PILOT-GM-VAE (Paper)
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.
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 , Part 2
and Part 3
Metadata
Release files for pilotgm 0.1.1
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| pilotgm-0.1.1.tar.gz | 26.4 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| pilotgm-0.1.1-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 54.2 kB
Release files / pilotgm-0.1.1.tar.gz
| Download URL | pilotgm-0.1.1.tar.gz |
|---|---|
| Size | 26.4 kB |
| Tags | Source |
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Release files / pilotgm-0.1.1-py3-none-any.whl
| Download URL | pilotgm-0.1.1-py3-none-any.whl |
|---|---|
| Size | 27.8 kB |
| Tags | Python 3 |
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SHA-256 checksum How to use checksums |
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twine/6.2.0 CPython/3.13.5
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