Question: Limma FDR and p.value
gravatar for SAURIN
14.4 years ago by
SAURIN1.1k wrote:
Hi, This might be simple question, but I am confused somehow..if anyone can guide me through this that would be great. > fit<- eBayes(fit); > results_fdr1<- classifyTestsP(fit, p.value=0.01, method="fdr") > vennDiagram(results_fdr1,include = "both"); (I get very nice vennDiagram, my all contrasts have nice numbers of genes) > coef1_Genes <- topTable(fit,coef = 1, adjust = "fdr",number = 50); Above when I get coef1_Genes, which has adjusted P.value way higher than 0.01, sometimes its range from 0.5.....0.904 or something like that. so, how can I decide my cutoff for FDR? I saw nice numbers in my vennDiagram above so, i thought I have lots of genes which has < 0.01 FDR but it not when I checked in coef1_Genes. In above results coef1_Genes P.value = FDR cutoff right? There is no option in topTable for howmuch FDR level..! I set p.value = 0.01 in classifyTestsP function so, I think classifyTestsP gives me FDR level < 0.01, because I am using method = "fdr", and those nice number I see in vennDiagram.......Is this right understanding? if yes then how can I access those genes? Please, let me know, I highly appreciate your help in advance. Thank you, Saurin ===== Saurin's WebWorld:
ADD COMMENTlink modified 3 months ago by Gordon Smyth37k • written 14.4 years ago by SAURIN1.1k
Answer: Limma FDR and p.value
gravatar for Gordon Smyth
3 months ago by
Gordon Smyth37k
Walter and Eliza Hall Institute of Medical Research, Melbourne, Australia
Gordon Smyth37k wrote:

I think you are assuming that classifyTestsP() adjusts for multiple testing across genes. Actually it adjusts only across contrasts, i.e., across rows rather than down columns. As the help says

"The adjustment for multiple testing is across the contrasts rather than the more usual control across genes."

You should use decideTests() instead. It does something closer to what you are probably expecting.

ADD COMMENTlink modified 3 months ago • written 3 months ago by Gordon Smyth37k
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