{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,17]],"date-time":"2026-10-17T19:44:19Z","timestamp":1792266259541,"version":"4.4.0"},"reference-count":40,"publisher":"Oxford University Press (OUP)","issue":"10","license":[{"start":{"date-parts":[[2026,9,30]],"date-time":"2026-09-30T00:00:00Z","timestamp":1790726400000},"content-version":"vor","delay-in-days":1,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,10,2]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Motivation<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Availability and implementation<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btag728","type":"journal-article","created":{"date-parts":[[2026,9,28]],"date-time":"2026-09-28T11:25:56Z","timestamp":1790594756000},"source":"Crossref","is-referenced-by-count":0,"title":["PRISM-GEP: Viewing single-cell expression through the lens of topic modeling"],"prefix":"10.1093","volume":"42","author":[{"ORCID":"https:\/\/orcid.org\/0009-0001-3282-1589","authenticated-orcid":false,"given":"Yanir","family":"Buznah","sequence":"first","affiliation":[{"name":"Bar-Ilan University Department of Computer Science, , Ramat Gan,","place":["Israel"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tal","family":"Ishon","sequence":"additional","affiliation":[{"name":"Bar-Ilan University Department of Computer Science, , Ramat 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