assignPOP

Population Assignment using Genetic, Non-Genetic or Integrated Data in a Machine-learning Framework

Description

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.

• Use principle component analysis (PCA) for dimensionality reduction (or data transformation)
• Use Monte-Carlo cross-validation to estimate mean and variance of assignment accuracy
• Use K-fold cross-validation to estimate membership probability
• Allow to resample various sizes of training datasets (proportions or fixed numbers of individuals and proportions of loci)
• Allow to choose from various proportions of training loci either randomly or based on locus Fst values
• Provide several machine-learning classification algorithms, including LDA, SVM, naive Bayes, decision tree, and random forest, to build tunable predictive models.
• Output results in publication-quality plots that can be modified using ggplot2 functions

Install assignPOP

You can install the released version from CRAN or the up-to-date version from this Github respository.

• To install from CRAN

  • Simply enter install.packages("assignPOP") in your R console

• To install from Github

  • step 1. Install devtools package by entering install.packages("devtools")
  • step 2. Import the library, library(devtools)
  • step 3. Then enter install_github("alexkychen/assignPOP")

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 install_github("alexkychen/assignPOP").

Package tutorial

Please visit our tutorial website for more infomration

• http://alexkychen.github.io/assignPOP/

What's new

Changes in ver. 1.1.6 (2019.6.8)

• fix multiprocess issue in assign.kfold function

Changes in ver. 1.1.5 (2018.3.23)

• Update assign.MC & assign.kfold to detect pop size and train.inds/k.fold setting
• Update accuracy.MC & assign.matrix to handle test individuals not from every pop
• Slightly modify levels method in accuracy.kfold
• fix bugs in accuracy.plot for K-fold results
• fix membership.plot title positioning and set text size to default

History

Changes in ver. 1.1.4 (2018.3.8)

• Fix missing assign.matrix function

Changes in ver. 1.1.3 (2017.6.15)

• Add unit tests (using package testthat)

Changes in ver. 1.1.2 (2017.5.13)

• Change function name read.genpop to read.Genepop; Add function read.Structure.
• Update read.genpop function, now can read haploid data

Cite this package

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 version

Previous packages can be found and downloaded at the releases page