Hierarchical Bayesian methods for integration of various types of genomics data


We propose methods to integrate data across several genomic platforms using a hierarchical Bayesian analysis framework that incorporates the biological relationships among the platforms to identify genes whose expression is related to clinical outcomes in cancer. This integrated approach combines information across all platforms, leading to increased statistical power in finding these predictive genes, and further provides mechanistic information about the manner of the effect on the outcome. We demonstrate the advantages of this approach (including improved estimation via effective estimate shrinkage) through a simulation, and finally we apply our method to a Glioblastoma Multiforme dataset and identify several genes significantly associated with patients’ survival.