[BioC] Deseq2 for down stream analysis
fabrice.ciup at gmail.com
Sun Aug 10 15:45:19 CEST 2014
I need this matrix as the input for Gene Set Enrichment Analysis
On Sun, Aug 10, 2014 at 9:41 PM, Fabrice Tourre <fabrice.ciup at gmail.com> wrote:
> 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.
>>> Thank you very much in advance.
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