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arXiv · 2601.15504

SAGE-FM: A lightweight and interpretable spatial transcriptomics foundation model

Xianghao Zhan·Jingyu Xu·Yuanning Zheng·Zinaida Good·Olivier Gevaert
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Abstract

Spatial transcriptomics enables spatial gene expression profiling, motivating computational models that capture spatially conditioned regulatory relationships. We introduce SAGE-FM, a lightweight spatial transcriptomics foundation model based on graph convolutional networks (GCNs) trained with a masked central spot prediction objective. Trained on 416 human Visium samples spanning 15 organs, SAGE-FM learns spatially coherent embeddings that robustly recover masked genes, with 91% of masked genes showing significant correlations (p < 0.05). The embeddings generated by SAGE-FM outperform MOFA and existing spatial transcriptomics methods in unsupervised clustering and preservation of biological heterogeneity. SAGE-FM generalizes to downstream tasks, enabling 81% accuracy in pathologist-defined spot annotation in oropharyngeal squamous cell carcinoma and improving glioblastoma subtype prediction relative to MOFA. In silico perturbation experiments further demonstrate that the model captures directional ligand-receptor and upstream-downstream regulatory effects consistent with ground truth. These results demonstrate that simple, parameter-efficient GCNs can serve as biologically interpretable and spatially aware foundation models for large-scale spatial transcriptomics.

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Xianghao Zhan, Jingyu Xu, Yuanning Zheng, Zinaida Good, Olivier Gevaert. 2026-01-21. SAGE-FM: A lightweight and interpretable spatial transcriptomics foundation model. https://arxiv.org/abs/2601.15504

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