{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,25]],"date-time":"2026-07-25T05:03:01Z","timestamp":1784955781745,"version":"3.55.0"},"reference-count":54,"publisher":"Oxford University Press (OUP)","issue":"2","license":[{"start":{"date-parts":[[2022,2,5]],"date-time":"2022-02-05T00:00:00Z","timestamp":1644019200000},"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":["62072003"],"award-info":[{"award-number":["62072003"]}],"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":["61873001"],"award-info":[{"award-number":["61873001"]}],"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":["U19A2064"],"award-info":[{"award-number":["U19A2064"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,3,10]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Rapid development of single-cell RNA sequencing (scRNA-seq) technology has allowed researchers to explore biological phenomena at the cellular scale. Clustering is a crucial and helpful step for researchers to study the heterogeneity of cell. Although many clustering methods have been proposed, massive dropout events and the curse of dimensionality in scRNA-seq data make it still difficult to analysis because they reduce the accuracy of clustering methods, leading to misidentification of cell types. In this work, we propose the scHFC, which is a hybrid fuzzy clustering method optimized by natural computation based on Fuzzy C Mean (FCM) and Gath-Geva (GG) algorithms. Specifically, principal component analysis algorithm is utilized to reduce the dimensions of scRNA-seq data after it is preprocessed. Then, FCM algorithm optimized by simulated annealing algorithm and genetic algorithm is applied to cluster the data to output a membership matrix, which represents the initial clustering result and is taken as the input for GG algorithm to get the final clustering results. We also develop a cluster number estimation method called multi-index comprehensive estimation, which can estimate the cluster numbers well by combining four clustering effectiveness indexes. The performance of the scHFC method is evaluated on 17 scRNA-seq datasets, and compared with six state-of-the-art methods. Experimental results validate the better performance of our scHFC method in terms of clustering accuracy and stability of algorithm. In short, scHFC is an effective method to cluster cells for scRNA-seq data, and it presents great potential for downstream analysis of scRNA-seq data. The source code is available at https:\/\/github.com\/WJ319\/scHFC.<\/jats:p>","DOI":"10.1093\/bib\/bbab588","type":"journal-article","created":{"date-parts":[[2021,12,23]],"date-time":"2021-12-23T12:12:39Z","timestamp":1640261559000},"source":"Crossref","is-referenced-by-count":17,"title":["scHFC: a hybrid fuzzy clustering method for single-cell RNA-seq data optimized by natural computation"],"prefix":"10.1093","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1345-1092","authenticated-orcid":false,"given":"Jing","family":"Wang","sequence":"first","affiliation":[{"name":"Key Lab of Intelligent Computing and Signal Processing of Ministry of Education, School of Computer Science and Technology, Anhui University, Hefei, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3024-1705","authenticated-orcid":false,"given":"Junfeng","family":"Xia","sequence":"additional","affiliation":[{"name":"Institutes of Physical Science and Information Technology, Anhui University, Hefei, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dayu","family":"Tan","sequence":"additional","affiliation":[{"name":"Institutes of Physical Science and Information Technology, Anhui University, Hefei, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rongxin","family":"Lin","sequence":"additional","affiliation":[{"name":"Key Lab of Intelligent Computing and Signal Processing of Ministry of Education, School of Computer Science and Technology, Anhui University, Hefei, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yansen","family":"Su","sequence":"additional","affiliation":[{"name":"Key Lab of Intelligent Computing and Signal Processing of Ministry of Education, School of Artificial Intelligence, Anhui University, Hefei, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chun-Hou","family":"Zheng","sequence":"additional","affiliation":[{"name":"Key Lab of Intelligent Computing and Signal Processing of Ministry of Education, School of Artificial Intelligence, Anhui University, Hefei, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2022,2,5]]},"reference":[{"key":"2022031506303319200_ref1","doi-asserted-by":"crossref","first-page":"236","DOI":"10.1038\/nature12172","article-title":"Single-cell transcriptomics reveals bimodality in expression and splicing in immune cells","volume":"498","author":"Shalek","year":"2013","journal-title":"Nature"},{"key":"2022031506303319200_ref2","doi-asserted-by":"crossref","first-page":"431","DOI":"10.1146\/annurev-genet-102209-163607","article-title":"Genomic analysis at the single-cell level","volume":"45","author":"Kalisky","year":"2011","journal-title":"Annu Rev Genet"},{"key":"2022031506303319200_ref3","doi-asserted-by":"crossref","first-page":"618","DOI":"10.1038\/nrg3542","article-title":"Single-cell sequencing-based technologies will revolutionize whole-organism science","volume":"14","author":"Shapiro","year":"2013","journal-title":"Nat Rev Genet"},{"key":"2022031506303319200_ref4","doi-asserted-by":"crossref","first-page":"286","DOI":"10.1093\/bfgp\/elaa003","article-title":"Identifying cell types to interpret scRNA-seq data: how, why and more possibilities","volume":"19","author":"Wang","year":"2020","journal-title":"Brief Funct Genomics"},{"key":"2022031506303319200_ref5","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1016\/j.cell.2016.01.047","article-title":"Heterogeneity in Oct4 and Sox2 targets biases cell fate in 4-cell mouse embryos","volume":"165","author":"Goolam","year":"2016","journal-title":"Cell"},{"key":"2022031506303319200_ref6","doi-asserted-by":"crossref","first-page":"251","DOI":"10.1038\/nature14966","article-title":"Single-cell messenger RNA sequencing reveals rare intestinal cell types","volume":"525","author":"Grun","year":"2015","journal-title":"Nature"},{"key":"2022031506303319200_ref7","doi-asserted-by":"crossref","first-page":"1138","DOI":"10.1126\/science.aaa1934","article-title":"Brain structure. 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