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HiCRep: assessing the reproducibility of Hi-C data using a stratum-adjusted correlation coefficient
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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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“Notably, we found that samples were clustered by cell types, not individuals, and showed high correlation between the same cell types from different individuals (NeuN+ = 0.95–0.97; NeuN− = 0.86-0.91; Supplementary Fig. 3a), demonstrating the reproducibility across Hi–C libraries.”
From Neuronal and glial 3D chromatin architecture informs the cellular etiology of brain disorders (2021) · cites this paper as [19]
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“Clustering analysis by Hi-CRep49 showed that 5-AZA treated and DNMT TKD cells were more similar to each other than the DMSO treated cells (Fig. 6e).”
From Subtype-specific 3D genome alteration in acute myeloid leukaemia (2022) · cites this paper as [49]
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“…replicates are available, it is a commonpracticetouseeitherPearsonorSpearmancorrelationco-efficients between the two Hi-C data matrices as a metric for data quality (Hu et al. 2012; Imakaev et al. 2012; Gorkin et al. 2014; Rao et al. 2014; Ay and Noble 2015; Dixon et al. 2015; Servant et al. 2015).”
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Promoter-proximal CTCF binding promotes distal enhancer-dependent gene activation
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Metadata
- Authors
- Yang, T., Zhang, F., Yardımcı, G. G., Song, F., Hardison, R. C., Noble, W. S., Yue, F., & Li, Q.
- Affiliations
- Pennsylvania State University (US)
- University of Washington (US)
- ORCID
- Venue
- Genome Research
- Publisher
- Cold Spring Harbor Laboratory
- Published
- 2017-08-30
- Volume
- 27
- Issue
- 11
- Pages
- 1939-1949
- ISSN
- 1088-9051 (ISSN-L)
- 1549-5469
- PMC
- PMC5668950
- OpenAlex
- W2952940726
- Cited by
- 533
- References
- 41
- Citations by year
- 2026: 48 · 2025: 68 · 2024: 98 · 2023: 69 · 2022: 57 · 2021: 73 · 2020: 60 · 2019: 38 · 2018: 18 · 2017: 4
- Type
- posted content
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- public-domain
- Locations
- Genome Research (publishedVersion, public-domain)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
- Europe PMC (PubMed Central) (publishedVersion, cc-by-nc)
- PubMed Central (publishedVersion, cc-by-nc)
- PubMed (publishedVersion)
- HighWire Press Open Archive (submittedVersion)
- Funders
- National Institutes of Health (T32 GM102057)
- Huck Graduate Research Innovation
- NIH (R01GM109453, U01CA200060, R24DK106766, U54HG006998, U41HG007000)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Chromatin Dynamics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genomic variations and chromosomal abnormalities
- Keywords
- Hi-C, domain structure, distance dependence, cell lineage
- MeSH terms
- Cell Line, Humans, Software, Reproducibility of Results, Sequence Analysis, RNA, Cell Lineage, Computational Biology, Chromatin Immunoprecipitation, High-Throughput Nucleotide Sequencing
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