Bayesian Structure Learning in Multilayered Genomic Networks

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 including cancer. 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 multilayered Gaussian graphical model (mlGGM) to investigate conditional independence structures in such multilevel genomic networks in human cancers. We implement a Bayesian node-wise selection (BANS) approach based on variable selection techniques that coherently accounts for the multiple types of dependencies in mlGGM; this flexible strategy exploits edge-specific prior knowledge and selects sparse and interpretable models. Through simulated data generated under various scenarios, we demonstrate that BANS outperforms other existing multivariate regression-based methodologies. Our integrative genomic network analysis for key signaling pathways across multiple cancer types highlights commonalities and differences of p53 integrative networks and epigenetic effects of BRCA2 on p53 and its interaction with T68 phosphorylated CHK2, that may have translational utilities of finding biomarkers and therapeutic targets. Supplementary materials for this article, including a standardized description of the materials available for reproducing the work, are available as an online supplement.

Keywords:

• Bayesian variable selection
• Multilayered Gaussian graphical models
• Multilevel data integration

Supplementary Materials

The online supplementary materials contain R codes including BANS R package and datasets. Supplementary pdf file includes proof and additional information on chain graphs, MCMC sampling, simulation studies and supplementary tables and figures.

Additional information

Funding

Min Jin Ha’s research is supported by by NIH/NCI grants P30CA01 and R21CA22029. Veerabhadran Baladandayuthapani’s research is supported by NIH R01-CA160736, R01-CA194391, P30-CA46592, R21CA22029, National Science Foundation Grant No. DMS 1922567, and UM Rogel Cancer Center and the School of Public Health.