An interactive power analysis tool for microarray hypothesis testing and generation
An interactive power analysis tool for microarray hypothesis testing and generation
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DOI：
10.1093/bioinformatics/btk052
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发表时间：
2006-04-01
期刊：
影响因子：
5.8
通讯作者：
Hoffman, EP
中科院分区：
文献类型：
--
作者：
Seo, J;Gordish-Dressman, H;Hoffman, EP
动机：人类临床项目通常需要先验的统计能力分析。为此，我们试图建立一个灵活和交互的能量分析工具，用于集成到我们的公共领域HCE 3.5软件包中的微阵列研究。结果：HCE 3.5能量分析工具可以导入任何已有的Affymetrix微阵列项目，并交互测试用户定义的α(显著性)、β(1次方)、样本量和效应量的影响。该工具为所有探测集或更专注的基于本体的子集生成过滤器，带有或不带有噪声过滤器，可用于将对未来项目的分析限制为适当供电的探测集。我们研究了三种生物(拟南芥、大鼠、人)和三种探针集算法(MAS5.0、RMA、dChip PM/MM)的项目。我们发现基于探针组算法选择和噪声过滤器的功率结果有很大差异。RMA为低数量的阵列提供了高灵敏度，但这是以高假阳性结果为代价的(在所研究的人类项目中，假阳性为24%)。我们的数据表明，先验功率计算对于假设检验和假设生成的实验设计以及优化数据分析参数的选择都是重要的。
Motivation: Human clinical projects typically require a priori statistical power analyses. Towards this end, we sought to build a flexible and interactive power analysis tool for microarray studies integrated into our public domain HCE 3.5 software package. We then sought to determine if probe set algorithms or organism type strongly influenced power analysis results.Results: The HCE 3.5 power analysis tool was designed to import any pre-existing Affymetrix microarray project, and interactively test the effects of user-defined definitions of alpha (significance), beta (1 - power), sample size and effect size. The tool generates a filter for all probe sets or more focused ontology-based subsets, with or without noise filters that can be used to limit analyses of a future project to appropriately powered probe sets. We studied projects from three organisms (Arabidopsis, rat, human), and three probe set algorithms (MAS5.0, RMA, dChip PM/MM). We found large differences in power results based on probe set algorithm selection and noise filters. RMA provided high sensitivity for low numbers of arrays, but this came at a cost of high false positive results (24% false positive in the human project studied). Our data suggest that a priori power calculations are important for both experimental design in hypothesis testing and hypothesis generation, as well as for the selection of optimized data analysis parameters.