{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,31]],"date-time":"2026-03-31T21:46:33Z","timestamp":1774993593604,"version":"3.50.1"},"reference-count":55,"publisher":"Oxford University Press (OUP)","issue":"19","license":[{"start":{"date-parts":[[2021,5,7]],"date-time":"2021-05-07T00:00:00Z","timestamp":1620345600000},"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 Science Foundation of China","doi-asserted-by":"publisher","award":["11931008"],"award-info":[{"award-number":["11931008"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Science Foundation of China","doi-asserted-by":"publisher","award":["61771009"],"award-info":[{"award-number":["61771009"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Science Foundation of China","doi-asserted-by":"publisher","award":["2020YFA0712400"],"award-info":[{"award-number":["2020YFA0712400"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000001","name":"US National Science Foundation","doi-asserted-by":"crossref","award":["DBI-1661332"],"award-info":[{"award-number":["DBI-1661332"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021,10,11]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Motivation<\/jats:title>\n                    <jats:p>Recent breakthroughs of single-cell RNA sequencing (scRNA-seq) technologies offer an exciting opportunity to identify heterogeneous cell types in complex tissues. However, the unavoidable biological noise and technical artifacts in scRNA-seq data as well as the high dimensionality of expression vectors make the problem highly challenging. Consequently, although numerous tools have been developed, their accuracy remains to be improved.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Here, we introduce a novel clustering algorithm and tool RCSL (Rank Constrained Similarity Learning) to accurately identify various cell types using scRNA-seq data from a complex tissue. RCSL considers both local similarity and global similarity among the cells to discern the subtle differences among cells of the same type as well as larger differences among cells of different types. RCSL uses Spearman\u2019s rank correlations of a cell\u2019s expression vector with those of other cells to measure its global similarity, and adaptively learns neighbor representation of a cell as its local similarity. The overall similarity of a cell to other cells is a linear combination of its global similarity and local similarity. RCSL automatically estimates the number of cell types defined in the similarity matrix, and identifies them by constructing a block-diagonal matrix, such that its distance to the similarity matrix is minimized. Each block-diagonal submatrix is a cell cluster\/type, corresponding to a connected component in the cognate similarity graph. When tested on 16 benchmark scRNA-seq datasets in which the cell types are well-annotated, RCSL substantially outperformed six state-of-the-art methods in accuracy and robustness as measured by three metrics.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Availability and implementation<\/jats:title>\n                    <jats:p>The RCSL algorithm is implemented in R and can be freely downloaded at https:\/\/cran.r-project.org\/web\/packages\/RCSL\/index.html.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Supplementary information<\/jats:title>\n                    <jats:p>Supplementary data are available at Bioinformatics online.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btab276","type":"journal-article","created":{"date-parts":[[2021,4,23]],"date-time":"2021-04-23T09:07:48Z","timestamp":1619168868000},"page":"3235-3242","source":"Crossref","is-referenced-by-count":14,"title":["Clustering single-cell RNA-seq data by rank constrained similarity learning"],"prefix":"10.1093","volume":"37","author":[{"given":"Qinglin","family":"Mei","sequence":"first","affiliation":[{"name":"Research Center for Mathematics and Interdisciplinary Sciences, Shandong University , Jinan 250100, China"},{"name":"School of Mathematics, Shandong University , Jinan 250100, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1581-5897","authenticated-orcid":false,"given":"Guojun","family":"Li","sequence":"additional","affiliation":[{"name":"Research Center for Mathematics and Interdisciplinary Sciences, Shandong University , Jinan 250100, China"},{"name":"School of Mathematics, Shandong University , Jinan 250100, China"},{"name":"School of Mathematical Science, Liaocheng University , Liaocheng 252000, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4636-3440","authenticated-orcid":false,"given":"Zhengchang","family":"Su","sequence":"additional","affiliation":[{"name":"Department of Bioinformatics and Genomics, The University of North Carolina at Charlotte , Charlotte, NC 28223, USA"}]}],"member":"286","published-online":{"date-parts":[[2021,5,7]]},"reference":[{"key":"2023051608272745100_btab276-B3","doi-asserted-by":"crossref","first-page":"937","DOI":"10.1038\/ng1616","article-title":"Contributions of low molecule number and chromosomal positioning to stochastic gene expression","volume":"37","author":"Becskei","year":"2005","journal-title":"Nat. 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