Lixin Lei, Kaitai Han, Zijun Wang, Chaojing Shi, Zhenghui Wang, Ruoyan Dai, Zhiwei Zhang, Mengqiu Wang, Qianjin Guo
The latest breakthroughs in spatially resolved transcriptomics technology offer comprehensive opportunities to delve into gene expression patterns within the tissue microenvironment. However, the precise identification of spatial domains within tissues remains challenging. In this study, we introduce AttentionVGAE (AVGN), which integrates slice images, spatial information and raw gene expression while calibrating low-quality gene expression. By combining the variational graph autoencoder with multi-head attention blocks (MHA blocks), AVGN captures spatial relationships in tissue gene expression, adaptively focusing on key features and alleviating the need for prior knowledge of cluster numbers, thereby achieving superior clustering performance...
March 27, 2024: Briefings in Bioinformatics