GATES: Graph Attention Network with Global Expression Fusion for Deciphering Spatial Transcriptome Architectures

TL;DR

GATES is a novel graph neural network model that integrates local and global spatial and gene expression information to improve the analysis of spatial transcriptomics data.

Contribution

GATES introduces a new approach combining local and global information fusion with adaptive attention for better spatial domain identification.

Findings

Outperforms existing methods in spatial domain detection

Effectively balances spatial proximity and gene expression similarity

Demonstrates robustness across multiple datasets

Abstract

Single-cell spatial transcriptomics (ST) offers a unique approach to measuring gene expression profiles and spatial cell locations simultaneously. However, most existing ST methods assume that cells in closer spatial proximity exhibit more similar gene expression patterns. Such assumption typically results in graph structures that prioritize local spatial information while overlooking global patterns, limiting the ability to fully capture the broader structural features of biological tissues. To overcome this limitation, we propose GATES (Graph Attention neTwork with global Expression fuSion), a novel model designed to capture structural details in spatial transcriptomics data. GATES first constructs an expression graph that integrates local and global information by leveraging both spatial proximity and gene expression similarity. The model then employs an autoencoder with adaptive attention to assign proper weights for neighboring nodes, enhancing its capability of feature extraction. By fusing features of both the spatial and expression graphs, GATES effectively balances spatial context with gene expression data. Experimental results across multiple datasets demonstrate that GATES significantly outperforms existing methods in identifying spatial domains, highlighting its potential for analyzing complex biological tissues. Our code can be accessed on GitHub at https://github.com/xiaoxiongtao/GATES.