{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,7,30]],"date-time":"2025-07-30T11:43:37Z","timestamp":1753875817049,"version":"3.41.2"},"reference-count":58,"publisher":"Oxford University Press (OUP)","issue":"4","license":[{"start":{"date-parts":[[2024,6,10]],"date-time":"2024-06-10T00:00:00Z","timestamp":1717977600000},"content-version":"vor","delay-in-days":18,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61533011","U1806202","61877064"],"award-info":[{"award-number":["61533011","U1806202","61877064"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,5,23]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Cluster analysis, a pivotal step in single-cell sequencing data analysis, presents substantial opportunities to effectively unveil the molecular mechanisms underlying cellular heterogeneity and intercellular phenotypic variations. However, the inherent imperfections arise as different clustering algorithms yield diverse estimates of cluster numbers and cluster assignments. This study introduces Single Cell Consistent Clustering based on Spectral Matrix Decomposition (SCSMD), a comprehensive clustering approach that integrates the strengths of multiple methods to determine the optimal clustering scheme. Testing the performance of SCSMD across different distances and employing the bespoke evaluation metric, the methodological selection undergoes validation to ensure the optimal efficacy of the SCSMD. A consistent clustering test is conducted on 15 authentic scRNA-seq datasets. The application of SCSMD to human embryonic stem cell scRNA-seq data successfully identifies known cell types and delineates their developmental trajectories. Similarly, when applied to glioblastoma cells, SCSMD accurately detects pre-existing cell types and provides finer sub-division within one of the original clusters. The results affirm the robust performance of our SCSMD method in terms of both the number of clusters and cluster assignments. Moreover, we have broadened the application scope of SCSMD to encompass larger datasets, thereby furnishing additional evidence of its superiority. The findings suggest that SCSMD is poised for application to additional scRNA-seq datasets and for further downstream analyses.<\/jats:p>","DOI":"10.1093\/bib\/bbae273","type":"journal-article","created":{"date-parts":[[2024,6,10]],"date-time":"2024-06-10T10:13:21Z","timestamp":1718014401000},"source":"Crossref","is-referenced-by-count":0,"title":["SCSMD: Single Cell Consistent Clustering based on Spectral Matrix Decomposition"],"prefix":"10.1093","volume":"25","author":[{"given":"Ran","family":"Jia","sequence":"first","affiliation":[{"name":"School of Mathematics and Statistics, Shandong University , Weihai 264209, Shandong, China"}]},{"given":"Ying-Zan","family":"Ren","sequence":"additional","affiliation":[{"name":"School of Mathematics and Statistics, Shandong University , Weihai 264209, Shandong, China"}]},{"given":"Po-Nian","family":"Li","sequence":"additional","affiliation":[{"name":"College of Mathematics and Informatics, South China Agricultural University , Guangzhou, Guangdong, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3599-7678","authenticated-orcid":false,"given":"Rui","family":"Gao","sequence":"additional","affiliation":[{"name":"School of Control Science and Engineering, Shandong University , Jinan 250100, Shandong, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3842-1153","authenticated-orcid":false,"given":"Yu-Sen","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Mathematics and Statistics, Shandong University , Weihai 264209, Shandong, China"}]}],"member":"286","published-online":{"date-parts":[[2024,6,10]]},"reference":[{"key":"2024061010093277600_ref1","doi-asserted-by":"crossref","first-page":"2445","DOI":"10.1109\/TCBB.2020.2971993","article-title":"Evolving transcriptomic profiles from single-cell RNA-seq data using nature-inspired multiobjective optimization[J]","volume":"18","author":"Li","year":"2020","journal-title":"IEEE\/ACM Trans Comput Biol Bioinform"},{"key":"2024061010093277600_ref2","doi-asserted-by":"crossref","first-page":"36","DOI":"10.1016\/j.mam.2017.07.003","article-title":"Single-cell RNA sequencing: technical advancements and biological applications[J]","volume":"59","author":"Hedlund","year":"2018","journal-title":"Mol Aspects Med"},{"key":"2024061010093277600_ref3","doi-asserted-by":"crossref","first-page":"1773","DOI":"10.1109\/TCBB.2019.2906601","article-title":"Single-cell RNA sequencing data interpretation by evolutionary multiobjective clustering[J]","volume":"17","author":"Li","year":"2019","journal-title":"IEEE\/ACM Trans Comput Biol Bioinform"},{"key":"2024061010093277600_ref4","doi-asserted-by":"crossref","first-page":"231","DOI":"10.1134\/S0006297923020074","article-title":"Complex analysis of single-cell RNA sequencing data[J]","volume":"88","author":"Khozyainova","year":"2023","journal-title":"Biochemistry (Moscow)"},{"key":"2024061010093277600_ref5","doi-asserted-by":"crossref","first-page":"BBI. 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