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Although great efforts have been devoted to either of them, the integrative analysis of single-cell multi-omics data is really limited because of the heterogeneity, noises and sparsity of single-cell profiles. In this study, a network-based integrative clustering algorithm (aka NIC) is present for the identification of cell types by fusing the parallel single-cell transcriptomic (scRNA-seq) and epigenomic profiles (scATAC-seq or DNA methylation). To avoid heterogeneity of multi-omics data, NIC automatically learns the cell\u2013cell similarity graphs, which transforms the fusion of multi-omics data into the analysis of multiple networks. Then, NIC employs joint non-negative matrix factorization to learn the shared features of cells by exploiting the structure of learned cell\u2013cell similarity networks, providing a better way to characterize the features of cells. The graph learning and integrative analysis procedures are jointly formulated as an optimization problem, and then the update rules are derived. Thirteen single-cell multi-omics datasets from various tissues and organisms are adopted to validate the performance of NIC, and the experimental results demonstrate that the proposed algorithm significantly outperforms the state-of-the-art methods in terms of various measurements. The proposed algorithm provides an effective strategy for the integrative analysis of single-cell multi-omics data (The software is coded using Matlab, and is freely available for academic https:\/\/github.com\/xkmaxidian\/NIC ).<\/jats:p>","DOI":"10.1093\/bib\/bbab546","type":"journal-article","created":{"date-parts":[[2021,11,29]],"date-time":"2021-11-29T12:10:12Z","timestamp":1638187812000},"source":"Crossref","is-referenced-by-count":23,"title":["Network-based integrative analysis of single-cell transcriptomic and epigenomic data for cell types"],"prefix":"10.1093","volume":"23","author":[{"given":"Wenming","family":"Wu","sequence":"first","affiliation":[{"name":"School of Computer Science and Technology, Xidian University, Xi\u00a0an, 710071, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wensheng","family":"Zhang","sequence":"additional","affiliation":[{"name":"Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, 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