Title: iDINGO: Integrative differential network analysis in genomics
Authors: Caleb Class - UT MD Anderson Cancer Center (United States) [presenting]
Min Jin Ha - UT MD Anderson Cancer Center (United States)
Veerabhadran Baladandayuthapani - University of Michigan (United States)
Kim-Anh Do - The University of Texas MD Anderson Cancer Center (United States)
Abstract: Differential network analysis is an important way to understand the network rewiring involved in disease progression and development. Building differential networks from multiple 'omics data provides insight into the holistic differences of the interactive system under different patient-specific groups. DINGO was developed to infer group-specific dependencies and build differential networks. However, DINGO and other existing tools are limited to analyze data arising from a single platform, and modeling each of the multiple 'omics data independently does not account for the hierarchical structure of the data. We developed the iDINGO R package to estimate group-specific dependencies and make inferences on the integrative differential networks, considering the biological hierarchy among the platforms. A Shiny application has also been developed to facilitate easier analysis and visualization of results, including integrative differential networks and hub gene identification across platforms.