[BioC] Calculating variance across probes

[Dax42@web.de]

Dear list,

I am trying to figure out what normalization method would suit my needs best.
To determine this, I thought about plotting mean expression value versus variance, both calculated across each probeset for one chip.

Calculating the mean over a probeset is easy, as I can use the expresso method for it:
expresso(data, bg.correct=FALSE, normalize=FALSE, pmcorrect.method="pmonly", summary.method="avgdiff")

Not as easy is the calculation of the variance over each probeset.  I wrote my own method for it, but it takes ages...
My data comes from the MOE 430 2 Affymetrix GeneChip with 45101 probesets. I got 6 chips in total.

Is anybody able to think of a faster way to compute the variance? Below is the code I was using.

Thanks for your help!
Sue
---------------

getprobes <- function(genelist,data){

	as.vector(t(pm(data,genelist)))
}

#####
### INPUT1: exprSet
### INPUT2: raw Data (AffyBatch)

meanvar <- function(exp,data){
	
	split.screen(c(3,2))	# 3 rows, 2 columns
	
	list<-geneNames(exp)
	list<-as.array(list)

	for(j in 1:6){
	
		r <- apply(list,1,getprobes,data[,j])
		v <- lapply(r,var)	
	
		screen(j)
		plot(exprs(exp)[,j],v,pch=".",main=paste("mean vs variance for chip ",deparse(j)))
	}
}

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