BAYESIAN ROBUST LEARNING IN CHAIN GRAPH MODELS FOR INTEGRATIVE PHARMACOGENOMICS

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 par-titioned into multiple ordered layers, consisting of both directed and undi-rected 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 identifi-cation of conditional sign dependencies among nonnormal nodes while still being able to infer conditional dependencies among normal nodes. In sim-ulations we demonstrate that RCGM outperforms existing Gaussian chain graph inference methods in data generated from various nonnormal mecha-nisms. 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 ico-tinib, erlotinib and osimertinib among other drugs, along with shared patterns of molecular mechanisms behind drug actions.