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» Articles » PMID: 29246102

An Eigenvalue Test for Spatial Principal Component Analysis

Overview
Journal BMC Bioinformatics
Publisher Biomed Central
Specialty Biology
Date 2017 Dec 17
PMID 29246102
Citations 8
Authors
V Montano
T Jombart
Affiliations
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Abstract

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.

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References
1.
Jombart T . adegenet: a R package for the multivariate analysis of genetic markers. Bioinformatics. 2008; 24(11):1403-5. DOI: 10.1093/bioinformatics/btn129. View
2.
Novembre J, Stephens M . Interpreting principal component analyses of spatial population genetic variation. Nat Genet. 2008; 40(5):646-9. PMC: 3989108. DOI: 10.1038/ng.139. View
3.
Jombart T, Devillard S, Balloux F . Discriminant analysis of principal components: a new method for the analysis of genetically structured populations. BMC Genet. 2010; 11:94. PMC: 2973851. DOI: 10.1186/1471-2156-11-94. View
4.
Jombart T, Pontier D, Dufour A . Genetic markers in the playground of multivariate analysis. Heredity (Edinb). 2009; 102(4):330-41. DOI: 10.1038/hdy.2008.130. View
5.
Montano V, Marcari V, Pavanello M, Anyaele O, Comas D, Destro-Bisol G . The influence of habitats on female mobility in Central and Western Africa inferred from human mitochondrial variation. BMC Evol Biol. 2013; 13:24. PMC: 3605107. DOI: 10.1186/1471-2148-13-24. View