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

[Jason Shoemaker]

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