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/.
|Number of pages||6|
|Journal||Molecular Ecology Resources|
|State||Published - Mar 2018|
Bibliographical noteFunding Information:
Funding for this project was provided by United States Department of Agriculture (USDA) Agricultural Research Service (ARS) and Animal and Plant Health Inspection Service (APHIS) Farm Bill Section 10,007 projects “Diagnostic Resources to Support Fruit Fly Exclusion and Eradication, 2012-2014” and “Genomic approaches to fruit fly exclusion and pathway analysis, 2015-2016” to USDA-ARS, USDA-APHIS and University of Hawai’i at Mµnoa (projects 3.0251.02 and 3.01251.03 (FY 2014), 3.0256.01 and 3.0256.02 (FY 2015), and 3.0392.02 and 3.0392.03 (FY 2016)). TJ is funded by the Medical Research Council Centre for Outbreak Analysis and Modelling and the National Institute for Health Research—Health Protection Research Unit for Modelling Methodology. Mention of trade names or commercial products in this publication is solely for the purpose of providing specific information and does not imply recommendation or endorsement by the USDA. USDA is an equal opportunity employer.
© 2017 John Wiley & Sons Ltd
- data visualization
- multivariate analyses
- ordinations in reduced space
- population genetics
ASJC Scopus subject areas
- Ecology, Evolution, Behavior and Systematics