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 (fewer 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")