Department of Neurology and Center for Translational Systems Biology, Mount Sinai School of Medicine, New York, NY 10029, USA.
Stat Methods Med Res. 2009 Dec;18(6):543-63. doi: 10.1177/0962280209351899.
The large-scale multiple testing problems resulting from the measurement of thousands of genes in microarray experiments have received increasing interest during the past several years. This article describes some commonly used criteria for controlling false positive errors, including familywise error rates, false discovery rates and false discovery proportion rates. Various statistical methods controlling these error rates are described. The advantages and disadvantages of these methods are discussed. These methods are applied to gene expression data from two microarray studies and the properties of these multiple testing procedures are compared.
近年来,由于在微阵列实验中测量数千个基因而导致的大规模多重检验问题受到了越来越多的关注。本文描述了一些常用的控制假阳性错误的标准,包括总体错误率、假发现率和假发现比例率。还描述了控制这些错误率的各种统计方法。讨论了这些方法的优缺点。将这些方法应用于来自两个微阵列研究的基因表达数据,并比较了这些多重检验程序的性质。