MATCHA: Probing multi-way chromatin interaction with hypergraph representation learning.

Recent advances in ligation-free, genome-wide chromatin interaction mapping such as SPRITE and ChIA-Drop have enabled the identification of simultaneous interactions involving multiple genomic loci within the same nuclei, which are informative to delineate higher-order genome organization and gene regulation mechanisms at single-nucleus resolution. Unfortunately, computational methods for analyzing multi-way chromatin interaction data are significantly underexplored. Here we develop an algorithm, called MATCHA, based on hypergraph representation learning where multi-way chromatin interactions are represented as hyperedges. Applications to SPRITE and ChIA-Drop data suggest that MATCHA is effective to denoise the data and make de novo predictions, which greatly enhances the data quality for analyzing the properties of multi-way chromatin interactions. MATCHA provides a promising framework to significantly improve the analysis of multi-way chromatin interaction data and has the potential to offer unique insights into higher-order chromosome organization and function. MATCHA is freely available for download here: https://github.com/ma-compbio/MATCHA.