{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,31]],"date-time":"2025-12-31T10:12:01Z","timestamp":1767175921799,"version":"build-2238731810"},"update-to":[{"DOI":"10.1371\/journal.pcbi.1013375","type":"new_version","label":"New version","source":"publisher","updated":{"date-parts":[[2025,9,8]],"date-time":"2025-09-08T00:00:00Z","timestamp":1757289600000}}],"reference-count":24,"publisher":"Public Library of Science (PLoS)","issue":"8","license":[{"start":{"date-parts":[[2025,8,18]],"date-time":"2025-08-18T00:00:00Z","timestamp":1755475200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62372303,62002234"],"award-info":[{"award-number":["62372303,62002234"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62131004"],"award-info":[{"award-number":["62131004"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100021171","name":"Basic and Applied Basic Research Foundation of Guangdong Province","doi-asserted-by":"publisher","award":["2024A1515010113"],"award-info":[{"award-number":["2024A1515010113"]}],"id":[{"id":"10.13039\/501100021171","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Shenzhen Science and Technology Program","award":["RCYX20231211090244048"],"award-info":[{"award-number":["RCYX20231211090244048"]}]},{"name":"HKRGC GRF","award":["17301519"],"award-info":[{"award-number":["17301519"]}]}],"content-domain":{"domain":["www.ploscompbiol.org"],"crossmark-restriction":false},"short-container-title":["PLoS Comput Biol"],"abstract":"<jats:p>The development of single-cell multi-omics sequencing technologies has enabled the simultaneous analysis of multi-omics data within the same cell. Accurate clustering of these cells is crucial for downstream analyses of complex biological functions. Despite significant advances in multi-omics integration approaches, current methodologies exhibit two major limitations. First, they inadequately incorporate prior biological knowledge from various omic layers. Second, these methods often conduct independent dimensionality reduction on individual omic datasets, thereby failing to capture the intrinsic complementary information and potentially overlooking crucial cross-platform interactions. Motivated by these, this study investigates a non-negative matrix factorization model called PLNMFG, which integrates the unified latent representation learning that retains the features between and within omics and the cluster structure learning that retains the intrinsic structure of the data into one joint framework. Specially, PLNMFG performs adaptive imputation to handle dropout events and uses prior pseudo-labels as constraints during the process of collective non-negative matrix factorization, as a result, a more robust latent representation that preserves the double similarity information is obtained. Graph Laplacian constraint is applied during clustering which further preserves structure characteristic of multi-omics data. In addition, the weight of each omic is adaptively learned based on the omic contribution. A series of experiments on 8 benchmark datasets show that our model performs well in terms of clustering accuracy and computational efficiency.<\/jats:p>","DOI":"10.1371\/journal.pcbi.1013375","type":"journal-article","created":{"date-parts":[[2025,8,18]],"date-time":"2025-08-18T18:02:45Z","timestamp":1755540165000},"page":"e1013375","update-policy":"https:\/\/doi.org\/10.1371\/journal.pcbi.corrections_policy","source":"Crossref","is-referenced-by-count":1,"title":["PLNMFG: Pseudo-label guided non-negative matrix factorization model with graph constraint for single-cell multi-omics data clustering"],"prefix":"10.1371","volume":"21","author":[{"given":"Hui","family":"Yuan","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mingzhu","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9393-3648","authenticated-orcid":true,"given":"Yushan","family":"Qiu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wai-Ki","family":"Ching","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Quan","family":"Zou","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"340","published-online":{"date-parts":[[2025,8,18]]},"reference":[{"issue":"2","key":"pcbi.1013375.ref001","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1038\/nrg3868","article-title":"Methods of integrating data to uncover genotype-phenotype interactions","volume":"16","author":"MD Ritchie","year":"2015","journal-title":"Nat Rev Genet"},{"issue":"4","key":"pcbi.1013375.ref002","doi-asserted-by":"crossref","first-page":"276","DOI":"10.1016\/j.tibtech.2015.12.013","article-title":"Trans-omics: how to reconstruct biochemical networks across multiple \u201cOmic\u201d layers","volume":"34","author":"K Yugi","year":"2016","journal-title":"Trends Biotechnol"},{"issue":"1","key":"pcbi.1013375.ref003","doi-asserted-by":"crossref","DOI":"10.1093\/bioinformatics\/btac736","article-title":"Clustering single-cell multi-omics data with MoClust","volume":"39","author":"M Yuan","year":"2023","journal-title":"Bioinformatics"},{"key":"pcbi.1013375.ref004","doi-asserted-by":"crossref","unstructured":"Gayoso A, et al. 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