Multi-GASTON is an unsupervised deep learning model that learns multiple spatial gradients simoutaineously from spatially resolved data such as spatial metabolomics and spatially-resolved transcriptomics (SRT). It is an extension of GASTON (https://pmc.ncbi.nlm.nih.gov/articles/PMC10592770/), which was designed for SRT data and learns a single topographic map of a 2-D tissue slice in terms of a 1-D coordinate called isodepth, where all genes can be expressed as a function of this isodepth. Now, allowing features like metabolites or genes to follow mulitple distinct spatial patterns, Multi-GASTON captures the feature topography by learning k isodepths, that smoothly vary across a tissue slice and capture spatial organizations of different groups of spatially variable features.
First install the conda environment from the environment.yml file:
cd multi_gaston
conda env create -f environment.yml
Then, activate the enviroment and install multi_gaston
conda activate multi_gaston_env
pip install -e .
The installation should take less than 10 minutes.
Example applications of Multi-Gaston to spatial metabolomics datasets of murine liver and small intestine can be found in https://github.com/raphael-group/MET-MAP, where the method is re-named as Metabolic Topography Mapper, MET-MAP.
- torch (=2.0.0)
- matplotlib (=3.8.0)
- pandas (=2.1.1)
- scikit-learn (=1.3.1)
- numpy (=1.23.4)
- jupyterlab (=4.0.6)
- seaborn (=0.12.2)
- tqdm (=4.66.1)
- scipy (=1.11.2)
- scanpy (=1.9.5)
- squidpy (=1.3.1)