DEseq for paired samples
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@prabhakar-chalise-6127
Last seen 9.6 years ago
Hi, I was looking for a method to identify differentially expressed genes using the paired tumor/normal samples. Is there a method under DEseq to test for such correlated data? Thanks, -Prabhakar Chalise [[alternative HTML version deleted]]
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Devon Ryan ▴ 200
@devon-ryan-6054
Last seen 8.3 years ago
Germany
Hi Prabhakar, The general method you're looking for is to add "patient" (or whatever term you prefer) as a factor in your design. So, if you denote tumor/normal by a factor called "status": counts ~ status + patient would likely be the formula you want. You could also allow an interaction if it makes sense for your dataset, by swapping an asterisk for the plus sign. You'll need to specify the individual patients in your dataframe, of course. Best, Devon ____________________________________________ Devon Ryan, Ph.D. Email: dpryan at dpryan.com Molecular and Cellular Cognition Lab German Centre for Neurodegenerative Diseases (DZNE) Ludwig-Erhard-Allee 2 53175 Bonn, Germany On Aug 30, 2013, at 12:12 AM, Prabhakar Chalise wrote: > Hi, > > I was looking for a method to identify differentially expressed genes using the paired tumor/normal samples. Is there a method under DEseq to test for such correlated data? > > Thanks, > -Prabhakar Chalise > > [[alternative HTML version deleted]] > > _______________________________________________ > 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
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Yes, exactly. See the section 'Multi-factor designs' in the vignette of DESeq or DESeq2. Note that for DESeq2, we recommend to put the variable of interest, say 'condition' taking values {normal, tumor} at the end of the design formula, because the results() and plotMA() functions by default will pull from the last variable of the design formula: ~ patient + condition Mike On Fri, Aug 30, 2013 at 8:39 AM, Devon Ryan <dpryan@dpryan.com> wrote: > Hi Prabhakar, > > The general method you're looking for is to add "patient" (or whatever > term you prefer) as a factor in your design. So, if you denote tumor/normal > by a factor called "status": > > counts ~ status + patient > > would likely be the formula you want. You could also allow an interaction > if it makes sense for your dataset, by swapping an asterisk for the plus > sign. You'll need to specify the individual patients in your dataframe, of > course. > > Best, > Devon > > ____________________________________________ > Devon Ryan, Ph.D. > Email: dpryan@dpryan.com > Molecular and Cellular Cognition Lab > German Centre for Neurodegenerative Diseases (DZNE) > Ludwig-Erhard-Allee 2 > 53175 Bonn, Germany > > On Aug 30, 2013, at 12:12 AM, Prabhakar Chalise wrote: > > > Hi, > > > > I was looking for a method to identify differentially expressed genes > using the paired tumor/normal samples. Is there a method under DEseq to > test for such correlated data? > > > > Thanks, > > -Prabhakar Chalise > > > > [[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 > > _______________________________________________ > 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]]
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