Deep generative modeling and clustering of single cell Hi-C data. Issue 1 (1st December 2022)

• Record Type: Journal Article
• Authors: Liu, Qiao; Zeng, Wanwen; Zhang, Wei; Wang, Sicheng; Chen, Hongyang; Jiang, Rui; Zhou, Mu; Zhang, Shaoting
• Is Part Of: Briefings in bioinformatics. Volume 24:Issue 1(2023)
• Publication Date: 2022-12-01
• Subjects: single cell -- 3D genome -- deep learning -- unsupervised learning; Genetics -- Data processing -- Periodicals; Molecular biology -- Data processing -- Periodicals; Genomes -- Data processing -- Periodicals; 572.80285
• DOI: 10.1093/bib/bbac494
• Languages: English
• ISSNs: 1467-5463

Abstract

Abstract: Deciphering 3D genome conformation is important for understanding gene regulation and cellular function at a spatial level. 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.