PRISM-GEP: Viewing single-cell expression through the lens of topic modeling.

Abstract

Motivation: Gene-expression programs (GEPs) are the co-regulated gene modules whose coordinated activity encodes biological processes. Recovering them from single-cell RNA-seq is hard because regulation is many-to-many. A cell can run several programs at once, and a gene can take part in several. Methods that resolve programs along a trajectory break this structure by assigning each gene to a single program.

Results: PRISM-GEP discovers overlapping GEPs with Latent Dirichlet Allocation, giving it a prior estimated from gene co-expression in place of the flat default it normally assumes. No reference dataset or pathway database is required. Across 15 human and mouse datasets, PRISM-GEP matches specialized factorizations such as cNMF and scHPF on Gene Ontology Biological Process metrics. Over the 43 rankable dataset-by-metric entries, its mean rank is level with the best, and unlike every specialized method it never ranks last. The same co-expression geometry orders the genes within each program, recovering developmental cascades on par with GeneTrajectory.

Availability and implementation: The implementation of our methods, including all code, datasets, and experimental workflows, is available in Python at https://github.com/shaham-lab/PRISM-GEP. The version used in this paper is archived at https://doi.org/10.5281/zenodo.22659056.