A review and performance evaluation of clustering frameworks for single-cell Hi-C data. Issue 6 (23rd September 2022)

• Record Type: Journal Article
• Authors: Zhen, Caiwei; Wang, Yuxian; Geng, Jiaquan; Han, Lu; Li, Jingyi; Peng, Jinghao; Wang, Tao; Hao, Jianye; Shang, Xuequn; Wei, Zhongyu; Zhu, Peican; Peng, Jiajie
• Is Part Of: Briefings in bioinformatics. Volume 23:Issue 6(2022)
• Publication Date: 2022-09-23
• Subjects: single-cell Hi-C -- clustering -- feature extraction; Genetics -- Data processing -- Periodicals; Molecular biology -- Data processing -- Periodicals; Genomes -- Data processing -- Periodicals; 572.80285
• DOI: 10.1093/bib/bbac385
• Languages: English
• ISSNs: 1467-5463

Abstract

Abstract: The three-dimensional genome structure plays a key role in cellular function and gene regulation. Single-cell Hi-C (high-resolution chromosome conformation capture) technology can capture genome structure information at the cell level, which provides the opportunity to study how genome structure varies among different cell types. Recently, a few methods are well designed for single-cell Hi-C clustering. In this manuscript, we perform an in-depth benchmark study of available single-cell Hi-C data clustering methods to implement an evaluation system for multiple clustering frameworks based on both human and mouse datasets. We compare eight methods in terms of visualization and clustering performance. Performance is evaluated using four benchmark metrics including adjusted rand index, normalized mutual information, homogeneity and Fowlkes–Mallows index. Furthermore, we also evaluate the eight methods for the task of separating cells at different stages of the cell cycle based on single-cell Hi-C data.