Permutation - based statistical tests for multiple hypotheses.
Permutation - based statistical tests for multiple hypotheses.
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DOI：
10.1186/1751-0473-3-15
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发表时间：
2008-10-21
影响因子：
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通讯作者：
Zheng H
中科院分区：
文献类型：
--
作者：
Camargo A;Azuaje F;Wang H;Zheng H
基因组学和蛋白质组学分析经常涉及同时测试数百个假设，无论是数字数据还是分类数据。为了校正假阳性的发生，经常使用基于多重测试校正的验证测试，例如Bonferroni和Benjamini和Hochberg，以及重新采样，例如置换测试。尽管基于排列的检验具有已知的功效，但大多数可用的工具仅为t检验或ANOVA提供此类检验。较少关注分类数据的检验，如卡方检验。该项目迈出的第一步是开发一个开放源码软件工具Ptest，以满足提供公共软件工具的需要，这些工具将这些和其他统计测试与纠正多种假设的选项结合起来。本研究开发了一个公共领域，用户友好的软件，其目的是双重的：第一，估计检验统计量的分类和数值数据;第二，通过Bonferroni，Benjamini和Hochberg验证检验统计量的显著性，以及数值和分类数据的排列检验。该工具允许对分类数据进行卡方检验，对配对和非配对数据进行ANOVA检验、Bartlett检验和t检验。一旦计算出检验统计量，就独立地实施Bonferroni、Benjamini和Hochberg以及排列检验，以控制I型错误。使用不同的公共数据集的软件的评估报告，这说明了多个假设评估和控制I型错误率的排列检验的力量。该软件提供的分析选项可以应用于支持功能基因组学中的一系列假设检验任务，使用数值和分类数据。
Genomics and proteomics analyses regularly involve the simultaneous test of hundreds of hypotheses, either on numerical or categorical data. To correct for the occurrence of false positives, validation tests based on multiple testing correction, such as Bonferroni and Benjamini and Hochberg, and re-sampling, such as permutation tests, are frequently used. Despite the known power of permutation-based tests, most available tools offer such tests for either t-test or ANOVA only. Less attention has been given to tests for categorical data, such as the Chi-square. This project takes a first step by developing an open-source software tool, Ptest, that addresses the need to offer public software tools incorporating these and other statistical tests with options for correcting for multiple hypotheses. This study developed a public-domain, user-friendly software whose purpose was twofold: first, to estimate test statistics for categorical and numerical data; and second, to validate the significance of the test statistics via Bonferroni, Benjamini and Hochberg, and a permutation test of numerical and categorical data. The tool allows the calculation of Chi-square test for categorical data, and ANOVA test, Bartlett's test and t-test for paired and unpaired data. Once a test statistic is calculated, Bonferroni, Benjamini and Hochberg, and a permutation tests are implemented, independently, to control for Type I errors. An evaluation of the software using different public data sets is reported, which illustrates the power of permutation tests for multiple hypotheses assessment and for controlling the rate of Type I errors. The analytical options offered by the software can be applied to support a significant spectrum of hypothesis testing tasks in functional genomics, using both numerical and categorical data.