Robust single-cell Hi-C clustering by convolution- and random-walk-based imputation.

ABSTRACT: 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 a

SUBMITTER: Zhou J 

PROVIDER: S-EPMC6628819 | biostudies-literature | 2019 Jul

REPOSITORIES: biostudies-literature