Shrinkage-based diagonal Hotelling's tests for high-dimensional small sample size data
Shrinkage-based diagonal Hotelling's tests for high-dimensional small sample size data
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DOI：
10.1016/j.jmva.2015.08.022
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发表时间：
2016-01-01
影响因子：
1.6
通讯作者：
Genton, Marc G.
中科院分区：
文献类型：
--
作者：
Dong, Kai;Pang, Herbert;Genton, Marc G.
高通量表达谱分析技术为遗传学研究带来了新的工具，也带来了统计学上的挑战。除了检测差异表达的基因外，检测基因组或途径分析的显著性也被认为是一个同样重要的问题。由于“大p小n”范式，传统的Hotelling T-2检验存在奇异性问题，因此在这种情况下无效。在本文中，我们提出了一个基于收缩的对角Hotelling的测试单样本和两个样本的情况下。我们还提出了几种不同的方法来获得近似零分布在不同的情况下的p和n为我们提出的基于收缩的测试。仿真研究表明，当n为中等或较大时，该方法对现有竞争者有较好的竞争效果，而当n较小时，该方法的竞争效果更好。此外，我们分析了四个基因表达数据集，他们证明了我们提出的基于收缩的对角Hotelling检验的优势。(C)2015 Elsevier Inc. All rights reserved.
High-throughput expression profiling techniques bring novel tools and also statistical challenges to genetic research. In addition to detecting differentially expressed genes, testing the significance of gene sets or pathway analysis has been recognized as an equally important problem. Owing to the "large p small n" paradigm, the traditional Hotelling's T-2 test suffers from the singularity problem and therefore is not valid in this setting. In this paper, we propose a shrinkage-based diagonal Hotelling's test for both one-sample and two-sample cases. We also suggest several different ways to derive the approximate null distribution under different scenarios of p and n for our proposed shrinkage-based test. Simulation studies show that the proposed method performs comparably to existing competitors when n is moderate or large, but it is better when n is small. In addition, we analyze four gene expression data sets and they demonstrate the advantage of our proposed shrinkage-based diagonal Hotelling's test. (C) 2015 Elsevier Inc. All rights reserved.