Gene set proximity analysis: expanding gene set enrichment analysis through learned geometric embeddings, with drug-repurposing applications in COVID-19

Motivation: Gene set analysis methods rely on knowledge-based representations of genetic interactions in the form of both gene set collections and protein-protein interaction (PPI) networks. However, explicit representations of genetic interactions often fail to capture complex interdependencies among genes, limiting the analytic power of such methods. Results: We propose an extension of gene set enrichment analysis to a latent embedding space reflecting PPI network topology, called gene set proximity analysis (GSPA). Compared with existing methods, GSPA provides improved ability to identify disease-associated pathways in disease-matched gene expression datasets, while improving reproducibility of enrichment statistics for similar gene sets. GSPA is statistically straightforward, reducing to a version of traditional gene set enrichment analysis through a single user-defined parameter. We apply our method to identify novel drug associations with SARS-CoV-2 viral entry. Finally, we validate our drug association predictions through retrospective clinical analysis of claims data from 8 million patients, supporting a role for gabapentin as a risk factor and metformin as a protective factor for severe COVID-19. Availability and implementation: GSPA is available for download as a command-line Python package at .Supplementary informationare available at Bioinformatics online.

Automated summary

This study proposes a gene set proximity analysis (GSPA) method based on protein-protein interaction (PPI) network topology, which improves the ability to identify disease-associated pathways and enhances the reproducibility of enrichment statistics. The method successfully identifies novel drug associations with SARS-CoV-2 viral entry and validates the predictions through clinical analysis.