[BioC] Deseq2 for down stream analysis

Fabrice Tourre fabrice.ciup at gmail.com
Sun Aug 10 15:41:00 CEST 2014

Dear Mike,

Thank you for your reply. I need a matrix for each gene and sample for
gene set enrichment analysis.

In you example, how will about this situation:

[0,0,0] vs [1,2,3]

[0,0,0] vs [10,10,10]

I have a lot such case genes.

On Sun, Aug 10, 2014 at 9:29 PM, Michael Love
<michaelisaiahlove at gmail.com> wrote:
> 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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