Integrating Local and Global Information to Decipher Spatial Domains of Spatial Transcriptomics by Attention-based Graph Convolutional Network

Recent developments in spatial transcriptomics technologies have made it possible to obtain gene expression profiles while maintaining spatial context. Precisely identifying spatial domains is essential for downstream analysis, requiring the effective integration of gene expression profiles with spatial information. To overcome the challenge of low accuracy in spatial domain identification, this paper proposed a deep learning model called LGAGCN based on local and global information. It used graph convolutional network to learn the features of local and global views and employed an attention mechanism to integrate embeddings from different views. Moreover, experiments were conducted on the human dorsolateral prefrontal cortex (DLPFC) dataset and the human breast cancer (HBC) dataset to evaluate the effectiveness of the model. The experimental results showed that LGAGCN outperformed state-of-the-art methods in spatial clustering task.