Hello,

I'm currently using Limma and Limma-Voom to analyze some Microarray and
RNA-Seq datasets, with the hope of finding some DEGs in my samples. Farther
downstream, I'm looking at some motif analysis regarding some specific
transcripts that have particular motifs present. The challenge is that not
all transcript variants of a gene necessarily have the motif, so I'm not
sure if I can accurately relate my DEGs to genes with the motif.

*What are the challenges or problems with feeding Limma (or even EdgeR and
other packages) transcript counts instead of gene counts?* I'd feed it a
custom library size so it won't double count using rowSums, but would it
cause other problems when I get my final list of differentially expressed
*transcripts*?

I also had some additional technical questions regarding limma:

   1. Is the standard practice to input all gene expression counts from an
   experiment into the same voom/limma objects? That is, I'm looking at
   different cell types, treatments, etc, and so is it better to keep them
   separate or to keep them together?
   2. My current method also only uses reads that map uniquely to a given
   gene; that is, it ignores reads that map to ambiguous areas of overlapping
   genes. I assume that this is also standard practice in calling DEGs?

Thanks,

Yogesh

Yogesh Saletore
yos2006@med.cornell.edu
http://www.ysaletore.com/
PhD Student
Christopher Mason Lab, http://www.masonlab.net
Department of Physiology and Biophysics and the Institute for
Computational Biomedicine
Weill Cornell Medical College of Cornell University
1305 York Ave., New York, NY 10021

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