How to calculate enrichment score (ES) in gene enrichment analysis?

[Chris Doan]

Hello GSEA team,

Would you please explain what value is measured at the beginning to calculate ES? I read this paper:

https://www.pnas.org/doi/10.1073/pnas.0506580102 and Googled the term and still haven't understood yet.

"The score is calculated by walking down the list L, increasing a running-sum statistic when we encounter a gene in S and decreasing it when we encounter genes not in S."

Thank you so much!

[Anthony Castanza]

Hi Chr

I'm not sure I understand what you mean by "what value is measured at the beginning to calculate ES", what beginning?
When you run GSEA you have two pieces of data, a ranked list of genes (i.e. a list of all expressed genes and their log2fc values) and a set of genes of interest. GSEA takes the ranked list of genes and walks down the list at each position asking "is this gene in the set of interest or is it not", if the gene is in the set, the running enrichment score is increased by an amount proportional to the ranking metric, if the gene is not in the set, the running enrichment score is decreased. At the end of the ranked list, GSEA finds the maximum deviation from zero and that value is given as the enrichment score. GSEA does some additional math to produce permutation based pValues and FDRs as well as to normalize this enrichment score for the effects of gene set size, but that is the core principle.

Hope this answered your question

-Anthony

Anthony S. Castanza, PhD

Curator, Molecular Signatures Database

Mesirov Lab, Department of Medicine

University of California, San Diego

[Chris Doan]

Hi Anthony,

Your answer gave me a better understanding! Would you please tell me in a ranked list of genes, how we rank these genes? Which value do we use to rank these genes? Is that log2fc?

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Chris Doan (he/him)

Master Bioinformatics student