Multiple hypothesis testing in microarray experiments
Multiple hypothesis testing in microarray experiments
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DOI：
10.1214/ss/1056397487
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发表时间：
2003-02-01
影响因子：
5.7
通讯作者：
Boldrick, JC
中科院分区：
文献类型：
--
作者：
Dudoit, S;Shaffer, JP;Boldrick, JC
DNA微阵列是一类新的和有前途的生物技术的一部分，它允许同时监测细胞中数千个基因的表达水平。DNA微阵列实验中一个重要且常见的问题是鉴别差异表达基因，即其表达水平与感兴趣的响应或协变量相关的基因。差异表达的生物学问题可以被重述为多假设检验中的问题：同时检验每个基因的零假设，即表达水平与响应或协变量之间没有关联。由于典型的微阵列实验同时测量数千个基因的表达水平，因此产生了大量的多重性问题。本文讨论了在DNA微阵列实验中进行多假设检验的不同方法，并比较了微阵列和模拟数据集上的程序。
DNA microarrays are part of a new and promising class of biotechnologies that allow the monitoring of expression levels in cells for thousands of genes simultaneously. An important and common question in DNA microarray experiments is the identification of differentially expressed genes, that is, genes whose expression levels are associated with a response or covariate of interest. The biological question of differential expression can be restated as a problem in multiple hypothesis testing: the simultaneous test for each gene of the null hypothesis of no association between the expression levels and the responses or covariates. As a typical microarray experiment measures expression levels for thousands of genes simultaneously, large multiplicity problems are generated. This article discusses different approaches to multiple hypothesis testing in the context of DNA microarray experiments and compares the procedures on microarray and simulated data sets.