RT Journal
A1 Yang, Tao
A1 Zhang, Feipeng
A1 Yardımcı, Galip Gürkan
A1 Song, Fan
A1 Hardison, Ross C.
A1 Noble, William Stafford
A1 Yue, Feng
A1 Li, Qunhua
T1 HiCRep: assessing the reproducibility of Hi-C data using a stratum-adjusted correlation coefficient
JF Genome Research 
JO Genome Research 
YR 2017 
FD November 01 
VO 27 
IS 11 
SP 1939 
OP 1949 
DO 10.1101/gr.220640.117 
UL http://genome.cshlp.org/content/27/11/1939.abstract 
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.