Analyzing gene expression data in terms of gene sets:: methodological issues
Analyzing gene expression data in terms of gene sets:: methodological issues
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DOI：
10.1093/bioinformatics/btm051
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发表时间：
2007-04-15
期刊：
影响因子：
5.8
通讯作者：
Buehlmann, Peter
中科院分区：
文献类型：
--
作者：
Goeman, Jelle J.;Buehlmann, Peter
动机：近年来，许多统计测试已经被提出用于分析基因表达数据的基因集，通常来自基因本体论。这些方法是基于非常不同的方法假设。一些方法测试每个基因组相对于其余基因的差异表达的差异表达，而其他方法测试每个基因组本身。此外，一些方法是基于一个模型，其中的基因是抽样单位，而其他处理的采样单位的主题。本文的目的是澄清不同的方法背后的假设，并表示一个优惠的方法基因集testing.Results：我们确定了一些关键的假设，这是大多数方法所需要的。从使用以基因为采样单位的模型的方法中获得的P值很容易被误解，因为它们基于与实际进行的生物实验不相似的统计模型。此外，由于这些模型是基于一个关键的和不切实际的基因之间的独立性假设，从这样的方法得到的P值可以是非常反保守的，如模拟实验所示。我们还认为，竞争性地测试每个基因集对其余基因的方法在单基因测试和基因集测试之间产生了不必要的裂痕。
Motivation: Many statistical tests have been proposed in recent years for analyzing gene expression data in terms of gene sets, usually from Gene Ontology. These methods are based on widely different methodological assumptions. Some approaches test differential expression of each gene set against differential expression of the rest of the genes, whereas others test each gene set on its own. Also, some methods are based on a model in which the genes are the sampling units, whereas others treat the subjects as the sampling units. This article aims to clarify the assumptions behind different approaches and to indicate a preferential methodology of gene set testing.Results: We identify some crucial assumptions which are needed by the majority of methods. P-values derived from methods that use a model which takes the genes as the sampling unit are easily misinterpreted, as they are based on a statistical model that does not resemble the biological experiment actually performed. Furthermore, because these models are based on a crucial and unrealistic independence assumption between genes, the P-values derived from such methods can be wildly anti-conservative, as a simulation experiment shows. We also argue that methods that competitively test each gene set against the rest of the genes create an unnecessary rift between single gene testing and gene set testing.