Hi, Dear all: I am looking for advice for what I observed in my analysis results using the same data but two very close related methods: limma-voom and edgeR. for purpose of consolidation and comparison, since I try to get as accurate DEGs lists for my next step downstream analysis, for the same dataset, I used both edgeR and limma-voom originally try consolidate each other. Surprisingly, as shown below the numbers of DEGs for 4 constrasts I am interested, there are consistency between the results of normal contrasts: e.g., for contrasts RasOnly.Normal_RasPNot.Normal and RasP.Normal_RasPNot.Normal (both involved comparison of normal samples of different types) in terms of numbers of DEGs (in very close ranges), however, there are large discrepancy between the results of tumor contrasts: e.g., RasP.Tumor_RasPNot.Tumor and RasOnly.Tumor_RasPNot.Tumor. The edgeR result has about 1k(416+420) DEGs for RasP.Tumor_RasPNot.Tumor and 890+552=1.3k DEGs for RasOnly.Tumor_RasPNot.Tumor at FDR 5%, whereas limma-voom method only picked up a bit more than 100 or less DEGs for the same contrasts (almost no DEGs at the default cutoff FDR or adjusted p-value level) shown below. Of course, maybe cutoffs (e.g., FDR vs adjusted.p-value) or model may have different impacts in the DEGs sets,however, the odd thing is: for the same cutoffs, we did see quite consistency in normal contrasts, but large dsicrepancy in the tumor contrasts. From almost no DEG (or a bit more than 100) to more than 1k DEGs seem a big difference to me. Since that would impact the downstream analysis such as pathway analysis etc. The other main difference is for the same data, which are claimed raw counts of RSEM for RNAseq data, were rounded to integers for edgeR and also we used the original RSEM raw count values for limma-voom. Both methods used the identical makeContrasts commands shown below. Any suggestions and advice would be highly appreciated. Thanks a lot in advance! Ming ATRF NCI-Frederick Maryland, USA EdgR result: currLrt<-glmLRT(fit, contrast=con.matrix[,colnames(con.matrix)[ii]]); show(summary(de <- decideTestsDGE(currLrt))); [1] "RasP.Tumor_RasPNot.Tumor" -1 416 0 17414 1 420 [1] "RasOnly.Tumor_RasPNot.Tumor" [,1] -1 890 0 16808 1 552 [1] "RasOnly.Normal_RasPNot.Normal" [,1] -1 4 0 18229 1 17 [1] "RasP.Normal_RasPNot.Normal" [,1] -1 481 0 16936 1 833 limma-voom result: > show(summary(de <- decideTests(fit))); RasP.Tumor_RasPNot.Tumor RasOnly.Tumor_RasPNot.Tumor -1 29 28 0 18202 18125 1 19 97 RasOnly.Normal_RasPNot.Normal RasP.Normal_RasPNot.Normal -1 0 682 0 18250 17184 1 0 384 critcal commands: EdgR: > y <- estimateGLMCommonDisp(y, design, verbose=TRUE); Disp = 0.42594 , BCV = 0.6526 > y <- estimateGLMTrendedDisp(y, design); Loading required package: splines > y <- estimateGLMTagwiseDisp(y, design) > fit <- glmFit(y, design); > con.matrix<-makeContrasts( + RasP.Tumor_RasPNot.Tumor=RasP.Tumor-RasPNot.Tumor, + RasOnly.Tumor_RasPNot.Tumor=RasOnly.Tumor-RasPNot.Tumor, + RasOnly.Normal_RasPNot.Normal=RasOnly.Normal-RasPNot.Normal, + RasP.Normal_RasPNot.Normal=RasP.Normal-RasPNot.Normal, + levels=design) currLrt<-glmLRT(fit, contrast=con.matrix[,colnames(con.matrix)[ii]]); show(summary(de <- decideTestsDGE(currLrt))); limma-voom: >design <- model.matrix(~0+RasTum); > con.matrix<-makeContrasts( + RasP.Tumor_RasPNot.Tumor=RasP.Tumor-RasPNot.Tumor, + RasOnly.Tumor_RasPNot.Tumor=RasOnly.Tumor-RasPNot.Tumor, + RasOnly.Normal_RasPNot.Normal=RasOnly.Normal-RasPNot.Normal, + RasP.Normal_RasPNot.Normal=RasP.Normal-RasPNot.Normal, + levels=design) > fit<- lmFit(v,design) > fit<-contrasts.fit(fit, con.matrix) > fit <- eBayes(fit) > show(summary(de <- decideTests(fit))); [[alternative HTML version deleted]]