ArticleBriefings in bioinformatics2026
An unsupervised method for spatial transcriptomics analysis based on adversarial autoencoder.
Article in Briefings in bioinformatics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Topology-guided dual-masked contrastive learning for weakly supervised cell-type annotation in single-cell multi-omics.Briefings in bioinformatics · 2026Article
- SRLST: a unified multimodal representation learning framework for spatial transcriptomics analysis.Bioinformatics (Oxford, England) · 2026Article
- Fractional Order Total Variation Low-Rank Representation on Single-Cell RNA Sequencing Clustering.IET systems biologyArticle
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Authors and funding
6 authors.
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Abstract
Spatial transcriptomics (ST) offers unprecedented opportunities to decode the spatial organization of gene expression, yet the inherent noise and complexity of ST data pose substantial challenges for accurate analysis. Here, we present DACN, a unified framework that integrates an improved adversarial autoencoder (AAE) with a graph convolutional network (GCN) to robustly analyze ST data across varying resolutions and throughputs. DACN employs a hybrid encoder that couples multi-head attention with residual connections to capture fine-grained local expression patterns while retaining critical global information. The hybrid encoder and generator jointly construct the AAE module, which denoises expression profiles and learns stable latent representations. The GCN component further exploits spatial neighborhood relationships to refine these embeddings. Across multiple ST datasets with varying resolutions, DACN consistently outperforms existing methods in accuracy and robustness. All code and datasets are publicly available at https://github.com/lanbiolab/DACN.
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