Downstream_tasks/Zero_shot_batch_effect/README.md

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

[![DOI](https://badgen.net/badge/DOI/10.1101%2F2023.10.16.561085/red)](https://www.biorxiv.org/content/10.1101/2023.10.16.561085) [![DOI](https://badgen.net/badge/figshare/10.6084%2Fm9.figshare.24747228/green)](https://doi.org/10.6084/m9.figshare.24747228)

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](https://www.biorxiv.org/content/10.1101/2023.10.16.561085).

## Project overview

In this project, we assess two proposed foundation models in the context of single-cell RNA-seq: Geneformer ([pub](https://www.nature.com/articles/s41586-023-06139-9), [code](https://huggingface.co/ctheodoris/Geneformer)) and scGPT ([pub](https://www.biorxiv.org/content/10.1101/2023.04.30.538439v2), [code](https://github.com/bowang-lab/scGPT)). 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](https://www.nature.com/articles/s41592-018-0229-2), [code](https://docs.scvi-tools.org/en/stable/user_guide/models/scvi.html)) 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)

<details>
<summary>Packages version</summary>

This code has been tested with the following versions of the packages:

- Python - tested with `3.9`
- PyTorch - tested with - `1.13`
- CUDA - tested with `11.7`
- [FlashAttention](https://github.com/Dao-AILab/flash-attention/tree/v1.0.4) - depends on `v1.0.4`
- [scGPT](https://github.com/bowang-lab/scGPT/tree/v0.1.6) - depends on `v0.1.6`
- [Geneformer](https://huggingface.co/ctheodoris/Geneformer/tree/5d0082c1e188ab88997efa87891414fdc6e4f6ff) - depends on commit `5d0082c`
- [scIB](https://github.com/theislab/scib/tree/v1.0.4) - tested with `v1.0.4`
- [sc_foundation_evals](https://github.com/microsoft/zero-shot-scfoundation) `v0.1.0`

</details>

## Installation

Below you can find the instructions on how to install the dependencies for this project. We provide two options: using conda/mamba or using Docker.

<details>
<summary>Conda / Mamba</summary>

### Conda / Mamba

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](https://docs.conda.io/en/latest/miniconda.html).

We recommend using [mamba](https://mamba.readthedocs.io/en/latest/user_guide/mamba.html), since it is faster in our experience. You can install mamba following the guide [here](https://mamba.readthedocs.io/en/latest/installation/micromamba-installation.html#operating-system-package-managers).

To simplify installation, we provide the installation script that creates a new conda environment with all the dependencies installed. You can run the following command to create the environment:

```bash
bash envs/installation.sh
```

If the installation is successful, you will see the following message:

```console
2024-08-22 19:49:26 SUCCESS: All packages installed successfully.
```

And you can activate the environment by running:

```bash
conda activate sc_foundation_evals
```

</details>

<details>
<summary>Docker</summary>

### Docker

The docker image is available on DockerHub [here](https://hub.docker.com/repository/docker/kzkedzierska/sc_foundation_evals/general). You can pull the image by running:

```bash
docker pull kzkedzierska/sc_foundation_evals
```

The image is based on the `cnstark/pytorch:1.13.0-py3.9.12-cuda11.7.1-ubuntu20.04` image, and has all the dependencies installed. The Dockerfile used to build the image can be found in the `envs/docker` directory.

You can also skip pulling the image since `docker` will pull it if needed. To run the interactive session with the image, you can use the following command:

```bash
docker run --gpus all -it kzkedzierska/sc_foundation_evals
```

If you want to be able to run the notebooks, run the image with the following tag:

```bash
 docker run --gpus all -it --rm -p 8888:8888 -v  ./:/workspace kzkedzierska/sc_foundation_evals:latest_notebook
```

And open the link provided in the terminal in your browser. It should look like this:

```console
[I 2024-08-23 22:15:13.015 ServerApp] Serving notebooks from local directory: /workspace
[I 2024-08-23 22:15:13.015 ServerApp] Jupyter Server 2.14.2 is running at:
[I 2024-08-23 22:15:13.015 ServerApp] http://localhost:8888/tree
[I 2024-08-23 22:15:13.015 ServerApp] http://127.0.0.1:8888/tree
```

For running the command on the server, consult the documentation of the server provider on how to forward the ports properly.

</details>

## Running the code

### Downloading the weights

To run notebooks you also need to have the weights of the models downloaded. scGPT weights are avaialble [here](https://github.com/bowang-lab/scGPT#pretrained-scgpt-model-zoo) 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.

### Copying this repository

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

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

And download and unpack the data, stored at figshare (see [here](https://doi.org/10.6084/m9.figshare.24747228) for more details).

```bash
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](notebooks/scGPT_zero_shot.ipynb) - notebook for running scGPT zero-shot evaluation
- [Geneformer_zero_shot](notebooks/Geneformer_zero_shot.ipynb) - notebook for running Geneformer zero-shot evaluation
- [Baselines_HVG_and_scVI](notebooks/Baselines_HVG_and_scVI.ipynb) - notebook for running the baselines used in the paper, i.e. HVG and scVI.

## 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](/SUPPORT.md). We also welcome any contributions to the code - be sure to checkout the **Contributing** section below.

## Contributing

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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>.

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This project has adopted the [Microsoft Open Source Code of Conduct](https://opensource.microsoft.com/codeofconduct/).
For more information see the [Code of Conduct FAQ](https://opensource.microsoft.com/codeofconduct/faq/) or
contact [opencode@microsoft.com](mailto:opencode@microsoft.com) with any additional questions or comments.

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