Question: GSEA on single-cell cluster data
0
gravatar for roy.granit
9 months ago by
roy.granit800
Israel/LabWorm
roy.granit800 wrote:

Hello Biostars, I have 10x SC expression data which I have processed using Seurat, and now I wish to test the association of several gene-sets with certain clusters. What approach/R package would you recommend?

I suppose I can run GSEA on the expression of individual cells, but next how do I summarize a cluster?

Thanks!

gsea R single-cell • 1.6k views
ADD COMMENTlink modified 4 months ago by marcovicari30 • written 9 months ago by roy.granit800
1

GSVA tries to estimate the pathway expression levels in every individual sample and then allows you to perform comparisons or unsupervised analysis like clustering on the transformed matrix.

ADD REPLYlink written 9 months ago by Martombo2.5k
1
gravatar for igor
9 months ago by
igor8.0k
United States
igor8.0k wrote:

Some options:

  • Take the top X cluster markers (genes upregulated in that cluster) and run GSEA (or any other pathway analysis) on those.
  • Calculate the fold changes between that cluster and other clusters and use that as a basis for a pre-ranked list for GSEA.
ADD COMMENTlink written 9 months ago by igor8.0k

Thanks, not sure about these suggestions, I believe they are correct for pathway over-representation but not for hyper-geometric/GSEA analysis. I was seeking some tool that would take the individual GSEA scores for each cell in a certain cluster and conduct a statistical comparison of these to the scores by cells in rest of cluster/other cluster. Something like GSVA which was suggested here above..

ADD REPLYlink modified 9 months ago • written 9 months ago by roy.granit800

Due to the dropouts, the result for individual cells will be extremely noisy. Cluster-level analysis would be much cleaner.

GSEA is not meant for over-representation, but I have seen it used that way many times. Regardless, as I mentioned, there are many other pathway analysis tools that are appropriate for that approach.

Single-sample enrichment approaches like GSVA are fine. However, unless the differences between your sub-populations are dramatic, they may not be able to effectively capture them. This is why I suggested to focus on the genes that are actually differentially expressed.

ADD REPLYlink modified 9 months ago • written 9 months ago by igor8.0k
0
gravatar for marcovicari3
4 months ago by
marcovicari30 wrote:

Hi Roy. What do you think about pagoda?

http://hms-dbmi.github.io/scde/pagoda.html

Best, Marco

ADD COMMENTlink written 4 months ago by marcovicari30
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