Pattern recognition (psychology)

Mathematics

Convolution (computer science)

Imputation (statistics)

Transcription factor

Biology

Missing data

Gene

Algorithm

Random walk

Data mining

Statistics

Enhancer

Machine learning

Genome

Artificial intelligence

Visualization

Computer science

Computational biology

Cluster analysis

Artificial neural network

Genetics

Chromosome conformation capture

Proceedings of the National Academy of Sciences

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.

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