Integrative analysis of multi-level 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 multi-level 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 non-normal 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 non-normality in continuous multivariate data. This flexible modeling strategy facilitates identification of conditional sign dependencies among non-normal 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 non-normal 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.

翻译：用于模拟不同生物领域依赖性的多层次药理学数据的综合分析对于发展基因组测试的治疗至关重要。链形图是这种多层次数据的有条件依赖结构的特点,在多层次数据中,变量自然地被分成多个定序层,由定向和无定向边缘组成。现有文献主要侧重于高斯链图,这些图不适合于不常见分布的长尾边缘,可能导致不准确的推论。我们提议一种贝叶斯式强势链图模型(RCGM),其基础是边际随机转换,使用高斯比例的混合物,以考虑到连续多变量数据中的无端非常性。这种灵活的模型战略有助于确定非正常节点之间的有条件标志依赖性,同时仍然能够推断正常节点之间的有条件依赖性。在模拟中,我们表明,在各种非正常机制生成的数据中,正值单项单项单项链图(RCGM)比现有的单项单项性图(RCGM)。我们运用了我们的方法,在系统内部抗御前的化学分析中,我们用于系统内部分析系统内部数据分析过程和化学分析,我们内部分析系统分析系统分析系统分析系统分析系统分析,我们的数据和系统分析系统分析系统分析。