Installation

LYNX targets Python 3.9.5. The deep-learning stack (PyTorch + PyTorch Geometric) and the spatial-omics libraries are the trickiest parts to reproduce, so a conda environment is recommended; a pip-only track is provided as a fallback.

Note

The PyTorch Geometric companion wheels (torch-sparse, torch-geometric) are not on plain PyPI for CUDA builds. They must be installed from the PyG find-links index matching your torch and CUDA versions (examples below use torch 2.3.1 + CUDA 12.1).

Conda (recommended)

conda env create -f environment.yml
conda activate lynx

# PyG companion wheels (match torch 2.3.1 + your CUDA build):
pip install torch-sparse==0.6.18 torch-geometric==2.6.1 \
    -f https://data.pyg.org/whl/torch-2.3.1+cu121.html

# Install the lynx package itself (and remaining pip deps):
pip install -e .

Pip (fallback)

python --version          # should report 3.9.5

pip install torch==2.3.1 torchvision==0.18.1
pip install torch-sparse==0.6.18 torch-geometric==2.6.1 \
    -f https://data.pyg.org/whl/torch-2.3.1+cu121.html

pip install -r requirements.txt
pip install -e .

Verify the install

python -c "import torch, torch_geometric, squidpy, scanpy, lynx; print('ok')"

If you are running directly from the project’s bundled interpreter, substitute env/bin/python for python in the commands above.

Installing into an existing interpreter

When installing into a specific interpreter such as env/bin/python, note that LYNX builds with the Poetry backend, so the build environment needs poetry-core. Always invoke the target interpreter’s own pip — pip on your PATH may belong to a different virtualenv:

env/bin/python -m pip install poetry-core
env/bin/python -m pip install -e . --no-deps   # --no-deps keeps the pinned stack untouched

Verify it resolves from any working directory (not just the repo root):

cd /tmp && env/bin/python -c "import lynx; print(lynx.__version__)"

Using the tutorials in Jupyter

The tutorial notebooks import lynx directly, so the running kernel must be the interpreter you installed LYNX into (e.g. env). Register that interpreter as a named kernel and select it in the notebook:

env/bin/python -m ipykernel install --user --name lynx-env \
    --display-name "Python (LYNX env)"

Then open a tutorial and choose the Python (LYNX env) kernel.