Identifying stable communities in Hi-C using multifractal network modularity

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 distill 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 contac,t 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.Author summary: Understanding the 3D structure of DNA inside cells is crucial for studying gene activity, as the two are often intricately connected. This DNA 3D structure is commonly analyzed using data from a technique called Hi-C, but the complex nature of DNA interactions makes this challenging. Our study introduces a new method to identify stable structures in DNA folding data that are resistant to noise. By generating multiple noisy versions of the Hi-C data and applying a specialized network clustering algorithm, we found that certain DNA structures remain more stable than others. These stable structures have stronger internal connections, are hierarchically organized at different scales, and consist of more active DNA regions. Our method not only improves the analysis of Hi-C data but also offers new ways to study complex networks to better understand gene regulation and organization.