MIOFlow

MIOFlow is a Python package for modeling and analyzing single-cell RNA-seq data using optimal flows. It leverages neural ordinary differential equations (neural ODEs) and optimal transport to reconstruct trajectories, compare cell populations, and study dynamic biological processes.

Features

• Trajectory inference using optimal transport and neural ODEs
• Comparison across conditions (e.g., control vs. perturbation)
• Visualization utilities for single-cell dynamics
• Flexible I/O for AnnData and standard scRNA-seq formats

Installation

Install from PyPI

pip install MIOFlow

Install from GitHub (Development Version)

pip install git+https://github.com/yourusername/MIOFlow.git

Usage

Basic Workflow

A basic workflow can be found on the tutorials. There is a Google Colab option as well.

tutorials/1_MIOFlow_Example.ipynb or tutorials/2_Colab_Training_MIOFlow

Citation

If you use MIOFlow in your research, please cite:

@misc{https://doi.org/10.48550/arxiv.2206.14928,
  doi = {10.48550/ARXIV.2206.14928},
  url = {https://arxiv.org/abs/2206.14928},
  author = {Huguet,  Guillaume and Magruder,  D. S. and Tong,  Alexander and Fasina,  Oluwadamilola and Kuchroo,  Manik and Wolf,  Guy and Krishnaswamy,  Smita},
  keywords = {Machine Learning (cs.LG),  FOS: Computer and information sciences,  FOS: Computer and information sciences},
  title = {Manifold Interpolating Optimal-Transport Flows for Trajectory Inference},
  publisher = {arXiv},
  year = {2022},
  copyright = {arXiv.org perpetual,  non-exclusive license}
}

License

MIOFlow is distributed under the terms of the Yale License.

Support

• 🐛 Issues: GitHub Issues
• 💬 Discussions: GitHub Discussions

Acknowledgments

• Built with PyTorch for neural ODE implementations
• Integrates with scanpy ecosystem for single-cell analysis
• Optimal transport implementations based on POT library