Attention-guided variational graph autoencoders reveal heterogeneity in spatial transcriptomics

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. Particularly, AVGN attempts to balance the model's attention focus on local and global structures by utilizing MHA blocks, an aspect that current graph neural networks have not extensively addressed. Benchmark testing demonstrates its significant efficacy in elucidating tissue anatomy and interpreting tumor heterogeneity, indicating its potential in advancing spatial transcriptomics research and understanding complex biological phenomena.PMID:38627939 | DOI:10.1093/bib/bbae173

Source: Briefings in Bioinformatics - April 17, 2024 Category: Bioinformatics Authors: Lixin Lei Kaitai Han Zijun Wang Chaojing Shi Zhenghui Wang Ruoyan Dai Zhiwei Zhang Mengqiu Wang Qianjin Guo Source Type: research