A masked generative graph representation learning framework empowering precise spatial domain identification
Year: 2026
Type: article
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.
Source: Bioinformatics
Institutions: Jilin University, Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Chinese Academy of Sciences, Center for Excellence in Molecular Cell Science, University of Chinese Academy of Sciences, State Key Laboratory of Cell Biology, Tongji University, Shanghai First Maternity and Infant Hospital, Zhuhai College of Science and Technology, King Abdullah University of Science and Technology, University of Missouri
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: gold
Estimated APC paid: $3,989
OpenAlex ID: W7162299751