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

Tao Yang, Feipeng Zhang, Galip Gürkan Yardımcı, Fan Song, Ross C. Hardison, William Stafford Noble, Feng Yue, Qunhua Li
doi: https://doi.org/10.1101/101386
Tao Yang
1Bioinformatics and Genomics Program, Pennsylvania State University, University Park, PA 16802
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Feipeng Zhang
2Department of Statistics, Pennsylvania State University, University Park, PA 16802
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Galip Gürkan Yardımcı
5Department of Genome Sciences, University of Washington, Seattle, WA 98105
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Fan Song
1Bioinformatics and Genomics Program, Pennsylvania State University, University Park, PA 16802
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Ross C. Hardison
1Bioinformatics and Genomics Program, Pennsylvania State University, University Park, PA 16802
4Department of Biochemistry and Molecular Biology, Pennsylvania State University, University Park, PA 16802
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William Stafford Noble
5Department of Genome Sciences, University of Washington, Seattle, WA 98105
6Department of Computer Science and Engineering, University of Washington, Seattle, WA 98105
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Feng Yue
1Bioinformatics and Genomics Program, Pennsylvania State University, University Park, PA 16802
3Department of Biochemistry and Molecular Biology, Pennsylvania State University College of Medicine, Hershey, PA 17033
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Qunhua Li
1Bioinformatics and Genomics Program, Pennsylvania State University, University Park, PA 16802
2Department of Statistics, Pennsylvania State University, University Park, PA 16802
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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 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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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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Posted August 04, 2017.
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HiCRep: assessing the reproducibility of Hi-C data using a stratum-adjusted correlation coefficient
Tao Yang, Feipeng Zhang, Galip Gürkan Yardımcı, Fan Song, 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 Gürkan Yardımcı, Fan Song, Ross C. Hardison, William Stafford Noble, Feng Yue, Qunhua Li
bioRxiv 101386; doi: https://doi.org/10.1101/101386

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