Bayesian joint analysis of heterogeneous genomics data.

Abstract

A non-parametric Bayesian factor model is proposed for joint analysis of multi-platform genomics data. The approach is based on factorizing the latent space (feature space) into a shared component and a data-specific component with the dimensionality of these components (spaces) inferred via a beta-Bernoulli process. The proposed approach is demonstrated by jointly analyzing gene expression/copy number variations and gene expression/methylation data for ovarian cancer patients, showing that the proposed model can potentially uncover key drivers related to cancer. The source code for this model is written in MATLAB and has been made publicly available at https://sites.google.com/site/jointgenomics/. catherine.ll.zheng@gmail.com Supplementary data are available at Bioinformatics online.