A masked generative graph representation learning framework empowering precise spatial domain identification

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ID: 314919
2026
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Abstract
MOTIVATION: Spatial transcriptomics (ST) enables the measurement of gene expression while preserving the spatial context of tissues. However, the sparsity of ST data leads to poor usage of gene expression and spatial information, resulting in the embeddings that are not well represented and challenging for downstream analyses. RESULTS: Here, we introduced GSG, a generative self-supervised representation learning framework for ST data that leverages a masking mechanism to learn informative representations. For spatial domain identification, GSG consistently outperformed state-of-the-art methods across benchmarking datasets, regardless of sequencing platforms. In addition, we applied GSG to an in-house human fetal heart dataset, revealing anatomically coherent spatial domains and identifying APCDD1 as an endocardial-specific marker potentially involved in congenital heart disease. Our results showcase GSG's superiority and underscore its valuable contributions to advancing ST analysis. AVAILABILITY AND IMPLEMENTATION: Our software package is available at https://github.com/keaml-Guan/GSG.Supplementary data are available at Bioinformatics online.
Reference Key
openalex_W7162299751 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors C. Wang, Tongdong Zhang, Hang Sun, Z W Wu, Shuo Liang, Xueting Wang, Meirong Du, Yanchun Liang, Xin Gao, Qi Tang, Dong Xu, Xiaoyue Feng, An Zeng, Renchu Guan
Journal BMC Bioinformatics
Year 2026
DOI
10.1093/bioinformatics/btag333
URL
https://doi.org/10.1093/bioinformatics/btag333
Keywords Keywords not found

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