Skip to content

Latest commit

 

History

History
59 lines (44 loc) · 1.84 KB

README.md

File metadata and controls

59 lines (44 loc) · 1.84 KB

GASTON-Mix

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.

Installation

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.

Getting started

See our tutorial tutorial.ipynb and check out our readthedocs.

Software dependencies

  • 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).

Citations

The GASTON-Mix pre-print is available on biorXiv. If you use GASTON-Mix for your work, please cite our paper.

@article {Chitra2025,
	author = {Chitra, Uthsav and Dan, Shu and Krienen, Fenna and Raphael, Benjamin J.},
	title = {GASTON-Mix: a unified model of spatial gradients and domains using spatial mixture-of-experts},
	elocation-id = {2025.01.31.635955},
	year = {2025},
	doi = {10.1101/2025.01.31.635955},
	publisher = {Cold Spring Harbor Laboratory},
	eprint = {https://www.biorxiv.org/content/early/2025/02/04/2025.01.31.635955.full.pdf},
	journal = {bioRxiv}
}

Support

For questions or comments, please file a Github issue and/or email Uthsav Chitra (uchitra@broadinstitute.org)