Hi Fatemehsadat, I`m no expert in R, there may be a more elegant solution. Try this, I haven't tested, so minor modifications may be required. tp <- topTags(lrt, n=15) # Extract the top 15 genes de_counts<-dge$counts[row.names(tp), order(dge$samples$group)] # Get raw counts for top genes rs<-rowSums(de_counts) # Assuming by high count, across all samples top.names <- names( rs[ order( rs, decreasing=TRUE ) ] ) de_counts[top.names, ] # DE genes Ranked ,highest first Thanks, Shreyartha On Wed, Dec 12, 2012 at 7:22 AM, Fatemehsadat Seyednasrollah <fatsey@utu.fi>wrote: > Hi, > > For an RNA-seq data, I have found the significant DE genes based on FDR < > 0.05 using the exactTest() function. > Now I need to rank these DE genes based on the expression level (count > values). What is the correct way of doing it? > > Thank you in advance. > > _______________________________________________ > 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 > [[alternative HTML version deleted]]