Pipeline_HOX_apiFISH

Getting started

    To download the pipeline_smFISH : git clone https://github.com/fish-quant/pipeline_HOX_apiFISH.git

Install environments

    1) Base environment: 

    a) conda create --name base_env_apifish python=3.11

    b) conda activate base_env_apifish

    c) pip install -r requirements_base_env_apifish.txt    
    
    d) add the kernel to jupyter :
    python -m ipykernel install --user --name base_env_apifish --display-name "base_env_apifish"

    2) Create second environment (ufish_env): 
    
        a) conda activate base
        
        b) conda create --name ufish_env python=3.11
        
        c) conda install pip
        
        d) pip install -r requirements_ufish.txt
        
        e) add new kernel to your conda environment:   
        python -m ipykernel install --user --name ufish_env --display-name "ufish_env"
        

    3) Test if cuda is available otherwise install it.
    
    python -c "import torch;
    print('CUDA available:', torch.cuda.is_available()); print('CUDA version:', torch.version.cuda)"

Code execution

    1) In Linux/Mac, open a terminal. In Windows open the Anaconda Prompt. Place the terminal's current working directory 
    at the pipelines root "../pipeline_HOX_apiFISH".
    
    2) Place yourself in the conda environment:
    
    conda activate base_env_apifish
    
    3) Launch the jupyter server:
    
    jupyter notebook.
    
    4 ) Execute the pipeline in the order given by the diagram.
    
    5)  All jupyter notebooks should be run in the environment "base_env_apifish", except the notebook called Spot_detection_part1. 

Authors

Jacques Bourg @ Florian Muller lab. Institut Pasteur. 04/06/25