1. Create environment with pytorch installed. Use cpuonly version if you don't have an nvidia gpu locally:

   conda create -p env python=3.8.10 -y
   conda activate ./env

pip install pre-commit

pip install -r requirements.txt
pip install -r requirements-dev.txt
pip install torch_scatter torch_sparse torch_cluster torch_spline_conv -f https://data.pyg.org/whl/torch-2.1.0+cpu.html

pre-commit install

1. Ensure docker is installed on your system
2. Modify the required parameters in the .env file according to your preferences.
3. Navigate to SUPREME_refactoring directory
4. Run the following command to build the docker image with a specified tag name (e.g., "supreme")

   sudo docker build . -t supreme

5. Run docker container

   docker run supreme

sudo docker container prune -f && sudo docker image prune -f && sudo docker system prune -a

Organize your multi-omics data by placing it in the data/sample_data/raw directory. Modify the prepare_data function within the pre_process_data.py module, specifically change lines 60 to 92. Then, execute main.py.