{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,24]],"date-time":"2026-07-24T16:27:40Z","timestamp":1784910460347,"version":"3.55.0"},"reference-count":42,"publisher":"Oxford University Press (OUP)","issue":"2","license":[{"start":{"date-parts":[[2025,4,27]],"date-time":"2025-04-27T00:00:00Z","timestamp":1745712000000},"content-version":"vor","delay-in-days":29,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000060","name":"National Institute of Allergy and Infectious Diseases","doi-asserted-by":"publisher","award":["19AI144181"],"award-info":[{"award-number":["19AI144181"]}],"id":[{"id":"10.13039\/100000060","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000049","name":"National Institute on Aging","doi-asserted-by":"publisher","award":["01AG073259"],"award-info":[{"award-number":["01AG073259"]}],"id":[{"id":"10.13039\/100000049","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000049","name":"National Institute on Aging","doi-asserted-by":"publisher","award":["01AI170959-01A1"],"award-info":[{"award-number":["01AI170959-01A1"]}],"id":[{"id":"10.13039\/100000049","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,3,29]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Accurate deconvolution of cell types from bulk gene expression is crucial for understanding cellular compositions and uncovering cell-type specific differential expression and physiological states of diseased tissues. Existing deconvolution methods have limitations, such as requiring complete cellular gene expression signatures or neglecting partial biological information. Moreover, these methods often overlook varying cell-type\u00a0messenger RNA\u00a0amounts, leading to biased proportion estimates. Additionally, they do not effectively utilize valuable reference information from external studies, such as means and ranges of population cell-type proportions. To address these challenges, we introduce an adaptive regularized tri-factor non-negative matrix factorization approach for deconvolution (ARTdeConv). We rigorously establish the numerical convergence of our algorithm. Through benchmark simulations, we demonstrate the superior performance of ARTdeConv compared to state-of-the-art semi-reference-based and reference-free methods as well as its robustness under challenges to its assumptions. In a real-world application to\u00a0a dataset from a trivalent influenza vaccine study, our method accurately estimates cellular proportions, as evidenced by the nearly perfect Pearson\u2019s correlation between ARTdeConv estimates and flow cytometry measurements. Moreover, our analysis of ARTdeConv estimates in COVID-19 patients reveals patterns consistent with important immunological phenomena observed in other studies. The proposed method, ARTdeConv, is implemented as an R package and can be accessed on GitHub for researchers and practitioners.<\/jats:p>","DOI":"10.1093\/nargab\/lqaf046","type":"journal-article","created":{"date-parts":[[2025,4,27]],"date-time":"2025-04-27T12:39:04Z","timestamp":1745757544000},"source":"Crossref","is-referenced-by-count":1,"title":["ARTdeConv: adaptive regularized tri-factor non-negative matrix factorization for\u00a0cell type deconvolution"],"prefix":"10.1093","volume":"7","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2484-2478","authenticated-orcid":false,"given":"Tianyi","family":"Liu","sequence":"first","affiliation":[{"name":"The University of North Carolina at Chapel Hill Department of Biostatistics, , Chapel Hill, NC, 27599,","place":["USA"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chuwen","family":"Liu","sequence":"additional","affiliation":[{"name":"The University of North Carolina at Chapel Hill Department of Biostatistics, , 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