[BioC] Deseq2 for down stream analysis

[Michael Love]

hi Fabrice,

On Sun, Aug 10, 2014 at 8:27 AM, Fabrice Tourre <fabrice.ciup at gmail.com> wrote:
> Dear expert,
>
> I've been using DESeq for my RNA-Seq differential expression analysis.
> Now I want to do GSEA. I have got follow expression value. which one
> should I used for the down stream analysis?

Please provide more details about the downstream analysis.

Do you need a matrix of values for each gene and sample, or just the
test statistic for each gene?

> rc, rld or vsd?
>
> rc <- counts(dds)
> rld <- rlog(dds)
> vsd <- varianceStabilizingTransformation(dds)
> rlogMat <- assay(rld)
> vstMat <- assay(vsd)
>
> Then I want to use the DESeq result to generate a ranked-list, which
> will be used as the input in GSEA. My question is: Should I rank the
> genes using the fold changes or using the q-values?
>

You can use the shrunken fold changes or p-values for ranking. The
fold change measures the effect itself, while the p-value is a
function of how distinct the changes are, so the signal over the
noise. For example, consider a comparison of two groups with three
values each (here continuous values just for demonstration): [3,4,5]
vs [1,2,3] has a fold change of 2, whereas [11,11,11] vs [10,10,10]
has a fold change of 1.1. but the second comparison will have a lower
p-value because the variance within groups is so small.

Mike

> Thank you very much in advance.
>
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