Bayesian robust learning in chain graph models for integrative pharmacogenomics

Abstract

Integrative analysis of multilevel pharmacogenomic data for modeling dependencies across various biological domains is crucial for developing genomic-testing based treatments. Chain graphs characterize conditional dependence structures of such multilevel data where variables are naturally partitioned into multiple ordered layers, consisting of both directed and undirected edges. Existing literature mostly focus on Gaussian chain graphs, which are ill-suited for nonnormal distributions with heavy-tailed marginals, potentially leading to inaccurate inferences. We propose a Bayesian robust chain graph model (RCGM) based on random transformations of marginals using Gaussian scale mixtures to account for node-level nonnormality in continuous multivariate data. This flexible modeling strategy facilitates identification of conditional sign dependencies among nonnormal nodes while still being able to infer conditional dependencies among normal nodes. In simulations we demonstrate that RCGM outperforms existing Gaussian chain graph inference methods in data generated from various nonnormal mechanisms. We apply our method to genomic, transcriptomic and proteomic data to understand underlying biological processes holistically for drug response and resistance in lung cancer cell lines. Our analysis reveals inter- and intra-platform dependencies of key signaling pathways to monotherapies of icotinib, erlotinib and osimertinib among other drugs, along with shared patterns of molecular mechanisms behind drug actions.