ArticleBriefings in bioinformatics2023
SGCAST: symmetric graph convolutional auto-encoder for scalable and accurate study of spatial transcriptomics.
Article in Briefings in bioinformatics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- SemanticST: A Scalable Multi-Contextual Graph Learning Framework for Uncovering Spatial Niches and Robust Multi-Sample Integration in Spatial Transcriptomics.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- A hybrid ST-ViT-driven multimodal architecture combining spatiotemporal MRI patterns and radiomic features for enhanced prediction of pCR in neoadjuvant breast cancer therapy.Biology direct · 2026Article
- CA-CAE: A deep learning-based multi-omics model for pan-cancer subtype classification and prognosis prediction.PLoS computational biology · 2026Article
- Deciphering spatial domains from spatially resolved transcriptomics through spatially regularized deep graph networks.BMC genomics · 2024Article
- MCGAE: unraveling tumor invasion through integrated multimodal spatial transcriptomics.Briefings in bioinformatics · 2024Article
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Authors and funding
3 authors.
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Abstract
Recent advances in spatial transcriptomics (ST) have enabled comprehensive profiling of gene expression with spatial information in the context of the tissue microenvironment. However, with the improvements in the resolution and scale of ST data, deciphering spatial domains precisely while ensuring efficiency and scalability is still challenging. Here, we develop SGCAST, an efficient auto-encoder framework to identify spatial domains. SGCAST adopts a symmetric graph convolutional auto-encoder to learn aggregated latent embeddings via integrating the gene expression similarity and the proximity of the spatial spots. This framework in SGCAST enables a mini-batch training strategy, which makes SGCAST memory-efficient and scalable to high-resolution spatial transcriptomic data with a large number of spots. SGCAST improves the overall accuracy of spatial domain identification on benchmarking data. We also validated the performance of SGCAST on ST datasets at various scales across multiple platforms. Our study illustrates the superior capacity of SGCAST on analyzing spatial transcriptomic data.
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