ArticleBMC bioinformatics2017
An Eigenvalue test for spatial principal component analysis.
Article in BMC bioinformatics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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12 citing papers in PubMed.
- From genomics to domestication: biocultural history of the Neotropical palms Acrocomia aculeata and Acrocomia totai.Annals of botany · 2026Article
- jsPCA: fast, scalable, and interpretable identification of spatial domains and variable genes across multi-slice and multi-sample spatial transcriptomics data.Bioinformatics advances · 2026Article
- Genetic connectivity and admixture zones shape the spread of African swine fever in wild Boar populations in North-western Italy.Scientific reports · 2025Article
- Relatedness of White-Tailed Deer from Culling Efforts Within Chronic Wasting Disease Management Zones in Minnesota.Pathogens (Basel, Switzerland) · 2025Article
- Dealing With Assumptions and Sampling Bias in the Estimation of Effective Population Size: A Case Study in an Amphibian Population.Evolutionary applications · 2024Article
- Southern marsh deer (Blastocerus dichotomus) populations assessed using Amplicon Sequencing on fecal samples.Scientific reports · 2024Article
- Large-scale assessment of genetic structure to assess risk of populations of a large herbivore to disease.Ecology and evolution · 2024Article
- Associations of major dietary patterns with cardiometabolic risk factors among Iranian patients with type 1 diabetes.Preventive medicine reports · 2024Article
- Article
- Deep structured populations of geographically isolated nipa (Frontiers in plant science · 2022Article
- Intact landscape promotes gene flow and low genetic structuring in the threatened Eastern Massasauga Rattlesnake.Ecology and evolution · 2021Article
- Does host socio-spatial behavior lead to a fine-scale spatial genetic structure in its associated parasites?Parasite (Paris, France) · 2019Article
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2 authors.
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Abstract
backgroundThe 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.
resultsWe 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.
conclusionsAs 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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