Dørum Guro, Snipen Lars, Solheim Margrete, Saebø Solve
Norwegian University of Life Sciences.
Stat Appl Genet Mol Biol. 2009;8:Article34. doi: 10.2202/1544-6115.1418. Epub 2009 Jul 27.
Gene Set Enrichment Analysis (GSEA) is a method for analysing gene expression data with a focus on a priori defined gene sets. The permutation test generally used in GSEA for testing the significance of gene set enrichment involves permutation of a phenotype vector and is developed for data from an indirect comparison design, i.e. unpaired data. In some studies the samples representing two phenotypes are paired, e.g. samples taken from a patient before and after treatment, or if samples representing two phenotypes are hybridised to the same two-channel array (direct comparison design). In this paper we will focus on data from direct comparison experiments, but the methods can be applied to paired data in general. For these types of data, a standard permutation test for paired data that randomly re-signs samples can be used. However, if the sample size is very small, which is often the case for a direct comparison design, a permutation test will give very imprecise estimates of the p-values. Here we propose using a rotation test rather than a permutation test for estimation of significance in GSEA of direct comparison data with a limited number of samples. Our proposed rotation test makes GSEA applicable to direct comparison data with few samples, by depending on rotations of the data instead of permutations. The rotation test is a generalisation of the permutation test, and can in addition be used on indirect comparison data and for testing significance of other types of test statistics outside the GSEA framework.
基因集富集分析（GSEA）是一种用于分析基因表达数据的方法，重点在于先验定义的基因集。GSEA中通常用于检验基因集富集显著性的置换检验涉及对一个表型向量进行置换，并且是为间接比较设计（即未配对数据）的数据而开发的。在一些研究中，代表两种表型的样本是配对的，例如从患者治疗前后采集的样本，或者代表两种表型的样本被杂交到同一个双通道阵列上（直接比较设计）。在本文中，我们将重点关注直接比较实验的数据，但这些方法通常也可应用于配对数据。对于这类数据，可以使用一种对配对数据进行随机重新标记样本的标准置换检验。然而，如果样本量非常小，这在直接比较设计中经常出现，那么置换检验对p值的估计会非常不精确。在这里，我们建议使用旋转检验而非置换检验来估计样本数量有限的直接比较数据在GSEA中的显著性。我们提出的旋转检验通过依赖数据的旋转而非置换，使GSEA适用于样本较少的数据。旋转检验是置换检验的一种推广，此外还可用于间接比较数据以及检验GSEA框架之外其他类型检验统计量的显著性。
