Ma Rui, Huang Jingong, Jiang Tao, Ma Wenxiu
Department of Statistics, University of California Riverside, 900 University Ave., Riverside, 92521, CA, USA.
Department of Computer Science and Engineering, University of California Riverside, 900 University Ave., Riverside, 92521, CA, USA.
Comput Struct Biotechnol J. 2024 Nov 7;23:4027-4035. doi: 10.1016/j.csbj.2024.11.002. eCollection 2024 Dec.
Single-cell Hi-C (scHi-C) techniques have significantly advanced our understanding of the 3D genome organization, providing crucial insights into the spatial genome architecture within individual nuclei. Numerous computational and statistical methods have been developed to analyze scHi-C data, with embedding methods playing a key role. Embedding reduces the dimensionality of complex scHi-C contact maps, making it easier to extract biologically meaningful patterns. These methods not only enhance cell clustering based on chromatin structures but also facilitate visualization and other downstream analyses. Most scHi-C embedding methods incorporate strategies such as normalization and imputation to address the inherent sparsity of scHi-C data, thereby further improving data quality and interpretability. In this review, we systematically examine the existing methods designed for scHi-C embedding, outlining their methodologies and discussing their capabilities in handling normalization and imputation. Additionally, we present a comprehensive benchmarking analysis to compare both embedding techniques and their clustering performances. This review serves as a practical guide for researchers seeking to select suitable scHi-C embedding tools, ultimately contributing to the understanding of the 3D organization of the genome.
单细胞Hi-C（scHi-C）技术极大地推进了我们对三维基因组组织的理解，为单个细胞核内的空间基因组结构提供了关键见解。已经开发了许多计算和统计方法来分析scHi-C数据，其中嵌入方法起着关键作用。嵌入降低了复杂scHi-C接触图谱的维度，使得更容易提取具有生物学意义的模式。这些方法不仅增强了基于染色质结构的细胞聚类，还便于可视化和其他下游分析。大多数scHi-C嵌入方法采用归一化和插补等策略来解决scHi-C数据固有的稀疏性，从而进一步提高数据质量和可解释性。在本综述中，我们系统地研究了为scHi-C嵌入设计的现有方法，概述了它们的方法，并讨论了它们在处理归一化和插补方面的能力。此外，我们进行了全面的基准分析，以比较嵌入技术及其聚类性能。本综述为寻求选择合适的scHi-C嵌入工具的研究人员提供了实用指南，最终有助于对基因组三维组织的理解。
