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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.