Bayesian Gene Set Analysis

TL;DR

This paper introduces a hierarchical Bayesian method for identifying differentially expressed gene sets, demonstrating superior performance over existing methods through simulations and real data application.

Contribution

A novel hierarchical Bayesian framework for gene set analysis that improves detection power and robustness compared to traditional methods.

Findings

Outperforms GSEA and GSA in simulated data

Effective in analyzing p53 mutation status data

Provides a Bayesian measure of gene set significance

Abstract

Gene expression microarray technologies provide the simultaneous measurements of a large number of genes. Typical analyses of such data focus on the individual genes, but recent work has demonstrated that evaluating changes in expression across predefined sets of genes often increases statistical power and produces more robust results. We introduce a new methodology for identifying gene sets that are differentially expressed under varying experimental conditions. Our approach uses a hierarchical Bayesian framework where a hyperparameter measures the significance of each gene set. Using simulated data, we compare our proposed method to alternative approaches, such as Gene Set Enrichment Analysis (GSEA) and Gene Set Analysis (GSA). Our approach provides the best overall performance. We also discuss the application of our method to experimental data based on p53 mutation status.