Empirical Bayes analysis of a microarray experiment
Empirical Bayes analysis of a microarray experiment
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DOI：
10.1198/016214501753382129
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发表时间：
2001-12-01
影响因子：
3.7
通讯作者：
Tusher, V
中科院分区：
文献类型：
--
作者：
Efron, B;Tibshirani, R;Tusher, V
微阵列是一种有助于同时测量数千个基因表达水平的新技术。一个典型的微阵列实验可以产生数百万个数据点，这带来了数据简化和同时推理的严重问题。我们考虑一个这样的实验，其中使用寡核苷酸阵列来评估电离辐射对7000个人类基因的遗传效应。介绍了一种简单的非参数经验贝叶斯模型，该模型用于指导将数据有效地简化为每个基因的单个汇总统计量，并同时推断哪些基因受到辐射的影响。虽然我们的重点是一个特定的实验，但所提出的方法可以相当普遍地应用。经验贝叶斯推断与频度错误发现率(FDR)准则密切相关。
Microarrays are a novel technology that facilitates the simultaneous measurement of thousands of gene expression levels. A typical microarray experiment can produce millions of data points, raising serious problems of data reduction, and simultaneous inference. We consider one such experiment in which oligonucleotide arrays were employed to assess the genetic effects of ionizing radiation on seven thousand human genes. A simple nonparametric empirical Bayes model is introduced, which is used to guide the efficient reduction of the data to a single summary statistic per gene, and also to make simultaneous inferences concerning which genes were affected by the radiation. Although our focus is on one specific experiment, the proposed methods can be applied quite generally. The empirical Bayes inferences are closely related to the frequentist false discovery rate (FDR) criterion.