Question: edgeR and DESeq2: model design and estimation of dispersion
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gravatar for Gordon Smyth
5.1 years ago by
Gordon Smyth37k
Walter and Eliza Hall Institute of Medical Research, Melbourne, Australia
Gordon Smyth37k wrote:
Dear Iddo, No, it is not valid to use a different design matrix for the dispersion estimation. edgeR will handle your model with 400 samples, but it will admitedly be slow. If this is too slow, then switch to voom() in the limma package, which will be very fast, or to glmQLFTest() in the edgeR package, which will still be relatively slow but faster than the glm routines in edgeR (or DESeq2). Best wishes Gordon > From: Iddo Ben-dov <iddobe at="" ekmd.huji.ac.il=""> > Subject: edgeR and DESeq2: model design and estimation of dispersion > Date: June 12, 2014 at 4:51:51 PM GMT+3 > To: bioconductor at r-project.org > > hi, > > in both edgeR and DESeq2, estimation of dispersion precedes negative > binomial GLM fitting. > > my question is, can I use a design formula when estimating dispersion > which is different from the formula used for GLM fitting? specifically, > I would like to use a simplified design when estimating dispersion and a > full design for GLM fitting. > > my motivation for doing so is that with the full design estimation of > dispersion is too demanding for my computer and time. > > my dataset includes 400 mRNAseq profiles (~22,000 genes). there are 100 > controls and 100 cases, and each was sampled twice - before and after > intervention. > > thus, the full design is: > ~ group*intervention + individual:group (blocking factor) > > as I mentioned, estimation of dispersion with the above design is not > practical, and I thus would like to simplify to: ~ group*intervention > > and introduce the 'individual' blocking factor only for NB GLM fitting. > > is this statistically valid? > > appreciate any help, > iddo ______________________________________________________________________ The information in this email is confidential and intend...{{dropped:4}}
limma edger deseq2 • 827 views
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