Bayesian Modeling for Biological Pathway Annotation of Genomic Signatures

We present Bayesian models and computational methods for the problem of matching predictions from molecular studies with known biological pathway databases the problem of pathway annotation of summary results of an experiment or observational study. In areas such as cancer genomics, linking quantified, experimentally defined gene expression signatures with known biological pathway gene sets is essential to improving the understanding of the complexity of molecular pathways related to outcome. Our probabilistic pathway annotation (PROPA) analysis involves new models for formal assessment and rankings of pathways putatively linked to an experimental or observational phenotype, integrates qualitative biological information into the analysis, and generates coherent inferences on uncertainties about gene pathway membership that can inform the revision of pathway databases. Our analysis relies on simulation-based computation in high-dimensional models, and introduces a novel extension of variational methods for computation of model evidence, or marginal likelihood functions, that are central to the comparison of multiple biological pathways. Examples highlight the methodology using both simulated and real data, and we develop detailed cases studies in breast cancer genomics involving hormonal pathways and pathway activities underlying cellular responses to lactic acidosis in breast cancer. The second study demonstrates the application of the method in decomposing the complexity of gene expression-based predictions about interacting biological pathway activation from both experimental (in vitro) and observational (in vivo) human cancer data.