A statistical physics approach to integrating multi-omics data for disease-module detection

Summary: Genes associated with the same disease frequently engage in mutual biological interactions, e.g., perturbation within a specific neighborhood in the molecular interactome, often referred to as the disease module. This has propelled the advancement of network-based approaches toward elucidating the molecular bases of human diseases. Although many computational methods have been developed to integrate the molecular interactome and omics profiles to extract such context-dependent disease modules, approaches that leverage multi-omics for disease-module detection are still lacking. Here, we developed a statistical physics approach based on the random-field O(n) model (RFOnM) to fill this gap. We applied the RFOnM approach to integrate gene-expression data and genome-wide association studies or mRNA data and DNA methylation for several complex diseases with the human interactome. We found that the RFOnM approach outperforms existing single omics methods in most of the complex diseases considered in this study. Motivation: Disease-module detection can help to uncover new mechanistic knowledge of complex diseases, reveal disease-disease relationships, discover therapeutic targets, and stratify biomarkers. However, computational methods for disease-module detection that integrate multi-omics are still lacking. To fill this gap, we developed a method based on the random-field O(n) model (RFOnM) to integrate multiple data types for several complex diseases with the human interactome for disease-module detection.