Network modelling of topological domains using Hi-C data

TL;DR

This paper introduces a novel network model for detecting topologically associating domains (TADs) in Hi-C data, accounting for non-exchangeability of genomic positions and incorporating biological covariates, leading to improved TAD identification.

Contribution

The authors propose a new non-exchangeable network model for TAD detection that integrates cell-type specific CTCF binding sites and provides an efficient likelihood optimization method.

Findings

Model accurately detects TADs with high probability when properly initialized.

Method outperforms spectral clustering in simulated data.

Identified TADs show relevant epigenetic features across cell types.

Abstract

Chromosome conformation capture experiments such as Hi-C are used to map the three-dimensional spatial organization of genomes. One specific feature of the 3D organization is known as topologically associating domains (TADs), which are densely interacting, contiguous chromatin regions playing important roles in regulating gene expression. A few algorithms have been proposed to detect TADs. In particular, the structure of Hi-C data naturally inspires application of community detection methods. However, one of the drawbacks of community detection is that most methods take exchangeability of the nodes in the network for granted; whereas the nodes in this case, i.e. the positions on the chromosomes, are not exchangeable. We propose a network model for detecting TADs using Hi-C data that takes into account this non-exchangeability. In addition, our model explicitly makes use of cell-type specific CTCF binding sites as biological covariates and can be used to identify conserved TADs across multiple cell types. The model leads to a likelihood objective that can be efficiently optimized via relaxation. We also prove that when suitably initialized, this model finds the underlying TAD structure with high probability. Using simulated data, we show the advantages of our method and the caveats of popular community detection methods, such as spectral clustering, in this application. Applying our method to real Hi-C data, we demonstrate the domains identified have desirable epigenetic features and compare them across different cell types.