epipackpy 1.0.1.dev2

EpiPack: scATAC-seq reference mapping, label transfer and OOR detection

EpiPack

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Description

EpiPack is a modular deep learning toolkit for single-cell ATAC-seq reference mapping, cell label annotation, and out-of-reference (OOR) detection.
By introducing heterogeneous transfer learning and peak-informed variational inference (PEIVI), EpiPack enables scalable construction of harmonized reference atlases and robust query mapping across diverse scATAC-seq datasets. It further provides global-local OOR detection frameworks for discovering novel cell types or perturbed cellular states with interpretable uncertainty estimation. Please see our manuscript for more details.

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Installation

The package is available on PyPI and can be installed with all required dependencies via:

pip install epipackpy

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Tutorial

Please refer to our full documentation and tutorials at
👉 epipack.readthedocs.io

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Dependencies

- Python >= 3.9  
- PyTorch >= 2.0.1  
- PyTorch-CUDA >= 11.8  
- NumPy >= 1.26.4  
- Pandas >= 1.5.3  
- SciPy >= 1.10.0  
- Scikit-learn >= 1.5.2  
- tqdm >= 4.66.1  
- Matplotlib >= 3.9.4  
- Seaborn >= 0.12.2  

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For PyTorch installation, we recommend users to follow the official PyTorch installation guide to select the correct build based on their CUDA version.

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