PT  - JOURNAL ARTICLE
AU  - Tao Yang
AU  - Feipeng Zhang
AU  - Galip Gürkan Yardimci
AU  - Fan Song
AU  - Ross C. Hardison
AU  - William Stafford Noble
AU  - Feng Yue
AU  - Qunhua Li
TI  - HiCRep: assessing the reproducibility of Hi-C data using a stratum-adjusted correlation coefficient
AID  - 10.1101/101386
DP  - 2017 Jan 01
TA  - bioRxiv
PG  - 101386
4099  - http://biorxiv.org/content/early/2017/07/27/101386.short
4100  - http://biorxiv.org/content/early/2017/07/27/101386.full
AB  - 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.