DicePlot: a package for high dimensional categorical data visualization

Important

Note for R users: We recommend using the newer ggdiceplot package for R. ggdiceplot is fully ggplot2-native, offering more flexibility, additional features, and better integration with the ggplot2 ecosystem. DicePlot remains available for compatibility but is no longer the primary R interface. See the ggdiceplot: The Recommended R Implementation chapter for details.

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.

ggdiceplot (Recommended for R)

The modern, ggplot2-native implementation for R users. You can find the ggdiceplot Source Code on github.

For users who still rely on the original DicePlot R package, a legacy R implementation remains available.

• ggdiceplot: The Recommended R Implementation
  • Overview
  • Why ggdiceplot Should Be Preferred Over DicePlot
  • Key Differences Between DicePlot and ggdiceplot
  • Installation
    • Load the Package
  • Basic Example Using geom_dice()
  • Advanced Example: Gene Expression Patterns
  • Domino Plot Example Using geom_dice()
  • Working with Real Data: miRNA Example
  • Taxonomy Example: Visualizing Hierarchical Data
  • Migration Guide: From DicePlot to ggdiceplot
    • Tips for Migration
  • Additional Resources
  • Comparison with Legacy DicePlot
  • Getting Help
  • Contributing

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 ggdiceplot in your research, please cite:

Matthias Flotho, Philipp Flotho, Andreas Keller, DicePlot: A package for high dimensional categorical data visualization, Bioinformatics, 2025;, btaf337, https://doi.org/10.1093/bioinformatics/btaf337 doi.org/10.1093/bioinformatics/btaf337 https://doi.org/10.1093/bioinformatics/btaf337

@article{flotho2025diceplot,
  title={DicePlot: A package for high dimensional categorical data visualization},
  author={Flotho, Matthias and Flotho, Philipp and Keller, Andreas},
  journal={Bioinformatics},
  pages={btaf337},
  year={2025},
  publisher={Oxford University Press}
}