SAGE-FM: A lightweight and interpretable spatial transcriptomics foundation model
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| Main Authors: | , , , , |
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| Format: | Preprint |
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2026
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| _version_ | 1866917216804405248 |
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| author | Zhan, Xianghao Xu, Jingyu Zheng, Yuanning Good, Zinaida Gevaert, Olivier |
| author_facet | Zhan, Xianghao Xu, Jingyu Zheng, Yuanning Good, Zinaida Gevaert, Olivier |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_15504 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | SAGE-FM: A lightweight and interpretable spatial transcriptomics foundation model Zhan, Xianghao Xu, Jingyu Zheng, Yuanning Good, Zinaida Gevaert, Olivier Machine Learning Genomics Quantitative Methods 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. |
| title | SAGE-FM: A lightweight and interpretable spatial transcriptomics foundation model |
| topic | Machine Learning Genomics Quantitative Methods |
| url | https://arxiv.org/abs/2601.15504 |