Nothing
### new class for quantiles over positions plots
setClass("qop", representation(data="list", genes="character", positions="numeric", samplenames="character", quantiles="numeric", mapping="character"), prototype=prototype(data=list(matrix(0, nrow=0, ncol=0))))
setValidity("qop", function(object){
if (any(length(object@data)!=length(object@samplenames),
!inherits(object@data[[1]], "matrix"),
nrow(object@data[[1]])!=length(object@quantiles),
ncol(object@data[[1]])!=length(object@positions)))
{warning("Object does not fulfill requirements for class 'qop'.\n"); return(FALSE)}
return(TRUE)
})# set validity of qop objects
setMethod("show",signature="qop", function(object){
cat("Quantiles over relative positions for",
length(object@data), "samples:\n")
cat(object@samplenames)
cat("\nPositions:\n", object@positions)
cat("\nQuantiles:\n", object@quantiles)
cat("\nbased on",length(object@genes),"genes.\n")
invisible(NULL)
})
setMethod("plot",signature=c("qop","ANY"), function(x, y, xlab="Distance to feature start [bp]", ylab="Probe level [log2]", ylim, ...){
if (missing(ylim))
ylim <- range(do.call("rbind", x@data), na.rm=TRUE)
plot(x=0,y=0, ylim=ylim, xlim=range(x@positions), xaxt="n", type="n", xlab=xlab, ylab=ylab,...)
pos.kb <- sort(unique(round(x@positions/1000)))
axis(side=1, at=pos.kb*1000,
labels=paste(pos.kb,"kb",sep=""))
if (missing(y))
mycolors <- rainbow(length(x@data))
else
mycolors <- y
for (i in 1:length(x@data)){
for (j in 1:length(x@quantiles))
lines(x=x@positions,y=x@data[[i]][j,], lwd=2, lty=j, col=mycolors[i])
}
legend(x="topleft", fill=mycolors, bty="n",
legend=x@samplenames)
legend(x="topright", lty=seq(length(x@quantiles)), lwd=2, bty="n",
legend=paste(round(x@quantiles*100),"% quantile", sep=""))
}) # plot method for qop objects
quantilesOverPositions <- function(xSet, selGenes, g2p,
positions= seq(-5000, 10000, by=250),
quantiles=c(0.1, 0.5, 0.9))
{
stopifnot(inherits(xSet,"ExpressionSet"),
is.list(g2p),
all(selGenes %in% names(g2p)))
### get values per gene
selGenesVal <- vector("list", length(selGenes))
for (i in 1:length(selGenes)){
if (i %% 1000 == 0) cat(i,"... ")
ix <- g2p[[selGenes[i]]]
if (length(ix)==0) next
iy <- exprs(xSet)[names(ix),,drop=FALSE]
if (sum(!is.na(iy[,1]))<2) next
iy <- apply(iy, 2, function(a)
approx(x=ix, y=a, xout=positions)$y)
selGenesVal[[i]] <- iy
}
names(selGenesVal) <- selGenes
### get quantiles per sample
valsBySample <- lapply(as.list(sampleNames(xSet)), function(thisSample){
do.call("cbind", lapply(selGenesVal, function(theseValues){
return(theseValues[,thisSample,drop=FALSE])}))})
names(valsBySample) <- sampleNames(xSet)
quantsBySample <- lapply(valsBySample, function(theseValues)
apply(theseValues, 1, quantile,
probs=quantiles, na.rm=TRUE))
### convert to densities
## a. probe positions (for normalizing the smoothed signal)
probePosDens <- density(unlist(g2p, use.names=FALSE))
probePosY <- approx(x=probePosDens$x, y=probePosDens$y,
xout=positions)$y
probePosY <- probePosY/max(probePosY)
## b. apply normalization
for (i in 1:length(quantsBySample)){
quantsBySample[[i]] <- quantsBySample[[i]] * matrix(probePosY, ncol=length(probePosY), nrow=nrow(quantsBySample[[i]]), byrow=TRUE)
}
# class(quantsBySample) <- c("qop", class(quantsBySample))
# return(quantsBySample)
res <- new("qop", data=quantsBySample, samplenames=sampleNames(xSet),
positions=positions, genes=selGenes, quantiles=quantiles,
mapping=deparse(substitute(g2p)))
return(res)
} #quantilesOverPositions
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