Metadata-Version: 2.1
Name: RGAST
Version: 0.0.1
Summary: Relational Graph Attention Network for Spatial Transcriptome Analysis
Home-page: https://github.com/GYQ-form/RGAST
Author: Yuqiao Gong
Author-email: gyq123@sjtu.edu.cn
License: MIT
Keywords: spatial transcriptomic,RGAT,representation learning,spatial domain identification
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Description-Content-Type: text/markdown
License-File: LICENSE

# RGAST

RGAST: Relational Graph Attention Network for Spatial Transcriptome Analysis

This document will help you easily go through the scBC model.

![fig1_00](https://github.com/GYQ-form/RGAST/assets/79566479/fe0655dc-2318-44e0-92bf-0aea3aad7163)


## Installation

To install our package, run

```bash
pip install RGAST
```



## Usage

RGAST (Relational Graph Attention network for Spatial Transcriptome analysis) constructs a relational graph attention network to learn the representation of each spot in the spatial transcriptome data. Plus the attention mechanism, RGAST considers both gene expression similarity and spatial neighbor relationships in constructing the graph network, enabling a more comprehensive and flexible representation of the spatial transcriptome data. RGAST can be used in many ST analysis:

- spatial domain identification
- cell trajectory inference
- spatially variable gene (SVG) detection
- uncover spatially resolved cell-cell interactions
- reveal intricate 3D spatial patterns across multiple sections of ST data



## Tutorial

We have prepared several basic tutorials  in https://github.com/GYQ-form/RGAST/tree/main/tutorial. You can quickly hands on RGAST by going through these tutorials. Model parameters trained in our study are also released in https://github.com/GYQ-form/RGAST/tree/main/model_path.
