{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,8,27]],"date-time":"2025-08-27T15:53:05Z","timestamp":1756309985417,"version":"3.41.2"},"reference-count":46,"publisher":"Oxford University Press (OUP)","issue":"1","license":[{"start":{"date-parts":[[2024,11,25]],"date-time":"2024-11-25T00:00:00Z","timestamp":1732492800000},"content-version":"vor","delay-in-days":3,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100000265","name":"Medical Research Council","doi-asserted-by":"publisher","award":["MR\/X014088\/1"],"award-info":[{"award-number":["MR\/X014088\/1"]}],"id":[{"id":"10.13039\/501100000265","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,11,22]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Single-cell multiomics have opened up tremendous opportunities for understanding gene regulatory networks underlying cell states by simultaneously profiling transcriptomes, epigenomes, and proteomes of the same cell. However, existing computational methods for integrative analysis of these high-dimensional multiomics data are either computationally expensive or limited in interpretation. These limitations pose challenges in the implementation of these methods in large-scale studies and hinder a more in-depth understanding of the underlying regulatory mechanisms. Here, we propose TriTan (Triple inTegrative fast non-negative matrix factorization), an efficient joint factorization method for single-cell multiomics data. TriTan implements a highly efficient factorization algorithm, greatly improving its computational performance. Three matrix factorization produced by TriTan helps in clustering cells, identifying signature features for each cell type, and uncovering feature associations across omics, which facilitates the identification of domains of regulatory chromatin and the prediction of cell-type-specific regulatory networks. We applied TriTan to the single-cell multiomics data obtained from different technologies and benchmarked it against the state-of-the-art methods where it shows highly competitive performance. Furthermore, we showed a range of downstream analyses conducted utilizing TriTan outputs, highlighting its capacity to facilitate interpretation in biological discovery.<\/jats:p>","DOI":"10.1093\/bib\/bbae615","type":"journal-article","created":{"date-parts":[[2024,11,25]],"date-time":"2024-11-25T02:56:45Z","timestamp":1732503405000},"source":"Crossref","is-referenced-by-count":1,"title":["TriTan: an efficient triple nonnegative matrix factorization method for integrative analysis of single-cell multiomics data"],"prefix":"10.1093","volume":"26","author":[{"given":"Xin","family":"Ma","sequence":"first","affiliation":[{"name":"Division of Informatics , Imaging and Data Sciences, Faculty of Biology, Medicine and Health, , Oxford Rd, Manchester, M13 9PL ,","place":["UK"]},{"name":"University of Manchester , Imaging and Data Sciences, Faculty of Biology, Medicine and Health, , Oxford Rd, Manchester, M13 9PL ,","place":["UK"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lijing","family":"Lin","sequence":"additional","affiliation":[{"name":"Centre for Health Informatics , Division of Informatics, Imaging and Data Sciences, Faculty of Biology, Medicine and Health, , Oxford Rd, Manchester, M13 9PL ,","place":["UK"]},{"name":"University of Manchester , Division of Informatics, Imaging and Data Sciences, Faculty of Biology, Medicine and Health, , Oxford Rd, Manchester, M13 9PL ,","place":["UK"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qian","family":"Zhao","sequence":"additional","affiliation":[{"name":"Division of Informatics , Imaging and Data Sciences, Faculty of Biology, Medicine and Health, , Oxford Rd, Manchester, M13 9PL ,","place":["UK"]},{"name":"University of Manchester , Imaging and Data Sciences, Faculty of Biology, Medicine and Health, , Oxford Rd, Manchester, M13 9PL ,","place":["UK"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mudassar","family":"Iqbal","sequence":"additional","affiliation":[{"name":"Division of Informatics , Imaging and Data Sciences, Faculty of Biology, Medicine and Health, , Oxford Rd, Manchester, M13 9PL ,","place":["UK"]},{"name":"University of Manchester , Imaging and Data Sciences, Faculty of Biology, Medicine and Health, , Oxford Rd, Manchester, M13 9PL ,","place":["UK"]}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"286","published-online":{"date-parts":[[2024,11,24]]},"reference":[{"key":"2024112502563807400_ref1","doi-asserted-by":"publisher","first-page":"14","DOI":"10.1038\/s41592-019-0692-4","article-title":"Computational methods for single-cell omics across modalities","volume":"17","author":"Mirjana","year":"2020","journal-title":"Nat 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