HiCRep: assessing the reproducibility of Hi-C data using a stratum-adjusted correlation coefficient

Tao Yang, Feipeng Zhang, Galip Gürkan Yardımci, Fan Song, Ross C. Hardison, William Stafford Noble, Feng Yue, Qunhua Li

Research output: Contribution to journalArticlepeer-review

64 Scopus citations

Abstract

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.

Original languageEnglish (US)
Pages (from-to)1939-1949
Number of pages11
JournalGenome research
Volume27
Issue number11
DOIs
StatePublished - Nov 2017

All Science Journal Classification (ASJC) codes

  • Genetics
  • Genetics(clinical)

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