[BioC] nsFilter and GSEA

[Paolo Innocenti]

Hi,

thanks for the prompt reply. I am a bit confused now.

Sean Davis wrote:
> Sounds like you probably need to take a closer look at the data.  I have 
> not used the nsFilter function, but it looks like the default variance 
> function is IQR (interquartile range) and the default cutoff is 0.5 for 
> that function value is 0.5.  If nearly 99% of your probes have an 
> IQR<0.5, I would look at the data quality closely to see if there are 
> data quality issues or preprocessing steps that do not make sense (can't 
> tell what was done before RMA).  

Boxplot, hist, RLE, NUSE, MAplot, RNAdeg: they all look fine (except for
one chip that is *a bit* strange, but that should just increase the
variance (?) . Can you suggest me other tests (and how to interpret them)?
And there is no preprocessing except for RMA (maybe is this the wrong
step?):

miame <- read.MIAME("miame")
phenodata<- read.AnnotatedDataFrame("phenodata",sep=" ")
mydata <- ReadAffy(sampleNames=sampleNames(phenodata),
	phenoData=phenodata,
	description=miame)
eset <- rma(mydata)
eset.f <- nsFilter(eset)$eset

What if the problem is that the data are TOO good? Makes sense to guess
that, if data mirror exactly the biology of the sample, I am expecting
heaps of genes with the exactly the same expression level, and "a few"
genes with differential expression? (the experimental design was
virginVSmated female flies: mating is expected to promote some change in
female physiology, probably affecting more that 60 genes, though).

  Cheers,
Paolo