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An improved procedure for gene selection from microarray experiments using false discovery rate criterion

机译:使用错误发现率标准从微阵列实验中选择基因的改进程序

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摘要

BackgroundA large number of genes usually show differential expressions in a microarray experiment with two types of tissues, and the p-values of a proper statistical test are often used to quantify the significance of these differences. The genes with small p-values are then picked as the genes responsible for the differences in the tissue RNA expressions. One key question is what should be the threshold to consider the p-values small. There is always a trade off between this threshold and the rate of false claims. Recent statistical literature shows that the false discovery rate (FDR) criterion is a powerful and reasonable criterion to pick those genes with differential expression. Moreover, the power of detection can be increased by knowing the number of non-differential expression genes. While this number is unknown in practice, there are methods to estimate it from data. The purpose of this paper is to present a new method of estimating this number and use it for the FDR procedure construction.
机译:背景技术大量的基因通常在具有两种类型的组织的微阵列实验中显示差异表达,并且通常使用适当的统计检验的p值来量化这些差异的重要性。然后选择具有较小p值的基因作为负责组织RNA表达差异的基因。一个关键问题是将p值视为小阈值应该是多少。在此阈值和虚假索赔率之间始终需要权衡取舍。最新的统计文献表明,错误发现率(FDR)标准是选择那些差异表达基因的有力且合理的标准。而且,通过知道非差异表达基因的数目可以增加检测的能力。尽管实际上不知道这个数字,但是有一些方法可以从数据中估算出这个数字。本文的目的是提出一种估算此数字的新方法,并将其用于FDR程序的构建。

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