Metadata-Version: 2.4
Name: HMAP-tool
Version: 1.0.2
Summary: Hierarchical Manifold Approximation and Projection for Single Cell Data
Home-page: https://github.com/ZengFLab/HMAP
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
Requires-Dist: dill==0.3.8
Requires-Dist: scanpy
Requires-Dist: datatable
Requires-Dist: scipy
Requires-Dist: numpy
Requires-Dist: scikit-learn
Requires-Dist: pandas
Requires-Dist: pyro-ppl
Requires-Dist: python-igraph
Requires-Dist: networkx
Requires-Dist: matplotlib
Requires-Dist: seaborn
Requires-Dist: fa2-modified
Dynamic: author
Dynamic: author-email
Dynamic: classifier
Dynamic: description
Dynamic: description-content-type
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# HMAP: Hierarchical Manifold Approximation and Projection

HMAP develop a hierarchical deep generative topographic mapping algorithm to realize the recovery of both **global and local** manifolds underlying the given data.


<div align="center">
    <img src="./img/figure1.png" alt="" width="60%">
</div>


## Installation
1. Create a virtual environment and activate it
```bash
conda create -n HMAP python=3.10 && conda activate HMAP
```

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 HMAP
```bash
pip install HMAP-tool
```
