GASTON-Mix is a spatial mixture-of-experts (MoE) model for learning domain-specific topographic maps of a tissue slice from spatially resolved transcriptomics (SRT) data.
We will make GASTON-Mix pip-installable soon. In the meanwhile, you can directly install the conda environment from the environment.yml file:
conda env create -f environment.yml
Then install GASTON-Mix using pip.
conda activate gaston-mix
pip install -e .
Installation should take <10 minutes.
See our tutorial tutorial.ipynb and check out our readthedocs.
- torch
- matplotlib
- pandas
- scikit-learn
- numpy
- jupyterlab
- seaborn
- tqdm
- scipy
- scanpy
See full list in environment.yml file. GASTON-Mix can be run on CPU or GPU.
We note that GASTON-Mix sometimes uses clusters from CellCharter to initialize its gating network. We suggest either making a separate environment to run CellCharter and follow their tutorial, or using a different initialization (see tutorial).
The GASTON-Mix pre-print is available at [add link] If you use GASTON-Mix for your work, please cite our paper.
@article{Chitra2025,
...
}
For questions or comments, please file a Github issue and/or email Uthsav Chitra (uchitra@broadinstitute.org)