{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,25]],"date-time":"2026-10-25T00:23:10Z","timestamp":1792887790581,"version":"4.5.0"},"reference-count":41,"publisher":"Oxford University Press (OUP)","issue":"3","license":[{"start":{"date-parts":[[2024,9,24]],"date-time":"2024-09-24T00:00:00Z","timestamp":1727136000000},"content-version":"vor","delay-in-days":84,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["R01HL160229"],"award-info":[{"award-number":["R01HL160229"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,7,2]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Single-cell gene set analysis (scGSA) provides a useful approach for quantifying molecular functions and pathways in high-throughput transcriptomic data, facilitating the biological interpretation of complex human datasets. However, various factors such as gene set size, quality of the gene sets and the dropouts\u00a0impact the performance of scGSA. To address these limitations, we present a single-cell Pathway Score (scPS) method to measure gene set activity at single-cell resolution. Furthermore, we benchmark our method with six other methods: AUCell, AddModuleScore, JASMINE, UCell, SCSE and ssGSEA. The comparison across all the methods using two different simulation approaches highlights the effect of cell count, gene set size, noise, condition-specific genes and zero imputation on their performance. The results of our study indicate that the scPS is comparable with other single-cell scoring methods and detects fewer false positives. Importantly, this work reveals critical variables in the scGSA.<\/jats:p>","DOI":"10.1093\/nargab\/lqae124","type":"journal-article","created":{"date-parts":[[2024,9,24]],"date-time":"2024-09-24T12:48:46Z","timestamp":1727182126000},"source":"Crossref","is-referenced-by-count":20,"title":["Comparative analysis of single-cell pathway scoring methods and a novel approach"],"prefix":"10.1093","volume":"6","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2013-4850","authenticated-orcid":false,"given":"Ruoqiao\u00a0H","family":"Wang","sequence":"first","affiliation":[{"name":"Department of Biomedical Genetics, University of Rochester , 601 Elmwood Ave, NY 14642 , USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4479-4183","authenticated-orcid":false,"given":"Juilee","family":"Thakar","sequence":"additional","affiliation":[{"name":"Department of Biomedical Genetics, University of Rochester , 601 Elmwood Ave, NY 14642 , USA"},{"name":"Department of Microbiology and Immunology, University of Rochester , 601 Elmwood Ave, NY 14642 , USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2024,9,24]]},"reference":[{"key":"2024092410562741200_B1","doi-asserted-by":"crossref","first-page":"1940010","DOI":"10.1142\/S0219720019400109","article-title":"Method choice in gene set analysis has important consequences for analysis outcome","volume":"17","author":"Maleki","year":"2019","journal-title":"J. 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