scgpt 0.1.2

Large-scale generative pretrain of single cell using transformer.

scGPT

This is the official codebase for scGPT: Towards Building a Foundation Model for Single-Cell Multi-omics Using Generative AI.

Installation

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

$ pip install scgpt

[Optional] We recommend using wandb for logging and visualization.

$ pip install wandb

For developing, we are using the Poetry package manager. To install Poetry, follow the instructions here.

$ git clone this-repo-url
$ cd scGPT
$ poetry install

Note: The flash-attn dependency usually requires specific GPU and CUDA version. If you encounter any issues, please refer to the flash-attn repository for installation instructions.

Pretrained scGPT checkpoints

Please download the pretrained scGPT checkpoints from here.

Fine-tune scGPT for scRNA-seq integration

Please see our example code in examples/finetune_integration.py. By default, the script assumes the scGPT checkpoint folder stored in the examples/save directory.

To-do-list

• Upload the pretrained model checkpoint
• Publish to pypi
• Provide the pretraining code with generative attention masking
• Finetuning examples for multi-omics integration, cell tyep annotation, perturbation prediction, cell generation
• Example code for Gene Regulatory Network analysis
• Documentation website with readthedocs
• Bump up to pytorch 2.0
• New pretraining on larger datasets
• Reference mapping example
• Finetuning with LORA
• Publish to huggingface model hub

Contributing

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

Acknowledgements

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

• flash-attention
• scanpy
• scvi-tools
• scib
• datasets
• transformers

Citing scGPT

@article{cui2023scGPT,
title={scGPT: Towards Building a Foundation Model for Single-Cell Multi-omics Using Generative AI},
author={Cui, Haotian and Wang, Chloe and Maan, Hassaan and Wang, Bo},
journal={bioRxiv},
year={2023},
publisher={Cold Spring Harbor Laboratory}
}