[BioC] Limma : post statistical gene filtering
stephanie.pierson at etumel.univmed.fr
Thu Jun 16 16:17:55 CEST 2011
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
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 ?
Universite de la Mediterranee (Aix-Marseille II)
Master 2 Pro Bioinformatique et Génomique
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