Dear AK, I assume that you have multiple replicates of each heart stage, so that the total number of samples is greater than 20. To rank genes by average up-regulation in heart across all stages, simply form a contrast that is the average of all Stage vs Control comparisons. For example, Group <- relevel(Group, ref="ControlX") design <- model.matrix(~Group) fit <- lmFit(y, design) cont <- c(-20,rep(1,20))/20 fit2 <- contrasts.fit(fit, contrast=cont) fit2 <- eBayes(fit) topTable(fit2) Best wishes Gordon > Date: Thu, 28 Aug 2014 21:21:03 -0400 > From: Atul <atulkakrana at="" outlook.com=""> > To: "Ryan C. Thompson" <rct at="" thompsonclan.org=""> > Cc: "Bioconductor at r-project.org" <bioconductor at="" r-project.org=""> > Subject: Re: [BioC] Combing Effects (t-stats) from experiment with > common reference design? > > Hi Ryan, > > Thanks for taking out time to reply to my question. I have samples from > two tissues - Heart (20 different developmental stages) and Control > (rest of the body, single fused sample from multiple time points). I > performed 'limma' analysis (GLM approach) to identify up-regulated genes > for each of the Heart stages (n=20). > > Ex comparisons: > Heart Stage-1 vs. Control-X > Heart Stage-2 vs. Control-X > ..... > Heart Stage-20 vs. Control-X > > Now I would like to rank genes on the basis of their enrichment in heart > across all stages. So that a gene which is highly enriched in heart > should rank high (on top) and genes which are not enriched in heart > should rank low (at bottom). Is there any way to combine 't-stats' for > each stage to a single metric? Or any other method rank genes that are > enriched in Heart across all stages? > > Actually I do have F-statistic. But I think that F-stat is high for gene > which shows variable enrichment i..e gene which is not enriched in 5 > stages but enriched in 15 stages will have better F-stat reather than a > gene with enrichment in all 20 stages. Therefore 'F-stat' doesn't seem > to be the correct indication of enrichment level across all stages. I > might be wrong, please correct me if that the case. > > Best > > AK > > On 08/28/2014 06:09 PM, Ryan C. Thompson wrote: >> Hi Atul, >> >> Typically if you are testing multiple contrasts simultaneously, you >> would use an ANOVA test that would five you an F statistics (and >> corresponding p-value). But it's not exactly clear if that's what >> you're asking for, Can you explain in more detail exactly which >> hypothesis you are trying to test? Ar you trying to test whether any >> of the Stages is different from the control, or are you trying to test >> whether genes are changing between all Stages? >> >> -Ryan >> >> On Thu 28 Aug 2014 12:52:47 PM PDT, Atul wrote: >>> Hi All, >>> >>> I was wondering whether there is any approach to combine 't-stat' from >>> different comparisons but using same control. These are my contrasts: >>> >>> Stage1 vs ControlX >>> Stage2 vs ControlX >>> Stage3 vs. ControlX >>> ......... >>> Stage 20 vs. ControlX >>> >>> Here the control is same i.e. same sample for all contrasts. From >>> 'limma' analysis I have Fold change, t-stats and p-values for each gene. >>> >>> Now, is it possible to combine 't-stats' from all different stages to >>> single value? Or compute a single combined value for all the contrasts. >>> So, that this single metric could be used to rank genes across all time >>> points. Is there any package available to do so? I can find methods to >>> combine p-values but not the 't-stat'. >>> >>> Thanks >>> >>> AK ______________________________________________________________________ The information in this email is confidential and intend...{{dropped:4}}