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176-deciphering-spatial-domains-from-spatial-multi-omics-with-spatialglue

https://github.com/szu-advtech-2024/176-deciphering-spatial-domains-from-spatial-multi-omics-with-spatialglue

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https://github.com/SZU-AdvTech-2024/176-Deciphering-Spatial-Domains-from-Spatial-Multi-omics-with-SpatialGlue/blob/main/ 

# Deciphering spatial domains from spatial multi-omics with SpatialGlue 

[![DOI](https://zenodo.org/badge/631763850.svg)](https://zenodo.org/badge/latestdoi/631763850)

![](https://github.com/JinmiaoChenLab/SpatialGlue/blob/main/Workflow.jpg)

## Overview
Integration of multiple data modalities in a spatially informed manner remains an unmet need for exploiting spatial multi-omics data. Here, we introduce SpatialGlue, a novel graph neural network with dual-attention mechanism, to decipher spatial domains by intra-omics integration of spatial location and omics measurement followed by cross-omics integration. We demonstrate that SpatialGlue can more accurately resolve spatial domains at a higher resolution across different tissue types and technology platforms, to enable biological insights into cross-modality spatial correlations. SpatialGlue is computation resource efficient and can be applied for data from various spatial multi-omics technological platforms, including Spatial-epigenome-transcriptome, Stereo-CITE-seq, SPOTS, and 10x Visium. Next, we will extend SpatialGlue to more platforms, such as 10x Genomics Xenium and Nanostring CosMx. 

## Requirements
You'll need to install the following packages in order to run the codes.
* python==3.8
* torch>=1.8.0
* cudnn>=10.2
* numpy==1.22.3
* scanpy==1.9.1
* anndata==0.8.0
* rpy2==3.4.1
* pandas==1.4.2
* scipy==1.8.1
* scikit-learn==1.1.1
* scikit-misc==0.2.0
* tqdm==4.64.0
* matplotlib==3.4.2
* R==4.0.3


## Citation
[Yahui Long](https://longyahui.github.io/Home//) et al. Deciphering spatial domains from spatial multi-omics with SpatialGlue. **Nature Methods**. 2024.

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