Spatially informed graph transformers for spatially resolved transcriptomics.

ABSTRACT: Spatially resolved transcriptomics (SRT) has emerged as a powerful technique for mapping gene expression landscapes within spatial contexts. However, significant challenges persist in effectively integrating gene expression with spatial information to elucidate the heterogeneity of biological tissues. Here, we present a Spatially informed Graph Transformers framework, SpaGT, which leverages both node and edge channels to model spatially aware graph representation for denoising gene expression and identifying spatial domains. Unlike conventional graph neural networks, which rely on static, localized convolutional aggregation, SpaGT employs a structure-reinforced self-attention mechanism that iteratively evolves topological structural information and transcriptional signal representation. By

SUBMITTER: Bao X 

PROVIDER: S-EPMC11972348 | biostudies-literature | 2025 Apr

REPOSITORIES: biostudies-literature