Metadata-Version: 2.4
Name: SpatialMST
Version: 0.0.3
Summary: Spatial Multimodal Self-supervised Transformer
Author-email: Suraj Verma <verma.surajcool@gmail.com>
License: MIT License
Project-URL: Homepage, https://github.com/Angione-Lab/SpatialMST
Keywords: SpatialMST
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: MIT License
Classifier: Natural Language :: English
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Requires-Python: >=3.9
Description-Content-Type: text/markdown
License-File: LICENSE
License-File: AUTHORS.rst
Requires-Dist: NumPy==1.26.4
Requires-Dist: scanpy
Requires-Dist: mudata
Requires-Dist: pandas==2.2.3
Requires-Dist: torch_geometric
Requires-Dist: torch
Requires-Dist: matplotlib
Requires-Dist: scikit-learn
Requires-Dist: scipy
Requires-Dist: beautifulsoup4
Requires-Dist: lightning
Requires-Dist: magic-impute
Provides-Extra: all
Requires-Dist: SpatialMST[extra]; extra == "all"
Provides-Extra: extra
Requires-Dist: pandas; extra == "extra"
Dynamic: license-file

# SpatialMST


[![image](https://img.shields.io/pypi/v/SpatialMST.svg)](https://pypi.python.org/pypi/SpatialMST)
[![image](https://img.shields.io/conda/vn/conda-forge/SpatialMST.svg)](https://anaconda.org/conda-forge/SpatialMST)

[![image](https://pyup.io/repos/github/SurajRepo/SpatialMST/shield.svg)](https://pyup.io/repos/github/SurajRepo/SpatialMST)


**Spatial Multimodal Self-supervised Transformer**
-   Free software: MIT License

## Installation
**Create environment**
```sh
conda create -n SpatialMSTEnv python=3.11
conda activate SpatialMSTEnv
```
**Install ipykernel**
```sh
conda install ipykernel
python -m ipykernel install --user --name SpatialMSTEnv --display-name "Python(SpatialMSTEnv)"
```
**Install POT: Python Optimal Transport**
```sh
conda install -c conda-forge pot
```
**Install Pytorch and pytorch-geometric**
```sh
pip3 install torch torchvision --index-url https://download.pytorch.org/whl/cu129
pip install torch_geometric
```

### Install SpatialMST
PyPI package: https://pypi.org/project/SpatialMST
```sh
pip install SpatialMST
```

The source files for spMetaTME can be downloaded from the [Github repo](https://github.com/Angione-Lab/SpatialMST.git).

You can either clone the public repository:

```sh
git clone https://github.com/Angione-Lab/SpatialMST.git
```

Once you have a copy of the source, you can install it with:

```sh
cd spmetatme
uv pip install .
```
## Generate metabolic module flux rates and metabolite abundances for spatial transcriptomics using scFEA.
The estimated metabolic module flux rates and metabolite abundances construct the two modalities and the spatial transcriptomics data represents the third modality.

https://www.biorxiv.org/content/10.1101/2020.09.23.310656v1.full [Github link](https://github.com/changwn/scFEA/tree/master)

## Integrating spatial transcriptomics with metabolic module fluxes and metabolite abundance: 
[Tutorial on spatial multimodal integration and analysis](https://github.com/Angione-Lab/Multimodal_breast_cancer_subtype_analysis/tree/main/Spatial_multi_omics_analysis)

## Download the datasets from figshare
[Example dataset](https://figshare.com/ndownloader/articles/30290779/versions/1?folder_path=data_spatial)
