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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Hi-C libraries from NeuN+ and NeuN− cells show high reproducibility, with samples clustering by cell type rather than individual and high correlation between same cell types from different individuals (NeuN+ = 0.95-0.97; NeuN− = 0.86-0.91).
“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]
Clustering analysis by Hi-CRep showed that 5-AZA treated and DNMT TKD cells were more similar to each other than DMSO treated cells.
“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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Mitter, M., Gasser, C., Takács, Z., Langer, C. C. H., Tang, W., Jessberger, G., Beales, C. T., Neuner, E., Ameres, S. L., Peters, J., et al. (2020) · Nature · doi:10.1038/s41586-020-2744-4 · Cited by 107 · see citing works
Condensin-Dependent Chromatin Compaction Represses Transcription Globally during Quiescence
Swygert, S. G., Kim, S., Wu, X., Fu, T., Hsieh, T.-H. S., Rando, O. J., Eisenman, R. N., Shendure, J., McKnight, J. N., & Tsukiyama, T. (2018) · Molecular Cell · doi:10.1016/j.molcel.2018.11.020 · Cited by 107 · see citing works
“For reproducibility analyses, the hicrep R package (Yang et al., 2017) was run on all pairs of experiments individually subsampled to 6,000,000 unique contacts (read pairs excluding unligated fragments), at 5 kb resolution for up to 100 kb interactions.”
MethodResultNeuronal and glial 3D chromatin architecture informs the cellular etiology of brain disorders
Hu, B., Won, H., Mah, W., Park, R. B., Kassim, B., Spiess, K., Kozlenkov, A., Crowley, C. A., Pochareddy, S., The PsychENCODE Consortium, et al. (2021) · Nature Communications · doi:10.1038/s41467-021-24243-0 · Cited by 103 · see citing works
Zhang, X.-L., Liu, X.-H., Du, Z., Wei, L., Fang, H., Dong, Q., Niu, J., Li, Y.-D., Gao, J., Zhang, M. Q., et al. (2021) · Genome Research · doi:10.1101/gr.275235.121 · Cited by 90 · see citing works
“HiCRep was used to examine the reproducibility (Yang et al. 2017).”
MethodHi-C analysis: from data generation to integration
Pal, K., Forcato, M., & Ferrari, F. (2018) · Biophysical Reviews · doi:10.1007/s12551-018-0489-1 · Cited by 82 · see citing works
“As more and more datasets become available, it will become increasingly important to establish common and standardized procedures to assess data quality (Wolff et al. 2018) and reproducibility of replicates (Yardimci et al. 2017; Yang et al. 2017).”
BackgroundMapping the Global Chromatin Connectivity Network for Sox2 Function in Neural Stem Cell Maintenance
Bertolini, J. A., Favaro, R., Zhu, Y., Pagin, M., Ngan, C. Y., Wong, C., Tjong, H., Vermunt, M. W., Martynoga, B., Barone, C., et al. (2019) · Cell stem cell · doi:10.1016/j.stem.2019.02.004 · Cited by 97 · see citing works
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“This approach leverages a similarity measure, stratum adjusted correlation coefficient, that was developed for comparing bulk Hi-C matrices [11], combined with multidimensional scaling (MDS) to preserve distances between individual scHi-C contact maps.”
MethodSelf-citationMultiscale modeling of genome organization with maximum entropy optimization
Lin, X., Qi, Y., Latham, A. P., & Zhang, B. (2021) · The Journal of Chemical Physics · doi:10.1063/5.0044150 · Cited by 96 · see citing works
Dynamic transcriptome and chromatin architecture in granulosa cells during chicken folliculogenesis
Li, D., Ning, C., Zhang, J., Wang, Y., Tang, Q., Kui, H., Wang, T., He, M., Jin, L. G., Li, J., et al. (2022) · Nature Communications · doi:10.1038/s41467-021-27800-9 · Cited by 101 · see citing works
SARS-CoV-2 restructures host chromatin architecture
Wang, R.-Y., Lee, J.-H., Kim, J., Xiong, F., Hasani, L. A., Shi, Y., Simpson, E. N., Zhu, X., Chen, Y.-T., Shivshankar, P., et al. (2023) · Nature Microbiology · doi:10.1038/s41564-023-01344-8 · Cited by 61 · see citing works
Oudelaar, A. M., Harrold, C. L., Hanssen, L. L. P., Telenius, J. M., Higgs, D. R., & Hughes, J. R. (2019) · Nature Communications · doi:10.1038/s41467-019-13404-x · Cited by 80 · see citing works
TADCompare: An R Package for Differential and Temporal Analysis of Topologically Associated Domains
Cresswell, K. G., & Dozmorov, M. G. (2020) · Frontiers in Genetics · doi:10.3389/fgene.2020.00158 · Cited by 80 · see citing works
“Future work will expand the utility of boundary score by developing a similarity/reproducibility score to measure the agreement between (multiple) Hi-C matrices, in the same vein as HiCRep (Yang et al., 2017), Selfish (Ardakany et al.”
BackgroundRamani, V., Deng, X., Qiu, R., Lee, C., Distèche, C. M., Noble, W. S., Shendure, J., & Duan, Z. (2019) · Methods · doi:10.1016/j.ymeth.2019.09.012 · Cited by 85 · see citing works
Self-citationChromatin architecture reorganization in murine somatic cell nuclear transfer embryos
Chen, M., Zhu, Q., Li, C., Kou, X.-C., Zhao, Y.-H., Li, Y.-H., Xu, R., Yang, L., Yang, L.-Y., Gu, L., et al. (2020) · Nature Communications · doi:10.1038/s41467-020-15607-z · Cited by 71 · see citing works
hicGAN infers super resolution Hi-C data with generative adversarial networks
Liu, Q., Lv, H., & Jiang, R. (2019) · Bioinformatics · doi:10.1093/bioinformatics/btz317 · Cited by 79 · see citing works
Böhm, K., Giacomelli, G., Schmidt, A., Imhof, A., Koszul, R., Marbouty, M., & Bramkamp, M. (2019) · Nature Communications · doi:10.1038/s41467-020-15238-4 · Cited by 75 · see citing works
Dong, P., Tu, X., Li, H., Zhang, J., Grierson, D. S., Li, P., & Zhong, S. (2019) · Journal of Integrative Plant Biology · doi:10.1111/jipb.12809 · Cited by 80 · see citing works
3D chromatin maps of the human pancreas reveal lineage-specific regulatory architecture of T2D risk
Su, C., Gao, L., May, C. L., Pippin, J. A., Boehm, K. D., Lee, M., Liu, C., Pahl, M. C., Golson, M. L., Naji, A., et al. (2022) · Cell Metabolism · doi:10.1016/j.cmet.2022.08.014 · Cited by 78 · see citing works
HiCAR is a robust and sensitive method to analyze open-chromatin-associated genome organization
Wei, X., Yu, X., Peters, D., Marius, C., Sun, T., Shan, R., Ou, J., Lin, X., Yue, F., Li, W., et al. (2022) · Molecular Cell · doi:10.1016/j.molcel.2022.01.023 · Cited by 83 · see citing works
Self-citationOrder and stochasticity in the folding of individual Drosophila genomes
Ulianov, S. V., Zakharova, V. V., Galitsyna, A. A., Kos, P. I., Polovnikov, K. E., Flyamer, I. M., Mikhaleva, E. A., Khrameeva, E. E., Germini, D., Logacheva, M. D., et al. (2021) · Nature Communications · doi:10.1038/s41467-020-20292-z · Cited by 64 · see citing works
Transcription-dependent cohesin repositioning rewires chromatin loops in cellular senescence
Olan, I., Parry, A. J., Schoenfelder, S., Narita, M., Ito, Y., Chan, A. S. L., Slater, G. S. C., Bihary, D., Bando, M., Shirahige, K., et al. (2020) · Nature Communications · doi:10.1038/s41467-020-19878-4 · Cited by 76 · see citing works
The impact of chromosomal fusions on 3D genome folding and recombination in the germ line
Vara, C., Paytuví-Gallart, A., Cuartero, Y., Álvarez-González, L., Marín-Gual, L., Garcia, F., Florit-Sabater, B., Capilla, L., Sánchez‐Guillén, R. A., Sarrate, Z., et al. (2021) · Nature Communications · doi:10.1038/s41467-021-23270-1 · Cited by 79 · see citing works
Chromatin compartment dynamics in a haploinsufficient model of cardiac laminopathy
Bertero, A., Fields, P., Smith, A. S. T., Leonard, A., Beussman, K. M., Sniadecki, N., Kim, D.-H., Tse, H., Pabon, L., Shendure, J., et al. (2019) · bioRxiv · doi:10.1083/jcb.201902117 · Cited by 59 · see citing works
Self-citationBastié, N., Chapard, C., Dauban, L., Gadal, O., Beckouët, F., & Koszul, R. (2022) · Nature Structural & Molecular Biology · doi:10.1038/s41594-022-00780-0 · Cited by 70 · see citing works
Quantitative prediction of enhancer–promoter interactionsPreprint
Belokopytova, P., Nuriddinov, M., Mozheiko, E., Fishman, D., & Fishman, V. (2019) · bioRxiv · doi:10.1101/gr.249367.119 · Cited by 62 · see citing works
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Zuo, W., Chen, G., Gao, Z., Li, S., Chen, Y., Huang, C., Chen, J., Chen, Z., Lei, M., & Bian, Q. (2021) · Nature Communications · doi:10.1038/s41467-021-26033-0 · Cited by 63 · see citing works
Unsupervised embedding of single-cell Hi-C data
Liu, J., Lin, D., Yardımcı, G. G., & Noble, W. S. (2018) · Bioinformatics · doi:10.1093/bioinformatics/bty285 · Cited by 70 · see citing works
“The first such method, HiCRep, first smooths the Hi-C contact matrix and then computes a weighted similarity measure separately at each genomic distance [20].”
MethodSelf-citationRen, B., Yang, J., Wang, C., Yang, G., Wang, H., Chen, Y.-H., Xu, R.-Y., Fan, X., You, L., Zhang, T.-P., et al. (2021) · Journal of Hematology & Oncology · doi:10.1186/s13045-021-01131-0 · Cited by 59 · see citing works
“The stratum-adjusted correlation coefficients (SCC) among libraries were calculated by the HiCRep package [14] and shown in Fig.”
MethodWhole-genome doubling drives oncogenic loss of chromatin segregation
Lambuta, R. A., Nanni, L., Liu, Y., Díaz-Miyar, J., Iyer, A., Tavernari, D., Katanayeva, N., Ciriello, G., & Oricchio, E. (2023) · Nature · doi:10.1038/s41586-023-05794-2 · Cited by 68 · see citing works
Architectural alterations of the fission yeast genome during the cell cycle
Tanizawa, H., Kim, K.-D., Iwasaki, O., & Noma, K.-I. (2017) · Nature Structural & Molecular Biology · doi:10.1038/nsmb.3482 · Cited by 65 · see citing works
Gene regulation in the 3D genome
Li, Y., Hu, M., & Shen, Y. (2018) · Human Molecular Genetics · doi:10.1093/hmg/ddy164 · Cited by 66 · see citing works
Comparison of the Hi-C, GAM and SPRITE methods using polymer models of chromatin
Fiorillo, L., Musella, F., Conte, M., Kempfer, R., Chiariello, A. M., Bianco, S., Kukalev, A., Irastorza-Azcárate, I., Esposito, A., Abraham, A., et al. (2021) · Nature Methods · doi:10.1038/s41592-021-01135-1 · Cited by 68 · see citing works
Functional genomics atlas of synovial fibroblasts defining rheumatoid arthritis heritability
Ge, X., Frank-Bertoncelj, M., Klein, K., McGovern, A., Kuret, T., Houtman, M., Burja, B., Micheroli, R., Shi, C., Marks, M., et al. (2020) · Genome Biology · doi:10.1186/s13059-021-02460-6 · Cited by 52 · see citing works
Zhou, X., & Wu, H. (2024) · Briefings in Bioinformatics · doi:10.1093/bib/bbaf009 · Cited by 60 · see citing works
“Common computational approaches for single-cell Hi-C data represent the data as matrices of chromosome interactions at a given resolution R [6, 7, 26, 27].”
Marchal, C., Singh, N., Batz, Z. A., Advani, J., Jaeger, C., Corso‐Díaz, X., & Swaroop, A. (2022) · Nature Communications · doi:10.1038/s41467-022-33427-1 · Cited by 57 · see citing works
“Gene body TSS 3 (136) 3 (64) 5 (118) 4 (86) 1 (57) 0 (0) 0 (162) 0 (65) Retina CREs 5 (122) Glaucoma GWAS lead variants 127 (12,658 variants in LD)”
Bauer, M., Vidal, E., Zorita, E. V., Üresin, N., Pinter, S. F., Filion, G. J. P., & Payer, B. (2021) · Nature Communications · doi:10.1038/s41467-021-23610-1 · Cited by 54 · see citing works
CTCF looping is established during gastrulation in medaka embryosPreprint
Nakamura, R., Motai, Y., Kumagai, M., Wike, C. L., Nishiyama, H., Nakatani, Y., Durand, N. C., Kondo, K., Kondo, T., Tsukahara, T., et al. (2021) · Genome Research · doi:10.1101/gr.269951.120 · Cited by 56 · see citing works
“The reproducibility between the replicates was calculated using HiCRep (Yang et al. 2017) using the default parameter.”
MethodThe chromatin remodeling enzyme Chd4 regulates genome architecture in the mouse brain
Goodman, J. V., Yamada, T., Yang, Y., Kong, L., Wu, D. Y., Zhao, G.-Y., Gabel, H. W., & Bonni, A. (2020) · Nature Communications · doi:10.1038/s41467-020-17065-z · Cited by 55 · see citing works
Comparative 3D genome architecture in vertebrates
Li, D., He, M., Tang, Q., Tian, S., Zhang, J., Li, Y., Wang, D., Jin, L. G., Ning, C., Zhu, W., et al. (2022) · BMC Biology · doi:10.1186/s12915-022-01301-7 · Cited by 55 · see citing works
OneD: increasing reproducibility of Hi-C samples with abnormal karyotypes
Vidal, E., Dily, F. L., Quilez, J., Stadhouders, R., Cuartero, Y., Graf, T., Martı́-Renom, M. A., Beato, M., & Filion, G. J. P. (2018) · Nucleic Acids Research · doi:10.1093/nar/gky064 · Cited by 55 · see citing works
Song, Y., Liang, Z., Zhang, J., Hu, G.-C., Wang, J.-H., Li, Y.-Y., Guo, R., Dong, X.-T., Babarinde, I. A., Ping, W., et al. (2022) · Cell Reports · doi:10.1016/j.celrep.2022.110626 · Cited by 46 · see citing works
Selfish: discovery of differential chromatin interactions via a self-similarity measure
Ardakany, A. R., Ay, F., & Lonardi, S. (2019) · Bioinformatics · doi:10.1093/bioinformatics/btz362 · Cited by 52 · see citing works
“The results show that SELFISH is by far more efficient than HiCRep and GenomeDISCO.”
MethodInfluentialFeng, Y.-L., Cai, L., Hong, W.-Z., Zhang, C.-X., Tan, N., Wang, M., Wang, C., Liu, F., Wang, X.-H., Ma, J.-Y., et al. (2022) · Circulation · doi:10.1161/circulationaha.121.055781 · Cited by 38 · see citing works
Boninsegna, L., Yıldırım, A., Polles, G., Zhan, Y., Quinodoz, S. A., Finn, E. H., Guttman, M., Zhou, X. J., & Alber, F. (2022) · Nature Methods · doi:10.1038/s41592-022-01527-x · Cited by 49 · see citing works
Normalization and de-noising of single-cell Hi-C data with BandNorm and scVI-3D
Zheng, Y., Shen, S., & Keleş, S. (2022) · Genome biology · doi:10.1186/s13059-022-02774-z · Cited by 51 · see citing works
“b HiCRep [13] similarity scores between aggregated scHi-C matrices”
BackgroundMethodInfluentialZhong, J., Niu, L., Lin, Z., Bai, X., Chen, Y., Luo, F., Hou, C., & Xiao, C. (2023) · Nature Communications · doi:10.1038/s41467-023-36899-x · Cited by 58 · see citing works
“2.0 (scc) 73 .”
InfluentialHigh-throughput Oligopaints identifies druggable 3D genome regulators
Park, D. S., Nguyen, S. C., Isenhart, R. L., Shah, P., Kim, W., Barnett, R. J., Chandra, A., Luppino, J. M., Harke, J., Wai, M., et al. (2023) · Nature · doi:10.1038/s41586-023-06340-w · Cited by 38 · see citing works
The 3D Genome Structure of Single Cells
Zhou, T., Zhang, R., & Ma, J. (2021) · Annual Review of Biomedical Data Science · doi:10.1146/annurev-biodatasci-020121-084709 · Cited by 50 · see citing works
“After imputation, only the top 20% of the interactions are kept, which reduces the bias introduced from uneven sequence coverage across different cells; these are then flattened into a one-dimensional feature vector. scHiCluster uses PCA (principal component analysis) as the dimensionality reduction method. scHiCluste…”
MethodInfluentialGranulosa cell mevalonate pathway abnormalities contribute to oocyte meiotic defects and aneuploidy
Liu, C., Zuo, W., Yan, G., Wang, S., Sun, S., Li, S., Tang, X., Li, Y., Cai, C., Wang, H., et al. (2023) · Nature Aging · doi:10.1038/s43587-023-00419-9 · Cited by 49 · see citing works
Comparative characterization of 3D chromatin organization in triple-negative breast cancers
Kim, T., Han, S., Chun, Y., Yang, H.-J., Min, H., Jeon, S. Y., Kim, J., Moon, H., & Lee, D. (2022) · Experimental & Molecular Medicine · doi:10.1038/s12276-022-00768-2 · Cited by 47 · see citing works
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“org/record/5362241/export/json [accessed July 12, 2022]) on three pseudoreplicates generated by sampling the merged contact maps, as described previously (Yang et al. 2017).”
BackgroundLocal chromatin fiber folding represses transcription and loop extrusion in quiescent cells
Swygert, S. G., Lin, D., Portillo‐Ledesma, S., Lin, P.-Y., Hunt, D. R., Kao, C.-F., Tamar Schlick, A., Noble, W. S., & Tsukiyama, T. (2021) · eLife · doi:10.7554/elife.72062 · Cited by 41 · see citing works
Self-citationLoss of Epigenetic Information as a Cause of Mammalian AgingPreprint
Yang, J.-H., Hayano, M., Griffin, P. T., Amorim, J. A., Bonkowski, M. S., Apostolides, J. K., Blanchette, M., Munding, E. M., Bhakta, M. S., Salfati, E., et al. (2021) · SSRN Electronic Journal · doi:10.2139/ssrn.3951490 · Cited by 45 · see citing works
“Normalized interaction matrices were binned with smoothMat (Yang et al., 2017).”
BackgroundMethodInterferons reshape the 3D conformation and accessibility of macrophage chromatin
Platanitis, E., Gruener, S., Geetha, A. R. S. J., Boccuni, L., Vogt, A., Novatchkova, M., Sommer, A., Barozzi, I., Müller, M. M., & Decker, T. (2022) · iScience · doi:10.1016/j.isci.2022.103840 · Cited by 45 · see citing works
Predicting scale-dependent chromatin polymer properties from systematic coarse-graining
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Extrusion fountains are hallmarks of chromosome organization emerging upon zygotic genome activation
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Theory of chromatin organization maintained by active loop extrusion
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Lineage-specific 3D genome organization is assembled at multiple scales by IKAROS
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“8% (41,111) of the OCRs to 35,215 genes (Figure 2B, Supplemental Table 4), among which 6,075 prOCRs were differentially accessible between NPCs and neurons, corresponding to 7,749 genes.”
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“Our Hi-C 3.0 data showed high concordance between replicates as shown by SCC analysis 40 (Extended Data Fig.”
InfluentialA Dynamic Folded Hairpin Conformation Is Associated with α-Globin Activation in Erythroid Cells
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- 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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