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Integrative analysis of multi-level pharmacogenomic data for modelingdependencies across various biological domains is crucial for developinggenomic-testing based treatments. Chain graphs characterize conditionaldependence structures of such multi-level data where variables are naturallypartitioned into multiple ordered layers, consisting of both directed andundirected edges. Existing literature mostly focus on Gaussian chain graphs,which are ill-suited for non-normal distributions with heavy-tailed marginals,potentially leading to inaccurate inferences. We propose a Bayesian robustchain graph model (RCGM) based on random transformations of marginals usingGaussian scale mixtures to account for node-level non-normality in continuousmultivariate data. This flexible modeling strategy facilitates identificationof conditional sign dependencies among non-normal nodes while still being ableto infer conditional dependencies among normal nodes. In simulations, wedemonstrate that RCGM outperforms existing Gaussian chain graph inferencemethods in data generated from various non-normal mechanisms. We apply ourmethod to genomic, transcriptomic and proteomic data to understand underlyingbiological processes holistically for drug response and resistance in lungcancer cell lines. Our analysis reveals inter- and intra- platform dependenciesof key signaling pathways to monotherapies of icotinib, erlotinib andosimertinib among other drugs, along with shared patterns of molecularmechanisms behind drug actions.

Bayesian Robust Learning in Chain Graph Models for Integrative Pharmacogenomics