A new multitest correction (SGoF) that increases its statistical power when increasing the number of tests.

A new multitest correction (SGoF) that increases its statistical power when increasing the number of tests.
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一种新的最大校正(SGOF),在增加测试数量时会增加其统计能力。

DOI:
10.1186/1471-2105-10-209
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发表时间:
2009-07-08
期刊:
影响因子:
3
通讯作者:
Rolán-Alvarez E
Rolán-Alvarez E
中科院分区:
生物学4区
文献类型:
--
作者:
Carvajal-Rodríguez A;de Uña-Alvarez J;Rolán-Alvarez E

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在分析高维生物数据时,多重测试下的真显著性检测正成为一个基本问题。不幸的是,已知的多重检验调整降低其统计功率的测试数量的增加。我们提出了一个新的多重检验调整,基于顺序拟合优度元检验(SGoF),这增加了它的统计能力与测试的数量。该方法与Bonferroni和FDR为基础的替代品进行了比较,通过模拟多测试的情况下,通过两种不同的测试:1)单样本t检验,和2)同质性G检验。它表明,SGoF的表现特别好,小样本量时,1)备择假设是弱到中度偏离零模型,2)有广泛的影响,通过家庭的测试,和3)测试的数量很大。因此,SGoF应该成为处理高维生物数据时进行多测试调整的重要工具。
The detection of true significant cases under multiple testing is becoming a fundamental issue when analyzing high-dimensional biological data. Unfortunately, known multitest adjustments reduce their statistical power as the number of tests increase. We propose a new multitest adjustment, based on a sequential goodness of fit metatest (SGoF), which increases its statistical power with the number of tests. The method is compared with Bonferroni and FDR-based alternatives by simulating a multitest context via two different kinds of tests: 1) one-sample t-test, and 2) homogeneity G-test. It is shown that SGoF behaves especially well with small sample sizes when 1) the alternative hypothesis is weakly to moderately deviated from the null model, 2) there are widespread effects through the family of tests, and 3) the number of tests is large. Therefore, SGoF should become an important tool for multitest adjustment when working with high-dimensional biological data.
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