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...

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Bibliographic Details
Published in:Briefings in bioinformatics 2023-01, Vol.24 (1)
Main Authors: Liu, Qiao, Zeng, Wanwen, Zhang, Wei, Wang, Sicheng, Chen, Hongyang, Jiang, Rui, Zhou, Mu, Zhang, Shaoting
Format: Article
Language:English
Subjects:
Chromatin
Chromatin - genetics
Chromosomes
Cluster Analysis
Clustering
Conformation
DNA
DNA fingerprinting
Gene regulation
Genome
Neural networks
Spatial data
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Summary: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 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.
ISSN:1467-5463
1477-4054
DOI:10.1093/bib/bbac494