[BioC] Limma : post statistical gene filtering

[Stephanie PIERSON]

Dear bioconductor listers,

I am analyzing agilent 2 color microarray data and i choose limma  
library to make normalization and statistical analysis because i only  
have 2 replicates per condition and i read in some paper that a  
moderated t test perform better when there are few replicates.

The problem is that when i performed the statistical test on the whole  
data set ( 35000 probes ),i have no differential expression, ie, all  
the adjusted p value are comprise between 0.5 and 0.9. So, i have seen  
on the list that the question on prefiltering genes have already been  
asked : some people on the list recommand to do the normalization,  
model fitting, etc, and then filter out before doing the multiplicity  
adjustment.
So, after the statistical analysis, i remove gene with log2FC<2  
(ebayes$coefficients), and i perform the FDR. But once again, i have  
no adj pvalue < 0.05.

So, i was wondering on wich criteria i could filter out genes before  
the multiple testing correction : pvalue ? log2FC ? other criteria ?

I have a lot of variabily between replicates, ie, for many genes, i  
have a fold change <0 in one replicate (for example, -5) and >0 on the  
other one replicate (for example, 3) ... do you think i should remove  
those gene before the statistical analysis or i can keep them ?


Thank you,
Best wishes
Stéphanie



-- 
Stéphanie PIERSON
Universite de la Mediterranee (Aix-Marseille II)
Master 2 Pro Bioinformatique et Génomique