BHiCect 2.0: Multi-resolution clustering of Hi-C data

TL;DR

BHiCect 2.0 introduces a multi-resolution spectral clustering method to analyze hierarchical chromosome structures from Hi-C data, providing a comprehensive view of genome organization across scales.

Contribution

It extends previous clustering approaches by integrating multiple Hi-C resolutions to describe nested chromosome architectures.

Findings

Effective multi-resolution clustering of Hi-C data

Reveals hierarchical chromosome organization

Provides an R package for broader use

Abstract

Chromatin conformation capture technologies such as Hi-C have revealed that the genome is organized in a hierarchy of structures spanning multiple scales observed at different resolutions. Current algorithms often focus on specific interaction patterns found at a specific Hi-C resolution. We present BHi-Cect 2.0, a method that leverages Hi-C data at multiple resolutions to describe chromosome architecture as nested preferentially self-interacting clusters using spectral clustering. This new version describes the hierarchical configuration of chromosomes by now integrating multiple Hi-C data resolutions. Our new implementation offers a more comprehensive description of the multi-scale architecture of the chromosomes. We further provide these functionalities as an R package to assist their integration with other computational pipelines. The BHiCect 2.0 R packages is available on github at https://github.com/princeps091-binf/BHiCect2with the version used for this manuscript on Zenodo at https://doi.org/10.5281/zenodo.17985844.