TY - JOUR
T1 - HiCRep
T2 - assessing the reproducibility of Hi-C data using a stratum-adjusted correlation coefficient
AU - Yang, Tao
AU - Zhang, Feipeng
AU - Yardımci, Galip Gürkan
AU - Song, Fan
AU - Hardison, Ross C.
AU - Noble, William Stafford
AU - Yue, Feng
AU - Li, Qunhua
N1 - Publisher Copyright:
© 2017 Yang et al.
PY - 2017/11
Y1 - 2017/11
N2 - 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.
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.
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U2 - 10.1101/gr.220640.117
DO - 10.1101/gr.220640.117
M3 - Article
C2 - 28855260
AN - SCOPUS:85041538366
SN - 1088-9051
VL - 27
SP - 1939
EP - 1949
JO - Genome research
JF - Genome research
IS - 11
ER -