[BioC] Using eBayes to find P values for individual contrasts

[Mark Cowley]

Hi Jason,
I think you're in danger of reinventing the wheel.

The adj.P.Val column in the topTable is the corrected p value. Don't forget about the coef topTable parameter to control which coefficient to look at. You can control what method to use via the adjust.method parameter. 

then take a look at the decideTests method to work out which genes are significant  for which contrasts.

cheers,
mark

On 16/11/2010, at 7:28 PM, Jason Shoemaker wrote:

> Dear all,
> 
> I have searched the archives but not found any advice on this issue. I am using the LIMMA package to determine differentially expressed genes. I have been using eBayes plus topTable to find the most differentially expressed genes, but now I want to determine the adjusted p values specific for each contrast. Should I simply do as follows (following the example from http://matticklab.com/index.php?title=Single_channel_analysis_of_Agilent_microarray_data_with_Limma):
> 
> contrast.matrix <- makeContrasts("Treatment1-Treatment2", "Treatment1-Treatment3", "Treatment2-Treatment1", levels=design);
> fit2 <- contrasts.fit(fit, contrast.matrix)
> fit2 <- eBayes(fit2)
> 
> P.values<-p.adjust(fit2$p.values,methods="fdr");
> 
> in doing so- can I make fair comparisons between p values for each contrast? Or more precisely, if a get a p value of 0.01 for "Treatment1-Treatment2" and large value (P>0.1) for the remaining 2 contrasts, is that gene significant only for "Treatment1-Treatment2"?
> Thank you!
> Jason
> 
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-----------------------------------------------------
Mark Cowley, PhD

Peter Wills Bioinformatics Centre
Garvan Institute of Medical Research, Sydney, Australia