SpaMV 1.0.27

The Python package for a spatial multi-omics integration algorithm called SpaMV.

SpaMV: An interpretable spatial multi-omics data integration and dimension reduction algorithm

Installation

1. Create and activate a conda environment with python 3.12

conda env create spamv python==3.12
conda activate spamv

2. (Optional) If you want to apply our algorithm to large datasets (with more than 10,000 spots), please make sure you have installed the pyg-lib package.

pip install pyg-lib -f https://data.pyg.org/whl/torch-${TORCH}+${CUDA}.html

where

• ${TORCH} should be replaced by either 1.13.0, 2.0.0, 2.1.0, 2.2.0, 2.3.0, 2.4.0, 2.5.0, 2.6.0, or 2.7.0
• ${CUDA} should be replaced by either cpu, cu102, cu117, cu118, cu121, cu124, cu126, or cu128

3. Then you can install our package as follows:

pip install spamv

Tutorial

We provide two jupyter notebooks (Tutorial_simulation.ipynb and Tutorial_realworld.ipynb) to reproduce the results in our paper. Before you run them, please make sure that you have downloaded the simulated data and/or real-world data from our Zenodo repositoy.