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

Brief Bioinform. 2023 Jan 19;24(1):bbac494. 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.

Keywords: 3D genome; deep learning; single cell; unsupervised learning.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Chromatin* / genetics
  • Chromosomes*
  • Cluster Analysis
  • DNA
  • Genome


  • Chromatin
  • DNA