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THESIS DEFENSE ABSTRACT

Application of Hierarchical Models in Microarray Data Analysis: Screening for Differentially Expressed Genes and Making Inference on Functional Classes

 Dongmei Liu, PhD Candidate, Johns Hopkins Department of Biostatistics

Two questions are important in microarray data analysis, detecting genes with differential expression in different samples and finding associations between disease and functional classes of genes. We develop models using empirical and hierarchical Bayes methods to answer the two questions. To screen differentially expressed genes, we propose four novel models that empirically investigate the impact of two critical choices: the specification of the goals of the selection procedure, and the specification of a dependence structure across genes. To make inference on functional classes, we propose a hierarchical model with two variations to study the association between disease and functional classes of genes. The method is based on the idea of Bayesian variable selection. The dissertation is closed by comparing various approaches that are available to test disease associations with functional classes.