An Eigenvalue Test for spatial Principal Component Analysis
File(s)
Published version
Author(s)
Montano, V
Jombart, T
Type
Journal Article
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.
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.
Date Issued
2017-12-16
Date Acceptance
2017-12-05
Citation
BMC Bioinformatics, 2017, 18
ISSN
1471-2105
Publisher
BioMed Central
Journal / Book Title
BMC Bioinformatics
Volume
18
Copyright Statement
© The Author(s). 2017 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0
International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and
reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to
the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver
(http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and
reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to
the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver
(http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
License URL
Sponsor
Medical Research Council (MRC)
Grant Number
MR/K010174/1B
Subjects
Science & Technology
Life Sciences & Biomedicine
Biochemical Research Methods
Biotechnology & Applied Microbiology
Mathematical & Computational Biology
Biochemistry & Molecular Biology
Eigenvalues
sPCA
Spatial genetic patterns
Monte-Carlo
MULTIVARIATE-ANALYSIS
GENETIC-MARKERS
R-PACKAGE
06 Biological Sciences
08 Information And Computing Sciences
01 Mathematical Sciences
Bioinformatics
Publication Status
Published
Article Number
562