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.ht ml 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 [[alternative HTML version deleted]] _______________________________________________ Bioconductor mailing list Bioconductor@r-project.org https://stat.ethz.ch/mailman/listinfo/bioconductor Search the archives: http://news.gmane.org/gmane.science.biology.informatics.conductor ______________________________________________________________________ This email has been scanned by the Symantec Email Security.cloud service. For more information please visit http://www.symanteccloud.com If you have any question, please contact MCRI IT Helpdesk for further assistance. ______________________________________________________________________ ______________________________________________________________________ This email has been scanned by the Symantec Email Security.cloud service. For more information please visit http://www.symanteccloud.com ______________________________________________________________________ [[alternative HTML version deleted]]

Hi Mark, Thank you very much for your reply. I understand that I can use the "separate" method to obtain the same results in the decideTest and topTable. I was just wondering if it was possible to obtain P-values using the "global" method. I am not sure if that makes sense. Maybe it is just my misunderstanding of the statistical concepts involving multiple testing for contrasts. Cheers Eduardo Ribeiro PhD student University of Florida ________________________________ From: Mark Cruickshank [mark.cruickshank@mcri.edu.au] Sent: Saturday, July 21, 2012 3:16 AM To: Ribeiro,Eduardo de Souza; bioconductor@r-project.org Subject: RE: [BioC] Limma: how to obtain P values using method "global" for multiple testing 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.ht ml 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 [[alternative HTML version deleted]] _______________________________________________ Bioconductor mailing list Bioconductor@r-project.org https://stat.ethz.ch/mailman/listinfo/bioconductor Search the archives: http://news.gmane.org/gmane.science.biology.informatics.conductor ______________________________________________________________________ This email has been scanned by the Symantec Email Security.cloud service. For more information please visit http://www.symanteccloud.com If you have any question, please contact MCRI IT Helpdesk for further assistance. ______________________________________________________________________ ______________________________________________________________________ This email has been scanned by the Symantec Email Security.cloud service. For more information please visit http://www.symanteccloud.com ______________________________________________________________________ [[alternative HTML version deleted]]