{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,11]],"date-time":"2026-06-11T04:35:29Z","timestamp":1781152529448,"version":"3.54.1"},"reference-count":54,"publisher":"Oxford University Press (OUP)","issue":"6","license":[{"start":{"date-parts":[[2024,10,15]],"date-time":"2024-10-15T00:00:00Z","timestamp":1728950400000},"content-version":"vor","delay-in-days":22,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Science Foundation of China","doi-asserted-by":"publisher","award":["62371423"],"award-info":[{"award-number":["62371423"]}],"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":["62271132"],"award-info":[{"award-number":["62271132"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Key Tech nologies Research and Development Program of China","award":["2022YFF1202100"],"award-info":[{"award-number":["2022YFF1202100"]}]},{"name":"Postdoctoral Science Foundation of Heilongjiang Province of China","award":["LBH-Z19106"],"award-info":[{"award-number":["LBH-Z19106"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,9,23]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Single-cell RNA sequencing (scRNA-seq) technology has revolutionized biological research by enabling high-throughput, cellular-resolution gene expression profiling. A critical step in scRNA-seq data analysis is cell clustering, which supports downstream analyses. However, the high-dimensional and sparse nature of scRNA-seq data poses significant challenges to existing clustering methods. Furthermore, integrating gene expression information with potential cell structure data remains largely unexplored. Here, we present scCFIB, a novel information bottleneck (IB)-based clustering algorithm that leverages the power of IB for efficient processing of high-dimensional sparse data and incorporates a cross-view fusion strategy to achieve robust cell clustering. scCFIB constructs a multi-feature space by establishing two distinct views from the original features. We then formulate the cell clustering problem as a target loss function within the IB framework, employing a collaborative information fusion strategy. To further optimize scCFIB\u2019s performance, we introduce a novel sequential optimization approach through an iterative process. Benchmarking against established methods on diverse scRNA-seq datasets demonstrates that scCFIB achieves superior performance in scRNA-seq data clustering tasks. Availability: the source code is publicly available on GitHub: https:\/\/github.com\/weixiaojiao\/scCFIB.<\/jats:p>","DOI":"10.1093\/bib\/bbae511","type":"journal-article","created":{"date-parts":[[2024,10,15]],"date-time":"2024-10-15T02:52:55Z","timestamp":1728960775000},"source":"Crossref","is-referenced-by-count":4,"title":["Clustering scRNA-seq data with the cross-view collaborative information fusion strategy"],"prefix":"10.1093","volume":"25","author":[{"given":"Zhengzheng","family":"Lou","sequence":"first","affiliation":[{"name":"School of Computer and Artificial Intelligence , Zhengzhou University, Zhengzhou 450000,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaojiao","family":"Wei","sequence":"additional","affiliation":[{"name":"School of Computer and Artificial Intelligence , Zhengzhou University, Zhengzhou 450000,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuanhao","family":"Hu","sequence":"additional","affiliation":[{"name":"School of Computer and Artificial Intelligence , Zhengzhou University, Zhengzhou 450000,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shizhe","family":"Hu","sequence":"additional","affiliation":[{"name":"School of Computer and Artificial Intelligence , Zhengzhou University, Zhengzhou 450000,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yucong","family":"Wu","sequence":"additional","affiliation":[{"name":"School of Computer and Artificial Intelligence , Zhengzhou University, Zhengzhou 450000,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhen","family":"Tian","sequence":"additional","affiliation":[{"name":"School of Computer and Artificial Intelligence , Zhengzhou University, Zhengzhou 450000,","place":["China"]},{"name":"Yangtze Delta Region Institute (Quzhou) , University of Electronic Science and Technology of China, Quzhou 324000,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2024,10,14]]},"reference":[{"key":"2024101502523783000_ref1","doi-asserted-by":"publisher","first-page":"1308","DOI":"10.1016\/j.cell.2016.07.054","article-title":"Comprehensive classification of retinal bipolar neurons by single-cell transcriptomics","volume":"166","author":"Shekhar","year":"2016","journal-title":"Cell"},{"key":"2024101502523783000_ref2","doi-asserted-by":"publisher","first-page":"1091","DOI":"10.1016\/j.cell.2018.02.001","article-title":"Mapping the mouse cell atlas by Microwell-Seq","volume":"172","author":"Han","year":"2018","journal-title":"Cell"},{"key":"2024101502523783000_ref3","doi-asserted-by":"publisher","first-page":"1202","DOI":"10.1016\/j.cell.2015.05.002","article-title":"Highly 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