Metadata-Version: 2.4
Name: pilotgm
Version: 0.1.1
Summary: Patient-Level Analysis of Single Cell Disease Atlas with Optimal Transport of Gaussian Mixtures Variational Autoencoders
Home-page: https://github.com/CostaLab/PILOT-GM-VAE
Author: Mehdi Joodaki
Author-email: judakimehdi@gmail.com
Requires-Python: >=3.9
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: torch
Requires-Dist: pilotpy
Requires-Dist: scanpy
Requires-Dist: joblib
Requires-Dist: tqdm
Requires-Dist: numba
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Dynamic: author-email
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[![GitHub license](https://img.shields.io/github/license/CostaLab/PILOT.svg)](https://github.com/CostaLab/PILOT?tab=MIT-1-ov-file#MIT-1-ov-file)

### PILOT-GM-VAE  ([Paper](https://academic.oup.com/bib/article/26/5/bbaf547/8287234))


Patient-Level Analysis of Single Cell Disease Atlas with Optimal Transport of Gaussian Mixtures Variational Autoencoders.
We introduce here PatIent-Level Analysis with Optimal Transport based on Gausian Mixture  Variational AutoEncoders. PILOT-GM-VAE explores the power of GM-VAE to estimate models describing complex single cell distributions with efficient optimal transport algorithms for estimating the distance between GMs. 



![plot](./img/plot1.png)

```terminal
git clone https://github.com/CostaLab/PILOT-GM-VAE.git

cd PILOT-GM-VAE

conda create --name PILOT-GM-VAE python

conda activate PILOT-GM-VAE

pip install pilotgm
```

### Navigate to Tutorial.

Then please use the provided Tutorial.


### Data sets

You can access the used data sets by PILOT-GM-VAE in Part 1 [![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.4740646.svg)](https://zenodo.org/records/8370081), Part 2 [![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.4740646.svg)](https://zenodo.org/records/7957118) and Part 3 
[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.4740646.svg)](https://zenodo.org/records/14615923)


