Bayesian Models for Integrative Genomics

Bayesian methods have found many successful applications in genomics. Methods that employ variable selection have been particularly successful, as they allow to handle situations where the amount of measured variables can be much greater than the number of observations. Here we describe Bayesian variable selection models for integrative genomics. We first look into models that incorporate external biological information into the analysis of experimental data, in particular gene expression data. We address linear settings, including regression and classification models, and mixture models, including clustering and discriminant analysis. We then focus on Bayesian models that achieve an even greater type of integration, by incorporating into the modeling experimental data from different platforms, together with prior knowledge. We look in particular at graphical models, integrating gene expression data with microRNA expression data. All modeling settings we consider exploit variable selection techniques and utilize prior constructions that cleverly incorporate biological knowledge about structural dependencies among the variables.