Question: GSEA preranking metric for RNA Seq
0
gravatar for bipin
21 months ago by
bipin20
bipin20 wrote:

I came across multiple posts regarding the pre-ranking metric for GSEA when using RNA seq data. However, there doesn't seem to be a consensus.

Some of the metrics I came across are:-

  • sign of log fold change * -log10(p-value[not adjusted p-val])
  • logfc shrink values from DESeq2
  • Inbuilt signal2noise from GSEA. However, this cannot be used in case of <3 replicates.

What metric do you use for ranking the genes or you know is widely used?

gsea rna-seq deseq2 • 2.1k views
ADD COMMENTlink modified 21 months ago by Kevin Blighe51k • written 21 months ago by bipin20
5
gravatar for Kevin Blighe
21 months ago by
Kevin Blighe51k
Kevin Blighe51k wrote:

Edit 31st July, 2019: I gave my original answer (below) assuming that you were referring to the general process of gene enrichment (or 'gene-set enrichment analysis'), and not that you were referring to GSEA, the Broad Institute's PROGRAM that hijacked the term GSEA

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It makes sense that there is no consensus, as there are countless ways to do this. My own recommendation would be to:

  1. Set an adjusted P value cut-off
  2. Rank genes based on absolute log (base 2) fold change

I believe the most widely used method is to just set an adjusted P value and log (base 2) fold change cut-off, and to then 'throw' the resulting gene list into the GSEA without any ranking.

The lack of consensus on a proper filtering strategy may in part be due to the fact that a substantial proportion of researchers do not pay much attention to the results of GSEA. GSEA results would certainly never stand as the sole evidence in a clinical test, neither would they be sufficient evidence on which conclusions could be made in most reputable journals.

Kevin

ADD COMMENTlink modified 3 months ago • written 21 months ago by Kevin Blighe51k
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