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

  1. Create a virtual environment and activate it
conda create -n HMAP python=3.10 scipy numpy pandas scikit-learn && conda activate HMAP
  1. Install PyTorch following the official instruction.
pip3 install torch torchvision --index-url https://download.pytorch.org/whl/cu126
  1. Install HMAP
pip3 install HMAP-tool

Reproducibility

See the Github repository for materials and tutorials required to reproduce the results.

Project links

Key dates

PyPI data
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  • Released:
Newer release available (1.3.0)

1 maintainer

PyPI data
Data sourced directly from PyPI's database.
Avatar for ZengFLab from gravatar.com ZengFLab

Credits

Author: Feng Zeng

License

MIT License

Requires

Python >=3.10

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