ggDAPC

ggDAPC is an R script that enhances the visualization of Discriminant Analysis of Principal Components (DAPC) results using the versatile ggplot2 package.
DAPC, available in the adegenet package, is a robust method for exploring genetic differences among groups without relying on genetic model assumptions.
By leveraging ggplot2, ggDAPC offers customizable and publication-ready graphics for your genetic data analyses.

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✨ Features

• Enhanced Visualization – Transform standard DAPC plots into customizable ggplot2 graphics.
• Flexibility – Modify colors, shapes, and layouts to suit your publication needs.
• Integration – Seamlessly works with outputs from the adegenet package.

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📦 Dependencies

To utilize ggDAPC, ensure the following R packages are installed:

• base R v4.1.1
• adegenet – Exploratory Analysis of Genetic and Genomic Data
• tidyverse – A collection of packages for data wrangling
• ggplot2 – Create Elegant Data Visualisations Using the Grammar of Graphics
• scico – Colour Palettes Based on the Scientific Colour-Maps (colorblind friendly)
• patchwork – The Composer of Plots - a ggplot2 arranger

It's recommended to review the documentation of these packages to fully leverage their capabilities.

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🚀 Getting Started

1. Download the DAPC.R File – Obtain the script from the repository.
2. Load the Script – Source the script in your R environment.

   source("path_to/DAPC.R")

3. Prepare Your Data – Ensure your genetic data is in the appropriate format (e.g., GENEPOP file with .gen extension).
4. Run the Analysis – Execute the functions provided in the script to perform DAPC and generate plots.

For a step-by-step guide, refer to the simple tutorial.

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🔍 Analysis Overview

ggDAPC facilitates two primary analyses:

1. A Priori Grouping – Analyzes groups as defined in your input file, typically representing predefined populations. This approach assesses whether the data supports expected group separations.

2. Optimal K Grouping – Determines the optimal number of clusters (K) based on your data, utilizing the find.clusters function from adegenet. This method identifies the most likely grouping without prior assumptions.

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📝 Important Considerations

• Data Responsibility – Ensure your data is appropriately formatted and that you understand the implications of your analytical choices.
• Customization – Feel free to modify graph parameters to better represent your findings.
• No Warranty – This script is provided as-is, without guarantees. Use it responsibly and validate your results.

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📚 Learn More

For detailed information on DAPC and its applications, consult the adegenet package documentation and relevant literature, such as Jombart et al. (2010).