Lee Mei-Ling Ting, Whitmore G A
Department of Medicine, Brigham and Women's Hospital, Boston, USA.
Stat Med. 2002 Dec 15;21(23):3543-70. doi: 10.1002/sim.1335.
A microarray study aims at having a high probability of declaring genes to be differentially expressed if they are truly expressed, while keeping the probability of making false declarations of expression acceptably low. Thus, in formal terms, well-designed microarray studies will have high power while controlling type I error risk. Achieving this objective is the purpose of this paper. Here, we discuss conceptual issues and present computational methods for statistical power and sample size in microarray studies, taking account of the multiple testing that is generic to these studies. The discussion encompasses choices of experimental design and replication for a study. Practical examples are used to demonstrate the methods. The examples show forcefully that replication of a microarray experiment can yield large increases in statistical power. The paper refers to cDNA arrays in the discussion and illustrations but the proposed methodology is equally applicable to expression data from oligonucleotide arrays.
微阵列研究的目标是，如果基因确实存在差异表达，那么就有很高的概率宣称这些基因存在差异表达，同时将做出错误表达宣称的概率控制在可接受的低水平。因此，从形式上来说，设计良好的微阵列研究在控制I型错误风险的同时将具有高功效。实现这一目标就是本文的目的。在这里，我们讨论概念性问题，并给出微阵列研究中统计功效和样本量的计算方法，同时考虑到这些研究中普遍存在的多重检验。讨论内容包括研究的实验设计和重复的选择。通过实际例子来演示这些方法。这些例子有力地表明，微阵列实验的重复可以显著提高统计功效。本文在讨论和举例时提及了cDNA阵列，但所提出的方法同样适用于来自寡核苷酸阵列的表达数据。
