Microarrays, empirical Bayes and the two-groups model
Microarrays, empirical Bayes and the two-groups model
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DOI：
10.1214/07-sts236
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发表时间：
2008-02-01
影响因子：
5.7
通讯作者：
Efron, Bradley
中科院分区：
文献类型：
--
作者：
Efron, Bradley
由奈曼、皮尔逊和费希尔提出的经典频率论假设检验理论被认为是世纪最有影响力的应用数学。世纪正在发生一些新的事情：高通量设备，如微阵列，通常需要同时对数千个单独的案例进行假设检验，而不是经典理论所设想的那样。在这些情况下，经验贝叶斯信息开始强迫频率论者和贝叶斯论者。两组模型是一个简单的贝叶斯结构，便于经验贝叶斯分析。本文关注的是贝叶斯和频率论思想在两组设置中的相互作用，特别关注Benjamini和Hochberg的错误发现率方法。主题包括在大规模测试情况下零假设的选择和意义，功率考虑，置换方法的局限性，组的情况下（如微阵列研究中的途径）的显著性检验，相关性效应，多个置信区间和贝叶斯竞争对手的两组模型。
The classic frequentist theory of hypothesis testing developed by Neyman, Pearson and Fisher has a claim to being the twentieth century's most influential piece of applied mathematics. Something new is happening in the twenty-first century: high-throughput devices, such as microarrays, routinely require simultaneous hypothesis tests for thousands of individual cases, not at all what the classical theory had in mind. In these situations empirical Bayes information begins to force itself upon frequentists and Bayesians alike. The two-groups model is a simple Bayesian construction that facilitates empirical Bayes analysis. This article concerns the interplay of Bayesian and frequentist ideas in the two-groups setting, with particular attention focused on Benjamini and Hochberg's False Discovery Rate method. Topics include the choice and meaning of the null hypothesis in large-scale testing situations, power considerations, the limitations of permutation methods, significance testing for groups of cases (such as pathways in microarray studies), correlation effects, multiple confidence intervals and Bayesian competitors to the two-groups model.