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Abstract: Gene expression microarray technologies provide the simultaneous measurementsof a large number of genes. Typical analyses of such data focus on theindividual genes, but recent work has demonstrated that evaluating changes inexpression across predefined sets of genes often increases statistical powerand produces more robust results. We introduce a new methodology foridentifying gene sets that are differentially expressed under varyingexperimental conditions. Our approach uses a hierarchical Bayesian frameworkwhere a hyperparameter measures the significance of each gene set. Usingsimulated data, we compare our proposed method to alternative approaches, suchas Gene Set Enrichment Analysis GSEA and Gene Set Analysis GSA. Ourapproach provides the best overall performance. We also discuss the applicationof our method to experimental data based on p53 mutation status.

Autor: Babak Shahbaba, Robert Tibshirani, Catherine M. Shachaf, Sylvia K. Plevritis


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