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 single-cell data.
Installation
- Create a virtual environment and activate it
conda create -n HMAP python=3.10 scipy numpy pandas scikit-learn && conda activate HMAP
- Install PyTorch following the official instruction.
pip3 install torch torchvision --index-url https://download.pytorch.org/whl/cu126
- Install HMAP
pip3 install HMAP-tool
Reproducibility
See the Github repository for materials and tutorials required to reproduce the results.
1 maintainer
Metadata
1 maintainer
Release files for HMAP-tool 1.3.1
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
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| hmap_tool-1.3.1.tar.gz | 28.0 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| hmap_tool-1.3.1-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 69.5 kB
Release files / hmap_tool-1.3.1.tar.gz
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| Size | 28.0 kB |
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Release files / hmap_tool-1.3.1-py3-none-any.whl
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| Size | 41.5 kB |
| Tags | Python 3 |
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