用于空间主成分分析的特征值检验。
An Eigenvalue test for spatial principal component analysis.
作者信息
Montano V, Jombart T
机构信息
School of Biology, University of St Andrews, Bute Building, St Andrews, KY16 9TS, UK.
Department of Infectious Disease Epidemiology, MRC Centre for Outbreak Analysis and Modelling, Imperial College, St Mary's Campus, Norfolk Place, London, W2 1PG, UK.
出版信息
BMC Bioinformatics. 2017 Dec 16;18(1):562. doi: 10.1186/s12859-017-1988-y.
BACKGROUND
The spatial Principal Component Analysis (sPCA, Jombart (Heredity 101:92-103, 2008) is designed to investigate non-random spatial distributions of genetic variation. Unfortunately, the associated tests used for assessing the existence of spatial patterns (global and local test; (Heredity 101:92-103, 2008) lack statistical power and may fail to reveal existing spatial patterns. Here, we present a non-parametric test for the significance of specific patterns recovered by sPCA.
RESULTS
We compared the performance of this new test to the original global and local tests using datasets simulated under classical population genetic models. Results show that our test outperforms the original global and local tests, exhibiting improved statistical power while retaining similar, and reliable type I errors. Moreover, by allowing to test various sets of axes, it can be used to guide the selection of retained sPCA components.
CONCLUSIONS
As such, our test represents a valuable complement to the original analysis, and should prove useful for the investigation of spatial genetic patterns.
背景
空间主成分分析（sPCA，Jombart，《遗传》101:92 - 103，2008年）旨在研究遗传变异的非随机空间分布。遗憾的是，用于评估空间模式存在性的相关检验（全局和局部检验；《遗传》101:92 - 103，2008年）缺乏统计效力，可能无法揭示现有的空间模式。在此，我们针对通过sPCA恢复的特定模式的显著性提出一种非参数检验。
结果
我们使用在经典群体遗传模型下模拟的数据集，将这种新检验的性能与原始的全局和局部检验进行了比较。结果表明，我们的检验优于原始的全局和局部检验，在保持相似且可靠的I型错误的同时，展现出更高的统计效力。此外，通过允许对各种轴集进行检验，它可用于指导保留的sPCA成分的选择。
结论
因此，我们的检验是对原始分析的一个有价值的补充，并且应该对空间遗传模式的研究有用。
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