| Name | scPEFT JSON |
| Version |
0.1.0
JSON |
| download |
| home_page | https://github.com/SELECT-FROM/scPEFT |
| Summary | Parameter-Efficient Fine-Tuning Enhances Adaptation of Single Cell Large Language Model. |
| upload_time | 2024-08-17 10:12:08 |
| maintainer | None |
| docs_url | None |
| author | Fei He |
| requires_python | <3.11,>=3.7.12 |
| license | MIT |
| keywords |
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
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| coveralls test coverage |
No coveralls.
|
# scPEFT
This is the official repository for **scPEFT: Parameter-Efficient Fine-Tuning Enhances Adaptation of Single Cell Large Language Model.**
[](https://www.biorxiv.org/content/10.1101/2024.01.27.577455v1)
## 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:
```bash
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](https://github.com/bowang-lab/scGPT/blob/main/README.md#pretrained-scgpt-model-zoo) and place it at e.g., `work_dir/scPEFT/save`. We recommend using the [whole-human](https://drive.google.com/drive/folders/1oWh_-ZRdhtoGQ2Fw24HP41FgLoomVo-y?usp=sharing) 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](https://github.com/SELECT-FROM/scPEFT/tree/main/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](https://github.com/SELECT-FROM/scPEFT/blob/main/tutorial_peft/Tutorial_Identification.ipynb) |
| batch correction | [Tutorial_BatchCorrection.ipynb](https://github.com/SELECT-FROM/scPEFT/blob/main/tutorial_peft/Tutorial_BatchCorrection.ipynb) |
| perturbation | [Tutorial_Perturbation.ipynb](https://github.com/SELECT-FROM/scPEFT/blob/main/tutorial_peft/Tutorial_Perturbation.ipynb) |
| case control | [Tutorial_CaseControl.ipynb](https://github.com/SELECT-FROM/scPEFT/blob/main/tutorial_peft/Tutorial_Perturbation.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](https://github.com/bowang-lab/scGPT)
- [scanpy](https://github.com/scverse/scanpy)
- [scvi-tools](https://github.com/scverse/scvi-tools)
- [scib](https://github.com/theislab/scib)
- [datasets](https://github.com/huggingface/datasets)
- [transformers](https://github.com/huggingface/transformers)
## Citing scPEFT
```bibtex
@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}
}
```
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