{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,6]],"date-time":"2026-06-06T17:13:31Z","timestamp":1780766011950,"version":"3.54.1"},"reference-count":41,"publisher":"Oxford University Press (OUP)","issue":"4","license":[{"start":{"date-parts":[[2024,3,28]],"date-time":"2024-03-28T00:00:00Z","timestamp":1711584000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62173235"],"award-info":[{"award-number":["62173235"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100021171","name":"Guangdong Basic and Applied Basic Research Foundation","doi-asserted-by":"publisher","award":["2022A1515010146"],"award-info":[{"award-number":["2022A1515010146"]}],"id":[{"id":"10.13039\/501100021171","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,3,29]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Motivation<\/jats:title>\n                  <jats:p>Single-cell clustering plays a crucial role in distinguishing between cell types, facilitating the analysis of cell heterogeneity mechanisms. While many existing clustering methods rely solely on gene expression data obtained from single-cell RNA sequencing techniques to identify cell clusters, the information contained in mono-omic data is often limited, leading to suboptimal clustering performance. The emergence of single-cell multi-omics sequencing technologies enables the integration of multiple omics data for identifying cell clusters, but how to integrate different omics data effectively remains challenging. In addition, designing a clustering method that performs well across various types of multi-omics data poses a persistent challenge due to the data\u2019s inherent characteristics.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>In this paper, we propose a graph-regularized multi-view ensemble clustering (GRMEC-SC) model for single-cell clustering. Our proposed approach can adaptively integrate multiple omics data and leverage insights from multiple base clustering results. We extensively evaluate our method on five multi-omics datasets through a series of rigorous experiments. The results of these experiments demonstrate that our GRMEC-SC model achieves competitive performance across diverse multi-omics datasets with varying characteristics.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Availability and implementation<\/jats:title>\n                  <jats:p>Implementation of GRMEC-SC, along with examples, can be found on the GitHub repository: https:\/\/github.com\/polarisChen\/GRMEC-SC.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btae169","type":"journal-article","created":{"date-parts":[[2024,3,28]],"date-time":"2024-03-28T19:34:48Z","timestamp":1711654488000},"source":"Crossref","is-referenced-by-count":22,"title":["Clustering single-cell multi-omics data via graph regularized multi-view ensemble learning"],"prefix":"10.1093","volume":"40","author":[{"given":"Fuqun","family":"Chen","sequence":"first","affiliation":[{"name":"College of Electronic and Information Engineering, Shenzhen University , Shenzhen 518060, Guangdong, China"},{"name":"Guangdong Key Laboratory of Intelligent Information Processing, Shenzhen University , Shenzhen 518060, Guangdong, China"},{"name":"Shenzhen Key Laboratory of Media Security and Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ), Shenzhen University , Shenzhen 518060, Guangdong, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guanhua","family":"Zou","sequence":"additional","affiliation":[{"name":"College of Electronic and Information Engineering, Shenzhen University , Shenzhen 518060, Guangdong, China"},{"name":"Guangdong Key Laboratory of Intelligent Information Processing, Shenzhen University , Shenzhen 518060, Guangdong, China"},{"name":"Shenzhen Key Laboratory of Media Security and Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ), Shenzhen University , Shenzhen 518060, Guangdong, 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