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The recent advances of single cell Hi-C technologies have enabled the profiling of the 3D architecture of DNA within individual cell, which allows us to study the cell-to-cell variability of 3D chromatin organization. Computational approaches are in urgent need to comprehensively analyze the sparse and heterogeneous single cell Hi-C data. Here, we proposed scDEC-Hi-C, a new framework for single cell Hi-C analysis with deep generative neural networks. scDEC-Hi-C outperforms existing methods in terms of single cell Hi-C data clustering and imputation. Moreover, the generative power of scDEC-Hi-C could help unveil the differences of chromatin architecture across cell types. We expect that scDEC-Hi-C could shed light on deepening our understanding of the complex mechanism underlying the formation of chromatin contacts.<\/jats:p>","DOI":"10.1093\/bib\/bbac494","type":"journal-article","created":{"date-parts":[[2022,10,25]],"date-time":"2022-10-25T09:23:05Z","timestamp":1666689785000},"source":"Crossref","is-referenced-by-count":16,"title":["Deep generative modeling and clustering of single cell Hi-C data"],"prefix":"10.1093","volume":"24","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9781-3360","authenticated-orcid":false,"given":"Qiao","family":"Liu","sequence":"first","affiliation":[{"name":"Department of Statistics, Stanford University , Stanford, CA 94305 , USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wanwen","family":"Zeng","sequence":"additional","affiliation":[{"name":"College of Software, Nankai University , Tianjin 300071 , 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