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


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.


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