Deciphering spatial domains from spatially resolved transcriptomics with an adaptive graph attention auto-encoder

Recent advances in spatially resolved transcriptomics have enabled comprehensive measurements of gene expression patterns while retaining the spatial context of the tissue microenvironment. STAGATE could substantially improve the identification accuracy of spatial domains, and denoise the data while preserving spatial expression patterns.

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• 1 Deciphering the spatial context of spots in a tissue needs to use their spatial information carefully.
• 2 To this end, we develop a graph attention auto-encoder framework STAGATE to accurately identify spatial domains by learning low-dimensional latent embeddings via integrating spatial information and gene expression profiles.
• 3 To better characterize the spatial similarity at the boundary of spatial domains, STAGATE adopts an attention mechanism to adaptively learn the similarity of neighboring spots, and an optional cell type-aware module through integrating the pre-clustering of gene expressions.

This research advances how AI systems learn, reason, and solve problems — with direct implications for automation and scientific discovery.

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