stGuide advances label transfer in spatial transcriptomics through attention-based supervised graph representation learning

Yupeng Xu¹Hao Dai²Jinwang Feng³Keren Xu⁴Qiu Wang⁵Pingting Gao^6*Chunman Zuo^7*

• ¹School of Computer Science and Technology, Donghua University, Shanghai, China
• ²Key Laboratory of Systems Biology, Shanghai Institute of Biochemistry and Cell Biology, Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences, Shanghai, China
• ³College of Computer and Information Science, Chongqing Normal University, Chongqing, China
• ⁴Department of Colorectal Surgery, Fudan University Shanghai Cancer Center, Shanghai, Shanghai Municipality, China
• ⁵Guangdong Institute of Intelligence Science and Technology, Zhuhai, China
• ⁶Endoscopy Center and Endoscopy Research Institute, Fudan University, Shanghai, China
• ⁷Institute of Artificial Intelligence, Donghua University, Shanghai, China

The growing availability of spatial transcriptomics data offers key resources for annotating query datasets using reference datasets. However, batch effects, unbalanced reference annotations, and tissue heterogeneity pose significant challenges to alignment analysis. Here, we present stGuide, an attention-based supervised graph learning model designed for cross-slice alignment and efficient label transfer from reference to query datasets. stGuide leverages supervised representations guided by reference annotations to map query slices into a shared embedding space using an attention-based mechanism. It then assigns spot-level labels by incorporating information from the nearest neighbors in the learned representation. Using human dorsolateral prefrontal cortex and breast cancer datasets, stGuide demonstrates its capabilities by (i) producing category-guided, low-dimensional features with well-mixed slices; (ii) transferring labels effectively across heterogeneous tissues; and(iii) uncovering relationships between clusters. Comparisons with state-of-the-art methods demonstrate that stGuide consistently outperforms existing approaches, positioning it as a robust and versatile tool for spatial transcriptomics analysis.