Abstract Bayesian Meta-analysis Models for Heterogeneous Genomics Data

The accumulation of high-throughput data from vast sources has drawn a lot atten-tions to develop methods for extracting meaningful information out of the massive data. More interesting questions arise from how to combine the disparate informa-tion, which goes beyond modeling sparsity and dimension reduction. This disserta-tion focuses on the innovations in the area of heterogeneous data integration. Chapter 1 contextualizes this dissertation by introducing different aspects of meta-analysis and model frameworks for high-dimensional genomic data. Chapter 2 introduces a novel technique, joint Bayesian sparse factor analysis model, to vertically integrate multi-dimensional genomic data from different plat-forms. Chapter 3 extends the above model to a nonparametric Bayes formula. It directly infers number of factors from a model-based approach. On the other hand, chapter 4 deals with horizontal integration of diverse gene expression data; the model infers pathway activities across various experimental conditions. All the methods mentioned above are demonstrated in both simulation studies and real data applications in chapters 2-4. Finally, chapter 5 summarizes the dissertation and discusses future directions. i