Liu Qiao, Zeng Wanwen, Zhang Wei, Wang Sicheng, Chen Hongyang, Jiang Rui, Zhou Mu, Zhang Shaoting
Department of Statistics, Stanford University, Stanford, CA 94305, USA.
College of Software, Nankai University, Tianjin 300071, China.
Brief Bioinform. 2023 Jan 19;24(1). doi: 10.1093/bib/bbac494.
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.
解析三维基因组构象对于在空间层面理解基因调控和细胞功能至关重要。单细胞Hi-C技术的最新进展使得对单个细胞内DNA三维结构的分析成为可能，这使我们能够研究三维染色质组织在细胞间的变异性。迫切需要计算方法来全面分析稀疏且异质的单细胞Hi-C数据。在此，我们提出了scDEC-Hi-C，这是一种利用深度生成神经网络进行单细胞Hi-C分析的新框架。scDEC-Hi-C在单细胞Hi-C数据聚类和插补方面优于现有方法。此外，scDEC-Hi-C的生成能力有助于揭示不同细胞类型间染色质结构的差异。我们期望scDEC-Hi-C能够为深化我们对染色质接触形成背后复杂机制的理解提供帮助。
