a package for high dimensional categorical data visualization — DicePlot 0.1 documentation (original) (raw)

Note

This project is under active development.

Displaying multidimensional categorical data often poses a challenge in life sciences to get a comprehensive overview of the underlying data. This is not limited to but holds in particular for pathway analysis across multiple conditions. Here we developed a visualization concept to create easy to understand and intuitive representation of such data. We provide the implementation as python as well as R package to ensure easy access and application.

Features

• Visualize Complex Data: Easily create plots for datasets with multiple categorical variables.
• DicePlot: Create DicePlots for datasets with more than two categorical variables.
• DominoPlot: Visualize gene expression data for different cell types and contrasts.
• R and python: Implementations in both R and python to ensure easy access and application.
• Customization: Customize plots with titles, labels, and themes.
• Integration with ggplot2: Leverages the power of ggplot2 for advanced plotting capabilities.
• Interactive Plots: Create interactive plots for easy exploration of your data using the plotly backend.

DicePlot

You can find the R Source Code on github.

• R - DicePlot
  • Requirements and installation
    * 1. Install R
    * 2. Install Required Packages
    * 3. Install DicePlot
    * 1. Load the Package
  • Diceplot: Tutorial
    * Real-World Example
    * Artificial example
  • Domino Plot Tutorial
    * Introduction to Domino Plots
    * Prerequisites
    * Dataset Overview
    * Step 1: Load Required Libraries
    * Step 2: Load and Prepare the Data
    * Step 3: Create a Basic Domino Plot
    * Step 4: Create a Customized Domino Plot
    * Step 5: Further Customizing the Plot
    * Step 6: Creating a Faceted Domino Plot
    * Understanding the Domino Plot Output
  • geom_dice_sf Tutorial
    * Prerequisites
    * Dataset Overview
    * Step 1: Load Required Libraries
    * Step 2: Load and Prepare the Data
    * Step 3: Create a Custom Legend Function
    * Step 4: Create a map with geom_dice_sf
  • References

pyDicePlot

You can find the python Source Code on github.

• Python - pyDicePlot
  • Requirements and installation
    * Using Conda
    * Installation via pip
  • DicePlot
    * Example Usage
  • DominoPlot
    * Example Usage

Contributing

We welcome contributions from the community! If you’d like to contribute:

1. Fork the repository on GitHub.
2. Create a new branch for your feature or bug fix.
3. Submit a pull request with a detailed description of your changes.

Contact

If you have any questions, suggestions, or issues, please open an issue on GitHub.

Citation

If you use this code or the R and Python packages for your own work, please cite DicePlot as:

M. Flotho, P. Flotho, A. Keller, “Diceplot: A package for high dimensional categorical data visualization,” arxiv, 2024. doi:10.48550/arXiv.2410.23897<https://doi.org/10.48550/arXiv.2410.23897>

BibTeX entry:

@article{flotea2024, author = {Flotho, M. and Flotho, P. and Keller, A.}, title = {Diceplot: A package for high dimensional categorical data visualization}, year = {2024}, journal = {arXiv preprint}, doi = {https://doi.org/10.48550/arXiv.2410.23897} }