hicrep

Hi-C is a 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. This limitation has created a need for an improved framework that can account for these features and provide a reliable and interpretable measure of reproducibility. HiCRep is one such framework that systematically addresses these limitations.

HiCRep introduces a novel similarity measure, the stratum-adjusted correlation coefficient (SCC), for quantifying the similarity between Hi-C interaction matrices. The SCC provides a statistically sound and reliable evaluation of reproducibility that accounts for the spatial features in Hi-C data. It can also quantify differences between Hi-C contact matrices and determine the optimal sequencing depth for a desired resolution. These capabilities make it a valuable tool for assessing Hi-C data quality and optimizing experimental design.

The SCC consistently shows higher accuracy than existing approaches in distinguishing subtle differences in reproducibility and depicting interrelationships of cell lineages. Additionally, 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 this approach, making it accessible to researchers and enabling them to assess the reproducibility of their Hi-C data accurately and reliably.