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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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Self-citationMeasuring the reproducibility and quality of Hi-C data
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“HiCRep [33], GenomeDISCO [34], HiC-Spector [32] and QuASAR-Rep (in submission) measure reproducibility, and”
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“Overall, we found high values of stratum-adjusted correlation coefficients (SCCs 21 ; for Pore-C VPC contact maps at five resolutions; Fig.”
BackgroundA map of cis-regulatory elements and 3D genome structures in zebrafish
Yang, H., Luan, Y., Liu, T., Lee, H. J., Li, F., Wang, Y., Wang, X., Zhang, B., Jin, Q., Ang, K. C., et al. (2020) · Nature · doi:10.1038/s41586-020-2962-9 · Cited by 146
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“Stratum-adjusted correlation coefficients ( 36 ) demonstrated high concordance of data between replicates and individuals from the same species (fig.”
BackgroundCondensin-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 113
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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
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“HiCRep was used to examine the reproducibility (Yang et al. 2017).”
MethodHi-C analysis: from data generation to integration
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“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
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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.”
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“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.”
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Quantitative prediction of enhancer–promoter interactionsPreprint
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Unsupervised embedding of single-cell Hi-C data
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“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].”
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“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 74
Architectural alterations of the fission yeast genome during the cell cycle
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Gene regulation in the 3D genome
Li, Y., Hu, M., & Shen, Y. (2018) · Human Molecular Genetics · doi:10.1093/hmg/ddy164 · Cited by 71
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 73
Functional genomics atlas of synovial fibroblasts defining rheumatoid arthritis heritability
Ge, X. J., Frank‐Bertoncelj, M., Klein, K., McGovern, A., Kuret, T., Houtman, M., Burja, B., Micheroli, R., Shi, C., Marks, M., et al. (2021) · Genome biology · doi:10.1186/s13059-021-02460-6 · Cited by 64
Zhou, X., & Wu, H. (2024) · Briefings in Bioinformatics · doi:10.1093/bib/bbaf009 · Cited by 66
“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 54
“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
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
“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., Gabel, H. W., & Bonni, A. (2020) · Nature Communications · doi:10.1038/s41467-020-17065-z · Cited by 57
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 61
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
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
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
“The results show that SELFISH is by far more efficient than HiCRep and GenomeDISCO.”
MethodInfluentialFeng, Y., Cai, L., Hong, W., Zhang, C., Tan, N., Wang, M., Wang, C., Liu, F., Wang, X., Ma, J., et al. (2022) · Circulation · doi:10.1161/circulationaha.121.055781 · Cited by 56
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 48
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 52
“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
“2.0 (scc) 73 .”
InfluentialHigh-throughput Oligopaint screen identifies druggable 3D genome regulators
Park, D. S., Nguyen, S. C., Isenhart, R. L., Shah, P. 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 56
The 3D Genome Structure of Single Cells
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“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
Comparative characterization of 3D chromatin organization in triple-negative breast cancers
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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 48
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 46
“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
Predicting coarse-grained chromatin polymer properties from nucleosome-level contact data
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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
Chiariello, A. M., Bianco, S., Oudelaar, A. M., Esposito, A., Annunziatella, C., Fiorillo, L., Conte, M., Corrado, A., Prisco, A., Larke, M., et al. (2020) · Cell Reports · doi:10.1016/j.celrep.2020.01.044 · Cited by 44
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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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