PT  - JOURNAL ARTICLE
AU  - Liu, Qiao
AU  - Zeng, Wanwen
AU  - Zhang, Wei
AU  - Wang, Sicheng
AU  - Chen, Hongyang
AU  - Jiang, Rui
AU  - Zhou, Mu
AU  - Zhang, Shaoting
TI  - Deep generative modeling and clustering of single cell Hi-C data
AID  - 10.1101/2022.07.19.500573
DP  - 2022 Jan 01
TA  - bioRxiv
PG  - 2022.07.19.500573
4099  - http://biorxiv.org/content/early/2022/07/20/2022.07.19.500573.short
4100  - http://biorxiv.org/content/early/2022/07/20/2022.07.19.500573.full
AB  - 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 pointsscDEC-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.Competing Interest StatementThe authors have declared no competing interest.