Use Monte-Carlo and K-fold cross-validation coupled with machine-learning classification algorithms to perform population assignment, with functionalities of evaluating discriminatory power of independent training samples, identifying informative loci, reducing data dimensionality for genomic data, integrating genetic and non-genetic data, and visualizing results.
Population Assignment using Genetic, Non-Genetic or Integrated Data in a Machine-learning Framework
This R package helps perform population assignment and infer population structure using a machine-learning framework. It employs supervised machine-learning methods to evaluate the discriminatory power of your data collected from source populations, and is able to analyze large genetic, non-genetic, or integrated (genetic plus non-genetic) data sets. This framework is designed for solving the upward bias issue discussed in previous studies. Main features are listed as follows.
You can install the released version from CRAN or the up-to-date version from this Github respository.
To install from CRAN
install.packages("assignPOP")in your R console
To install from Github
Note: When you install the package from Github, you may need to install additional packages before the assignPOP can be successfully installed. Follow the hints that R provided and then re-run
Please visit our tutorial website for more infomration
Changes in ver. 1.1.4
Changes in ver. 1.1.3
Changes in ver. 1.1.2
Chen K-Y, Marschall EA, Sovic MG, Fries AC, Gibbs HL, Ludsin SA. assignPOP: An R package for population assignment using genetic, non-genetic, or integrated data in a machine-learning framework. Methods in Ecology and Evolution. 2018;9:439–446. https://doi.org/10.1111/2041-210X.12897
Previous packages can be found and downloaded at archive branch