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Construction of null statistics in permutation-based multiple testing for multi-factorial microarray experiments

机译:基于置换的多重测试多因素微阵列实验中无效统计的构建

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Motivation: The parametric F-test has been widely used in the analysis of factorial microarray experiments to assess treatment effects. However, the normality assumption is often untenable for microarray experiments, with small replications. Therefore, permutation-based methods are called for help to assess the statistical significance. The distribution of the F-statistics across all the genes on the array can be regarded as a mixture distribution with a proportion of statistics generated from the null distribution of no differential gene expression whereas the other proportion of statistics generated from the alternative distribution of genes differentially expressed. This results in the fact that the permutation distribution of the F-statistics may not approximate well to the true null distribution of the F-statistics. Therefore, the construction of a proper null statistic to better approximate the null distribution of F-statistic is of great importance to the permutation-based multiple testing in microarray data analysis.
机译:动机:参数F检验已广泛用于分析阶乘微阵列实验以评估治疗效果。但是,对于微阵列实验而言,具有小复制的正态性假设通常是站不住脚的。因此,需要使用基于排列的方法来帮助评估统计意义。 F统计量在阵列上所有基因上的分布可被视为混合分布,其中一部分统计量由无差异基因表达的零分布产生,而另一部分统计量则由基因的差异分布产生表达。这导致以下事实:F统计量的置换分布可能无法很好地近似于F统计量的真实零分布。因此,构建适当的无效统计量以更好地近似F统计量的无效分布对于微阵列数据分析中基于置换的多重检验非常重要。

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