MORE interpretable multi-omic regulatory networks to characterise phenotypes

Abstract

Studying phenotype-specific regulatory mechanisms is crucial to understanding the molecular basis of diseases and other complex traits. However, existing approaches for constructing multi-omic regulatory networks MO-RN are scarce, and most cannot integrate diverse omics modalities, incorporate prior biological knowledge, or infer phenotype-specific networks. To address these challenges, we present MORE (Multi-Omics REgulation), a novel R package for inferring multi-modal regulatory networks. MORE is available at https://github.com/BiostatOmics/MORE and supports any number and type of omics layers while optionally incorporating prior regulatory knowledge. Leveraging advanced regression-based models and variable selection techniques, MORE identifies significant regulatory relationships. This tool also provides useful functionalities for the biological interpretation of MO-RN: network visualisations, differential regulatory networks, and functional enrichment analyses of key network features. We evaluated MORE on simulated multi-omic datasets and benchmarked it against state-of-The-Art tools. Our tool consistently outperformed other methods regarding accuracy in identifying significant regulators, model goodness-of-fit, and computational efficiency. We further applied MORE to a multi-omic ovarian cancer dataset to uncover tumour subtype-specific regulatory mechanisms associated with distinct survival outcomes. This analysis revealed differential regulatory patterns to understand the molecular basis of each subtype. By addressing the limitations of methods for multi-omic network inference, MORE represents a valuable resource for studying regulatory systems. Its ability to construct phenotype-specific regulatory networks with high accuracy and interpretability positions it as a useful resource for researchers seeking to unravel the complexities of molecular interactions and regulatory mechanisms across diverse biological contexts.