Bair Eric
Department of Endodontics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA ; Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
Wiley Interdiscip Rev Comput Stat. 2013 Jul;5(4). doi: 10.1002/wics.1260.
DNA microarrays are a relatively new technology that can simultaneously measure the expression level of thousands of genes. They have become an important tool for a wide variety of biological experiments. One of the most common goals of DNA microarray experiments is to identify genes associated with biological processes of interest. Conventional statistical tests often produce poor results when applied to microarray data owing to small sample sizes, noisy data, and correlation among the expression levels of the genes. Thus, novel statistical methods are needed to identify significant genes in DNA microarray experiments. This article discusses the challenges inherent in DNA microarray analysis and describes a series of statistical techniques that can be used to overcome these challenges. The problem of multiple hypothesis testing and its relation to microarray studies are also considered, along with several possible solutions.
DNA微阵列是一项相对较新的技术，它能够同时测量数千个基因的表达水平。它们已成为各种生物学实验的重要工具。DNA微阵列实验最常见的目标之一是识别与感兴趣的生物学过程相关的基因。由于样本量小、数据有噪声以及基因表达水平之间的相关性，传统统计检验应用于微阵列数据时往往产生不佳结果。因此，需要新的统计方法来识别DNA微阵列实验中的显著基因。本文讨论了DNA微阵列分析中固有的挑战，并描述了一系列可用于克服这些挑战的统计技术。还考虑了多重假设检验问题及其与微阵列研究的关系，以及几种可能的解决方案。
