Hi-C is a powerful technology for studying genome-wide chromatin interactions. However, current methods for assessing Hi-C data reproducibility can produce misleading results because they ignore spatial features in Hi-C data, such as domain structure and distance dependence. We present HiCRep, a framework for assessing the reproducibility of Hi-C data that systematically accounts for these features. In particular, we introduce a novel similarity measure, the stratum adjusted correlation coefficient (SCC), for quantifying the similarity between Hi-C interaction matrices. Not only does it provide a statistically sound and reliable evaluation of reproducibility, SCC can also be used to quantify differences between Hi-C contact matrices and to determine the optimal sequencing depth for a desired resolution. The measure consistently shows higher accuracy than existing approaches in distinguishing subtle differences in reproducibility and depicting interrelationships of cell lineages. The proposed measure is straightforward to interpret and easy to compute, making it well-suited for providing standardized, interpretable, automatable, and scalable quality control. The freely available R package HiCRep implements our approach.

Hi-C 是一项用于研究全基因组染色质相互作用的强大技术。然而，当前评估 Hi-C 数据再现性的方法可能会产生误导性结果，因为它们忽略了 Hi-C 数据中的空间特征，例如域结构和距离依赖性。我们介绍了 HiCRep，这是一个用于评估 Hi-C 数据可重复性的框架，该框架系统地说明了这些特征。特别是，我们引入了一种新的相似性度量，即层调整相关系数 (SCC)，用于量化 Hi-C 交互矩阵之间的相似性。它不仅提供统计上合理且可靠的重现性评估，SCC 还可用于量化 Hi-C 接触矩阵之间的差异，并确定所需分辨率的最佳测序深度。在区分可重复性的细微差异和描述细胞谱系的相互关系方面，该措施始终显示出比现有方法更高的准确性。拟议的措施易于解释和计算，使其非常适合提供标准化、可解释、可自动化和可扩展的质量控制。免费提供的 R 包 HiCRep 实现了我们的方法。