RNAgif - 15 RNA Velocity Analysis Tools

Welcome to RNAgif, a comprehensive collection and comparison of 15 state-of-the-art RNA velocity analysis tools. This project integrates methods across multiple categories including machine learning models, deep learning frameworks, and statistical models for RNA velocity inference. Each method is well-documented and includes links to their respective GitHub repositories, along with detailed instructions and descriptions.

Table of Contents

• About RNAFusion
• 15 RNA Velocity Tools
• Project Structure
• Getting Started
• Dependencies
• Running Locally
• Contributing
• License

About RNAgif

RNAFusion provides a unified platform for exploring and analyzing RNA velocity tools. These tools help predict cellular dynamics and understand gene expression regulation. Our project includes an interactive Sphinx-generated website that allows users to browse through various methods, view key innovations, and access the respective research papers and repositories.

15 RNA Velocity Tools

This project incorporates the following RNA velocity tools:

1. velocyto - A tool for RNA velocity analysis based on steady-state splicing kinetics.
2. scVelo - A dynamic modeling framework with EM and latent time for RNA velocity.
3. VeloAE - An autoencoder-based method incorporating GCN and attention mechanisms.
4. VeloVAE - Variational autoencoder for latent state RNA velocity estimation.
5. UniTVelo - Temporally unified, top-down method using radial basis functions.
6. DeepVelo (2022) - A neural differential equation model for RNA velocity.
7. cellDancer - Single-cell resolution inference for velocity kinetics using DNN.
8. veloVI - Variational inference with uncertainty modeling for RNA velocity.
9. LatentVelo - Neural ODE and variational autoencoder for RNA velocity in latent space.
10. DeepVelo (2024) - GCN-based method for multi-lineage RNA velocity estimation.
11. STT - Multiscale dynamical modeling for spatial transcriptomics and RNA velocity.
12. dynamo - GMM-based transcriptomic vector field model for RNA velocity.
13. MultiVelo - Multi-omic integration of RNA velocity and chromatin accessibility.
14. PhyloVelo - Phylogenetic analysis with RNA velocity incorporating gene expression.
15. TFvelo - Focuses on transcription factors and regulatory gene expression for RNA velocity.

Each tool is documented with:

• Publication details.
• Core algorithms.
• Key innovations and features.
• Links to research papers and GitHub repositories.

Project Structure

The project is structured as follows:

.
├── docs/                # Sphinx documentation source files
│   ├── _static/         # Static files for styling and customization
│   ├── _templates/      # HTML templates
│   ├── projects/        # RST files for each RNA velocity tool
│   └── index.rst        # Main Sphinx documentation page
├── source/              # Source code and configuration files
│   └── conf.py          # Sphinx configuration file
├── images/              # Image assets for the documentation
├── README.md            # Project readme file
└── requirements.txt     # Python dependencies file

Getting Started

1. Clone the Repository

To get a local copy of the repository, use:

git clone https://github.com/your_username/RNAFusion.git
cd RNAFusion

2. Install Dependencies

This project uses Sphinx to generate documentation. Install the necessary dependencies using:

pip install all the requirements such as sphinx phython3 and etc..

3. Build the Documentation Locally

You can build the documentation locally using the following command:

make html

The generated HTML files will be in the _build/html directory. Open the index.html file in a browser to view the project documentation.

Dependencies

• Python 3.6+
• Sphinx: Documentation generator.
• sphinx_rtd_theme: Read the Docs theme for Sphinx.
• Other tools: See requirements.txt.

Running Locally

Ensure you have Python and pip installed.

Install the dependencies using pip install -r requirements.txt.

Run Sphinx to generate the documentation:

make html

Open _build/html/index.html to view the documentation locally.

Contributing

Contributions are welcome! If you would like to contribute to this project, please follow these steps:

1. Fork the repository.
2. Create a new branch: git checkout -b feature-branch-name.
3. Make your changes and commit them: git commit -m 'Add some feature'.
4. Push to the branch: git push origin feature-branch-name.
5. Open a pull request.

License

This project is licensed under the MIT License - see the LICENSE file for details.