wingen

Continuous mapping of genetic diversity

https://github.com/anushapb/wingen

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Continuous mapping of genetic diversity

README.Rmd

---
output: github_document
---


# wingen 


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

![](man/figures/README-gif.gif)

## 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](https://methodsblog.com/2023/05/03/wingen-mapping-genetic-diversity-using-moving-windows/) about wingen for a quick overview of the package and its uses.

## Installation

Install the released version of wingen from CRAN:
``` r
install.packages("wingen")
```

Or install the development version from GitHub:
``` r
# 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)](http://doi.org/10.1111/2041-210X.14090)**.

```{r, comment = FALSE, message = FALSE, results = FALSE, warning = FALSE}
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.

```{r window_gd, warning = FALSE, message = FALSE, results = FALSE, fig.height = 4, fig.width = 4.5}
# 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")
```
```{r, fig.width = 5, fig.height = 4}
ggplot_count(wgd) +
  ggtitle("Moving window sample counts")
```

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

```{r krig_gd, warning = FALSE, message = FALSE, results = FALSE}
# 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.

```{r mask_gd, warning = FALSE, message = FALSE, results = FALSE}
# Mask results that fall outside of the "range"
mgd <- mask_gd(kgd, lotr_range)
```

```{r result, warning = FALSE, message = FALSE, results = FALSE, fig.height = 4, fig.width = 4.5}
# Plot results
ggplot_gd(kgd) +
  ggtitle("Kriged pi")

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

For an extended walk through, see the package vignette:

``` r
vignette("wingen-vignette")
```

A pdf of the vignette can also be found [here](https://github.com/AnushaPB/wingen/blob/main/vignettes/wingen-vignette.pdf)

Example analyses from [Bishop et al. (2023)](http://doi.org/10.1111/2041-210X.14090) can be found in the [paperex](https://github.com/AnushaPB/wingen/tree/main/paperex) directory.

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