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.
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.
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)
The amount of time that the installation takes depends on (1) whether you chose mamba over conda (former is much faster in my experience), (2) how many dependencies are already present in your environment, (3) the speed of your internet connection, and (4) the speed of your machine. The following steps, took me about 1 hour to complete on a remote HPC with fast internet connection.
You can install the dependencies using conda. To do so, you need to have conda installed on your machine. If you don't have it, you can install it from here.
We strongly recommend using mamba instead of conda, since it is much faster in our experience. If you are starting from scratch, i.e. don't have conda installed, you can install mamba instead of conda by following their guide here.
If you already have conda install and want to benefit from the speed and enhanced experience of mamba, you can do so by running:
# install mamba in your base environment
conda install -c conda-forge mambaBe warned though, this is not a recommended way by the creators of mamba.
Note: If you installed mamba from scratch, in all commands below you can replace conda with mamba. However, if you just installed mamba in your existing conda install use mamba only for creating the 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.ymlTo activate the environment, run:
# activate conda environment
conda activate sc_foundation_evalsThis 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==11.0.4 --no-build-isolation
# then install v1.0.6 version of scGPT
pip install git+https://github.com/bowang-lab/scGPT.git@v0.1.6
pip install wandbpip install git+https://huggingface.co/ctheodoris/Geneformer.git
And finally, install the sc_foundation_evals package (the code to run evaluations on zero-shot scFoundation models) itself.
cd sc_foundation_evals
pip install .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/GeneformerSupport for docker is coming soon.
To run the code, you need to clone this repository.
git clone https://github.com/microsoft/zero-shot-scfoundationAnd 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.zipTo 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.
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.
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