Hi Dr. Huber, I believe you are correct in terms of the weakness of the experimental design. Unfortunately, I could not change it. I have 9 biological replicates sequenced for one group and 5 for another. Is there a down side of using DEseq to compare conditions with unbalanced sample number? One can argue that choosing either one of the normalization factors does not matter because I have about 85% of concordance of the results, but others can easily argue that it matters because of the difference on the results. And to add to the discrepancy on the results, when I analyze the data using one or the other normalization factor, there is a shift on the number of up regulated genes. For example: Genes Up in A genes Up in B RLM 2953 3322 TMM 3114 2632 I think it may easily affect the biological interpretation. I appreciated your reply, Thanks, Fernando -----Original Message----- From: bioconductor-bounces@r-project.org [mailto:bioconductor- bounces@r-project.org] On Behalf Of Wolfgang Huber Sent: Saturday, March 19, 2011 5:21 AM To: bioconductor at r-project.org Subject: Re: [BioC] choosing normalization method for RNA-seq analysis Fernando, does it matter? I.e., for your data, do you get substantially different results from the different methods? If so, that might indicate a weakness of the data quality or of the experimental design that you would need to investigate in a manner specific to the dataset. Unsurprisingly, my personal favorite is "RLE". Wolfgang Biase, Fernando scripsit 17/03/11 22:53: > Hi > > Working with edgeR package to analyze RNA-seq data, we have three > options of normalization within calcNormFactors() function. Is there a > way to test which one is more suitable for the data? If there is no > test, is there a criteria to choose one over the other? > > Thanks in advance, Fernando > > _______________________________________________ Bioconductor mailing > list Bioconductor at r-project.org > https://stat.ethz.ch/mailman/listinfo/bioconductor Search the > archives: > http://news.gmane.org/gmane.science.biology.informatics.conductor -- Wolfgang Huber EMBL http://www.embl.de/research/units/genome_biology/huber _______________________________________________ Bioconductor mailing list Bioconductor at r-project.org https://stat.ethz.ch/mailman/listinfo/bioconductor Search the archives: http://news.gmane.org/gmane.science.biology.informatics.conductor