[BioC] Limma p-values, fdr and classifyTests

[Matthew Hannah]

Hi,

I'm using Limma and have some questions related to p-values and gene
selection. 

Looking in the classifyTests help I noticed "The adjustment for multiple
testing is across the contrasts rather than the more usual control
across genes." There is also a multiple testing procedure for the
topTable function but this appears to give a different result (<sig.
genes) - is this the more usual control across genes? Why are they
different? Is it possible to take both into account?

Basically I'm not just interested in the top 50 genes, I'd like to
identify all 'significant' changes. I thought the output from
classifyTestsP (0.01, fdr) would be good but this doesn't account for
across gene multiple testing. Is there an easy way to get this output
rather than calling topTable (if the fdr is across genes?) for all
genes?

classifyTestsF could be useful as I'm looking at a treatment effect on
different lines. However, again there is no account of across gene
multiple testing. Is there any possibility to do this?

Also, all this talk of p-values but there is a note saying they are
nominal. How far does this hold true - do you always have to select a
cut-off based on some criteria (eg:control genes) or is there a way they
can be applied quantitatively?

Finally is it ok to pass an eBayes fit to topTable? What's the
difference compared to toptable?
fit <- lmFit(esetgcrma, design)
con.fit <- contrasts.fit(fit, cont.matrix)
ebfit <- eBayes(con.fit)
topTable(ebfit,coef=1,number=50,adjust="fdr")

Thanks alot,

Matt