Identifying stable communities in Hi-C data using a multifractal null model

TL;DR

This paper introduces a pipeline for identifying stable, noise-resistant 3D chromatin communities in Hi-C data by leveraging multifractal null models and local modularity maximization.

Contribution

It presents a novel method combining multifractal analysis and community detection to extract robust chromatin communities from noisy Hi-C datasets.

Findings

Stable communities have higher internal contact frequencies.

Stable communities are enriched in active chromatin marks.

Stable communities form more nested hierarchies.

Abstract

Chromosome capture techniques like Hi-C have expanded our understanding of mammalian genome 3D architecture and how it influences gene activity. To analyze Hi-C data sets, researchers increasingly treat them as DNA-contact networks and use standard community detection techniques to identify mesoscale 3D communities. However, there are considerable challenges in finding significant communities because the Hi-C networks have cross-scale interactions and are almost fully connected. This paper presents a pipeline to distil 3D communities that remain intact under experimental noise. To this end, we bootstrap an ensemble of Hi-C datasets representing noisy data and extract 3D communities that we compare with the unperturbed dataset. Notably, we extract the communities by maximizing local modularity (using the Generalized Louvain method), which considers the multifractal spectrum recently discovered in Hi-C maps. Our pipeline finds that stable communities (under noise) typically have above-average internal contact frequencies and tend to be enriched in active chromatin marks. We also find they fold into more nested cross-scale hierarchies than less stable ones. Apart from presenting how to systematically extract robust communities in Hi-C data, our paper offers new ways to generate null models that take advantage of the network's multifractal properties. We anticipate this has a broad applicability to several network applications.