Robust single-cell Hi-C clustering by convolution- And random-walk–based imputation

Jingtian Zhou, Jianzhu Ma, Yusi Chen, Chuankai Cheng, Bokan Bao, Jian Peng, Terrence J. Sejnowski, Jesse R. Dixon, Joseph R. Ecker

Research output: Contribution to journalArticlepeer-review


Three-dimensional genome structure plays a pivotal role in gene regulation and cellular function. Single-cell analysis of genome architecture has been achieved using imaging and chromatin conformation capture methods such as Hi-C. To study variation in chromosome structure between different cell types, computational approaches are needed that can utilize sparse and heterogeneous single-cell Hi-C data. However, few methods exist that are able to accurately and efficiently cluster such data into constituent cell types. Here, we describe scHiCluster, a single-cell clustering algorithm for Hi-C contact matrices that is based on imputations using linear convolution and random walk. Using both simulated and real single-cell Hi-C data as benchmarks, scHiCluster significantly improves clustering accuracy when applied to low coverage datasets compared with existing methods. After imputation by scHiCluster, topologically associating domain (TAD)-like structures (TLSs) can be identified within single cells, and their consensus boundaries were enriched at the TAD boundaries observed in bulk cell Hi-C samples. In summary, scHiCluster facilitates visualization and comparison of single-cell 3D genomes.

Original languageEnglish (US)
Pages (from-to)14011-14018
Number of pages8
JournalProceedings of the National Academy of Sciences of the United States of America
Issue number28
StatePublished - 2019


  • 3D chromosome structure
  • Hi-C
  • Random walk
  • Single cell

ASJC Scopus subject areas

  • General


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