scgpt
Towards Building a Foundation Model for Single-Cell Multi-omics Using Generative AI
Generative pre-trained models have achieved remarkable success in various domains such as natural language processing and computer vision. Specifically, the combination of large-scale diverse datasets and pre-trained transformers has emerged as a promising approach for developing foundation models. Drawing parallels between linguistic constructs and cellular biology - where texts comprise words, similarly, cells are defined by genes - our study probes the applicability of foundation models to advance cellular biology and genetics research. Utilizing the burgeoning single-cell sequencing data, we have pioneered the construction of a foundation model for single-cell biology, scGPT, which is based on generative pre-trained transformer across a repository of over 33 million cells. Our findings illustrate that scGPT, a generative pre-trained transformer, effectively distills critical biological insights concerning genes and cells. Through the further adaptation of transfer learning, scGPT can be optimized to achieve superior performance across diverse downstream applications. This includes tasks such as cell-type annotation, multi-batch integration, multi-omic integration, genetic perturbation prediction, and gene network inference.
Haotian Cui , Chloe Wang , Hassaan Maan , Kuan Pang , Fengning Luo , Bo Wang ,
Monthly downloads
1000
scGPT ¶
This is the official codebase for scGPT: Towards Building a Foundation Model for Single-Cell Multi-omics Using Generative AI.
!UPDATE: We have released several new pretrained scGPT checkpoints. Please see the Pretrained scGPT checkpoints section for more details.
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. For now, May 2023, we recommend using CUDA 11.7 and flash-attn<1.0.5 due to various issues reported about installing new versions of flash-attn.
Pretrained scGPT Model Zoo ¶
Here is the list of pretrained models. Please find the links for downloading the checkpoint folders. We recommend using the whole-human model for most applications by default. If your fine-tuning dataset shares similar cell type context with the training data of the organ-specific models, these models can usually demonstrate competitive performance as well.
| Model name | Description | Download |
|---|---|---|
| whole-human (recommended) | Pretrained on 33 million normal human cells. | link |
| brain | Pretrained on 13.2 million brain cells. | link |
| blood | Pretrained on 10.3 million blood and bone marrow cells. | link |
| heart | Pretrained on 1.8 million heart cells | link |
| lung | Pretrained on 2.1 million lung cells | link |
| kidney | Pretrained on 814 thousand kidney cells | link |
| pan-cancer | Pretrained on 5.7 million cells of various cancer types | link |
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 type 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
- 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:
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 Pang, Kuan and Luo, Fengning and Wang, Bo},
journal={bioRxiv},
year={2023},
publisher={Cold Spring Harbor Laboratory}
}
Related notebook
BioTuring
Doublets are a characteristic error source in droplet-based single-cell sequencing data where two cells are encapsulated in the same oil emulsion and are tagged with the same cell barcode. Across type doublets manifest as fictitious phenotypes that can be incorrectly interpreted as novel cell types. DoubletDetection present a novel, fast, unsupervised classifier to detect across-type doublets in single-cell RNA-sequencing data that operates on a count matrix and imposes no experimental constraints. This classifier leverages the creation of in silico synthetic doublets to determine which cells in the input count matrix have gene expression that is best explained by the combination of distinct cell types in the matrix. In this notebook, we will illustrate an example workflow for detecting doublets in single-cell RNA-seq count matrices.
BioTuring
Build single-cell trajectories with the software that introduced **pseudotime**. Find out about cell fate decisions and the genes regulated as they're made. Group and classify your cells based on gene expression. Identify new cell types and states and the genes that distinguish them. Find genes that vary between cell types and states, over trajectories, or in response to perturbations using statistically robust, flexible differential analysis. In development, disease, and throughout life, cells transition from one state to another. Monocle introduced the concept of **pseudotime**, which is a measure of how far a cell has moved through biological progress. Many researchers are using single-cell RNA-Seq to discover new cell types. Monocle 3 can help you purify them or characterize them further by identifying key marker genes that you can use in follow-up experiments such as immunofluorescence or flow sorting. **Single-cell trajectory analysis** shows how cells choose between one of several possible end states. The new reconstruction algorithms introduced in Monocle 3 can robustly reveal branching trajectories, along with the genes that cells use to navigate these decisions.
BioTuring
Required GPU
PopV uses popular vote of a variety of cell-type transfer tools to classify cell-types in a query dataset based on a test dataset. Using this variety of algorithms, they compute the agreement between those algorithms and use this agreement to predict which cell-types have a high likelihood of the same cell-types observed in the reference.