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dc.contributor.authorMontano, V.
dc.contributor.authorJombart, T.
dc.date.accessioned2019-08-16T12:30:03Z
dc.date.available2019-08-16T12:30:03Z
dc.date.issued2017-12-16
dc.identifier.citationMontano , V & Jombart , T 2017 , ' An Eigenvalue test for spatial principal component analysis ' , BMC Bioinformatics , vol. 18 , 562 . https://doi.org/10.1186/s12859-017-1988-yen
dc.identifier.issn1471-2105
dc.identifier.otherPURE: 260615998
dc.identifier.otherPURE UUID: a197ff6d-ee3f-414e-85fb-9739aff8bbef
dc.identifier.otherScopus: 85038100880
dc.identifier.otherPubMed: 29246102
dc.identifier.otherWOS: 000418142000001
dc.identifier.urihttp://hdl.handle.net/10023/18323
dc.description.abstractBackground: 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.
dc.format.extent7
dc.language.isoeng
dc.relation.ispartofBMC Bioinformaticsen
dc.rights© The Author(s). 2017. 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.en
dc.subjectEigenvaluesen
dc.subjectsPCAen
dc.subjectSpatial genetic patternsen
dc.subjectMonte-Carloen
dc.subjectQA75 Electronic computers. Computer scienceen
dc.subjectQH301 Biologyen
dc.subjectBiochemistryen
dc.subjectStructural Biologyen
dc.subjectMolecular Biologyen
dc.subjectComputer Science Applicationsen
dc.subjectApplied Mathematicsen
dc.subjectDASen
dc.subject.lccQA75en
dc.subject.lccQH301en
dc.titleAn Eigenvalue test for spatial principal component analysisen
dc.typeJournal articleen
dc.description.versionPublisher PDFen
dc.contributor.institutionUniversity of St Andrews.School of Biologyen
dc.identifier.doihttps://doi.org/10.1186/s12859-017-1988-y
dc.description.statusPeer revieweden


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