Hi Steve, A little bit of background as to the biological question would actually explain this odd request. Basically, I am trying to see if copy number aberrations for a given gene across n number of samples is affecting its expression level (defined as cis-correlated). And so for each gene, I have x number of cases that are deleted/amplification and y number of cases that are neutral. So essentially I just want to know if the cases that have deletion/amplification have expression different than those that are neutral. It's a classical differential expression test between two groups, but I am only interested in the expression of that gene in the context of cases with aberration vs. neutral. So while I could run DESeq for just those two groups to find the expression of one gene, what if I wanted to actually investigate the cis-correlation across all genes (say my search space is 15000). Would I have to run DESeq 15000 times with a different grouping of samples each time just to get the cis-correlation status of these 15000 genes? Seems computational expensive. This is why I was wondering if I could run DESeq for just one gene, but all the information across all samples to estimateSizeFactors, estimateDispersions, etc. This would save on complexity and significant computational time. Fong On Fri, Feb 1, 2013 at 6:37 PM, Steve Lianoglou < mailinglist.honeypot@gmail.com> wrote: > Hi, > > > On Friday, February 1, 2013, Fong Chun Chan wrote: > >> Hi, >> >> This is somewhat of a odd request to run DESeq. Basically, I am interested >> finding whether a single gene is differentially expressed between two >> groups of RNA-seq libraries. I could run DESeq just to get the >> differential >> expression of that one gene. But that seems to be computationally >> expensive >> just to find whether a single gene is differentially expressed. > > > Have you tried doing it on all of your data? I suspect it won't take much > time at all. > > Is there another reason that you want to be blind to the rest of the > changes that are happening in your data? > > > >> I understand that DESeq needs to estimateSizeFactors() and >> estimateDispersions() on the entire library using all the reads. But is >> there a way in the nbinomTest() to just restrict the analyses to just one >> gene? One way I was thinking was to create another CountDataSet with just >> that one gene, and then fill in the slots of sizeFactors and DIspersions >> with the data from the whole CountDataSet. >> >> Has anyone else tried to do this? >> >> Thanks, >> >> Fong >> >> [[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 >> > > > -- > Steve Lianoglou > Graduate Student: Computational Systems Biology > | Memorial Sloan-Kettering Cancer Center > | Weill Medical College of Cornell University > Contact Info: http://cbio.mskcc.org/~lianos/contact > [[alternative HTML version deleted]]