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

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 heterogeneity of chromatin architecture across different 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. scDEC-Hi-C is freely available at https://github.com/kimmo1019/scDEC-Hi-C . Key points scDEC-Hi-C provides an end-to-end framework based on autoencoder and deep generative model to comprehensively analyze single cell Hi-C data, including low-dimensional embedding and clustering. Through a series of experiments including single cell Hi-C data clustering and structural difference identification, scDEC-Hi-C demonstrates suprioir performance over existing methods. In the downstream analysis of chromatin loops from single cell Hi-C data, scDEC-Hi-C is capable of significantly enhancing the ability for identifying single cell chromatin loops by data imputation.