[BioC] number of genes for DESeq analysis

[Abhishek Pratap]

Hi Vladimir


One way to do this without cutting down on your gene set is to do the
dispersion estimation  and binomial test for all the genes you  have
in your annotation model and then take a subset of the genes you are
interested from the resulting data frame spitted out after the
binomial test.

I am not sure what kind of impact will it have on the statistical
model if you reduce the number of genes. I guess the estimates are
taken for each gene but since your gene sample size will be very small
may be the model will have issues on estimation a dispersion
parameter. I am not sure. Simon or Wolfgang can best answer that.

HTH,
-Abhi

On Tue, Feb 14, 2012 at 6:37 AM, vladimir mashanov
<mashanovvlad at googlemail.com> wrote:
> Dear All,
>
> I have carried out an RNAseq experiment with 4 conditions, 2
> biological replicates of each. In the moment, I am interested in how
> my conditions differ in terms of expression of a subset of 36 genes.
> The idea is to count only the reads, which correspond to those 36
> genes and use this piece of data for the analysis of their
> differential expression across the conditions. Will this approach be
> valid? What is the minimum number of genes required by the statistical
> model implemented in DESeq? I apologize if the question are too naive.
>
> Thank you
>
> Vladimir.
>
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