Deep generative modeling and clustering of single cell Hi -C data

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.