Recent advances in spatially resolved transcriptomics have enabled comprehensive measurements of gene expression patterns while retaining spatial context of tissue microenvironment. Deciphering the spatial context of spots in a tissue needs to use their spatial information carefully. To this end, we developed a graph attention auto- encoder framework STGATE to accurately identify spatial domains by learning low-dimensional latent embeddings via integrating spatial information and gene

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... profiles. To better characterize the spatial similarity at the boundary of spatial domains, STGATE 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. We validated STGATE on diverse spatial transcriptomics datasets generated by different platforms with different spatial resolutions. STGATE could substantially improve the identification accuracy of spatial domains, and denoise the data while preserving spatial expression patterns. Importantly, STGATE could be extended to multiple consecutive sections for reducing batch effects between sections and extracting 3D expression domains from the reconstructed 3D tissue effectively.