GSEA on single-cell cluster data
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4.0 years ago
roy.granit ▴ 880

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!

single-cell GSEA R • 10k views
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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.

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4.0 years ago
igor 13k

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.
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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..

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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.

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3.6 years ago

Hi Roy. What do you think about pagoda?

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

Best, Marco

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