{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,15]],"date-time":"2026-03-15T07:44:11Z","timestamp":1773560651766,"version":"3.50.1"},"reference-count":42,"publisher":"Oxford University Press (OUP)","issue":"19","license":[{"start":{"date-parts":[[2022,8,19]],"date-time":"2022-08-19T00:00:00Z","timestamp":1660867200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62076109"],"award-info":[{"award-number":["62076109"]}],"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":["61972174"],"award-info":[{"award-number":["61972174"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"the Fundamental Research Funds for the Central Universities"},{"name":"Research Grants Council of the Hong Kong Special Administrative Region [CityU","award":["11200218"],"award-info":[{"award-number":["11200218"]}]},{"DOI":"10.13039\/501100005847","name":"Health and Medical Research Fund","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100005847","id-type":"DOI","asserted-by":"publisher"}]},{"name":"the Food and Health Bureau"},{"name":"The Government of the Hong Kong Special Administrative Region","award":["07181426"],"award-info":[{"award-number":["07181426"]}]},{"name":"Hong Kong Institute for Data Science (HKIDS) at City University of Hong Kong. The work described in this article was partially supported by two grants from City University of Hong Kong","award":["CityU 11202219"],"award-info":[{"award-number":["CityU 11202219"]}]},{"name":"Hong Kong Institute for Data Science (HKIDS) at City University of Hong Kong. The work described in this article was partially supported by two grants from City University of Hong Kong","award":["CityU 11203520"],"award-info":[{"award-number":["CityU 11203520"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,9,30]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Motivation<\/jats:title><jats:p>Single-cell RNA sequencing (scRNA-seq) can provide insight into gene expression patterns at the resolution of individual cells, which offers new opportunities to study the behavior of different cell types. However, it is often plagued by dropout events, a phenomenon where the expression value of a gene tends to be measured as zero in the expression matrix due to various technical defects.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>In this article, we argue that borrowing gene and cell information across column and row subspaces directly results in suboptimal solutions due to the noise contamination in imputing dropout values. Thus, to impute more precisely the dropout events in scRNA-seq data, we develop a regularization for leveraging that imperfect prior information to estimate the true underlying prior subspace and then embed it in a typical low-rank matrix completion-based framework, named scWMC. To evaluate the performance of the proposed method, we conduct comprehensive experiments on simulated and real scRNA-seq data. Extensive data analysis, including simulated analysis, cell clustering, differential expression analysis, functional genomic analysis, cell trajectory inference and scalability analysis, demonstrate that our method produces improved imputation results compared to competing methods that benefits subsequent downstream analysis.<\/jats:p><\/jats:sec><jats:sec><jats:title>Availability and implementation<\/jats:title><jats:p>The source code is available at https:\/\/github.com\/XuYuanchi\/scWMC and test data is available at https:\/\/doi.org\/10.5281\/zenodo.6832477.<\/jats:p><\/jats:sec><jats:sec><jats:title>Supplementary information<\/jats:title><jats:p>Supplementary data are available at Bioinformatics online.<\/jats:p><\/jats:sec>","DOI":"10.1093\/bioinformatics\/btac570","type":"journal-article","created":{"date-parts":[[2022,8,19]],"date-time":"2022-08-19T13:18:25Z","timestamp":1660915105000},"page":"4537-4545","source":"Crossref","is-referenced-by-count":7,"title":["scWMC: weighted matrix completion-based imputation of scRNA-seq data via prior subspace information"],"prefix":"10.1093","volume":"38","author":[{"given":"Yanchi","family":"Su","sequence":"first","affiliation":[{"name":"School of Artificial Intelligence, Jilin University , Changchun 130012, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9028-5382","authenticated-orcid":false,"given":"Fuzhou","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Computer Science, City University of Hong Kong , Hong Kong 999077, Hong Kong SAR"}]},{"given":"Shixiong","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Xidian University , Xian 710000, China"}]},{"given":"Yanchun","family":"Liang","sequence":"additional","affiliation":[{"name":"Zhuhai Laboratory of Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education, Zhuhai College of Science and Technology , Zhuhai 519041, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6062-733X","authenticated-orcid":false,"given":"Ka-Chun","family":"Wong","sequence":"additional","affiliation":[{"name":"Department of Computer Science, City University of Hong Kong , Hong Kong 999077, Hong Kong SAR"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8716-9823","authenticated-orcid":false,"given":"Xiangtao","family":"Li","sequence":"additional","affiliation":[{"name":"School of Artificial Intelligence, Jilin University , Changchun 130012, China"}]}],"member":"286","published-online":{"date-parts":[[2022,8,19]]},"reference":[{"key":"2023041408241170400_","doi-asserted-by":"crossref","first-page":"1083","DOI":"10.1038\/nmeth.4463","article-title":"Scenic: single-cell regulatory network inference and clustering","volume":"14","author":"Aibar","year":"2017","journal-title":"Nat. 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