A novel statistical approach for replicating multi-omics networks across study groups and cohorts.

Music City Center 

Multiple omics data provide researchers with a more comprehensive understanding of the mechanisms underlying complex diseases. Network-based approaches are effective in integrating multiple omics data and simultaneously capture interactions between different molecules. Understanding how multi-omics networks replicate across experimental conditions, demographic groups, and study cohorts can uncover conserved and differential biological changes associated with disease outcomes. While replication analyses have been well-established for single biomarkers, there is a lack of methods specifically addressing the replication of intermolecular interactions in biological networks. To bridge the gap, we propose developing a novel approach to facilitate network replication across study groups and cohorts while leveraging machine learning to identify consistent molecular signatures most relevant to outcomes of interest. To demonstrate the utility of the proposed method, we will use multi-omics data from studies of chronic obstructive pulmonary disease (COPD), the Genetic Epidemiology of COPD (COPDGene) and the Study of COPD Subgroups and Biomarkers (SPIROMICS) cohorts.

multi-omics data

network analysis

network replication

COPD 

WNAR