Multivariate analysis of variance test for gene set analysis
Multivariate analysis of variance test for gene set analysis
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DOI：
10.1093/bioinformatics/btp098
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发表时间：
2009-04-01
期刊：
影响因子：
5.8
通讯作者：
Chen, James J.
中科院分区：
文献类型：
--
作者：
Tsai, Chen-An;Chen, James J.
动机：基因分类测试（GCT）或基因集分析（GSA）是一种统计方法，用于确定某些功能预定义的基因集是否在不同的实验条件下表达不同。通过实例说明了Fishers精确检验在过度代表分析中的不足之处。大多数替代的GSA方法是针对从两个实验条件收集的数据开发的，并且大多数是基于单变量逐基因检验统计量或假设基因集中的基因之间的独立性。结果：当基因集的基因数大于样本数时，样本协方差矩阵是奇异的，是病态的。使用标准的多变量方法可能会导致分析中的偏差。建议的MANOVA检验使用收缩协方差矩阵估计样本协方差矩阵。MANOVA测试和其他六个GSA公布的方法，主成分分析，SAM-GS，协方差分析，全球，GSEA和MaxMean，使用模拟进行评估。MANOVA检验似乎在模拟中考虑的模型下在I型误差和功效的控制方面表现最好。几个公开可用的微阵列数据集下的两个和三个实验条件进行了分析GSA的插图。除了GSEA和MaxMean之外，大多数方法在鉴定显著基因集的能力方面通常是相当的。
Motivation: Gene class testing (GCT) or gene set analysis (GSA) is a statistical approach to determine whether some functionally predefined sets of genes express differently under different experimental conditions. Shortcomings of the Fishers exact test for the overrepresentation analysis are illustrated by an example. Most alternative GSA methods are developed for data collected from two experimental conditions, and most is based on a univariate gene-by-gene test statistic or assume independence among genes in the gene set. A multivariate analysis of variance (MANOVA) approach is proposed for studies with two or more experimental conditions.Results: When the number of genes in the gene set is greater than the number of samples, the sample covariance matrix is singular and ill-condition. The use of standard multivariate methods can result in biases in the analysis. The proposed MANOVA test uses a shrinkage covariance matrix estimator for the sample covariance matrix. The MANOVA test and six other GSA published methods, principal component analysis, SAM-GS, analysis of covariance, Global, GSEA and MaxMean, are evaluated using simulation. The MANOVA test appears to perform the best in terms of control of type I error and power under the models considered in the simulation. Several publicly available microarray datasets under two and three experimental conditions are analyzed for illustrations of GSA. Most methods, except for GSEA and MaxMean, generally are comparable in terms of power of identification of significant gene sets.