Improve spatial domain identification for spatial transcriptomics using high-order neighbor feature hybrid graph convolutional networks

Recent developments in spatial transcriptomics (ST) technologies have afforded us a profound understanding of gene expression patterns in the tissue microenvironment. Recently, several prominent spatial domain identification methods have been introduced to employ both spatial and expression information for precisely deciphering tissue structures. However, existing methods only focus on information from immediate neighbors, failing to capture the mixed relationships of neighbors at various scales and learn a general mixed feature from neighbors at different distances. To this end, we propose ST-HNHG, which fuses gene expression profiles, spatial information, and morphological images for deciphering spatial domains. Specifically, the high-order neighbor feature hybrid graph convolutional network (HNHGCN) is designed to capture feature representations between neighbors at different distances and learn their linear mixing. A data augmentation module is also proposed to enhance data diversity and model robustness. The attention mechanism is also introduced to integrate the embeddings learned from morphological and expression information, obtaining the latent representation for spatial domain identification. We test ST-HNHG on two ST datasets. The results indicate that ST-HNHG outperforms most existing methods, and considering the linear mixing between neighbors at various scales is beneficial for improving the accuracy of recognizing spatial domains.