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Although several deep learning-based clustering algorithms have been proposed, most existing clustering methods have limitations in capturing the precise distribution types of the data or fully utilizing the relationships between cells, leaving a considerable scope for improving the clustering performance, particularly in detecting rare cell populations from large scRNA-seq data. We introduce DeepScena, a novel single-cell hierarchical clustering tool that fully incorporates nonlinear dimension reduction, negative binomial-based convolutional autoencoder for data fitting, and a self-supervision model for cell similarity enhancement. In comprehensive evaluation using multiple large-scale scRNA-seq datasets, DeepScena consistently outperformed seven popular clustering tools in terms of accuracy. Notably, DeepScena exhibits high proficiency in identifying rare cell populations within large datasets that contain large numbers of clusters. When applied to scRNA-seq data of multiple myeloma cells, DeepScena successfully identified not only previously labeled large cell types but also subpopulations in CD14 monocytes, T cells and natural killer cells, respectively.<\/jats:p>","DOI":"10.1093\/bib\/bbad335","type":"journal-article","created":{"date-parts":[[2023,9,28]],"date-time":"2023-09-28T22:35:50Z","timestamp":1695940550000},"source":"Crossref","is-referenced-by-count":31,"title":["Self-supervised deep clustering of single-cell RNA-seq data to hierarchically detect rare cell populations"],"prefix":"10.1093","volume":"24","author":[{"given":"Tianyuan","family":"Lei","sequence":"first","affiliation":[{"name":"Tianjin Normal University College of Computer and Information Engineering, , Tianjin 300387 , China"}]},{"given":"Ruoyu","family":"Chen","sequence":"additional","affiliation":[{"name":"Moorestown High School , Moorestown, NJ 08057 , USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4127-0539","authenticated-orcid":false,"given":"Shaoqiang","family":"Zhang","sequence":"additional","affiliation":[{"name":"Tianjin Normal University College of Computer and Information Engineering, , Tianjin 300387 , China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6827-4321","authenticated-orcid":false,"given":"Yong","family":"Chen","sequence":"additional","affiliation":[{"name":"Rowan University Department of Biological and Biomedical Sciences, , NJ 08028 , USA"}]}],"member":"286","published-online":{"date-parts":[[2023,9,28]]},"reference":[{"issue":"1","key":"2023092821540893000_ref1","doi-asserted-by":"crossref","first-page":"e57","DOI":"10.1002\/cpmb.57","article-title":"Introduction to single-cell RNA sequencing","volume":"122","author":"Olsen","year":"2018","journal-title":"Curr Protoc Mol 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