An Eigenvalue test for spatial principal component analysis

V. Montano; T. Jombart

doi:10.1186/s12859-017-1988-y

An Eigenvalue test for spatial principal component analysis

V. Montano^*, T. Jombart

^*Corresponding author for this work

School of Biology

Research output: Contribution to journal › Article › peer-review

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.

Original language	English
Article number	562
Number of pages	7
Journal	BMC Bioinformatics
Volume	18
DOIs	https://doi.org/10.1186/s12859-017-1988-y
Publication status	Published - 16 Dec 2017

Keywords

Eigenvalues
sPCA
Spatial genetic patterns
Monte-Carlo

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Montano-2017-An-Eigenvalue-BMCBioinformatics-562
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Cite this

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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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N2 - 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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An Eigenvalue test for spatial principal component analysis

Abstract

Keywords

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