Multi-omics data integration with prior knowledge to decipher signaling and metabolic deregulation in complex diseases with COSMOS

Trainers: Aurelien Dugourd

Overview: Multi-omics datasets can provide molecular insights beyond the sum of individual omics. Various tools have been recently developed to integrate such datasets, but there are limited strategies to systematically extract mechanistic hypotheses from them. Here, we present COSMOS (Causal Oriented Search of Multi-Omics Space, https://github.com/saezlab/cosmosR), a method that integrates phosphoproteomics, transcriptomics, and metabolomics datasets. COSMOS combines extensive prior knowledge of signaling, metabolic, and gene regulatory networks with computational methods to estimate activities of transcription factors and kinases as well as network-level causal reasoning. COSMOS provides mechanistic hypotheses for experimental observations across multi-omics datasets, leveraging prior knowledge such as signalling events, ligand-receptor interactions and metabolic networks. In this talk, we will present the concepts behind COSMOS and its latest advances, notably its integration with factor-based analysis tools such as MOFA. 

Materials: 
COSMOS – slides