################################################
## present
setMethod("present", "xcmsSet", function(object, class, minfrac) {
if ( nrow(object@groups)<1 || length(object@groupidx) <1) {
stop("No group information. Use group().")
}
classlabel <- sampclass(object)
classlabel <- levels(classlabel)[as.vector(unclass(classlabel))]
sampidx <- which(classlabel %in% class)
if (length(sampidx) == 0) {
stop("Class ", class, "not found")
}
classnum <- length(sampidx)
minpresent <- classnum * minfrac
filled <- rep(FALSE, nrow(peaks(object)))
## exists(object@filled) always returns FALSE ??
sloti <- try(slot(object, "filled"), silent = TRUE)
if (class(sloti) != "try-error") {
filled[object@filled] <- TRUE
}
apply (groupval(object), 1, function(x) {
length(which( (!(is.na(x[sampidx]) | is.nan(x[sampidx])))
& !filled[x[sampidx]])) >= minpresent
})
})
############################################################
## absent
setMethod("absent", "xcmsSet", function(object, class, minfrac) {
if ( nrow(object@groups)<1 || length(object@groupidx) <1) {
stop("No group information. Use group().")
}
classlabel <- sampclass(object)
classlabel <- levels(classlabel)[as.vector(unclass(classlabel))]
sampidx <- which(classlabel %in% class)
if (length(sampidx) == 0) {
stop("Class ", class, "not found")
}
classnum <- length(sampidx)
minabsent <- classnum * minfrac
filled <- rep(FALSE, nrow(peaks(object)))
## exists(object@filled) always returns FALSE ??
sloti <- try(slot(object, "filled"), silent = TRUE)
if (class(sloti) != "try-error") {
filled[object@filled] <- TRUE
}
apply (groupval(object), 1, function(x) {
length(which(is.na(x[sampidx]) | is.nan(x[sampidx]) | filled[x[sampidx]])) >= minabsent
})
})
############################################################
## specDist
setMethod("specDist", signature(object="xcmsSet"),
function(object, peakIDs1, peakIDs2,
method=getOption("BioC")$xcms$specDist.method,
...) {
if (missing(peakIDs1)) {
stop("missing argument peakIDs1")
}
if (missing(peakIDs2)) {
stop("missing argument peakIDs2")
}
peaks <- object@peaks
peakTable1 <- peaks[peakIDs1,c("mz","into")]
peakTable2 <- peaks[peakIDs2,c("mz","into")]
method <- match.arg(method, getOption("BioC")$xcms$specDist.methods)
if (is.na(method))
stop("unknown method : ", method)
method <- paste("specDist", method, sep=".")
distance <- do.call(method, alist<-list(peakTable1, peakTable2, ...))
distance
})
############################################################
## showError
#' @title Extract processing errors
#'
#' @aliases showError
#'
#' @description If peak detection is performed with \code{\link{findPeaks}}
#' setting argument \code{stopOnError = FALSE} eventual errors during the
#' process do not cause to stop the processing but are recorded inside of
#' the resulting \code{\linkS4class{xcmsSet}} object. These errors can be
#' accessed with the \code{showError} method.
#'
#' @param object An \code{\linkS4class{xcmsSet}} object.
#'
#' @param message. Logical indicating whether only the error message, or the
#' error itself should be returned.
#'
#' @param ... Additional arguments.
#'
#' @return A list of error messages (if \code{message. = TRUE}) or errors or an
#' empty list if no errors are present.
#'
#' @author Johannes Rainer
setMethod("showError", signature(object = "xcmsSet"),
function(object, message. = TRUE, ...) {
errs <- .getProcessErrors(object, ...)
if (length(errs) > 0) {
res <- lapply(errs, function(z) {
if (message.)
return(z@info)
else return(z@error)
})
return(res)
}
return(list())
})
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Defines functions .retcor.peakgroups_orig
## All methods for xcmsSet should go here.
#' @include functions-xcmsSet.R
############################################################
## show
setMethod("show", "xcmsSet", function(object) {
cat("An \"xcmsSet\" object with", nrow(object@phenoData), "samples\n\n")
if (nrow(object@peaks)) {
cat("Time range: ", paste(round(range(object@peaks[,"rt"]), 1),
collapse = "-"),
" seconds (", paste(round(range(object@peaks[,"rt"])/60, 1),
collapse = "-"),
" minutes)\n", sep = "")
cat("Mass range:", paste(round(range(object@peaks[,"mz"], na.rm = TRUE), 4),
collapse = "-"), "m/z\n")
cat("Peaks:", nrow(object@peaks), "(about",
round(nrow(object@peaks)/nrow(object@phenoData)), "per sample)\n")
cat("Peak Groups:", nrow(object@groups), "\n")
} else
cat("Peaks: 0\n")
cat("Sample classes:", paste(levels(sampclass(object)), collapse = ", "), "\n\n")
## Processing info.
## Feature detection errors.
cat("Feature detection:\n")
if(.hasSlot(object, "mslevel")){
MSn <- mslevel(object)
if(is.null(MSn))
MSn <- 1
cat(paste0(" o Peak picking performed on MS", MSn, ".\n"))
}
if(.hasSlot(object, "scanrange")){
if(!is.null(scanrange(object))){
cat(" o Scan range limited to ", scanrange(object)[1], "-",
scanrange(object)[2], "\n")
}
}
errs <- .getProcessErrors(object, PROCSTEP = .PROCSTEP.PEAK.DETECTION)
if (length(errs) > 0) {
cat(" o Detection errors: ", length(errs), " files failed.\n",
" Use method 'showError' to list the error(s).\n\n", sep ="")
}
if (length(object@profinfo)) {
cat("Profile settings: ")
for (i in seq(along = object@profinfo)) {
if (i != 1) cat(" ")
cat(names(object@profinfo)[i], " = ", object@profinfo[[i]],
"\n", sep = "")
}
cat("\n")
}
memsize <- object.size(object)
cat("Memory usage:", signif(memsize/2^20, 3), "MB\n")
})
#' @description This method updates an \emph{old} \code{\linkS4class{xcmsSet}}
#' object to the latest definition.
#'
#' @title Update an \code{\linkS4class{xcmsSet}} object
#'
#' @param object The \code{\linkS4class{xcmsSet}} object to update.
#'
#' @param ... Optional additional arguments. Currently ignored.
#'
#' @param verbose Currently ignored.
#'
#' @return An updated \code{\linkS4class{xcmsSet}} containing all data from
#' the input object.
#'
#' @author Johannes Rainer
setMethod("updateObject", "xcmsSet", function(object, ..., verbose = FALSE) {
## Create a new empty xcmsSet and start filling it with the slot
## values (if present.)
## Define the default values.
pks <- matrix(nrow = 0, ncol = 0)
grps <- matrix(nrow = 0, ncol = 0)
grpidx <- list()
flld <- integer(0)
phnD <- data.frame()
theRt <- list()
flpth <- character(0)
prfnf <- vector("list")
datCorr <- integer(0)
pol <- character(0)
prgInfo <- list()
msl <- numeric(0)
scnr <- numeric(0)
prgCb <- function(progress) NULL
procH <- list()
## Now replace the values with the slots... if present.
if (.hasSlot(object, "peaks"))
pks <- object@peaks
if (.hasSlot(object, "groups"))
grps <- object@groups
if (.hasSlot(object, "groupidx"))
grpidx <- object@groupidx
if (.hasSlot(object, "filles"))
flld <- object@filles
if (.hasSlot(object, "phenoData"))
phnD <- object@phenoData
if (.hasSlot(object, "rt"))
theRt <- object@rt
if (.hasSlot(object, "filepaths"))
flpth <- object@filepaths
if (.hasSlot(object, "profinfo"))
prfnf <- object@profinfo
if (.hasSlot(object, "dataCorrection"))
datCorr <- object@dataCorrection
if (.hasSlot(object, "polarity"))
pol <- object@polarity
if (.hasSlot(object, "polarity"))
prgInfo <- object@progressInfo
if (.hasSlot(object, "mslevel"))
msl <- object@mslevel
if (.hasSlot(object, "scanrange"))
scnr <- object@scanrange
if (.hasSlot(object, "progressCallback"))
prgCb <- object@progressCallback
if (.hasSlot(object, ".processHistory"))
procH <- object@.processHistory
## Generate the new object.
newXs <- new("xcmsSet",
peaks = pks,
groups = grps,
groupidx = grpidx,
filled = flld,
phenoData = phnD,
rt = theRt,
filepaths = flpth,
profinfo = prfnf,
dataCorrection = datCorr,
polarity = pol,
progressInfo = prgInfo,
mslevel = msl,
scanrange = scnr,
progressCallback = prgCb,
.processHistory = procH
)
return(newXs)
})
############################################################
## peaks
setMethod("peaks", "xcmsSet", function(object) object@peaks)
setReplaceMethod("peaks", "xcmsSet", function(object, value) {
object@peaks <- value
object
})
############################################################
## groups
setMethod("groups", "xcmsSet", function(object) object@groups)
setReplaceMethod("groups", "xcmsSet", function(object, value) {
object@groups <- value
object
})
############################################################
## groupidx
setMethod("groupidx", "xcmsSet", function(object) object@groupidx)
setReplaceMethod("groupidx", "xcmsSet", function(object, value) {
object@groupidx <- value
object
})
############################################################
## sampnames
setMethod("sampnames", "xcmsSet", function(object) rownames(object@phenoData))
setReplaceMethod("sampnames", "xcmsSet", function(object, value) {
if (length(object@phenoData)==0) {
object@phenoData <- data.frame(class=rep("dummy", length(value)))
}
rownames(object@phenoData) <- value
object
})
############################################################
## sampclass
setMethod("sampclass", "xcmsSet", function(object) {
if (ncol(object@phenoData) >0) {
if(any(colnames(object@phenoData)=="class")){
sclass <- object$class
## in any rate: transform class to a character vector
## and generate a new factor on that with the levels
## being in the order of the first occurrence of the
## elements (i.e. no alphanumeric ordering).
sclass <- as.character(sclass)
sclass <- factor(sclass, levels=unique(sclass))
return(sclass)
}
interaction(object@phenoData, drop=TRUE)
} else {
factor()
}
})
setReplaceMethod("sampclass", "xcmsSet", function(object, value) {
## if we're submitting a data.frame, we're using interaction on that.
if(class(value)=="data.frame"){
message("Setting the class labels as the interaction of the data.frame columns.")
value <- as.character(interaction(value, drop=TRUE))
}
if (!is.factor(value))
value <- factor(value, unique(value))
object@phenoData$class <- value
object
})
############################################################
## phenoData
setMethod("phenoData", "xcmsSet", function(object) object@phenoData)
setReplaceMethod("phenoData", "xcmsSet", function(object, value) {
if (is.matrix(value))
value <- as.data.frame(value)
## if (is.data.frame(value) && !("class" %in% colnames(value)))
## value[,"class"] <- interaction(value)
## else
if (!is.data.frame(value))
value <- data.frame(class = value)
object@phenoData <- value
object
})
############################################################
## filepaths
setMethod("filepaths", "xcmsSet", function(object) object@filepaths)
setReplaceMethod("filepaths", "xcmsSet", function(object, value) {
object@filepaths <- value
object
})
############################################################
## profinfo
setMethod("profinfo", "xcmsSet", function(object) object@profinfo)
setReplaceMethod("profinfo", "xcmsSet", function(object, value) {
object@profinfo <- value
object
})
############################################################
## calibrate
setMethod("calibrate", "xcmsSet", function(object, calibrants,
method = "linear",
mzabs = 0.0001, mzppm = 5,
neighbours = 3, plotres = FALSE) {
nsamp = length(unique(object@peaks[,"sample"]))
match.arg(method, c("shift", "linear", "edgeshift"))
if (is.list(calibrants))
if (length(calibrants) != nsamp)
stop("Error: Number of masslists differs with number of samples")
## Loop over samples, estimate calibration and apply it.
for (s in 1:nsamp) {
peaklist = object@peaks[which(object@peaks[,"sample"] == s), ]
if (is.list(calibrants)) {
masslist <- calibrants[s]
} else {
masslist <- calibrants
}
## Check that peaks are ordered by mz value (issue #200)
if (is.unsorted(peaklist[, "mz"]))
warning("Peaks in sample ", s, " are not sorted by mz value")
masses <- matchpeaks(peaklist, masslist, mzabs, mzppm, neighbours)
if (length(masses) == 0) {
warning("No masses close enough!\n")
next
}
if (nrow(masses) == 1 & method != "shift") {
warning("Sample ", s, ": only one peak found, falling back to ",
"method = 'shift'")
method = "shift"
}
## Estimate the adjustment.
params <- estimate(masses, method)
mzu <- peaklist[, "mz"]
mposs <- masses[, "pos"]
mdiffs <- masses[, "dif"]
a <- params[1]
b <- params[2]
## cat("a=",a,"b=",b)
if (method != "edgeshift"){
mzu <- mzu - (a * mzu + b)
} else {
## Different adjustment for peaks below the smallest and peaks above
## the largest mz
mzu[c(1:(min(mposs) - 1))] <- mzu[c(1:(min(mposs) - 1))] -
(a * mzu[min(mposs)] + b)
mzu[c((min(mposs)):(max(mposs)))] <-
mzu[c((min(mposs)):(max(mposs)))] -
(a * mzu[c((min(mposs)):(max(mposs)))] + b)
mzu[c((max(mposs)+1):length(mzu))] <-
mzu[c((max(mposs)+1):length(mzu))] - (a * mzu[max(mposs)] + b)
}
peaklist[,"mz"] <- mzu
object@peaks[which(object@peaks[,"sample"] == s), ] <- peaklist
}
if (plotres) {
plot(mzu[mposs],mdiffs, xlim=c(min(mzu),max(mzu)))
if (method!="edgeshift") {
abline(b,a)
} else {
lines(c(min(mzu), mzu[min(mposs)]),
c(a * mzu[min(mposs)] + b,a * mzu[min(mposs)] + b))
lines(c(mzu[min(mposs)], mzu[max(mposs)]),
c(a * mzu[min(mposs)] + b,a * mzu[max(mposs)] + b))
lines(c(mzu[max(mposs)], max(mzu)),
c(a * mzu[max(mposs)] + b,a * mzu[max(mposs)] + b))
}
}
invisible(object)
})
############################################################
## groupnames
setMethod("groupnames", "xcmsSet", function(object, mzdec = 0, rtdec = 0,
template = NULL) {
if ( nrow(object@groups)<1 || length(object@groupidx) <1) {
stop("No group information. Use group().")
}
if (!missing(template)) {
tempsplit <- strsplit(template[1], "[T_]")
tempsplit <- strsplit(unlist(tempsplit), "\\.")
if (length(tempsplit[[1]]) > 1)
mzdec <- nchar(tempsplit[[1]][2])
else
mzdec <- 0
if (length(tempsplit[[2]]) > 1)
rtdec <- nchar(tempsplit[[2]][2])
else
rtdec <- 0
}
mzfmt <- paste("%.", mzdec, "f", sep = "")
rtfmt <- paste("%.", rtdec, "f", sep = "")
gnames <- paste("M", sprintf(mzfmt, groups(object)[,"mzmed"]), "T",
sprintf(rtfmt, groups(object)[,"rtmed"]), sep = "")
if (any(dup <- duplicated(gnames)))
for (dupname in unique(gnames[dup])) {
dupidx <- which(gnames == dupname)
gnames[dupidx] <- paste(gnames[dupidx], seq(along = dupidx), sep = "_")
}
gnames
})
############################################################
## group.density
setMethod("group.density", "xcmsSet", function(object, bw = 30, minfrac = 0.5,
minsamp = 1, mzwid = 0.25,
max = 50, sleep = 0) {
## Using now the do_groupChromPeaks_density function:
res <- do_groupChromPeaks_density(peaks(object),
sampleGroups = sampclass(object),
bw = bw,
minFraction = minfrac,
minSamples = minsamp,
binSize = mzwid,
maxFeatures = max,
sleep = sleep)
groups(object) <- as.matrix(res[, -match("peakidx", colnames(res))])
groupidx(object) <- res$peakidx
object
})
############################################################
## group.mzClust
setMethod("group.mzClust", "xcmsSet", function(object,
mzppm = 20,
mzabs = 0,
minsamp = 1,
minfrac=0.5) {
res <- do_groupPeaks_mzClust(peaks = peaks(object),
sampleGroups = sampclass(object),
ppm = mzppm,
absMz = mzabs,
minFraction = minfrac,
minSamples = minsamp)
groups(object) <- res$featureDefinitions
groupidx(object) <- res$peakIndex
object
})
############################################################
## group.nearest
setMethod("group.nearest", "xcmsSet", function(object, mzVsRTbalance=10,
mzCheck=0.2, rtCheck=15, kNN=10) {
res <- do_groupChromPeaks_nearest(peaks = peaks(object),
sampleGroups = sampclass(object),
mzVsRtBalance = mzVsRTbalance,
absMz = mzCheck,
absRt = rtCheck,
kNN = kNN)
groups(object) <- res$featureDefinitions
groupidx(object) <- res$peakIndex
invisible(object)
})
############################################################
## group
setMethod("group", "xcmsSet", function(object, method=getOption("BioC")$xcms$group.method,
...) {
method <- match.arg(method, getOption("BioC")$xcms$group.methods)
if (is.na(method))
stop("unknown method : ", method)
method <- paste("group", method, sep=".")
invisible(do.call(method, alist(object, ...)))
})
############################################################
## groupval
setMethod("groupval", "xcmsSet", function(object, method = c("medret", "maxint"),
value = "index", intensity = "into") {
if ( nrow(object@groups)<1 || length(object@groupidx) <1) {
stop("xcmsSet is not been group()ed.")
}
method <- match.arg(method)
peakmat <- peaks(object)
groupmat <- groups(object)
groupindex <- groupidx(object)
sampnum <- seq(length = length(sampnames(object)))
retcol <- match("rt", colnames(peakmat))
intcol <- match(intensity, colnames(peakmat))
sampcol <- match("sample", colnames(peakmat))
values <- matrix(nrow = length(groupindex), ncol = length(sampnum))
if (method == "medret") {
for (i in seq(along = groupindex)) {
gidx <- groupindex[[i]][order(abs(peakmat[groupindex[[i]],retcol] - median(peakmat[groupindex[[i]],retcol])))]
values[i,] <- gidx[match(sampnum, peakmat[gidx,sampcol])]
}
} else {
for (i in seq(along = groupindex)) {
gidx <- groupindex[[i]][order(peakmat[groupindex[[i]],intcol], decreasing = TRUE)]
values[i,] <- gidx[match(sampnum, peakmat[gidx,sampcol])]
}
}
if (value != "index") {
values <- peakmat[values,value]
dim(values) <- c(length(groupindex), length(sampnum))
}
colnames(values) <- sampnames(object)
rownames(values) <- paste(round(groupmat[,"mzmed"],1), round(groupmat[,"rtmed"]), sep = "/")
values
})
############################################################
## retcor
setMethod("retcor", "xcmsSet", function(object, method=getOption("BioC")$xcms$retcor.method,
...) {
## Backward compatibility for old "methods"
if (method == "linear" || method == "loess") {
args <- list(...)
if (any(names(args) == "smooth"))
warning("Provided argument 'smooth' will be replaced with the ",
"value of 'method', i.e. with ", method)
args$smooth <- method
## Overwriting eventually provided smooth parameter.
return(invisible(do.call(retcor.peakgroups, c(list(object), args))))
}
method <- match.arg(method, getOption("BioC")$xcms$retcor.methods)
if (is.na(method))
stop("unknown method : ", method)
method <- paste("retcor", method, sep=".")
invisible(do.call(method, alist(object, ...)))
})
############################################################
## retcor.peakgroups
setMethod("retcor.peakgroups", "xcmsSet", function(object, missing = 1, extra = 1,
smooth = c("loess", "linear"),
span = .2,
family = c("gaussian", "symmetric"),
plottype = c("none", "deviation", "mdevden"),
col = NULL, ty = NULL) {
peakmat <- peaks(object)
groupmat <- groups(object)
if (length(groupmat) == 0)
stop("No group information found")
samples <- sampnames(object)
classlabel <- as.vector(unclass(sampclass(object)))
n <- length(samples)
corpeaks <- peakmat
smooth <- match.arg(smooth)
plottype <- match.arg(plottype)
family <- match.arg(family)
if (length(object@rt) == 2)
rtcor <- object@rt$corrected
else {
fnames <- filepaths(object)
rtcor <- vector("list", length(fnames))
for (i in seq(along = fnames)) {
xraw <- xcmsRaw(fnames[i])
rtcor[[i]] <- xraw@scantime
}
object@rt <- list(raw = rtcor, corrected = rtcor)
}
minFr <- (n - missing) / n
res <- do_adjustRtime_peakGroups(peaks = peakmat,
peakIndex = object@groupidx,
rtime = rtcor,
minFraction = minFr,
extraPeaks = extra,
smooth = smooth,
span = span,
family = family)
rtdevsmo <- vector("list", n)
for (i in 1:n) {
rtdevsmo[[i]] <- rtcor[[i]] - res[[i]]
}
## rtdevsmo <- mapply(FUN = function(a, b) {
## return(a - b)
## }, rtcor, res)
if (plottype == "mdevden") {
split.screen(matrix(c(0, 1, .3, 1, 0, 1, 0, .3), ncol = 4, byrow = TRUE))
screen(1)
par(mar = c(0, 4.1, 4.1, 2), xaxt = "n")
}
if (plottype %in% c("deviation", "mdevden")) {
## Need also the 'rt' matrix:
rt <- .getPeakGroupsRtMatrix(peakmat, object@groupidx, seq_len(n),
missing, extra)
rtdev <- rt - apply(rt, 1, median, na.rm = TRUE)
## define the colors and line types and returns a list of
## mypal, col and ty. Uses the original code if no colors are
## submitted. Supports manually selected colors (e.g. in hex)
vals <- defineColAndTy(col, ty, classlabel)
col <- vals$col
mypal <- vals$mypal
ty <- vals$ty
rtrange <- range(do.call(c, rtcor))
devrange <- range(do.call(c, rtdevsmo))
plot(0, 0, type="n", xlim = rtrange, ylim = devrange,
main = "Retention Time Deviation vs. Retention Time",
xlab = "Retention Time", ylab = "Retention Time Deviation")
legend(rtrange[2], devrange[2], samples, col = mypal[col], lty = ty,
pch = ceiling(1:n/length(mypal)), xjust = 1)
for (i in 1:n) {
points(data.frame(rt = rt[,i], rtdev = rtdev[,i]),
col = mypal[col[i]], pch = ty[i], type = "p")
points(rtcor[[i]], rtdevsmo[[i]], type="l", col = mypal[col[i]],
lty = ty[i])
}
}
if (plottype == "mdevden") {
screen(2)
par(mar = c(5.1, 4.1, 0, 2), yaxt = "n")
allden <- density(peakmat[,"rt"], bw = diff(rtrange)/200,
from = rtrange[1], to = rtrange[2])[c("x","y")]
corden <- density(rt, bw = diff(rtrange)/200, from = rtrange[1],
to = rtrange[2], na.rm = TRUE)[c("x","y")]
allden$y <- allden$y / sum(allden$y)
corden$y <- corden$y / sum(corden$y)
maxden <- max(allden$y, corden$y)
plot(c(0,0), xlim = rtrange, ylim = c(0, maxden), type = "n",
main = "", xlab = "Retention Time", ylab = "Peak Density")
points(allden, type = "l", col = 1)
points(corden, type = "l", col = 2)
abline(h = 0, col = "grey")
legend(rtrange[2], maxden, c("All", "Correction"), col = 1:2,
lty = c(1,1), xjust = 1)
close.screen(all.screens = TRUE)
}
for (i in 1:n) {
cfun <- stepfun(rtcor[[i]][-1] - diff(rtcor[[i]]) / 2,
rtcor[[i]] - rtdevsmo[[i]])
rtcor[[i]] <- rtcor[[i]] - rtdevsmo[[i]]
sidx <- which(corpeaks[,"sample"] == i)
corpeaks[sidx, c("rt", "rtmin", "rtmax")] <-
cfun(corpeaks[sidx, c("rt", "rtmin", "rtmax")])
}
object@rt$corrected <- rtcor
peaks(object) <- corpeaks
groups(object) <- matrix(nrow = 0, ncol = 0)
groupidx(object) <- list()
invisible(object)
})
## The original code!
.retcor.peakgroups_orig <- function(object, missing = 1, extra = 1,
smooth = c("loess", "linear"), span = .2,
family = c("gaussian", "symmetric"),
plottype = c("none", "deviation", "mdevden"),
col = NULL, ty = NULL) {
peakmat <- peaks(object)
groupmat <- groups(object)
if (length(groupmat) == 0)
stop("No group information found")
samples <- sampnames(object)
classlabel <- as.vector(unclass(sampclass(object)))
n <- length(samples)
corpeaks <- peakmat
smooth <- match.arg(smooth)
plottype <- match.arg(plottype)
family <- match.arg(family)
if (length(object@rt) == 2)
rtcor <- object@rt$corrected
else {
fnames <- filepaths(object)
rtcor <- vector("list", length(fnames))
for (i in seq(along = fnames)) {
xraw <- xcmsRaw(fnames[i])
rtcor[[i]] <- xraw@scantime
}
object@rt <- list(raw = rtcor, corrected = rtcor)
}
nsamp <- rowSums(groupmat[,match("npeaks", colnames(groupmat))+unique(classlabel),drop=FALSE])
idx <- which(nsamp >= n-missing & groupmat[,"npeaks"] <= nsamp + extra)
if (length(idx) == 0)
stop("No peak groups found for retention time correction")
## Ordering the peaks by the rtmed might not represent the ordering
## of the below selected "representative" peak for each peak.
## See issue #110
## idx <- idx[order(groupmat[idx,"rtmed"])]
rt <- groupval(object, "maxint", "rt")[idx,, drop=FALSE]
## And now order them by median retention time: issue #110
rt <- rt[order(rowMedians(rt, na.rm = TRUE)), , drop = FALSE]
cat("Retention Time Correction Groups:", nrow(rt), "\n")
rtdev <- rt - apply(rt, 1, median, na.rm = TRUE)
if (smooth == "loess") {
mingroups <- min(colSums(!is.na(rt)))
if (mingroups < 4) {
smooth <- "linear"
warning("Too few peak groups, reverting to linear method")
} else if (mingroups*span < 4) {
span <- 4/mingroups
warning("Span too small, resetting to ", round(span, 2))
}
}
rtdevsmo <- vector("list", n)
## Code for checking to see if retention time correction is overcorrecting
rtdevrange <- range(rtdev, na.rm = TRUE)
warn.overcorrect <- FALSE
for (i in 1:n) {
pts <- na.omit(data.frame(rt = rt[,i], rtdev = rtdev[,i]))
if (smooth == "loess") {
lo <- suppressWarnings(loess(rtdev ~ rt, pts, span = span, degree = 1, family = family))
rtdevsmo[[i]] <- xcms:::na.flatfill(predict(lo, data.frame(rt = rtcor[[i]])))
### Remove singularities from the loess function
rtdevsmo[[i]][abs(rtdevsmo[[i]]) > quantile(abs(rtdevsmo[[i]]), 0.9)*2] <- NA
if (length(naidx <- which(is.na(rtdevsmo[[i]]))))
rtdevsmo[[i]][naidx] <- suppressWarnings(approx(na.omit(data.frame(rtcor[[i]], rtdevsmo[[i]])),
xout = rtcor[[i]][naidx], rule = 2)$y)
while (length(decidx <- which(diff(rtcor[[i]] - rtdevsmo[[i]]) < 0))) {
d <- diff(rtcor[[i]] - rtdevsmo[[i]])[tail(decidx, 1)]
rtdevsmo[[i]][tail(decidx, 1)] <- rtdevsmo[[i]][tail(decidx, 1)] - d
if (abs(d) <= 1e-06)
break;
}
rtdevsmorange <- range(rtdevsmo[[i]])
if (any(rtdevsmorange/rtdevrange > 2)) warn.overcorrect <- TRUE
} else {
if (nrow(pts) < 2) {
stop("Not enough ``well behaved'' peak groups even for linear smoothing of retention times")
}
fit <- lsfit(pts$rt, pts$rtdev)
rtdevsmo[[i]] <- rtcor[[i]] * fit$coef[2] + fit$coef[1]
ptsrange <- range(pts$rt)
minidx <- rtcor[[i]] < ptsrange[1]
maxidx <- rtcor[[i]] > ptsrange[2]
rtdevsmo[[i]][minidx] <- rtdevsmo[[i]][head(which(!minidx), n = 1)]
rtdevsmo[[i]][maxidx] <- rtdevsmo[[i]][tail(which(!maxidx), n = 1)]
}
}
if (warn.overcorrect) {
warning(paste("Fitted retention time deviation curves exceed points by more than 2x.",
"This is dangerous and the algorithm is probably overcorrecting your data.",
"Consider increasing the span parameter or switching to the linear smoothing method.",
sep = "\n"))
}
if (plottype == "mdevden") {
split.screen(matrix(c(0, 1, .3, 1, 0, 1, 0, .3), ncol = 4, byrow = TRUE))
screen(1)
par(mar = c(0, 4.1, 4.1, 2), xaxt = "n")
}
if (plottype %in% c("deviation", "mdevden")) {
## define the colors and line types and returns a list of
## mypal, col and ty. Uses the original code if no colors are
## submitted. Supports manually selected colors (e.g. in hex)
vals <- defineColAndTy(col, ty, classlabel)
col <- vals$col
mypal <- vals$mypal
ty <- vals$ty
rtrange <- range(do.call(c, rtcor))
devrange <- range(do.call(c, rtdevsmo))
plot(0, 0, type="n", xlim = rtrange, ylim = devrange,
main = "Retention Time Deviation vs. Retention Time",
xlab = "Retention Time", ylab = "Retention Time Deviation")
legend(rtrange[2], devrange[2], samples, col = mypal[col], lty = ty,
pch = ceiling(1:n/length(mypal)), xjust = 1)
for (i in 1:n) {
points(data.frame(rt = rt[,i], rtdev = rtdev[,i]), col = mypal[col[i]], pch = ty[i], type="p")
points(rtcor[[i]], rtdevsmo[[i]], type="l", col = mypal[col[i]], lty = ty[i])
}
}
if (plottype == "mdevden") {
screen(2)
par(mar = c(5.1, 4.1, 0, 2), yaxt = "n")
allden <- density(peakmat[,"rt"], bw = diff(rtrange)/200,
from = rtrange[1], to = rtrange[2])[c("x","y")]
corden <- density(rt, bw = diff(rtrange)/200, from = rtrange[1],
to = rtrange[2], na.rm = TRUE)[c("x","y")]
allden$y <- allden$y / sum(allden$y)
corden$y <- corden$y / sum(corden$y)
maxden <- max(allden$y, corden$y)
plot(c(0,0), xlim = rtrange, ylim = c(0, maxden), type = "n", main = "", xlab = "Retention Time", ylab = "Peak Density")
points(allden, type = "l", col = 1)
points(corden, type = "l", col = 2)
abline(h = 0, col = "grey")
legend(rtrange[2], maxden, c("All", "Correction"), col = 1:2, lty = c(1,1), xjust = 1)
close.screen(all.screens = TRUE)
}
for (i in 1:n) {
cfun <- stepfun(rtcor[[i]][-1] - diff(rtcor[[i]])/2, rtcor[[i]] - rtdevsmo[[i]])
rtcor[[i]] <- rtcor[[i]] - rtdevsmo[[i]]
sidx <- which(corpeaks[,"sample"] == i)
corpeaks[sidx, c("rt", "rtmin", "rtmax")] <- cfun(corpeaks[sidx, c("rt", "rtmin", "rtmax")])
}
object@rt$corrected <- rtcor
peaks(object) <- corpeaks
groups(object) <- matrix(nrow = 0, ncol = 0)
groupidx(object) <- list()
invisible(object)
}
############################################################
## retcor.obiwarp
setMethod("retcor.obiwarp", "xcmsSet", function(object, plottype = c("none", "deviation"),
profStep=1, center=NULL,
col = NULL, ty = NULL,
response=1, distFunc="cor_opt",
gapInit=NULL, gapExtend=NULL,
factorDiag=2, factorGap=1,
localAlignment=0, initPenalty=0) {
if (is.null(gapInit)) {
if (distFunc=="cor") {gapInit=0.3}
if (distFunc=="cor_opt") {gapInit=0.3}
if (distFunc=="cov") {gapInit=0.0}
if (distFunc=="euc") {gapInit=0.9}
if (distFunc=="prd") {gapInit=0.0}
}
if (is.null(gapExtend)) {
if (distFunc=="cor") {gapExtend=2.4}
if (distFunc=="cor_opt") {gapExtend=2.4}
if (distFunc=="cov") {gapExtend= 11.7}
if (distFunc=="euc") {gapExtend= 1.8}
if (distFunc=="prd") {gapExtend= 7.8}
}
peakmat <- peaks(object)
samples <- sampnames(object)
classlabel <- as.vector(unclass(sampclass(object)))
N <- length(samples)
corpeaks <- peakmat
plottype <- match.arg(plottype)
if (length(object@rt) == 2) {
rtcor <- object@rt$corrected
} else {
fnames <- filepaths(object)
rtcor <- vector("list", length(fnames))
for (i in seq(along = fnames)) {
xraw <- xcmsRaw(fnames[i])
rtcor[[i]] <- xraw@scantime
}
object@rt <- list(raw = rtcor, corrected = rtcor)
}
rtimecor <- vector("list", N)
rtdevsmo <- vector("list", N)
plength <- rep(0, N)
if (missing(center)) {
for(i in 1:N){
plength[i] <- length(which(peakmat[,"sample"]==i))
}
center <- which.max(plength)
}
cat("center sample: ", samples[center], "\nProcessing: ")
idx <- which(seq(1,N) != center)
obj1 <- xcmsRaw(object@filepaths[center], profmethod="bin", profstep=0)
## Scanrange checking: fix for issue #194
## Check if we've got the scanrange slot.
scanrange <- NULL
if (.hasSlot(object, "scanrange")) {
if (length(object@scanrange) == 2)
scanrange <- object@scanrange
} else {
if(length(obj1@scantime) != length(object@rt$raw[[center]])){
## This is in case the xcmsSet was read using a scanrange, i.e. if
## the data was read in with defining a scan range, then we would
## have a mismatch here. This code essentially ensures that the
## retention time of the raw object would match the retention time
## present in the xcmsSet.
## This was before the days in which @scanrange was added as a slot
## to xcmsSet.
##figure out the scan time range
scantime.start <- object@rt$raw[[center]][1]
scantime.end <- object@rt$raw[[center]][length(object@rt$raw[[center]])]
scanrange.start <- which.min(abs(obj1@scantime - scantime.start))
scanrange.end <- which.min(abs(obj1@scantime - scantime.end))
scanrange <- c(scanrange.start, scanrange.end)
}
}
## Subset the object if scanrange not NULL
if (!is.null(scanrange))
obj1 <- obj1[scanrange[1]:scanrange[2]]
## ## added t automatically find the correct scan range from the xcmsSet object
## if(length(obj1@scantime) != length(object@rt$raw[[center]])){
## ## This is in case the xcmsSet was read using a scanrange, i.e. if
## ## the data was read in with defining a scan range, then we would have a
## ## mismatch here. This code essentially ensures that the retention time
## ## of the raw object would match the retention time present in the xcmsSet.
## ## This was before the days in which @scanrange was added as a slot to
## ## xcmsSet.
## ##figure out the scan time range
## scantime.start <-object@rt$raw[[center]][1]
## scantime.end <-object@rt$raw[[center]][length(object@rt$raw[[center]])]
## scanrange.start <-which.min(abs(obj1@scantime - scantime.start))
## scanrange.end <-which.min(abs(obj1@scantime - scantime.end))
## scanrange<-c(scanrange.start, scanrange.end)
## obj1 <- xcmsRaw(object@filepaths[center], profmethod="bin",
## profstep=0, scanrange=scanrange)
## } else{
## scanrange<-NULL
## }
for (si in 1:length(idx)) {
s <- idx[si]
cat(samples[s], " ")
##
## Might be better to just get the profile matrix from the center object
## outside of the for loop and then modifying a internal variable within
## the loop - avoids creation of two profile matrices in each iteration.
profStepPad(obj1) <- profStep ## (re-)generate profile matrix, since it
## might have been modified during previous iteration
if(is.null(scanrange)){
obj2 <- xcmsRaw(object@filepaths[s], profmethod="bin", profstep=0)
} else{
obj2 <- xcmsRaw(object@filepaths[s], profmethod="bin", profstep=0,
scanrange = scanrange)
}
profStepPad(obj2) <- profStep ## generate profile matrix
mzmin <- min(obj1@mzrange[1], obj2@mzrange[1])
mzmax <- max(obj1@mzrange[2], obj2@mzrange[2])
mz <- seq(mzmin,mzmax, by=profStep)
mz <- as.double(mz)
mzval <- length(mz)
scantime1 <- obj1@scantime
scantime2 <- obj2@scantime
## median difference between spectras' scan times.
mstdiff <- median(c(diff(scantime1), diff(scantime2)))
rtup1 <- c(1:length(scantime1))
rtup2 <- c(1:length(scantime2))
mst1 <- which(diff(scantime1)>5*mstdiff)[1]
if(!is.na(mst1)) {
rtup1 <- which(rtup1<=mst1)
cat("Found gaps: cut scantime-vector at ", scantime1[mst1],"seconds", "\n")
}
mst2 <- which(diff(scantime2)>5*mstdiff)[1]
if(!is.na(mst2)) {
rtup2 <- which(rtup2<=mst2)
cat("Found gaps: cut scantime-vector at ", scantime2[mst2],"seconds", "\n")
}
scantime1 <- scantime1[rtup1]
scantime2 <- scantime2[rtup2]
## Drift of measured scan times - expected to be largest at the end.
rtmaxdiff <- abs(diff(c(scantime1[length(scantime1)],
scantime2[length(scantime2)])))
## If the drift is larger than the threshold, cut the matrix up to the
## max allowed difference.
if(rtmaxdiff>(5*mstdiff)){
rtmax <- min(scantime1[length(scantime1)],
scantime2[length(scantime2)])
rtup1 <- which(scantime1<=rtmax)
rtup2 <- which(scantime2<=rtmax)
}
scantime1 <- scantime1[rtup1]
scantime2 <- scantime2[rtup2]
valscantime1 <- length(scantime1)
valscantime2 <- length(scantime2)
## Restrict the profile matrix to columns 1:valscantime
if(length(obj1@scantime)>valscantime1) {
obj1@env$profile <- obj1@env$profile[,-c((valscantime1+1):length(obj1@scantime))]
}
if(length(obj2@scantime)>valscantime2) {
obj2@env$profile <- obj2@env$profile[,-c((valscantime2+1):length(obj2@scantime))]
}
## Now ensure that the nrow of the profile matrix matches.
## Add empty rows at the beginning
if(mzmin < obj1@mzrange[1]) {
## The profile matrices should not get larger than mz!
max_missing_rows <- mzval - nrow(obj1@env$profile)
low_mz <- seq(mzmin, obj1@mzrange[1], profStep)
## keep all mz bins that are smaller than mzrange, but ensure that
## we're not adding more rows than needed.
seqlen <- min(sum(low_mz < obj1@mzrange[1]), max_missing_rows)
## seqlen <- length(seq(mzmin, obj1@mzrange[1], profStep)) - 1
x <- matrix(0, seqlen, dim(obj1@env$profile)[2])
obj1@env$profile <- rbind(x, obj1@env$profile)
}
## Add emtpy rows at the end.
if(mzmax > obj1@mzrange[2]){
max_missing_rows <- mzval - nrow(obj1@env$profile)
high_mz <- seq(obj1@mzrange[2], mzmax, profStep)
seqlen <- min(sum(high_mz > obj1@mzrange[2]), max_missing_rows)
## seqlen <- length(seq(obj1@mzrange[2], mzmax, profStep)) - 1
x <- matrix(0, seqlen, dim(obj1@env$profile)[2])
obj1@env$profile <- rbind(obj1@env$profile, x)
}
if(mzmin < obj2@mzrange[1]){
max_missing_rows <- mzval - nrow(obj2@env$profile)
low_mz <- seq(mzmin, obj2@mzrange[1], profStep)
seqlen <- min(sum(low_mz < obj2@mzrange[1]), max_missing_rows)
## seqlen <- length(seq(mzmin, obj2@mzrange[1], profStep))-1
x <- matrix(0, seqlen, dim(obj2@env$profile)[2])
obj2@env$profile <- rbind(x, obj2@env$profile)
}
if(mzmax > obj2@mzrange[2]){
max_missing_rows <- mzval - nrow(obj2@env$profile)
high_mz <- seq(obj2@mzrange[2], mzmax, profStep)
seqlen <- min(sum(high_mz > obj2@mzrange[2]), max_missing_rows)
## seqlen <- length(seq(obj2@mzrange[2], mzmax, profStep)) - 1
x <- matrix(0, seqlen, dim(obj2@env$profile)[2])
obj2@env$profile <- rbind(obj2@env$profile, x)
}
## OK, now that the matrices are "aligned" extract the intensities
intensity1 <- obj1@env$profile
intensity2 <- obj2@env$profile
## Final check to ensure that our expansion of profile matrix rows was
## correct.
if ((mzval * valscantime1 != length(intensity1)) ||
(mzval * valscantime2 != length(intensity2)))
stop("Dimensions of profile matrices do not match !\n")
## Would it be possible to supply non-binned data too???
rtimecor[[s]] <-.Call("R_set_from_xcms",
valscantime1,scantime1,mzval,mz,intensity1,
valscantime2,scantime2,mzval,mz,intensity2,
response, distFunc,
gapInit, gapExtend,
factorDiag, factorGap,
localAlignment, initPenalty)
## Hm, silently add the raw retention times if we cut the retention time
## vector above - would merit at least a warning I believe.
if(length(obj2@scantime) > valscantime2) {
object@rt$corrected[[s]] <- c(rtimecor[[s]],
obj2@scantime[(max(rtup2)+1):length(obj2@scantime)])
} else {
object@rt$corrected[[s]] <- rtimecor[[s]]
}
## Why are we rounding here, but NOT in the retcor.peakgroups?
## -> issue #122
## The point is we're using the un-rounded adjusted rt for the rt, BUT
## use the rounded values for the adjustment of the peak rts.
rtdevsmo[[s]] <- round(rtcor[[s]]-object@rt$corrected[[s]],2)
rm(obj2)
gc()
## updateProgressInfo
object@progressInfo$retcor.obiwarp <- si / length(idx)
xcms:::progressInfoUpdate(object)
}
cat("\n")
## Why are we rounding here, but NOT in the retcor.peakgroups?
## -> issue #122
rtdevsmo[[center]] <- round(rtcor[[center]] - object@rt$corrected[[center]], 2)
if (plottype == "deviation") {
## define the colors and line types and returns a list of
## mypal, col and ty. Uses the original code if no colors are
## submitted. Supports manually selected colors (e.g. in hex)
vals <- defineColAndTy(col, ty, classlabel)
col <- vals$col
mypal <- vals$mypal
ty <- vals$ty
rtrange <- range(do.call("c", rtcor))
devrange <- range(do.call("c", rtdevsmo))
layout(matrix(c(1, 2),ncol=2, byrow=F),widths=c(1,0.3))
par(mar=c(4,4,2,0))
plot(0, 0, type="n", xlim = rtrange, ylim = devrange,
main = "Retention Time Deviation vs. Retention Time",
xlab = "Retention Time", ylab = "Retention Time Deviation")
for (i in 1:N) {
points(rtcor[[i]], rtdevsmo[[i]], type="l", col = mypal[col[i]], lty = ty[i])
}
plot.new() ; par(mar= c(2, 0, 2, 0))
plot.window(c(0,1), c(0,1))
legend(0,1.04, basename(samples), col = mypal[col], lty = ty)
}
for (i in 1:N) {
cfun <- stepfun(rtcor[[i]][-1] - diff(rtcor[[i]])/2, rtcor[[i]] - rtdevsmo[[i]])
rtcor[[i]] <- rtcor[[i]] - rtdevsmo[[i]]
sidx <- which(corpeaks[,"sample"] == i)
corpeaks[sidx, c("rt", "rtmin", "rtmax")] <- cfun(corpeaks[sidx, c("rt", "rtmin", "rtmax")])
}
peaks(object) <- corpeaks
groups(object) <- matrix(nrow = 0, ncol = 0)
groupidx(object) <- list()
invisible(object)
})
############################################################
## plotrt
setMethod("plotrt", "xcmsSet", function(object, col = NULL, ty = NULL, leg = TRUE, densplit = FALSE) {
samples <- sampnames(object)
classlabel <- as.vector(unclass(sampclass(object)))
n <- length(samples)
rtuncor <- object@rt$raw
rtcor <- object@rt$corrected
## define the colors and line types and returns a list of
## mypal, col and ty. Uses the original code if no colors are
## submitted. Supports manually selected colors (e.g. in hex)
vals <- defineColAndTy(col, ty, classlabel)
col <- vals$col
mypal <- vals$mypal
ty <- vals$ty
rtdevsmo <- vector("list", n)
for (i in 1:n)
rtdevsmo[[i]] <- rtuncor[[i]] - rtcor[[i]]
rtrange <- range(do.call(c, rtuncor))
devrange <- range(do.call(c, rtdevsmo))
if (densplit) {
split.screen(matrix(c(0, 1, .3, 1, 0, 1, 0, .3), ncol = 4, byrow = TRUE))
screen(1)
par(mar = c(0, 4.1, 4.1, 2), xaxt = "n")
}
plot(0, 0, type="n", xlim = rtrange, ylim = devrange, main = "Retention Time Deviation vs. Retention Time", xlab = "Retention Time", ylab = "Retention Time Deviation")
if (leg)
legend(rtrange[2], devrange[2], samples, col = mypal[col], lty = ty, pch = ceiling(1:n/length(mypal)), xjust = 1)
for (i in 1:n)
points(rtuncor[[i]], rtdevsmo[[i]], type="l", col = mypal[col[i]], lty = ty[i])
if (densplit) {
screen(2)
par(mar = c(5.1, 4.1, 0, 2), yaxt = "n")
allden <- density(object@peaks[,"rt"], bw = diff(rtrange)/200,
from = rtrange[1], to = rtrange[2])[c("x","y")]
plot(allden, xlim = rtrange, type = "l", main = "", xlab = "Retention Time", ylab = "Peak Density")
abline(h = 0, col = "grey")
close.screen(all.screens = TRUE)
}
})
############################################################
## fillPeaks.chrom
## New version using BiocParallel instead of nSlaves and manual setup.
setMethod("fillPeaks.chrom", "xcmsSet", function(object, nSlaves = 0,
expand.mz = 1,expand.rt = 1,
BPPARAM = bpparam()) {
## development mockup:
if (FALSE) {
library(xcms)
library(faahKO)
object <- group(faahko)
gf <- fillPeaks(object)
pkgEnv = getNamespace("xcms")
attach(pkgEnv)
}
if (!is.null(nSlaves)) {
if (nSlaves > 0) {
message("Use of argument 'nSlaves' is deprecated,",
" please use 'BPPARAM' instead.")
options(mc.cores = nSlaves)
}
}
peakmat <- peaks(object)
groupmat <- groups(object)
if (length(groupmat) == 0)
stop("No group information found")
files <- filepaths(object)
samp <- sampnames(object)
classlabel <- as.vector(unclass(sampclass(object)))
prof <- profinfo(object)
rtcor <- object@rt$corrected
## Remove groups that overlap with more "well-behaved" groups
sampclasscols <- seq(match("npeaks", colnames(groupmat))+1,
length.out=length(levels(sampclass(object))))
numsamp <- rowSums(groupmat[ , sampclasscols, drop=FALSE])
uorder <- order(-numsamp, groupmat[,"npeaks"])
uindex <- rectUnique(groupmat[,c("mzmin","mzmax","rtmin","rtmax"),drop=FALSE],
uorder)
groupmat <- groupmat[uindex,]
groupindex <- groupidx(object)[uindex]
gvals <- groupval(object)[uindex,]
peakrange <- matrix(nrow = nrow(gvals), ncol = 4)
colnames(peakrange) <- c("mzmin","mzmax","rtmin","rtmax")
mzmin <- peakmat[gvals,"mzmin"]
dim(mzmin) <- c(nrow(gvals), ncol(gvals))
peakrange[,"mzmin"] <- apply(mzmin, 1, median, na.rm = TRUE)
mzmax <- peakmat[gvals,"mzmax"]
dim(mzmax) <- c(nrow(gvals), ncol(gvals))
peakrange[,"mzmax"] <- apply(mzmax, 1, median, na.rm = TRUE)
retmin <- peakmat[gvals,"rtmin"]
dim(retmin) <- c(nrow(gvals), ncol(gvals))
peakrange[,"rtmin"] <- apply(retmin, 1, median, na.rm = TRUE)
retmax <- peakmat[gvals,"rtmax"]
dim(retmax) <- c(nrow(gvals), ncol(gvals))
peakrange[,"rtmax"] <- apply(retmax, 1, median, na.rm = TRUE)
lastpeak <- nrow(peakmat)
lastpeakOrig <- lastpeak
## peakmat <- rbind(peakmat, matrix(nrow = sum(is.na(gvals)), ncol = ncol(peakmat)))
cnames <- colnames(object@peaks)
## Making gvals environment so that when it is repeated for each file it only uses the memory one time
gvals_env <- new.env(parent=baseenv())
assign("gvals", gvals, envir = gvals_env)
ft <- cbind(file=files,id=1:length(files))
argList <- apply(ft,1,function(x) {
## Add only those samples which actually have NA in them
if (!any(is.na(gvals[,as.numeric(x["id"])]))) {
## nothing to do.
list()
} else {
list(file=x["file"],id=as.numeric(x["id"]),
params=list(method="chrom",
gvals=gvals_env,
prof=prof,
dataCorrection=object@dataCorrection,
polarity=object@polarity,
rtcor=object@rt$corrected[[as.numeric(x["id"])]],
peakrange=peakrange,
expand.mz=expand.mz,
expand.rt=expand.rt))
}
})
nonemptyIdx <- (sapply(argList, length) > 0)
if (!any(nonemptyIdx)) {
## Nothing to do
return(invisible(object))
}
argList <- argList[nonemptyIdx]
## Use BiocParallel for parallel computing.
newpeakslist <- bplapply(argList, fillPeaksChromPar, BPPARAM = BPPARAM)
o <- order(sapply(newpeakslist, function(x) x$myID))
newpeaks <- do.call(rbind, lapply(newpeakslist[o], function(x) x$newpeaks))
## Make sure colnames are compatible
newpeaks <- newpeaks[, match(cnames, colnames(newpeaks)), drop = FALSE]
colnames(newpeaks) <- cnames
peakmat <- rbind(peakmat, newpeaks)
for (i in seq(along = files)) {
naidx <- which(is.na(gvals[,i]))
for (j in seq(along = naidx))
groupindex[[naidx[j]]] <- c(groupindex[[naidx[j]]], lastpeak+j)
lastpeak <- lastpeak + length(naidx)
}
peaks(object) <- peakmat
object@filled <- seq((lastpeakOrig+1),nrow(peakmat))
groups(object) <- groupmat
groupidx(object) <- groupindex
invisible(object)
})
############################################################
## fillPeaks.MSW
setMethod("fillPeaks.MSW", "xcmsSet", function(object, mrange=c(0,0), sample=NULL) {
peakmat <- peaks(object)
groupmat <- groups(object)
if (length(groupmat) == 0)
stop("No group information found")
files <- filepaths(object)
samp <- sampnames(object)
classlabel <- as.vector(unclass(sampclass(object)))
prof <- profinfo(object)
rtcor <- object@rt$corrected
## Remove groups that overlap with more "well-behaved" groups
numsamp <- rowSums(groupmat[,(match("npeaks", colnames(groupmat))+1):ncol(groupmat),drop=FALSE])
uorder <- order(-numsamp, groupmat[,"npeaks"])
uindex <- rectUnique(groupmat[,c("mzmin","mzmax","rtmin","rtmax"),drop=FALSE],
uorder)
groupmat <- groupmat[uindex,]
groupindex <- groupidx(object)[uindex]
gvals <- groupval(object)[uindex,]
peakrange <- matrix(nrow = nrow(gvals), ncol = 4)
colnames(peakrange) <- c("mzmin","mzmax","rtmin","rtmax")
mzmin <- peakmat[gvals,"mzmin"]
dim(mzmin) <- c(nrow(gvals), ncol(gvals))
peakrange[,"mzmin"] <- apply(mzmin, 1, min, na.rm = TRUE)
mzmax <- peakmat[gvals,"mzmax"]
dim(mzmax) <- c(nrow(gvals), ncol(gvals))
peakrange[,"mzmax"] <- apply(mzmax, 1, min, na.rm = TRUE)
retmin <- peakmat[gvals,"rtmin"]
dim(retmin) <- c(nrow(gvals), ncol(gvals))
peakrange[,"rtmin"] <- apply(retmin, 1, median, na.rm = TRUE)
retmax <- peakmat[gvals,"rtmax"]
dim(retmax) <- c(nrow(gvals), ncol(gvals))
peakrange[,"rtmax"] <- apply(retmax, 1, median, na.rm = TRUE)
lastpeak <- nrow(peakmat)
peakmat <- rbind(peakmat, matrix(nrow = sum(is.na(gvals)), ncol = ncol(peakmat)))
cnames <- colnames(object@peaks)
for (i in seq(along = files)) {
cat(samp[i], "")
flush.console()
naidx <- which(is.na(gvals[,i]))
newpeaks <- matrix(nrow=length(naidx), ncol=9)
nppos<-0 ## line position in the newpeaks matrix
if (length(naidx)) {
lcraw <- xcmsRaw(files[i], profmethod = prof$method, profstep = 0)
ngs <- as.vector(naidx)
for (g in ngs)
{
nppos <- nppos+1
## Get the index of the mz value(s) closest to the mzmed of the
## group; could eventually be more than one.
mzpos <- which(abs(lcraw@env$mz - groupmat[g,"mzmed"]) ==
min(abs(lcraw@env$mz - groupmat[g,"mzmed"])))
## Get the index of the maximum intensity for the above mz index.
mmzpos <- mzpos[which(lcraw@env$intensity[mzpos] ==
max(lcraw@env$intensity[mzpos]))]
## Eventually increase the range around the mzmed.
mmzr <- seq((mmzpos-mrange[1]),(mmzpos+mrange[2]))
## maxo is the maximum signal for mz values that are closest to
## the mzmed of the feature.
maxo <- max(lcraw@env$intensity[mmzr])
## this is the new one, summing the scale-range
## calculating scale, adding intensities in this scale
medMZmin <- median(peakmat[groupindex[[g]],"mzmin"])
medMZmax <- median(peakmat[groupindex[[g]],"mzmax"])
## mz values to be considered: from median mzmin of all peaks in
## the current peak group/feature to median mzmax.
## Might eventually be easier to just check for $mz >= medMZmin
minMzpos <- min(which(abs(lcraw@env$mz - medMZmin) ==
min(abs(lcraw@env$mz - medMZmin))))
maxMzpos <- max(which(abs(lcraw@env$mz - medMZmax) ==
min(abs(lcraw@env$mz - medMZmax))))
## into: the sum of intensities in this range.
into = sum(lcraw@env$intensity[minMzpos:maxMzpos])
newpeaks[nppos,] <- c(groupmat[g,"mzmed"],medMZmin,medMZmax,-1,-1,-1,into,maxo,i)
}
colnames(newpeaks) <- c("mz","mzmin","mzmax","rt","rtmin","rtmax","into","maxo","sample")
rm(lcraw)
gc()
newcols <-colnames(newpeaks)[colnames(newpeaks) %in% cnames]
peakmat[lastpeak+seq(along = naidx),newcols] <- newpeaks[,newcols]
for (i in seq(along = naidx))
groupindex[[naidx[i]]] <- c(groupindex[[naidx[i]]], lastpeak+i)
lastpeak <- lastpeak + length(naidx)
}
}
cat("\n")
peaks(object) <- peakmat
object@filled <- seq((lastpeak+1),nrow(peakmat))
groups(object) <- groupmat
groupidx(object) <- groupindex
invisible(object)
})
############################################################
## fillPeaks
setMethod("fillPeaks", "xcmsSet", function(object, method=getOption("BioC")$xcms$fillPeaks.method,...) {
method <- match.arg(method, getOption("BioC")$xcms$fillPeaks.methods)
if (is.na(method))
stop("unknown method : ", method)
method <- paste("fillPeaks", method, sep=".")
invisible(do.call(method, alist(object, ...)))
})
############################################################
## getEIC
setMethod("getEIC", "xcmsSet", function(object, mzrange, rtrange = 200,
groupidx, sampleidx = sampnames(object),
rt = c("corrected", "raw")) {
files <- filepaths(object)
grp <- groups(object)
samp <- sampnames(object)
prof <- profinfo(object)
rt <- match.arg(rt)
if (is.numeric(sampleidx))
sampleidx <- sampnames(object)[sampleidx]
sampidx <- match(sampleidx, sampnames(object))
if (!missing(groupidx)) {
if (is.numeric(groupidx))
groupidx <- groupnames(object)[unique(as.integer(groupidx))]
grpidx <- match(groupidx, groupnames(object, template = groupidx))
}
if (missing(mzrange)) {
if (missing(groupidx))
stop("No m/z range or groups specified")
if (any(is.na(groupval(object, value = "mz"))))
warning(
"`NA` values in xcmsSet. Use fillPeaks() on the object to fill",
"-in missing peak values. Note however that this will also ",
"insert intensities of 0 for peaks that can not be filled in.")
mzmin <- apply(groupval(object, value = "mzmin"), 1, min, na.rm = TRUE)
mzmax <- apply(groupval(object, value = "mzmax"), 1, max, na.rm = TRUE)
mzrange <- matrix(c(mzmin[grpidx], mzmax[grpidx]), ncol = 2)
## if (any(is.na(groupval(object, value = "mz"))))
## stop('Please use fillPeaks() to fill up NA values !')
## mzmin <- -rowMax(-groupval(object, value = "mzmin"))
## mzmax <- rowMax(groupval(object, value = "mzmax"))
## mzrange <- matrix(c(mzmin[grpidx], mzmax[grpidx]), ncol = 2)
} else if (all(c("mzmin","mzmax") %in% colnames(mzrange)))
mzrange <- mzrange[,c("mzmin", "mzmax"),drop=FALSE]
else if (is.null(dim(mzrange)))
stop("mzrange must be a matrix")
colnames(mzrange) <- c("mzmin", "mzmax")
if (length(rtrange) == 1) {
if (missing(groupidx))
rtrange <- matrix(rep(range(object@rt[[rt]][sampidx]), nrow(mzrange)),
ncol = 2, byrow = TRUE)
else {
rtrange <- retexp(grp[grpidx,c("rtmin","rtmax"),drop=FALSE], rtrange)
}
} else if (is.null(dim(rtrange)))
stop("rtrange must be a matrix or single number")
colnames(rtrange) <- c("rtmin", "rtmax")
## Ensure that we've got corrected retention time if requested.
if (is.null(object@rt[[rt]]))
stop(rt, " retention times not present in 'object'!")
## Ensure that the defined retention time range is within the rtrange of the
## object: we're using the max minimal rt of all files and the min maximal rt
rtrs <- lapply(object@rt[[rt]], range)
rtr <- c(max(unlist(lapply(rtrs, "[", 1))),
min(unlist(lapply(rtrs, "[", 2))))
## Check if we've got a range which is completely off:
if (any(rtrange[, "rtmin"] >= rtr[2] | rtrange[, "rtmax"] <= rtr[1])) {
outs <- which(rtrange[, "rtmin"] >= rtr[2] |
rtrange[, "rtmax"] <= rtr[1])
stop(length(outs), " of the specified 'rtrange' are completely outside ",
"of the retention time range of 'object' which is (", rtr[1], ", ",
rtr[2], "). The first was: (", rtrange[outs[1], "rtmin"], ", ",
rtrange[outs[1], "rtmax"], "!")
}
lower_rt_outside <- rtrange[, "rtmin"] < rtr[1]
upper_rt_outside <- rtrange[, "rtmax"] > rtr[2]
if (any(lower_rt_outside) | any(upper_rt_outside)) {
## Silently fix these ranges.
rtrange[lower_rt_outside, "rtmin"] <- rtr[1]
rtrange[upper_rt_outside, "rtmax"] <- rtr[2]
}
if (missing(groupidx))
gnames <- character(0)
else
gnames <- groupidx
eic <- vector("list", length(sampleidx))
names(eic) <- sampleidx
for (i in seq(along = sampidx)) {
## cat(sampleidx[i], "")
flush.console()
## getXcmsRaw takes care of rt correction, susetting to scanrage and other
## stuff.
lcraw <- getXcmsRaw(object, sampleidx = sampidx[i], rt=rt)
currenteic <- xcms::getEIC(lcraw, mzrange, rtrange, step = prof$step)
eic[[i]] <- currenteic@eic[[1]]
rm(lcraw)
gc()
}
## cat("\n")
invisible(new("xcmsEIC", eic = eic, mzrange = mzrange, rtrange = rtrange,
rt = rt, groupnames = gnames))
})
############################################################
## peakTable
setMethod("peakTable", "xcmsSet", function(object, filebase = character(), ...) {
if (length(sampnames(object)) == 1) {
return(object@peaks)
}
if (nrow(object@groups) < 1) {
stop ('First argument must be an xcmsSet with group information or contain only one sample.')
}
groupmat <- groups(object)
if (! "value" %in% names(list(...))) {
ts <- data.frame(cbind(groupmat,groupval(object, value="into", ...)), row.names = NULL)
} else {
ts <- data.frame(cbind(groupmat,groupval(object, ...)), row.names = NULL)
}
cnames <- colnames(ts)
if (cnames[1] == 'mzmed') {
cnames[1] <- 'mz'
} else {
stop ('mzmed column missing')
}
if (cnames[4] == 'rtmed') {
cnames[4] <- 'rt'
} else {
stop ('mzmed column missing')
}
colnames(ts) <- cnames
if (length(filebase))
write.table(ts, paste(filebase, ".tsv", sep = ""), quote = FALSE, sep = "\t", col.names = NA)
ts
})
############################################################
## diffreport
setMethod("diffreport", "xcmsSet", function(object,
class1 = levels(sampclass(object))[1],
class2 = levels(sampclass(object))[2],
filebase = character(),
eicmax = 0, eicwidth = 200,
sortpval = TRUE,
classeic = c(class1,class2),
value = c("into","maxo","intb"),
metlin = FALSE,
h = 480, w = 640, mzdec=2,
missing = numeric(), ...) {
if ( nrow(object@groups)<1 || length(object@groupidx) <1) {
stop("No group information. Use group().")
}
if (!is.numeric(w) || !is.numeric(h))
stop("'h' and 'w' have to be numeric")
if (!requireNamespace("multtest", quietly = TRUE))
stop("The use of 'diffreport' requires package 'multtest'. Please ",
"install with 'Biobase::install(\"multtest\")'")
value <- match.arg(value)
groupmat <- groups(object)
if (length(groupmat) == 0)
stop("No group information found")
samples <- sampnames(object)
n <- length(samples)
classlabel <- sampclass(object)
classlabel <- levels(classlabel)[as.vector(unclass(classlabel))]
values <- groupval(object, "medret", value=value)
indecies <- groupval(object, "medret", value = "index")
if (!all(c(class1,class2) %in% classlabel))
stop("Incorrect Class Labels")
## c1 and c2 are column indices of class1 and class2 resp.
c1 <- which(classlabel %in% class1)
c2 <- which(classlabel %in% class2)
ceic <- which(classlabel %in% classeic)
if (length(intersect(c1, c2)) > 0)
stop("Intersecting Classes")
## Optionally replace NA values with the value provided with missing
if (length(missing)) {
if (is.numeric(missing)) {
## handles NA, Inf and -Inf
values[, c(c1, c2)][!is.finite(values[, c(c1, c2)])] <- missing[1]
} else
stop("'missing' should be numeric")
}
## Check against missing Values
if (any(is.na(values[, c(c1, c2)])))
warning("`NA` values in xcmsSet. Use fillPeaks() on the object to fill",
"-in missing peak values. Note however that this will also ",
"insert intensities of 0 for peaks that can not be filled in.")
mean1 <- rowMeans(values[,c1,drop=FALSE], na.rm = TRUE)
mean2 <- rowMeans(values[,c2,drop=FALSE], na.rm = TRUE)
## Calculate fold change.
## For foldchange <1 set fold to 1/fold
## See tstat to check which was higher
fold <- mean2 / mean1
fold[!is.na(fold) & fold < 1] <- 1/fold[!is.na(fold) & fold < 1]
testval <- values[,c(c1,c2)]
## Replace eventual infinite values with NA (CAMERA issue #33)
testval[is.infinite(testval)] <- NA
testclab <- c(rep(0,length(c1)),rep(1,length(c2)))
if (min(length(c1), length(c2)) >= 2) {
tstat <- multtest::mt.teststat(testval, testclab, ...)
pvalue <- pval(testval, testclab, tstat)
} else {
message("Too few samples per class, skipping t-test.")
tstat <- pvalue <- rep(NA,nrow(testval))
}
stat <- data.frame(fold = fold, tstat = tstat, pvalue = pvalue)
if (length(levels(sampclass(object))) >2) {
pvalAnova<-c()
for(i in 1:nrow(values)){
var<-as.numeric(values[i,])
ano<-summary(aov(var ~ sampclass(object)) )
pvalAnova<-append(pvalAnova, unlist(ano)["Pr(>F)1"])
}
stat<-cbind(stat, anova= pvalAnova)
}
if (metlin) {
neutralmass <- groupmat[,"mzmed"] + ifelse(metlin < 0, 1, -1)
metlin <- abs(metlin)
digits <- ceiling(-log10(metlin))+1
metlinurl <-
paste("http://metlin.scripps.edu/simple_search_result.php?mass_min=",
round(neutralmass - metlin, digits), "&mass_max=",
round(neutralmass + metlin, digits), sep="")
values <- cbind(metlin = metlinurl, values)
}
twosamp <- cbind(name = groupnames(object), stat, groupmat, values)
if (sortpval) {
tsidx <- order(twosamp[,"pvalue"])
twosamp <- twosamp[tsidx,]
rownames(twosamp) <- 1:nrow(twosamp)
values<-values[tsidx,]
} else
tsidx <- 1:nrow(values)
if (length(filebase))
write.table(twosamp, paste(filebase, ".tsv", sep = ""), quote = FALSE, sep = "\t", col.names = NA)
if (eicmax > 0) {
if (length(unique(peaks(object)[,"rt"])) > 1) {
## This looks like "normal" LC data
eicmax <- min(eicmax, length(tsidx))
eics <- getEIC(object, rtrange = eicwidth*1.1, sampleidx = ceic,
groupidx = tsidx[seq(length = eicmax)])
if (length(filebase)) {
eicdir <- paste(filebase, "_eic", sep="")
boxdir <- paste(filebase, "_box", sep="")
dir.create(eicdir)
dir.create(boxdir)
if (capabilities("png")){
xcmsBoxPlot(values[seq(length = eicmax),],
sampclass(object), dirpath=boxdir, pic="png",
width=w, height=h)
png(file.path(eicdir, "%003d.png"), width = w, height = h,
units = "px")
} else {
xcmsBoxPlot(values[seq(length = eicmax),],
sampclass(object), dirpath=boxdir, pic="pdf",
width=w, height=h)
pdf(file.path(eicdir, "%003d.pdf"), width = w/72,
height = h/72, onefile = FALSE)
}
}
plot(eics, object, rtrange = eicwidth, mzdec=mzdec)
if (length(filebase))
dev.off()
} else {
## This looks like a direct-infusion single spectrum
if (length(filebase)) {
specdir <- paste(filebase, "_spec", sep="")
dir.create(specdir)
if (capabilities("png")){
png(file.path(specdir, "%003d.png"), width = w, height = h)
}else{
pdf(file.path(specdir, "%003d.pdf"), width = w/72,
height = h/72, onefile = FALSE)
}
}
plotSpecWindow(object, gidxs = tsidx[seq(length = eicmax)], borderwidth=1)
if (length(filebase))
dev.off()
}
}
invisible(twosamp)
})
############################################################
## progressCallback
setMethod("progressCallback", "xcmsSet", function(object) object@progressCallback)
setReplaceMethod("progressCallback", "xcmsSet", function(object, value) {
object@progressCallback <- value
object
})
############################################################
## progressInfoUpdate
setMethod("progressInfoUpdate", "xcmsSet", function(object)
do.call(object@progressCallback, list(progress=object@progressInfo))
)
############################################################
## $
## eSet/ExpressionSet like methods to access columns in @phenoData
setMethod("$", "xcmsSet", function(x, name) {
eval(substitute(phenoData(x)$NAME_ARG, list(NAME_ARG=name)))
})
setReplaceMethod("$", "xcmsSet", function(x, name, value) {
phenoData(x)[[name]] = value
x
})
############################################################
## getXcmsRaw
## read the raw data for a xset.
## sampleidx: argument which allows to specify which file from the xcmsSet
## should be read, if length > 1 a list of xcmsRaw is returned
## Note: if scanrange is submitted here I have to subset the xcmsRaw objects
## AFTER having filled in adjusted retention times etc.
setMethod("getXcmsRaw", "xcmsSet", function(object, sampleidx = 1,
profmethod = profMethod(object),
profstep = profStep(object),
profparam = profinfo(object),
mslevel = NULL,
scanrange = NULL,
rt = c("corrected", "raw"),
BPPARAM = bpparam()) {
if (is.numeric(sampleidx))
sampleidx <- sampnames(object)[sampleidx]
sampidx <- match(sampleidx, sampnames(object)) ## numeric
if (length(sampidx) == 0)
stop("submitted value for sampleidx outside of the",
" available files!")
fn <- filepaths(object)[sampidx]
rt <- match.arg(rt)
if (rt == "corrected" & !any(names(object@rt) == "corrected")) {
message("No RT correction has been performed, thus returning",
" raw retention times.")
rt <- "raw"
}
if (missing(mslevel)) {
msl <- mslevel(object)
} else {
msl <- mslevel
}
## if (missing(scanrange)) {
## srange <- scanrange(object)
## } else {
## srange <- scanrange
## }
## If scanrange is NULL we don't have to do subset and can skip some stuff.
## If scanrange is provided we don't want to subset in xcmsRaw but subset
## at the very end using []
srange <- NULL ## ensures we're reading all data.
if (is.null(scanrange) | length(scanrange) < 2) {
srange <- scanrange(object)
} else {
scanrange <- range(scanrange)
}
## include MSn?
includeMsn <- FALSE
if (!is.null(msl)) {
if(msl > 1)
includeMsn <- TRUE
}
ret <- bplapply(as.list(fn), FUN = function(z) {
raw <- xcmsRaw(z, profmethod = profmethod,
profstep = profstep, mslevel = msl,
scanrange = srange,
includeMSn = includeMsn)
return(raw)
}, BPPARAM=BPPARAM)
## do corrections etc.
for(i in 1:length(ret)){
if(length(object@dataCorrection) > 1){
if(object@dataCorrection[[sampidx[i]]] == 1){
ret[[i]] <- stitch(ret[[i]], AutoLockMass(ret[[i]]))
message(paste0("Applying lock Waters mass correction",
" to ", basename(fn[i])))
}
}
if(rt == "corrected"){
## check if there is any need to apply correction...
## This includes fix for the bug reported by Aleksandr (issue 44)
if(all(object@rt$corrected[[sampidx[i]]] ==
object@rt$raw[[sampidx[i]]])){
message("No need to perform retention time correction,",
" raw and corrected rt are identical for ",
basename(fn[i]), ".")
ret[[i]]@scantime <- object@rt$raw[[sampidx[i]]]
}else{
message(paste0("Applying retention time correction",
" to ", basename(fn[i]), "."))
ret[[i]]@scantime <- object@rt$corrected[[sampidx[i]]]
}
}
## Finally doing the sub-setting...
if (length(scanrange) > 1) {
## Doing the sub-setting here ensures that the scantime, profile
## matrix and everything matches the scanrange.
ret[[i]] <- ret[[i]][scanrange[1]:scanrange[2]]
}
}
## what's missing?
## + consider calibration(s)?
## + what with polarity?
if(length(ret)==1)
return(ret[[1]])
return(ret)
})
############################################################
## levelplot
setMethod("levelplot", "xcmsSet",
function(x, log=TRUE, sampleidx=1,
col.regions=colorRampPalette(brewer.pal(9, "YlOrRd"))(256),
highlight.peaks=FALSE, highlight.col="#377EB880",
rt="raw", ...){
if (is.numeric(sampleidx))
sampleidx <- sampnames(x)[sampleidx]
sampidx <- match(sampleidx, sampnames(x))
if(length(sampidx) > 1)
stop("A levelplot can only be created for one file at a time!")
xraw <- getXcmsRaw(x, sampleidx=sampidx, rt=rt)
if(highlight.peaks){
pks <- peaks(x)
pks <- pks[pks[, "sample"]==sampidx, ]
## call the levelplot AND specify the panel...
## some code taken from plotSurf...
sel <- profRange(xraw, ...)
zvals <- xraw@env$profile[sel$massidx, sel$scanidx]
if(log){
zvals <- log(zvals+max(c(-min(zvals), 1)))
}
## y axis is time
yvals <- xraw@scantime[sel$scanidx]
yrange <- range(yvals)
## x is m/z
xvals <- profMz(xraw)[sel$massidx]
## 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
fileName <- sampnames(x)[sampidx]
plt <- levelplot(zvals~xvals*yvals,
panel=function(...){
panel.levelplot(...)
panel.rect(xleft=pks[, "mzmin"], xright=pks[, "mzmax"],
ybottom=pks[, "rtmin"], ytop=pks[, "rtmax"],
border=highlight.col)
},
xlab="m/z", ylab="Time",
colorkey=list(height=1, width=0.7),
main=list(fileName, side=1, line=0.5), col.regions=col.regions)
## plt <- plt + layer(panel.rect(xleft=pks[, "mzmin"], xright=pks[, "mzmax"],
## ybottom=pks[, "rtmin"], ytop=pks[, "rtmax"],
## border="#377EB820")
## )
}else{
plt <- levelplot(xraw, col.regions = col.regions, log=log)
}
plt
})
############################################################
## mslevel
## getter methods for the slots mslevel and scanrange of the xcmsSet object.
setMethod("mslevel", "xcmsSet", function(object){
## for xcmsSet objects that don't have (yet) the slot...
if(!.hasSlot(object, "mslevel")){
message("No slot mslevel available, consider updating the",
" object with the 'updateObject' method.")
return(NULL)
}else{
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(mlevel)
}
})
setReplaceMethod("mslevel", "xcmsSet", function(object, value){
if(.hasSlot(object, "mslevel")){
object@mslevel <- value
}else{
message("Object has no slot mslevel, consider updating",
" the object using 'updateObject'.")
}
object
})
############################################################
## scanrange
setMethod("scanrange", "xcmsSet", function(object){
if(!.hasSlot(object, "scanrange")){
message("No slot scanrange available, consider updating the",
" object with the 'updateObject' method.")
return(NULL)
}else{
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)
}
})
setReplaceMethod("scanrange", "xcmsSet", function(object, value){
if(.hasSlot(object, "scanrange")){
object@scanrange <- value
}else{
message("Object has no slot scanrange, consider updating",
" the object with the 'updateObject' method.")
}
object
})
############################################################
## profMethod
## getter methods for the prof method and step.
setMethod("profMethod", "xcmsSet", function(object) {
return(profinfo(object)$method)
})
setMethod("profStep", "xcmsSet", function(object) {
return(profinfo(object)$step)
})
## sub setting an xcmsSet object...
setMethod("[", "xcmsSet", function(x, i, j, ..., drop = FALSE) {
if (missing(drop))
drop <- FALSE
if (missing(i) && missing(j)) {
if (length(list(...))!=0)
stop("specify samples to subset; use '",
substitute(x), "$", names(list(...))[[1]],
"' to access phenoData variables")
return(x)
}
## Update the xcmsSet
x <- updateObject(x)
## don't allow i, but allow j to be: numeric or logical. If
## it's a character vector <- has to fit to sampnames(x)
if (!missing(i))
stop("Subsetting to rows is not supported!")
if (missing(j)) {
j <- 1:length(sampnames(x))
} else {
if (is.character(j)) {
## check if these match to the sampnames.
matches <- match(j, sampnames(x))
if(length(matches)!=length(j))
stop("All provided sample names have to match the",
" sample names in the xcmsSet!")
j <- matches
}
if (is.logical(j)) {
if (length(j) != length(sampnames(x)))
stop("If j is a logical its length has to match",
" the number of samples in the xcmsSet!")
j <- which(j)
}
if (!length(j)) {
tmp <- new("xcmsSet")
cn <- c("rtmin", "rtmax", "rt", "mzmin", "mzmax", "mz",
"into", "maxo", "sample")
pks <- matrix(ncol = length(cn), nrow = 0, dimnames = list(c(), cn))
tmp@peaks <- pks
return(tmp)
}
}
if (class(j) == "numeric")
j <- as.integer(j)
if(!is.integer(j))
stop("j has to be an integer vector specifying the",
" index of the samples for which the data has to be extracted")
## check if j is within the range of 1:length(sampnames)
if(any(!j %in% (1:length(sampnames(x)))))
stop("j has to be a numeric with values between 1 and ",
length(sampnames(x)), "!")
## OK, j is now an integer vector...
## "copy" the xcmsSet, that way we keep parameters mslevel, scanrange,
## profinfo, polarity.
xsub <- x
## first of all, subset the phenoData
phenoData(xsub) <- droplevels(phenoData(xsub)[j,, drop=FALSE])
## then the file paths
filepaths(xsub) <- filepaths(x)[j]
## now starting to subset data:
## 1) @rt$raw, @rt$corrected
xsub@rt$raw <- x@rt$raw[j]
xsub@rt$corrected <- x@rt$corrected[j]
## 2) @peaks
keep.peaks <- x@peaks
rownames(keep.peaks) <- as.character(1:nrow(keep.peaks))
## subsetting the peaks. Since we want to also allow reverse ordering we have to
## do it a little more complicated...
keep.peaks <- split(data.frame(keep.peaks), f=keep.peaks[, "sample"])
keep.peaks <- keep.peaks[as.character(j)]
names(keep.peaks) <- NULL
keep.peaks <- as.matrix(do.call(rbind, keep.peaks))
##keep.peaks <- keep.peaks[as.character(keep.peaks[, "sample"]) %in% as.character(j), ]
## have to replace the sample index.
newsample <- numeric(nrow(keep.peaks))
for(idx in 1:length(j)){
newsample[keep.peaks[, "sample"]==j[idx]] <- idx
}
keep.peaks[, "sample"] <- newsample
xsub@peaks <- keep.peaks
rownames(xsub@peaks) <- NULL
## 3) groupidx if present. subset this to indices of peaks which we will keep.
## will use the rownames of the keep.peaks matrix for that (represents the
## original index).
if(length(x@groupidx) > 0){
keep.groupidx <- lapply(x@groupidx, function(z){
newidx <- match(as.character(z), rownames(keep.peaks))
return(newidx[!is.na(newidx)])
## that way I just return the indices as they are!
##return(z[as.character(z) %in% rownames(keep.peaks)])
})
xsub@groupidx <- keep.groupidx
## 4) groups have to be re-calculated.
keep.groups <- x@groups
keep.groups <- keep.groups[, -(which(colnames(keep.groups)=="npeaks"):ncol(keep.groups))]
sampclasses <- sampclass(xsub)
peakCounts <- matrix(ncol=length(levels(sampclasses)), nrow=nrow(keep.groups), 0)
colnames(peakCounts) <- levels(sampclasses)
## loop throught the peakcounts
for(idx in 1:nrow(keep.groups)){
groupidx <- keep.groupidx[[idx]]
if(length(groupidx) == 0)
next
tab <- keep.peaks[groupidx, "sample"]
tab <- table(sampclasses[tab])
peakCounts[idx, names(tab)] <- as.numeric(tab)
}
keep.groups <- cbind(keep.groups, npeaks=rowSums(peakCounts), peakCounts)
xsub@groups <- keep.groups
}
## 5) filled
if(length(x@filled) > 0){
xsub@filled <- (1:nrow(keep.peaks))[rownames(keep.peaks) %in%
as.character(x@filled)]
}
## 6) dataCorrection
if(length(x@dataCorrection)>0)
xsub@dataCorrection <- x@dataCorrection[j]
## 7) processHistory: we want to make sure that the ordering of the objects stays
procHist <- .getProcessHistory(x, fileIndex = j)
procHist <- lapply(procHist, updateFileIndex, old = j, new = 1:length(j))
xsub@.processHistory <- procHist
OK <- .validProcessHistory(xsub)
if (!is.logical(OK))
stop(OK)
return(xsub)
})
############################################################
## present
setMethod("present", "xcmsSet", function(object, class, minfrac) {
if ( nrow(object@groups)<1 || length(object@groupidx) <1) {
stop("No group information. Use group().")
}
classlabel <- sampclass(object)
classlabel <- levels(classlabel)[as.vector(unclass(classlabel))]
sampidx <- which(classlabel %in% class)
if (length(sampidx) == 0) {
stop("Class ", class, "not found")
}
classnum <- length(sampidx)
minpresent <- classnum * minfrac
filled <- rep(FALSE, nrow(peaks(object)))
## exists(object@filled) always returns FALSE ??
sloti <- try(slot(object, "filled"), silent = TRUE)
if (class(sloti) != "try-error") {
filled[object@filled] <- TRUE
}
apply (groupval(object), 1, function(x) {
length(which( (!(is.na(x[sampidx]) | is.nan(x[sampidx])))
& !filled[x[sampidx]])) >= minpresent
})
})
############################################################
## absent
setMethod("absent", "xcmsSet", function(object, class, minfrac) {
if ( nrow(object@groups)<1 || length(object@groupidx) <1) {
stop("No group information. Use group().")
}
classlabel <- sampclass(object)
classlabel <- levels(classlabel)[as.vector(unclass(classlabel))]
sampidx <- which(classlabel %in% class)
if (length(sampidx) == 0) {
stop("Class ", class, "not found")
}
classnum <- length(sampidx)
minabsent <- classnum * minfrac
filled <- rep(FALSE, nrow(peaks(object)))
## exists(object@filled) always returns FALSE ??
sloti <- try(slot(object, "filled"), silent = TRUE)
if (class(sloti) != "try-error") {
filled[object@filled] <- TRUE
}
apply (groupval(object), 1, function(x) {
length(which(is.na(x[sampidx]) | is.nan(x[sampidx]) | filled[x[sampidx]])) >= minabsent
})
})
############################################################
## specDist
setMethod("specDist", signature(object="xcmsSet"),
function(object, peakIDs1, peakIDs2,
method=getOption("BioC")$xcms$specDist.method,
...) {
if (missing(peakIDs1)) {
stop("missing argument peakIDs1")
}
if (missing(peakIDs2)) {
stop("missing argument peakIDs2")
}
peaks <- object@peaks
peakTable1 <- peaks[peakIDs1,c("mz","into")]
peakTable2 <- peaks[peakIDs2,c("mz","into")]
method <- match.arg(method, getOption("BioC")$xcms$specDist.methods)
if (is.na(method))
stop("unknown method : ", method)
method <- paste("specDist", method, sep=".")
distance <- do.call(method, alist<-list(peakTable1, peakTable2, ...))
distance
})
############################################################
## showError
#' @title Extract processing errors
#'
#' @aliases showError
#'
#' @description If peak detection is performed with \code{\link{findPeaks}}
#' setting argument \code{stopOnError = FALSE} eventual errors during the
#' process do not cause to stop the processing but are recorded inside of
#' the resulting \code{\linkS4class{xcmsSet}} object. These errors can be
#' accessed with the \code{showError} method.
#'
#' @param object An \code{\linkS4class{xcmsSet}} object.
#'
#' @param message. Logical indicating whether only the error message, or the
#' error itself should be returned.
#'
#' @param ... Additional arguments.
#'
#' @return A list of error messages (if \code{message. = TRUE}) or errors or an
#' empty list if no errors are present.
#'
#' @author Johannes Rainer
setMethod("showError", signature(object = "xcmsSet"),
function(object, message. = TRUE, ...) {
errs <- .getProcessErrors(object, ...)
if (length(errs) > 0) {
res <- lapply(errs, function(z) {
if (message.)
return(z@info)
else return(z@error)
})
return(res)
}
return(list())
})
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