{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,8]],"date-time":"2025-12-08T22:35:24Z","timestamp":1765233324483,"version":"3.37.3"},"reference-count":46,"publisher":"Oxford University Press (OUP)","issue":"15","license":[{"start":{"date-parts":[[2022,6,14]],"date-time":"2022-06-14T00:00:00Z","timestamp":1655164800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"funder":[{"name":"Xinjiang Autonomous Region University Research Program","award":["XJEDU2019Y002"],"award-info":[{"award-number":["XJEDU2019Y002"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["U19A2064","61873001"],"award-info":[{"award-number":["U19A2064","61873001"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Graduate innovation project of Xinjiang Uygur Autonomous Region","award":["XJ2021G023"],"award-info":[{"award-number":["XJ2021G023"]}]},{"name":"Information Materials and Intelligent Sensing Laboratory of Anhui Province","award":["IMIS202105"],"award-info":[{"award-number":["IMIS202105"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,8,2]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Motivation<\/jats:title><jats:p>A large number of studies have shown that clustering is a crucial step in scRNA-seq analysis. Most existing methods are based on unsupervised learning without the prior exploitation of any domain knowledge, which does not utilize available gold-standard labels. When confronted by the high dimensionality and general dropout events of scRNA-seq data, purely unsupervised clustering methods may not produce biologically interpretable clusters, which complicate cell type assignment.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>In this article, we propose a semi-supervised clustering method based on a capsule network named scCNC that integrates domain knowledge into the clustering step. Significantly, we also propose a Semi-supervised Greedy Iterative Training method used to train the whole network. Experiments on some real scRNA-seq datasets show that scCNC can significantly improve clustering performance and facilitate downstream analyses.<\/jats:p><\/jats:sec><jats:sec><jats:title>Availability and implementation<\/jats:title><jats:p>The source code of scCNC is freely available at https:\/\/github.com\/WHY-17\/scCNC.<\/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\/btac393","type":"journal-article","created":{"date-parts":[[2022,6,14]],"date-time":"2022-06-14T13:40:09Z","timestamp":1655214009000},"page":"3703-3709","source":"Crossref","is-referenced-by-count":10,"title":["scCNC: a method based on capsule network for clustering scRNA-seq data"],"prefix":"10.1093","volume":"38","author":[{"given":"Hai-Yun","family":"Wang","sequence":"first","affiliation":[{"name":"College of Mathematics and System Sciences, Xinjiang University , Urumqi 830046, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8486-744X","authenticated-orcid":false,"given":"Jian-Ping","family":"Zhao","sequence":"additional","affiliation":[{"name":"College of Mathematics and System Sciences, Xinjiang University , Urumqi 830046, China"},{"name":"Institute of Mathematics and Physics, Xinjiang University , Urumqi 830046, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chun-Hou","family":"Zheng","sequence":"additional","affiliation":[{"name":"College of Mathematics and System Sciences, Xinjiang University , Urumqi 830046, China"},{"name":"School of Artificial Intelligence, Anhui University , Hefei 230039, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yan-Sen","family":"Su","sequence":"additional","affiliation":[{"name":"School of Artificial Intelligence, Anhui University , Hefei 230039, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"286","published-online":{"date-parts":[[2022,6,14]]},"reference":[{"key":"2023041405354234900_","doi-asserted-by":"crossref","first-page":"38","DOI":"10.1038\/nbt.4314","article-title":"Dimensionality reduction for visualizing single-cell data using UMAP","volume":"37","author":"Becht","year":"2019","journal-title":"Nat. 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