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

Tao Yang, Feipeng Zhang, Galip Gurkan Yardimci, Ross C Hardison, William Stafford Noble, Feng Yue, Qunhua Li
doi: https://doi.org/10.1101/101386
Tao Yang
Pennsylvania State University;
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Feipeng Zhang
Pennsylvania State University;
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Galip Gurkan Yardimci
University of Washington
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Ross C Hardison
Pennsylvania State University;
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William Stafford Noble
University of Washington
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Feng Yue
Pennsylvania State University;
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Qunhua Li
Pennsylvania State University;
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  • For correspondence: qunhua.li@psu.edu
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Abstract

Hi-C is a powerful technology for studying genome-wide chromatin interactions. However, current methods for assessing Hi-C data reproducibility ignore spatial features in Hi-C data, such as domain structure and distance dependence. We present the stratum-adjusted correlation coefficient (SCC), a reproducibility measure that accounts for these features. SCC can assess pairwise differences between Hi-C matrices under a wide range of settings and can be used to determine optimal sequencing depth. The measure consistently shows higher accuracy than existing approaches in distinguishing subtle differences in reproducibility and depicting interrelationships of cell lineages. The R package HiCRep implements our approach.

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The copyright holder for this preprint is the author/funder. It is made available under a CC-BY-NC-ND 4.0 International license.
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  • Posted January 18, 2017.

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HiCRep: assessing the reproducibility of Hi-C data using a stratum-adjusted correlation coefficient
Tao Yang, Feipeng Zhang, Galip Gurkan Yardimci, Ross C Hardison, William Stafford Noble, Feng Yue, Qunhua Li
bioRxiv 101386; doi: https://doi.org/10.1101/101386
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HiCRep: assessing the reproducibility of Hi-C data using a stratum-adjusted correlation coefficient
Tao Yang, Feipeng Zhang, Galip Gurkan Yardimci, Ross C Hardison, William Stafford Noble, Feng Yue, Qunhua Li
bioRxiv 101386; doi: https://doi.org/10.1101/101386

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