Integrating multi-modal information to detect spatial domains of spatial transcriptomics by graph attention network

Abstract

Recent advances in spatially resolved transcriptomic technologies have enabled unprecedented opportunities to elucidate tissue architecture and function in situ. Spatial transcriptomics can provide multimodal and complementary information simultaneously, including gene expression profiles, spatial locations, and histology images. However, most existing methods have limitations in efficiently utilizing spatial information and matched high-resolution histology images. To fully leverage the multi-modal information, we propose a SPAtially embedded Deep Attentional graph Clustering (SpaDAC) method to identify spatial domains while reconstructing denoised gene expression profiles. This method can efficiently learn the low-dimensional embeddings for spatial transcriptomics data by constructing multi-view graph modules to capture both spatial location connectives and morphological connectives. Benchmark results demonstrate that SpaDAC outperforms other algorithms on several recent spatial transcriptomics datasets. SpaDAC is a valuable tool for spatial domain detection, facilitating the comprehension of tissue architecture and cellular microenvironment. The source code of SpaDAC is freely available at Github (https://github.com/huoyuying/SpaDAC.git).