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
Quick Start
pip install MIOFlow
See the Installation page for full setup instructions, or jump straight into the Tutorials.
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},
title = {Manifold Interpolating Optimal-Transport Flows for Trajectory Inference},
publisher = {arXiv},
year = {2022},
copyright = {arXiv.org perpetual, non-exclusive license}
}
Acknowledgments
- Built with PyTorch for neural ODE implementations
- Integrates with the scanpy ecosystem for single-cell analysis
- Optimal transport based on the POT library