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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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FreeHi-C: high fidelity Hi-C data simulation for benchmarking and data augmentationPreprint
Zheng, Y., & Keleş, S. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/629923
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MethodAssessing stationary distributions derived from chromatin contact maps
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Detecting local changes in chromatin architecture with false discovery controlPreprint
Koch, H., Yang, T., Imakaev, M., Hardison, R. C., & Li, Q. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.09.03.281972
Self-citationessHi-C: essential component analysis of Hi-C matrices
Franzini, S., Stefano, M. D., & Micheletti, C. (2021) · Bioinformatics · doi:10.1093/bioinformatics/btab062
“Owing to the importance of comparative analysis, it is increasingly crucial to crossvalidate data gathered with different protocols, resolution, and sequencing depth [20, 21, 22, 23, 24], thus identifying the common and statistically-significant features [25] in Hi-C matrices.”
BackgroundAnalysis of Genome Architecture Mapping Data with a Machine Learning and Polymer-Physics-Based Tool
Fiorillo, L., Conte, M., Esposito, A., Musella, F., Flora, F., Chiariello, A. M., & Bianco, S. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-71593-9_25 · Cited by 1
“2a, b), as witnessed by the high values of the Pearson (r) and the Stratum Adjusted (scc) [39] correlation coefficients (r = 0.93 and scc = 0.96 for the co-segregation matrices, while r = 0.86 and scc = 0.97 for the D’ case).”
BackgroundInfluentialBhowmick, B. K. (2021) · Springer eBooks · doi:10.1007/978-981-16-3997-5_1
From compartments to gene loops: Functions of the 3D genome in the human brainPreprint
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Faculty Opinions recommendation of Predicting 3D genome folding from DNA sequence with Akita.
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Castillo, D., Mendieta-Esteban, J., & Martı́-Renom, M. A. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.09.19.508575
“4b ) results in a Stratum-adjusted Correlation Coefficient (SCC), a metric designed specifically to compare Hi-C matrices [25], of 0.81.”
ResultInfluentialStructural modeling of the 3D genome using machine learning
Highsmith, M. R. (2021) · MOspace Institutional Repository (University of Missouri) · doi:10.32469/10355/93230
High changing curvature regions detect chromatin enrichment in single cell dataPreprint
Amici, G., Papale, A., Lando, D., Boucher, W., & Holcman, D. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.03.31.535120
“To evaluate the convergence of the probability series, we computed the stratum-adjusted correlation coefficient SCC [44] between the simulated and the experimental contact matrices, computed as the weighted average of the Pearson correlation coefficients for each sub-diagonal: where the index n…”
MethodPredicting chromosomal compartments directly from the nucleotide sequence with DNA-DDA
Lainscsek, X., & Taher, L. (2023) · Briefings in Bioinformatics · doi:10.1093/bib/bbad198
Dependency of 3D chromatin folding on RNA polymerase II at different cell cycle stages
Übelmesser, N. (2023) · eDiss (Georg-August-Universität Göttingen) · doi:10.53846/goediss-9998
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Posterior inference of Hi-C contact frequency through sampling
Zhang, Y., Cameron, C. J. F., & Blanchette, M. (2024) · Frontiers in Bioinformatics · doi:10.3389/fbinf.2023.1285828
“Although Hi-C datasets with multiple replicates are becoming available and are being analyzed using different tools in recent years (Yang et al., 2017; Stans fi eld et al., 2019), HiCSampler currently only infers posterior distributions of interaction frequencies based on a single Hi-C contact map.”
MethodLenz, T., Sirel, M., Hoppe, H., Shafeeq, S., Roch, K. G. L., & Ribacke, U. (2024) · eLife · doi:10.7554/elife.93632 · Cited by 4
“Correlation among biological replicates was evaluated with HiCRep ( Yang et al., 2017 ), then replicates were merged to generate a single representative sample.”
Zhang, J., Liu, P., He, M., Wang, Y., Kui, H., Jin, L. G., Li, D., & Li, M. (2022) · Figshare · doi:10.6084/m9.figshare.19350071
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Additional file 1 of 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) · Figshare · doi:10.6084/m9.figshare.19728043
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Pasadas, M. C. (2024) · UPCommons institutional repository (Universitat Politècnica de Catalunya) · doi:10.5821/dissertation-2117-422069
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“Also note that we seek to quantify the heterogeneity of an ensemble of polymer conformations in 3D, not its population-averaged Hi-C map [55].”
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“Moreover, because MFPT reflects effective topological distances rather than raw contact frequencies, its variation shows substantially weaker dependence on genomic distance, reducing the need for complex distance-dependent normalization in downstream analyses [ 20 ].”
InfluentialSHICEDO: single-cell Hi-C data enhancement with reduced over-smoothing
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Additional file 2 of Comparative 3D genome architecture in vertebrates
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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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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