wingen

Generate continuous maps of genetic diversity using moving windows with options for rarefaction, interpolation, and masking.

Citation

Please cite the original Bishop et al. (2023) paper if you use this package:

Bishop, A. P., Chambers, E. A., & Wang, I. J. (2023). Generating continuous maps of genetic diversity using moving windows. Methods in Ecology and Evolution, 14, 1175–1181. http://doi.org/10.1111/2041-210X.14090

Checkout our Methods blog post about wingen for a quick overview of the package and its uses.

Installation

Install the released version of wingen from CRAN:

install.packages("wingen")

Or install the development version from GitHub:

# install.packages("devtools")
devtools::install_github("AnushaPB/wingen")

Example

The following example demonstrates the basic functionality of wingen using a small subset (100 variant loci x 100 samples) of the simulated data from Bishop et al. (2023).

library(wingen)

# Load ggplot for plotting
library(ggplot2)

# Load example data
load_middle_earth_ex()

The core function of this package is window_gd(), which takes as inputs a vcfR object (or a path to a .vcf file), sample coordinates (as a data.frame, matrix, or sf object), and a raster layer (as a SpatRaster or RasterLayer) which the moving window will slide across. Users can control the genetic diversity statistic that is calculated (stat), the window dimensions (wdim), the aggregation factor to use on the raster (fact), whether to perform rarefaction (rarify), and other aspects of the moving window calculations. Additional arguments for this function are described in the vignette and function documentation.

# Run moving window calculations of pi with rarefaction
wgd <- window_gd(lotr_vcf,
  lotr_coords,
  lotr_lyr,
  stat = "pi",
  wdim = 7,
  fact = 3,
  rarify = TRUE
)

# Use ggplot_gd() to plot the genetic diversity layer and ggplot_count() to plot the sample counts layer
ggplot_gd(wgd) +
  ggtitle("Moving window pi")

ggplot_count(wgd) +
  ggtitle("Moving window sample counts")

Next, the output from window_gd() can be interpolated using kriging with the wkrig_gd() function.

# Interpolate to a higher resolution
krige_layer <- disagg(wgd, 2) 
kgd <- wkrig_gd(wgd, krige_layer)

Finally, the output from wkrig_gd() (or window_gd()) can be masked to exclude areas that fall outside of the study area or that were undersampled.

# Mask results that fall outside of the "range"
mgd <- mask_gd(kgd, lotr_range)

# Plot results
ggplot_gd(kgd) +
  ggtitle("Kriged pi")

ggplot_gd(mgd) +
  ggtitle("Masked pi")

For an extended walk through, see the package vignette:

vignette("wingen-vignette")

A pdf of the vignette can also be found here

Example analyses from Bishop et al. (2023) can be found in the paperex directory.