ArticleBriefings in bioinformatics2024
Attention-guided variational graph autoencoders reveal heterogeneity in spatial transcriptomics.
Article in Briefings in bioinformatics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed, 17 citations in OpenAlex.
- SpaHNR: a spatial domain identification method via sparse attention-based hierarchical node representation and multi-view contrastive learning.BMC bioinformatics · 2026Article
- SpatialDG: a novel spatial domain identification method for spatially resolved transcriptomics data based on dual-graph neural network.Briefings in bioinformatics · 2026Article
- DuaST: an integrated deep learning framework for spatial transcriptomics with cross-branch interaction.Briefings in bioinformatics · 2026Article
- A multimodal vision knowledge graph of cardiovascular disease.Nature cardiovascular research · 2026Article
- Variational graph autoencoder for reconstructed transcriptomic data associated with NLRP3 mediated pyroptosis in periodontitis.Scientific reports · 2025Article
- Graph-Based 3-Dimensional Spatial Gene Neighborhood Networks of Single Cells in Gels and Tissues.BME frontiers · 2025Article
- Latent representation of H&E images retains biological information in a breast cancer cohort.PloS one · 2025Article
- Article
- Inferring single-cell resolution spatial gene expression via fusing spot-based spatial transcriptomics, location, and histology using GCN.Briefings in bioinformatics · 2024Article
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Authors and funding
9 authors at 1 institution in 1 country.
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Abstract
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. 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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.