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STAIG: Spatial transcriptomics analysis via image-aided graph contrastive learning for domain exploration and alignment-free integration

Author

Listed:
  • Yitao Yang

    (the University of Tokyo)

  • Yang Cui

    (the University of Tokyo)

  • Xin Zeng

    (the University of Tokyo)

  • Yubo Zhang

    (the University of Tokyo)

  • Martin Loza

    (the University of Tokyo)

  • Sung-Joon Park

    (the University of Tokyo)

  • Kenta Nakai

    (the University of Tokyo
    the University of Tokyo)

Abstract

Spatial transcriptomics is an essential application for investigating cellular structures and interactions and requires multimodal information to precisely study spatial domains. Here, we propose STAIG, a deep-learning model that integrates gene expression, spatial coordinates, and histological images using graph-contrastive learning coupled with high-performance feature extraction. STAIG can integrate tissue slices without prealignment and remove batch effects. Moreover, it is designed to accept data acquired from various platforms, with or without histological images. By performing extensive benchmarks, we demonstrate the capability of STAIG to recognize spatial regions with high precision and uncover new insights into tumor microenvironments, highlighting its promising potential in deciphering spatial biological intricates.

Suggested Citation

  • Yitao Yang & Yang Cui & Xin Zeng & Yubo Zhang & Martin Loza & Sung-Joon Park & Kenta Nakai, 2025. "STAIG: Spatial transcriptomics analysis via image-aided graph contrastive learning for domain exploration and alignment-free integration," Nature Communications, Nature, vol. 16(1), pages 1-15, December.
    • Handle: RePEc:nat:natcom:v:16:y:2025:i:1:d:10.1038_s41467-025-56276-0
    DOI: 10.1038/s41467-025-56276-0
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