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Servant N, Varoquaux N, Heard E, et al. Effective normalization for copy number variation in Hi-C data. BMC Bioinformatics. 2018;19(1):313doi: 10.1186/s12859-018-2256-5.
Servant, N., Varoquaux, N., Heard, E., Barillot, E., & Vert, J. P. (2018). Effective normalization for copy number variation in Hi-C data. BMC bioinformatics, 19(1), 313. https://doi.org/10.1186/s12859-018-2256-5
Servant, Nicolas, et al. "Effective normalization for copy number variation in Hi-C data." BMC bioinformatics vol. 19,1 (2018): 313. doi: https://doi.org/10.1186/s12859-018-2256-5
Servant N, Varoquaux N, Heard E, Barillot E, Vert JP. Effective normalization for copy number variation in Hi-C data. BMC Bioinformatics. 2018 Sep 06;19(1):313. doi: 10.1186/s12859-018-2256-5. PMID: 30189838; PMCID: PMC6127909.
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BMC Bioinformatics. 2018 Sep 06;19(1):313. doi: 10.1186/s12859-018-2256-5.

Effective normalization for copy number variation in Hi-C data.

BMC bioinformatics

Nicolas Servant, Nelle Varoquaux, Edith Heard, Emmanuel Barillot, Jean-Philippe Vert

Affiliations

  1. Institut Curie, PSL Research University, Paris, F-75005, France. [email protected].
  2. INSERM, U900, Paris, F-75005, France. [email protected].
  3. Mines ParisTech, PSL Research University, CBIO-Centre for Computational Biology, Paris, F-75006, France. [email protected].
  4. Department of Statistics, University of California, Berkeley, USA.
  5. Berkeley Institute for Data Science, Berkeley, USA.
  6. Institut Curie, PSL Research University, CNRS UMR3215, INSERM U934, Paris, F-75005, France.
  7. Institut Curie, PSL Research University, Paris, F-75005, France.
  8. INSERM, U900, Paris, F-75005, France.
  9. Mines ParisTech, PSL Research University, CBIO-Centre for Computational Biology, Paris, F-75006, France.
  10. Ecole Normale Supérieure, PSL Research University, Department of Mathematics and Applications, Paris, F-75005, France.

PMID: 30189838 PMCID: PMC6127909 DOI: 10.1186/s12859-018-2256-5

Abstract

BACKGROUND: Normalization is essential to ensure accurate analysis and proper interpretation of sequencing data, and chromosome conformation capture data such as Hi-C have particular challenges. Although several methods have been proposed, the most widely used type of normalization of Hi-C data usually casts estimation of unwanted effects as a matrix balancing problem, relying on the assumption that all genomic regions interact equally with each other.

RESULTS: In order to explore the effect of copy-number variations on Hi-C data normalization, we first propose a simulation model that predict the effects of large copy-number changes on a diploid Hi-C contact map. We then show that the standard approaches relying on equal visibility fail to correct for unwanted effects in the presence of copy-number variations. We thus propose a simple extension to matrix balancing methods that model these effects. Our approach can either retain the copy-number variation effects (LOIC) or remove them (CAIC). We show that this leads to better downstream analysis of the three-dimensional organization of rearranged genomes.

CONCLUSIONS: Taken together, our results highlight the importance of using dedicated methods for the analysis of Hi-C cancer data. Both CAIC and LOIC methods perform well on simulated and real Hi-C data sets, each fulfilling different needs.

Keywords: Cancer; Copy-number; Hi-C; Normalization

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