Dear Eduardo,

I understand that "method" relates to how contrasts are compared. There is a description in these posts:

https://stat.ethz.ch/pipermail/bioconductor/2008-January/020646.html
https://mailman.stat.ethz.ch/pipermail/bioconductor/2012-May/045488.html

You will see that "separate" method in decideTests will give the same result as topTable. This is a very easy way to interpret the results. P-vals from your two contrasts from topTable will correspond to the correlated (1) or anti-correlated (-1) probes. You can get a sense if your contrasts are correlated by displaying your decideTests results Venn or with heatDiagram in limma.

i'm very new to arrays & interested if others have different views. This is something you could try.

cont.matrix <- makeContrasts(
  'contA'=(TreatmentA - Control),
  'contB'=(TreatmentB - Control),
  
  levels=design)

fit<-lmFit(M,design)
fit2 <- contrasts.fit(fit, cont.matrix)
fit2 <- eBayes(fit2)

DECIDE<-decideTests(fit2,method="separate",adjust.method="BH",p.value=0.05,lfc=0)

contA<-topTable(fit2,coef=1,adjust.method="BH",number=480000,sort.by="B")
contB<-topTable(fit2,coef=2,adjust.method="BH",number=480000,sort.by="B")

DECIDE<-decideTests(fit2,method="separate",adjust.method="BH",p.value=0.05,lfc=0)
heatDiagram(DECIDE,coef=fit2$coef)
vennDiagram(DECIDE)


Cheers, Mark



-----Original Message-----
From: bioconductor-bounces@r-project.org on behalf of Ribeiro,Eduardo de Souza
Sent: Sat 7/21/2012 12:02 PM
To: bioconductor@r-project.org
Subject: [BioC] Limma: how to obtain P values using method "global" for multiple testing
 
Hi everyone,

I am analyzing a Affymetrix microarray data set using the Limma package. According to my experimental design, I understand that my contrasts are closely related and that I should use the "global" method for multiple testing. My question is: Is there a way to obtain P-values using the "global" method for multiple testing? The decideTest function allows to specify the method "global" but just give the matrix of the results and not the P-values. And if I understood correctly, the topTable function does not allow to specify the method for multiple testing and uses the "separate" method as default. Comparing the decideTest matrices, I realize that the results of "separate" and "global" methods are slightly different and I am concerned that this difference may change the final interpretation of my study.

Thank you very much.

Best regards

Eduardo Ribeiro
PhD student
University of Florida


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