[BioC] nsFilter and GSEA
paolo.innocenti at ebc.uu.se
Fri Jan 11 16:41:28 CET 2008
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
miame <- read.MIAME("miame")
phenodata<- read.AnnotatedDataFrame("phenodata",sep=" ")
mydata <- ReadAffy(sampleNames=sampleNames(phenodata),
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).
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