Foundation models in single-cell biology: evaluating zero-shot capabilities

This repository contains the code that accompanies our paper, Assessing the limits of zero-shot foundation models in single-cell biology. You can find the preprint of the paper here.

Project overview

In this project, we assess two proposed foundation models in the context of single-cell RNA-seq: Geneformer (pub, code) and scGPT (pub, code). We focus on evaluating the zero-shot capabilities of these models, specifically their ability to generalize beyond their original training objectives. Our evaluation targets two main tasks: cell type clustering and batch integration. In these tasks, we compare the performance of Geneformer and scGPT against two baselines: scVI (pub, code) and a heuristic method that selects highly variable genes (HVGs). We also investigate the performence of the models in reconstructing the gene expression profiles of cells, and compare it against the baselines - such as a mean expression value or average ranking.

Dependencies

Currently the code requires the GPUs supported by flash attention, required for scGPT to run.

GPUs supported by flash attention are:

• Ampere, Ada, or Hopper GPUs (e.g., A100, RTX 3090, RTX 4090, H100).
• Turing GPUs (T4, RTX 2080)

Installation

1. Installing conda environment

# install conda environment from conda_env.yml file
# in this step, you can use mamba instead of conda for speed
conda env create -f envs/conda_env.yml

To activate the environment, run:

# activate conda environment
conda activate sc_foundation_evals

2. Installing scGPT

This can be tricky, as scGPT requires specific flash-attn version, and flash attention can be difficult to install. If you get any issues with installation, check out the instructions from the flash-attn authors here, but bear in mind that they have significantly updated their code with 2.0 release, so the instructions might not entirely work for this version.

# make sure sc_foundation_evals env is activated
# We have found it easier to install flash attention first, and then scGPT
pip install flash-attn==1.0.1 --no-build-isolation
# then install v1.0.6 version of scGPT
pip install git+https://github.com/bowang-lab/[email protected]
pip install wandb
pip install PyComplexHeatmap

3. Installing Geneformer

To run notebooks you also need to have the weights of the models downloaded. scGPT weights are avaialble here and Geneformer weights are available in its repository. As per the instructions in the Geneformer repository, make sure you have git lfs installed before downloading the weights via repository cloning.

# Make sure you have git-lfs installed (https://git-lfs.com)
git lfs install
git clone https://huggingface.co/ctheodoris/Geneformer
echo "recursive-include geneformer *" >> Geneformer/MANIFEST.in
cd Geneformer
pip install .

4. Installing sc_foundation_evals package

And finally, install the sc_foundation_evals package (the code to run evaluations on zero-shot scFoundation models) itself.

cd ..
pip install .

Docker

Support for docker is coming soon.

Running the code

Copying this repository

To run the code, you need to clone this repository.

git clone https://github.com/microsoft/zero-shot-scfoundation

And download and unpack the data, stored at figshare (see here for more details).

cd zero-shot-scfoundation
# download and unpack the data
wget https://figshare.com/ndownloader/files/43480497 -O data.zip
unzip data.zip && rm data.zip

Notebooks

To best understand the code and it's organization, please have a look at the notebooks. The notebooks directory currently contains the following notebooks:

• scGPT_zero_shot - notebook for running scGPT zero-shot evaluation
• Geneformer_zero_shot - notebook for running Geneformer zero-shot evaluation
• Baselines_HVG_and_scVI - notebook for running the baselines used in the paper, i.e. HVG and scVI.

To run scGPT evaluations from an executable python script, use scGPT_zero_shot.py. To run an example for this script, use Example_scGPT_zero_shot.sh.

Any questions?

If you have any questions, or find any issues with the code, please open an issue in this repository. You can find more information on how to file an issue in here. We also welcome any contributions to the code - be sure to checkout the Contributing section below.

Contributing

This project welcomes contributions and suggestions. Most contributions require you to agree to a Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us the rights to use your contribution. For details, visit https://cla.opensource.microsoft.com.

When you submit a pull request, a CLA bot will automatically determine whether you need to provide a CLA and decorate the PR appropriately (e.g., status check, comment). Simply follow the instructions provided by the bot. You will only need to do this once across all repos using our CLA.

This project has adopted the Microsoft Open Source Code of Conduct. For more information see the Code of Conduct FAQ or contact [email protected] with any additional questions or comments.

Trademarks

This project may contain trademarks or logos for projects, products, or services. Authorized use of Microsoft trademarks or logos is subject to and must follow Microsoft's Trademark & Brand Guidelines. Use of Microsoft trademarks or logos in modified versions of this project must not cause confusion or imply Microsoft sponsorship. Any use of third-party trademarks or logos are subject to those third-party's policies.