PreprintCited by 577Genomics and Chromatin DynamicsGenomics and Phylogenetic StudiesGenomic variations and chromosomal abnormalities

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

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Yang, Tao, Zhang, Feipeng, Yardımcı, Galip Gürkan, Song, Fan, Hardison, Ross C., Noble, William Stafford, Yue, Feng, Li, Qunhua·2017·Genome Research

DOI 10.1101/gr.220640.117PMC PMC5668950

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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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Identification of Enhancers and Promoters in the Genome by Multidimensional Scaling
Ishibashi, R., & Taguchi, Y. (2021) · Genes · doi:10.3390/genes12111671 · Cited by 2
“The framework HiCRep evaluates the reproducibility of Hi-C data [14] using the stratum-coordinated correlation coefficient as a similarity measure to quantify differences between Hi-C contact matrices.”
Method
A Heuristic Strategy for Multi-Mapping Reads to Enhance Hi-C Data
Bulathsinghalage, C., & Liu, L. (2021) · IEEE International Conference on Bioinformatics and Bioengineering (BIBE) · doi:10.1109/bibe52308.2021.9635215 · Cited by 2
“We calculate the reproducibility scores among hESC and IMR90’s replicates by chromosome (from chromosome 1 to chromosome 22) with HiCRep [19].”
Method
FreeHi-C spike-in simulations for benchmarking differential chromatin interaction detection
Zheng, Y., Zhou, P., & Keleş, S. (2020) · Methods · doi:10.1016/j.ymeth.2020.07.001 · Cited by 2
Statistical curve models for inferring 3D chromatin architecture
Tuzhilina, E., Hastie, T. J., & Segal, M. R. (2024) · The Annals of Applied Statistics · doi:10.1214/24-aoas1917 · Cited by 2
Stratification of TAD boundaries identified in reproducible Hi-C contact matrices reveals preferential insulation of super-enhancers by strong boundariesPreprint
Gong, Y., Lazaris, C., Sakellaropoulos, T., Lozano, A., Kambadur, P., Ntziachristos, P., Aifantis, I., & Tsirigos, A. (2017) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/141481 · Cited by 2
Topological Data Analysis of Single-cell Hi-C Contact MapsPreprint
Carrière, M., & Rabadán, R. (2018) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/486936 · Cited by 5
Three-dimensional genome structure and chromatin accessibility reorganization during in vivo induction of human T cell tolerancePreprint
Li, R., Guo, H., Hou, Y., Liu, S., Wang, M., Peng, T., Zhao, X., Lu, L., Han, Y., Shao, Y., et al. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.03.11.988253 · Cited by 2
“HiCRep’s SCC scores (Yang et al., 2017) of the Hi-C matrices showed that cells of different lineages have different 3D genome structures (Figure 2B).”
BackgroundMethod
The three-dimensional chromatin structure of the major human pancreatic cell types reveals lineage-specific regulatory architecture of T2D riskPreprint
Su, C., Gao, L., May, C., Pippin, J., Boehm, K., Lee, M. Y. Y., Liu, C.-Y., Pahl, M. C., Golson, M., Naji, A., et al. (2021) · bioRxiv · doi:10.1101/2021.11.30.470653 · Cited by 4
Context-dependent 3D genome regulation by cohesin and related factorsPreprint
Nakato, R., Sakata, T., Wang, J., Nagai, L. A. E., Oba, G. M., Bando, M., & Shirahige, K. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.05.24.493188 · Cited by 2
An Igh novel enhancer modulates antigen receptor diversity by determining locus conformationPreprint
Bhat, K. H., Priyardarshi, S., Naiyer, S., Qu, X., Farooq, H., Kleiman, E., Xu, J., Lei, X., Cantillo, J. F., Wuerffel, R., et al. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.05.23.492988 · Cited by 2
Extreme structural heterogeneity rewires glioblastoma chromosomes to sustain patient-specific transcriptional programsPreprint
Xie, T., Danieli‐Mackay, A., Buccarelli, M., Barbieri, M., Papadionysiou, I., D’Alessandris, Q. G., Übelmesser, N., Vinchure, O. S., Lauretti, L., Fotia, G., et al. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.04.20.537702 · Cited by 2
Schizophrenia Risk Mapping and Functional Engineering of the 3D Genome in Three Neuronal SubtypesPreprint
Powell, S. K., Liao, W., O’Shea, C. P., Kammourh, S., Ghorbani, S., Rigat, R., Elahi, R., Deans, P. M., Le, D. J., Agarwal, P., et al. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.07.17.549339 · Cited by 2
Genome folding principles revealed in condensin-depleted mitotic chromosomesPreprint
Zhao, H., Lin, Y., Lin, E., Liu, F., Shu, L., Jing, D., Wang, B., Wang, M., Shan, F., Zhang, L., et al. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.11.09.566494 · Cited by 2
Self-citation
Genome Restructuring around Innate Immune Genes in Monocytes in Alcohol-associated HepatitisPreprint
Kim, A., McMullen, M. R., Bellar, A., Streem, D. W., Dasarathy, J., Welch, N., & Dasarathy, S. (2024) · eLife · doi:10.1101/2024.08.07.607014 · Cited by 2
Insulated Outlier Chromosomes Enable Metabolic Innovation in Minimal Eukaryotic AlgaePreprint
Valiadi, M., Harrison, K., Loe-Mie, Y., Williams, B., Ankrett, D. N., Smirnoff, N., & Monier, A. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.04.21.649811 · Cited by 2
Does multi-way, long-range chromatin contact data advance 3D genome reconstruction?
Olshen, A. B., & Segal, M. R. (2023) · BMC Bioinformatics · doi:10.1186/s12859-023-05170-x · Cited by 1
“However, it has been argued [34] 2], again with the goal of comparing the competing weighting schemes.”
Background
SpriteHyper2vec: A SCSPRITE Data Completion Method Based on Hypergraph Random Walk
Liu, Y., Ye, Y., & Gao, L. (2023) · IEEE International Conference on Cloud Computing and Intelligence Systems (CCIS) · doi:10.1109/ccis59572.2023.10262832 · Cited by 1
Application of Dimension Reduction Methods to High-Dimensional Single-Cell 3D Genomic Contact Data
Wang, Z., Zhang, P., Sun, W., & Li, D. (2024) · IECE transactions on internet of things. · doi:10.62762/tiot.2024.186430 · Cited by 1
“According to the previous research, single-cell Hi-C data are inherently variable, and there are differences between different cells and different cell states, and between different types of cells [2].”
BackgroundMethodResult
scHiCSRS: a self-representation smoothing method with Gaussian mixture model for imputing single cell Hi-C data
Xie, Q. S., Meng, W., & Lin, S. (2025) · BMC Bioinformatics · doi:10.1186/s12859-025-06147-8 · Cited by 1
“In a recent contribution, we explored the potential of using scRNA methods for analyzing scHi-C data and achieved some success [7].”
The challenge of chromatin model comparison and validation: A project from the first international 4D Nucleome Hackathon
Kubica, J., Korsak, S., Clerkin, A. B., Kouřil, D., Schirman, D., Yadavalli, A. D., Banecki, K. H., Kadlof, M., Busby, B., & Plewczyński, D. (2025) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1013358 · Cited by 1
“For that reason, state-of-the-art frameworks such as HiCRep [113] or GenomeDISCO [112] have been developed to assess the reproducibility of Hi-C that take into account the sensitivity-to-noise ratio and the unique spatial features of the data, including domain structures and distance-dependence.”
Can random walking on a Hi-C contact matrix lead to data quality improvement? An assessment
Liu, Y., & Lin, S. (2025) · PLoS ONE · doi:10.1371/journal.pone.0327100 · Cited by 1
High-throughput capture of actively transcribed region-interacting sequences reveals an intricate promoter-centered regulatory network
Li, X., Xu, J., Yan, X., Zhong, J., Hou, C., Xiao, C., Niu, L., & Chi, W. (2025) · Molecular Cell · doi:10.1016/j.molcel.2025.10.018 · Cited by 4
Exuberant long noncoding RNA expression may sculpt Igh locus topology
Drake, E., Naiyer, S., Qu, X., Bhat, K. H., Farooq, H., Maienschein‐Cline, M., Liang, J., & Kenter, A. L. (2025) · Frontiers in Immunology · doi:10.3389/fimmu.2025.1678105 · Cited by 1
Accurate prediction of cohesin and RNA Polymerase II-associated chromatin interactions using convolutional neural networks
Abbas, A. A., Chandratre, K. R., Liu, C., Zhang, M. Q., & Mani, R. S. (2026) · Nucleic Acids Research · doi:10.1093/nar/gkag258 · Cited by 1
4D nucleome landscape reveals dissolution of bipartite active X chromosome in liver cancer development
Xie, X., Tan, D., Mo, J., Xu, Y., Ni, Q., Liu, T., Wang, H., Chen, B., Liao, Y., Lin, Y., et al. (2026) · Nature Cell Biology · doi:10.1038/s41556-026-02063-y · Cited by 1
“Stratum-adjusted correlation coefficient (SCC) analysis using HiCRep 28 was performed to quantify the similarity between Hi-C interaction matrices of the male mice across all diseased and control groups.”
Assessing chromatin relocalization in 3D using the patient rule induction method
Segal, M. R. (2021) · Biostatistics · doi:10.1093/biostatistics/kxab033 · Cited by 1
Bottom-up data integration in polymer models of chromatin organization
Zhang, A. C. Y., Rosa, A., & Sanguinetti, G. (2023) · Biophysical Journal · doi:10.1016/j.bpj.2023.12.006 · Cited by 1
PLNorm: A Power-Law Distribution-Based Normalization Method for Single-Cell Hi-C Data Analysis
Zhao, B., & Liu, L. (2023) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm58861.2023.10385540 · Cited by 1
“These evaluation tools include HiCRep [32] and cell embeddings .”
Method
SnapFISH-IMPUTE: an imputation method for multiplexed DNA FISH data
Yu, H., Wu, D., Mishra, S. V., Shen, G., Sun, H., Hu, M., & Li, Y. (2024) · Communications Biology · doi:10.1038/s42003-024-06428-7 · Cited by 2
Novel AI-powered computational method using tensor decomposition for identification of common optimal bin sizes when integrating multiple Hi-C datasets
Taguchi, Y., & Turki, T. (2025) · Scientific Reports · doi:10.1038/s41598-025-91355-8 · Cited by 1
Histo-LCM-Hi-C Reveals 3D Chromatin Conformations of Spatially Localized Rare Cells in Tissues at High Resolution
Liu, Y.-Q., Chen, M., Liu, X., Xu, Z., Li, X., Guo, Y., Czajkowsky, D. M., & Shao, Z. (2025) · Genomics Proteomics & Bioinformatics · doi:10.1093/gpbjnl/qzaf091 · Cited by 2
“In addition, the maps obtained with our method were highly re-producible, exhibiting a stratum-adjusted correlation coefficient (SCC, bin size 500 kb) [17] of 0.98 between biological replicates ( Figure 2 A).”
Influential
Resources and challenges for integrative analysis of nuclear architecture data
Jung, Y. L., Kırlı, K., Alver, B., & Park, P. J. (2021) · Current Opinion in Genetics & Development · doi:10.1016/j.gde.2020.12.009 · Cited by 1
Probabilistic method corrects previously uncharacterized Hi-C artifactPreprint
Shen, Y., & Kingsford, C. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.10.07.325332 · Cited by 1
“For each method pair (for example HiC-Pro and LLC-HiC), we get overall 22 (autosomes) × 4 (cell types) = 88 HiCRep scores.”
MethodInfluential
Low input capture Hi-C (liCHi-C) identifies promoter-enhancer interactions at high-resolutionPreprint
Tomás-Daza, L., Rovirosa, L., López-Martí, P., Nieto-Aliseda, A., Serra, F., Planas‐Riverola, A., Molina, Ò., McDonald, R., Ghevaert, C., Cuatrecasas, E., et al. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.11.04.515239 · Cited by 1
Allele-specific Effects of Three-dimensional Genome Architecture in Hybrid PigsPreprint
Li, M. (2022) · Research Square · doi:10.21203/rs.3.rs-2392032/v1 · Cited by 1
Allele-specific Effects of Three-dimensional Genome Architecture in Hybrid PigsPreprint
Lin, Y., Li, J., Gu, Y., Jin, L. G., Bai, J., Zhang, J., Wang, Y., Liu, P., Long, K., He, M., et al. (2022) · Research Square · doi:10.21203/rs.3.rs-2392032/v2 · Cited by 1
Examining dynamics of three-dimensional genome organization with multi-task matrix factorizationPreprint
Lee, D.-I., & Roy, S. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.08.25.554883 · Cited by 1
High-resolution diploid 3D genome reconstruction using Pore-C dataPreprint
Chen, Y., Lin, Z., Wang, S., Wu, B. M., Niu, L., Zhong, J., Sun, Y., Bai, X., Liu, L.-R., Xie, W., et al. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.08.29.555243 · Cited by 1
Multi-omics analysis in primary T cells elucidates mechanisms behind disease associated genetic lociPreprint
Orozco, G., Shi, C., Zhao, D., Rossi, S., Frantzeskos, A., Ding, J., Ferrazzano, C., Wynn, C., Hum, R. M., Richards, E., et al. (2023) · Research Square · doi:10.21203/rs.3.rs-3256438/v1 · Cited by 1
Epigenetics and chromatin structure regulate var2csa expression and the placental binding phenotype in Plasmodium falciparumPreprint
Lenz, T., Sirel, M., Hoppe, H., Shafeeq, S., Roch, K. G. L., & Ribacke, U. (2024) · eLife · doi:10.7554/elife.93632.1 · Cited by 2
CTCF mutation at R567 causes developmental disorders via 3D genome rearrangement and abnormal neurodevelopmentPreprint
Zhang, J., Hu, G., Lu, Y., Ren, H., Huang, Y., Wen, Y., Ji, B., Wang, D., Wang, H., Liu, H., et al. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.04.07.588438 · Cited by 1

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: Yang, Tao
Zhang, Feipeng
Yardımcı, Galip Gürkan
Hardison, Ross C.
Noble, William Stafford
Yue, Feng
Li, Qunhua
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
DOI: 10.1101/gr.220640.117
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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HiCRep: assessing the reproducibility of Hi-C data using a stratum-adjusted correlation coefficient

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