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However, the high dimensionality and complexity of scRNA-seq data pose significant challenges to achieving accurate clustering from a singular perspective. To address these challenges, we propose a novel approach, called multi-level multi-view network based on structural consistency contrastive learning (scMMN), for scRNA-seq data clustering. Firstly, the proposed method constructs shallow views through the $k$-nearest neighbor ($k$NN) and diffusion mapping (DM) algorithms, and then deep views are generated by utilizing the graph Laplacian filters. These deep multi-view data serve as the input for representation learning. To improve the clustering performance of scRNA-seq data, contrastive learning is introduced to enhance the discrimination ability of our network. Specifically, we construct a group contrastive loss for representation features and a structural consistency contrastive loss for structural relationships. Extensive experiments on eight real scRNA-seq datasets show that the proposed method outperforms other state-of-the-art methods in scRNA-seq data clustering tasks. Our source code has already been available at https:\/\/github.com\/szq0816\/scMMN.<\/jats:p>","DOI":"10.1093\/bib\/bbae562","type":"journal-article","created":{"date-parts":[[2024,11,4]],"date-time":"2024-11-04T09:40:22Z","timestamp":1730713222000},"source":"Crossref","is-referenced-by-count":12,"title":["Multi-level multi-view network based on structural contrastive learning for scRNA-seq data clustering"],"prefix":"10.1093","volume":"25","author":[{"given":"Zhenqiu","family":"Shu","sequence":"first","affiliation":[{"name":"Faculty of Information Engineering and Automation, Kunming University of Science and Technology , Chenggong, 650500, Yunnan ,","place":["China"]}]},{"given":"Min","family":"Xia","sequence":"additional","affiliation":[{"name":"Faculty of Information Engineering and Automation, Kunming University of Science and Technology , Chenggong, 650500, Yunnan ,","place":["China"]}]},{"given":"Kaiwen","family":"Tan","sequence":"additional","affiliation":[{"name":"Faculty of Information Engineering and Automation, Kunming University of Science and Technology , Chenggong, 650500, Yunnan ,","place":["China"]}]},{"given":"Yongbing","family":"Zhang","sequence":"additional","affiliation":[{"name":"Faculty of Information Engineering and Automation, Kunming University of Science and Technology , Chenggong, 650500, Yunnan ,","place":["China"]}]},{"given":"Zhengtao","family":"Yu","sequence":"additional","affiliation":[{"name":"Faculty of Information Engineering and Automation, Kunming University of Science and Technology , Chenggong, 650500, Yunnan ,","place":["China"]}]}],"member":"286","published-online":{"date-parts":[[2024,11,4]]},"reference":[{"key":"2024110409401099500_ref1","doi-asserted-by":"publisher","first-page":"8845","DOI":"10.1093\/nar\/gku555","article-title":"Single-cell rna-seq: advances and future challenges","volume":"42","author":"Saliba","year":"2014","journal-title":"Nucleic Acids 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