hi Amandine,

I'll defer to the GOseq authors for the recommendation but can answer your
questions about DESeq2


On Thu, Feb 20, 2014 at 9:03 AM, <amandine.fournier@chu-lyon.fr> wrote:

>
>
> I don't know which step filters outliers out. It seems that they are
> taking into account to estimate library sizes, but I am not sure if they
> are filtered before or after estimating dispersion values.
> Low expressed genes are filtered out at the end of the analysis, juste
> before calculating FDR, so they are taking into account for calculating
> library sizes, estimating dispersion values, fitting the GLM model and
> testing, but not in the final FDR.
>
>
Filtering out outliers and genes with low mean count are both performed by
the results() function, after estimation of size factors, dispersion, and
the GLM. (estimates of dispersion and estimates of the GLM coefficients are
necessary to test for outliers, as here we use Cook's distance, which is
 based on the influence of individual samples on the GLM coefficients).

I would suggest that the background not include the outlier-containing or
low mean count genes (so just use the genes with adjusted p-values that are
not NA). This seems to be analogous to the filtering of genes with logFC of
0 in the GOseq vignette:

Finally, we Format the DE genes into a vector suitable for use with goseq
> > genes=as.integer(p.adjust(tested$table$PValue[tested$table$logFC!=0],
> + method="BH")<.05)


But again, I would defer to the package authors on this question.

Mike

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