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Inference of genome 3D architecture by modeling overdispersion of Hi-C data

Abstract : We address the challenge of inferring a consensus 3D model of genome architecture from Hi-C data. Existing approaches most often rely on a two step algorithm: first convert the contact counts into distances, then optimize an objective function akin to multidimensional scaling (MDS) to infer a 3D model. Other approaches use a maximum likelihood approach, modeling the contact counts between two loci as a Poisson random variable whose intensity is a decreasing function of the distance between them. However, a Poisson model of contact counts implies that the variance of the data is equal to the mean, a relationship that is often too restrictive to properly model count data. We first confirm the presence of overdispersion in several real Hi-C data sets, and we show that the overdispersion arises even in simulated data sets. We then propose a new model, called Pastis-NB, where we replace the Poisson model of contact counts by a negative binomial one, which is parametrized by a mean and a separate dispersion parameter. The dispersion parameter allows the variance to be adjusted independently from the mean, thus better modeling overdispersed data. We compare the results of Pastis-NB to those of several previously published algorithms: three MDS-based methods (ShRec3D, ChromSDE, and Pastis-MDS) and a statistical methods based on a Poisson model of the data (Pastis-PM). We show that the negative binomial inference yields more accurate structures on simulated data, and more robust structures than other models across real Hi-C replicates and across different resolutions. A Python implementation of Pastis-NB is available at under the BSD license Supplementary information is available at
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Pré-publication, Document de travail
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Soumis le : mercredi 13 octobre 2021 - 10:29:30
Dernière modification le : mercredi 17 novembre 2021 - 12:31:41

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Nelle Varoquaux, William Noble, Jean-Philippe Vert. Inference of genome 3D architecture by modeling overdispersion of Hi-C data. 2021. ⟨hal-03375900⟩



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