Metadata-Version: 2.1
Name: scegot
Version: 0.2.2
Summary: single cell trajectory inference framework based on Entropic Gaussian mixture Optimal Transport
Home-page: https://yachimura-lab.github.io/scEGOT/
License: MIT
Author: Toshiaki Yachimura
Author-email: toshiaki.yachimura.a4@tohoku.ac.jp
Requires-Python: >=3.9,<3.12
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Requires-Dist: POT (>=0.9.3,<0.10.0)
Requires-Dist: anndata (>=0.8,<0.9)
Requires-Dist: cellmap (>=1.0.7,<2.0.0)
Requires-Dist: graphviz (>=0.20.1,<0.21.0)
Requires-Dist: kaleido (==0.2.1)
Requires-Dist: llvmlite (>=0.41.1,<0.42.0)
Requires-Dist: matplotlib (>=3.8.0,<4.0.0)
Requires-Dist: natsort (>=8.4.0,<9.0.0)
Requires-Dist: networkx (>=3.2.1,<4.0.0)
Requires-Dist: numpy (>=1.26.3,<2.0.0)
Requires-Dist: pandas (>=2.2.0,<3.0.0)
Requires-Dist: plotly (>=5.18.0,<6.0.0)
Requires-Dist: pyarrow (>=15.0.0,<16.0.0)
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Requires-Dist: tqdm (>=4.66.1,<5.0.0)
Requires-Dist: umap-learn (>=0.5.5,<0.6.0)
Project-URL: Documentation, https://yachimura-lab.github.io/scEGOT/tutorial.html
Project-URL: Repository, https://github.com/yachimura-lab/scEGOT
Description-Content-Type: text/markdown

# scEGOT

[![Downloads](https://static.pepy.tech/badge/scegot)](https://pepy.tech/project/scegot)

single cell trajectory inference framework based on Entropic Gaussian mixture Optimal Transport

![Fig_scEGOT_abstract](https://github.com/yachimura-lab/scEGOT/assets/87163501/83a42b90-6cb4-402a-87d0-65815c44962c)

## Installation

```sh
$ pip install scegot
```

## Requirements

- Python3
- GraphViz

  Click [here](https://graphviz.org/) to learn how to install GraphViz (official documentation).

## Documentation

The tutorial is available [here](https://yachimura-lab.github.io/scEGOT/).

## Preprint

Please see the following paper:
[T. Yachimura et al. scEGOT: Single-cell trajectory inference framework based on entropic Gaussian mixture optimal transport. bioRxiv](https://doi.org/10.1101/2023.09.11.557102)

