mvmapper: Interactive spatial mapping of genetic structures

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Title: mvmapper: Interactive spatial mapping of genetic structures
Author(s): Dupuis, JR
Bremer, FT
Jombart, T
Sim, SB
Geib, SM
Item Type: Journal Article
Abstract: Characterizing genetic structure across geographic space is a fundamental challenge in population genetics. Multivariate statistical analyses are powerful tools for summarizing genetic variability, but geographic information and accompanying metadata are not always easily integrated into these methods in a user-friendly fashion. Here, we present a deployable Python-based web-tool, mvmapper, for visualizing and exploring results of multivariate analyses in geographic space. This tool can be used to map results of virtually any multivariate analysis of georeferenced data, and routines for exporting results from a number of standard methods have been integrated in the R package adegenet, including principal components analysis (PCA), spatial PCA, discriminant analysis of principal components, principal coordinates analysis, nonmetric dimensional scaling and correspondence analysis. mvmapper's greatest strength is facilitating dynamic and interactive exploration of the statistical and geographic frameworks side by side, a task that is difficult and time-consuming with currently available tools. Source code and deployment instructions, as well as a link to a hosted instance of mvmapper, can be found at https://popphylotools.github.io/mvMapper/.
Publication Date: 1-Mar-2018
Date of Acceptance: 3-Oct-2017
URI: http://hdl.handle.net/10044/1/55977
DOI: https://dx.doi.org/10.1111/1755-0998.12724
ISSN: 1755-098X
Publisher: Wiley
Start Page: 362
End Page: 367
Journal / Book Title: Molecular Ecology Resources
Volume: 18
Issue: 2
Copyright Statement: © 2017 John Wiley & Sons Ltd. This is the pre-peer reviewed version of the following article, which has been published in final form at https://onlinelibrary.wiley.com/doi/abs/10.1111/1755-0998.12724
Sponsor/Funder: Medical Research Council (MRC)
Funder's Grant Number: MR/K010174/1B
Keywords: Science & Technology
Life Sciences & Biomedicine
Biochemistry & Molecular Biology
Ecology
Evolutionary Biology
Environmental Sciences & Ecology
data visualization
multivariate analyses
ordinations in reduced space
population genetics
Python
software
POPULATION-STRUCTURE
LANDSCAPE GENETICS
MULTIVARIATE-ANALYSIS
INFERENCE
PACKAGE
CONSERVATION
CHALLENGES
DIVERSITY
PATTERNS
HISTORY
Python
data visualization
multivariate analyses
ordinations in reduced space
population genetics
software
Python
data visualization
multivariate analyses
ordinations in reduced space
population genetics
software
06 Biological Sciences
Evolutionary Biology
Publication Status: Published
Online Publication Date: 2017-10-07
Appears in Collections:Faculty of Medicine
Epidemiology, Public Health and Primary Care



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