[BioC] question on the cutoff for limma package

[Yi, Ming (NIH/NCI) [C]]

Dear List:

Recently, I used limma package to analyze some miRNA array data. One of the differential lists I derived for one of the contrasts in our limma model just used P.Value <0.01 as cutoff combined FC cutoff, we noticed that in this particular contrast, all the differential miRNAs have rather high adj.P.Val almost all miRNAs are 1 or very close to 1 (e.g., 0.973 etc)  (I used adj="fdr" in topTable...) although the other contrasts in the same model we set up in limma does have "normal" looking adj.P.Val ranged from 1 to about 0.01. 

>From our previous experience, sometimes, even with very high adj.P.Val, with decent P.Value (e.g., <0.01), we can have good validation. In this case, now we validated two miRNAs from the list both with good P.Value <0.01 but with rather high adj.P.Val (both are around 0.97 or 1). We did validate one of them as good miRNA but the other one is bad (we can not validate it as differential). 

I understood it is more subjective aspect and we only validated two of chosen miRNAs in this case (and we encountered similar situation before for validation of other dataset), and many people used FDR or adjusted p-value varied from 5% to 30% commonly. 
my first question is: what kind of situation could lead to adj.P.Val for all of genes in the list as high as 0.97 to 1 (there are about 6k features in the dataset? 

What shall be the cutoff for P.Value and adj.P.Val in the situation like this? Considering both or more specifically on adj.P.Val? In our case, if rely on adj.P.Val only for cutoff, which are all so high, we do not have any single miRNA that we can choose, however, our biological validation experiment indeed validate a good one (although we only validated just two of them, still kind of much higher than expected considering the fact that none of them has decent adj.P.Val but rather bad ones). If rely on P.Value (e.g. <0.01), we do have quite a few mRNAs in the list, but each one with sky high adj.P.Val! and we only can validate 1 of the 2 chosen candidates as good one.

Any insight or experience to share with?

Thanks a lot!

Ming

ABCC
NCI-Frederick,
Frederick, MD