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Showing content from https://github.com/alexkychen/assignPOP below:

alexkychen/assignPOP: Population Assignment using Genetic, Non-genetic or Integrated Data in a Machine-learning Framework. Methods in Ecology and Evolution. 2018;9:439–446.

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.

• 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

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").

Please visit our tutorial website for more infomration

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

Changes in ver. 1.3.0 (2024.3.13)

• Update accuracy.plot - adjust ggplot's aes_string() due to its deprecation.
• Update testthat test_accuracy and test_membership to meet ggplot2 3.5.0 requirements

Changes in ver. 1.2.4 (2021.10.27)

• Update membership.plot - add argument 'plot.k' and 'plot.loci' to skip related question prompt.

Changes in ver. 1.2.3 (2021.8.17)

• Update assign.X - (1)Add argument 'common' to specify whether stopping the analysis when inconsistent features between data sets were found. (2)Add argument 'skipQ' to skip data type checking on non-genetic data. (3)Modify argument 'mplot' to handle membership probability plot output.

Changes in ver. 1.2.2 (2020.11.6)

• Update read.Genepop and read.Structure - locus has only one allele across samples will be kept. Use reduce.allele to remove single-allele or low variance loci.
• In ver. 1.2.1, errors might be generated when running assign.MC (and other assignment test functions) due to existence of single-allele loci. (fixed in ver. 1.2.2)

Changes in ver. 1.2.1 (2020.8.24)

• Update read.Genepop to increase file reading speed (~40 times faster)
• Update read.Structure to increase file reading speed (~90 times faster)
• read.Structure now also can handle triploid and tetraploid organisms (see arg. ploidy)
• fix bug in allele.reduce to handle small p threshold across all loci

Changes in ver. 1.2.0 (2020.7.24)

• Add codes to check model name in assign.MC, assign.kfold, assign.X
• Add text to SVM description
• Fix cbind/stringsAsFactors issues in several places for R 4.0
• Able to inject arugments used in models (e.g., gamma in SVM)

Changes in ver. 1.1.9 (2020.3.16)

• Fix input non-genetic data (x1) error in assign.X

Changes in ver. 1.1.8 (2020.2.28)

• update following functions to work with R 4.0.0
• accuracy.MC, accuracy.kfold, assign.matrix, compile.data, membership.plot
• add stringsAsFactor=T to read.table and read.csv
• temporarily turn off testthat due to its current failure to pass test in Debian system

Changes in ver. 1.1.7 (2019.8.26)

• add broken-stick method for principal component selection in assign.MC, assign.kfold, and assign.X functions
• update accuracy.MC, accuracy.kfold, assign.matrix to handle missing levels of predicted population in test results
• update assign. and accuracy. functions to handle numeric population names

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

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

Chen, K. Y., Marschall, E. A., Sovic, M. G., Fries, A. C., Gibbs, H. L., & Ludsin, S. A. (2018). assign POP: An R package for population assignment using genetic, non-genetic, or integrated data in a machine-learning framework. Methods in Ecology and Evolution. 9(2)439-446. https://doi.org/10.1111/2041-210X.12897

Papers citing our package

Previous packages can be found and downloaded at the releases page

assignPOP version 1.1.9 and earlier are not fully compatible with newly released R 4.0.0. If you're using R 4.0.0 (or newer), please update your assignPOP to 1.2.0.

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