Modeling causal signal propagation in multi-omic factor space with COSMOS

Abstract

Understanding complex diseases requires approaches that jointly analyze omic data across multiple biological layers, including signaling, gene regulation, and metabolism. Existing data-driven multi-omic analysis methods, such as multi-omic factor analysis (MOFA), can identify associations between molecular features and phenotypes, but they are not designed to integrate existing mechanistic molecular knowledge, which can provide further actionable insights. We introduce an approach that connects data-driven analysis of multi-omic data with systematic integration of mechanistic prior knowledge using COSMOS+ (Causal Oriented Search of Multi-Omics Space). We show how factor analysis’ output can be used to estimate activities of transcription factors and kinases as well as ligand-receptor interactions, which in turn are integrated with network-level prior-knowledge to generate mechanistic hypotheses about paths connecting deregulated molecular features. Our approach offers an interpretable framework to generate actionable insights from multi-omic data particularly suited for high dimensional datasets such as patient cohorts.Abstract Figure