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B1959
Title: Graphical models for data integration and mediation analysis Authors:  Min Jin Ha - UT MD Anderson Cancer Center (United States) [presenting]
Veerabhadran Baladandayuthapani - University of Michigan (United States)
Francesco Stingo - University of Florence (Italy)
Abstract: Integrative network modeling of data arising from multiple genomic platforms provides insight into the holistic picture of the interactive system, as well as the flow of information across many disease domains. The basic data structure consists of a sequence of hierarchically ordered datasets for each individual subject, which facilitates integration of diverse inputs, such as genomic, transcriptomic, and proteomic data. A primary analytical task in such contexts is to model the layered architecture of networks where the vertices can be naturally partitioned into ordered layers, dictated by multiple platforms, and exhibit both undirected and directed relationships. We propose a multi-layered Gaussian graphical model (mlGGM) to investigate conditional independence structures in such multi-level genomic networks. We use a Bayesian node-wise selection approach that coherently accounts for the multiple types of dependencies in mlGGM, that is used for finding causal factors for outcome variables via mediation analysis.