Metadata-Version: 2.4
Name: SpaMV
Version: 1.0.36
Summary: The Python package for a spatial multi-omics integration algorithm called SpaMV.
Author-email: Yang Liu <yangliu1214@hkbu.edu.hk>
License-Expression: MIT
Project-URL: Homepage, https://github.com/enderlogic/SpaMV
Project-URL: Issues, https://github.com/enderlogic/SpaMV/issues
Classifier: Programming Language :: Python :: 3
Classifier: Operating System :: OS Independent
Requires-Python: >=3.10
Description-Content-Type: text/markdown
Requires-Dist: pyro-ppl
Requires-Dist: scanpy
Requires-Dist: torch_geometric
Requires-Dist: squidpy==1.8.0
Requires-Dist: dask==2026.1.2
Requires-Dist: numpy<2.3
Requires-Dist: scikit-misc

# 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.
