INCORPORATING BIOLOGICAL INFORMATION INTO LINEAR MODELS: A BAYESIAN APPROACH TO THE SELECTION OF PATHWAYS AND GENES

• 成果类型: Article
• 署名作者: Stingo, Francesco C.; Chen, Yian A.; Tadesse, Mahlet G.; Vannucci, Marina
• 署名单位: Rice University; H Lee Moffitt Cancer Center & Research Institute; Georgetown University
• 刊物名称: ANNALS OF APPLIED STATISTICS
• ISSN/ISSBN: 1932-6157
• DOI: 10.1214/11-AOAS463
• 发表日期: 2011
• 页码: 1978-2002
• 关键词: network-based analysis; random-field model; variable selection; Penalized regression; microarray data; expression; cancer; prediction; signatures; tool

摘要

The vast amount of biological knowledge accumulated over the years has allowed researchers to identify various biochemical interactions and define different families of pathways. There is an increased interest in identifying pathways and pathway elements involved in particular biological processes. Drug discovery efforts, for example, are focused on identifying biomarkers as well as pathways related to a disease. We propose a Bayesian model that addresses this question by incorporating information on pathways and gene networks in the analysis of DNA microarray data. Such information is used to define pathway summaries, specify prior distributions, and structure the MCMC moves to fit the model. We illustrate the method with an application to gene expression data with censored survival outcomes. In addition to identifying markers that would have been missed otherwise and improving prediction accuracy, the integration of existing biological knowledge into the analysis provides a better understanding of underlying molecular processes.