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Bayesian Joint Modeling for Integrative Gene Set Enrichment Analysis involving RNA-Seq Xinlei (Sherry) Wang Department of Statistical Science Southern Methodist University 2014 Conference of Texas Statisticians University of Texas at Dallas March 22, 2014 Abstract To understand molecular mechanisms underlying complex human diseases, one important task is to identify groups of related genes that are combinatorially involved in such biological processes, mainly through gene set enrichment analysis (GSEA). In the past, many statistical methods have been developed for GSEA. However, there is very limited literature in its integrative analysis, despite a pressing need in an emerging big data era. In this project, we propose a Bayesian joint modeling approach to combine multiple gene set enrichment studies that involve microarray and/or RNA-seq expression data, which can capture isoform-phenotype relationships, gene-phenotype relationships, isoform-gene relationships, gene-gene interactions, (potential) co-expression within the same gene group in one integrated model, while accounting for between-study heterogeneities explicitly.