Metadata-Version: 2.4
Name: SURE-tools
Version: 4.4.5
Summary: Succinct Representation of Single Cells
Home-page: https://github.com/ZengFLab/SURE
Author: Feng Zeng
Author-email: zengfeng@xmu.edu.cn
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.10
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: dill==0.3.8
Requires-Dist: scanpy
Requires-Dist: pytorch-ignite
Requires-Dist: datatable
Requires-Dist: scipy
Requires-Dist: numpy
Requires-Dist: scikit-learn
Requires-Dist: pandas
Requires-Dist: pyro-ppl
Requires-Dist: jax[cuda12]
Requires-Dist: leidenalg
Requires-Dist: python-igraph
Requires-Dist: networkx
Requires-Dist: matplotlib
Requires-Dist: seaborn
Requires-Dist: fa2-modified
Requires-Dist: zuko
Requires-Dist: plotly
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# SURE: SUccinct REpresentation of cells
 SURE implements a discrete latent state model with normalizing flow encoder for exact estimation of cellular populations. 


## Installation
1. Create a virtual environment
```bash
conda create -n SURE python=3.10 scipy numpy pandas scikit-learn && conda activate SURE
```

2. Install [PyTorch](https://pytorch.org/get-started/locally/) following the official instruction. 
```bash
pip3 install torch torchvision --index-url https://download.pytorch.org/whl/cu126
```

3. Install SURE
```bash
pip3 install SURE-tools
```



