Log-Linear Models for Gene Association

• 成果类型: Article
• 署名作者: Hu, Jianhua; Joshi, Adarsh; Johnson, Valen E.
• 署名单位: University of Texas System; UTMD Anderson Cancer Center; Texas A&M University System; Texas A&M University College Station
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1198/jasa.2009.0025
• 发表日期: 2009
• 页码: 597-607
• 关键词: breast-cancer; variable selection; expression

摘要

We describe a class of log-linear models for the detection of interactions in high-dimensional genomic data. This class of models leads to a Bayesian model selection algorithm that can be applied to data that have been reduced to contingency tables using ranks of observations within subjects, and discretization of these ranks within gene/network components. Many normalization issues associated with the analysis of genomic data are thereby avoided. A prior density based on Ewens' sampling distribution is used to restrict the number of interacting components assigned high posterior probability, and the calculation of posterior model probabilities is expedited by approximations based on the likelihood ratio statistic. Simulations studies are used to evaluate the efficiency of the resulting structures. Finally, the algorithm is validated in a microarray study for which it was possible to obtain biological confirmation of detected interactions.