scPEFT

This is the official repository for scPEFT: Parameter-Efficient Fine-Tuning Enhances Adaptation of Single Cell Large Language Model.

 

Installation

scPEFT works with Python >= 3.7.13. Please make sure you have the correct version of Python installed pre-installation.

scPEFT is available on PyPI. To install scPEFT, run the following command:

pip install scpeft

For developing, run the following command:

git clone https://github.com/SELECT-FROM/scPEFT
cd scPEFT

Get Started

1. Download the upstream model scGPT model checkpoint and place it at e.g., work_dir/scPEFT/save. We recommend using the whole-human model for most applications by default, which pretrained on 33 million normal human cells..

2. The tutorials of scPEFT for downstream tasks in tutorial_peft. Here are the links to the downstream tasks and tutorials mentioned in our article

   Downstream task	Link
   cell type identification	Tutorial_Identification.ipynb
   batch correction	Tutorial_BatchCorrection.ipynb
   perturbation	Tutorial_Perturbation.ipynb
   cell population discovery	Tutorial_CellPopulationDiscovery.ipynb
   marker gene detection	Tutorial_MarkerGeneDetection.ipynb

Contributing

We greatly welcome contributions to scPEFT. Please submit a pull request if you have any ideas or bug fixes. We also welcome any issues you encounter while using scPEFT.

Built With

We sincerely thank the authors of following open-source projects:

• scGPT
• scanpy
• scib
• pytorch

Citing scPEFT

@article {He2024.01.27.577455,
	author = {Fei He and Ruixin Fei and Mingyue Gao and Li Su and Xinyu Zhang and Dong Xu},
	title = {Parameter-Efficient Fine-Tuning Enhances Adaptation of Single Cell Large Language Model for Cell Type Identification},
	year = {2024},
	doi = {10.1101/2024.01.27.577455},
	publisher = {Cold Spring Harbor Laboratory},
	URL = {https://www.biorxiv.org/content/early/2024/01/30/2024.01.27.577455},
	journal = {bioRxiv}
}