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Graph Fourier transform for spatial omics representation and analyses of complex organs

Author

Listed:
  • Yuzhou Chang

    (Ohio State University
    The Ohio State University)

  • Jixin Liu

    (Shandong University)

  • Yi Jiang

    (Ohio State University)

  • Anjun Ma

    (Ohio State University
    The Ohio State University)

  • Yao Yu Yeo

    (Beth Israel Deaconess Medical Center
    Harvard Medical School)

  • Qi Guo

    (Ohio State University)

  • Megan McNutt

    (Ohio State University)

  • Jordan E. Krull

    (Ohio State University
    The Ohio State University)

  • Scott J. Rodig

    (Dana Farber Cancer Institute
    Brigham & Women’s Hospital)

  • Dan H. Barouch

    (Beth Israel Deaconess Medical Center
    Ragon Institute of MGH, MIT, and Harvard)

  • Garry P. Nolan

    (Stanford University School of Medicine)

  • Dong Xu

    (University of Missouri)

  • Sizun Jiang

    (Beth Israel Deaconess Medical Center
    Harvard Medical School
    Dana Farber Cancer Institute)

  • Zihai Li

    (The Ohio State University)

  • Bingqiang Liu

    (Shandong University)

  • Qin Ma

    (Ohio State University
    The Ohio State University)

Abstract

Spatial omics technologies decipher functional components of complex organs at cellular and subcellular resolutions. We introduce Spatial Graph Fourier Transform (SpaGFT) and apply graph signal processing to a wide range of spatial omics profiling platforms to generate their interpretable representations. This representation supports spatially variable gene identification and improves gene expression imputation, outperforming existing tools in analyzing human and mouse spatial transcriptomics data. SpaGFT can identify immunological regions for B cell maturation in human lymph nodes Visium data and characterize variations in secondary follicles using in-house human tonsil CODEX data. Furthermore, it can be integrated seamlessly into other machine learning frameworks, enhancing accuracy in spatial domain identification, cell type annotation, and subcellular feature inference by up to 40%. Notably, SpaGFT detects rare subcellular organelles, such as Cajal bodies and Set1/COMPASS complexes, in high-resolution spatial proteomics data. This approach provides an explainable graph representation method for exploring tissue biology and function.

Suggested Citation

  • Yuzhou Chang & Jixin Liu & Yi Jiang & Anjun Ma & Yao Yu Yeo & Qi Guo & Megan McNutt & Jordan E. Krull & Scott J. Rodig & Dan H. Barouch & Garry P. Nolan & Dong Xu & Sizun Jiang & Zihai Li & Bingqiang , 2024. "Graph Fourier transform for spatial omics representation and analyses of complex organs," Nature Communications, Nature, vol. 15(1), pages 1-22, December.
    • Handle: RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-51590-5
    DOI: 10.1038/s41467-024-51590-5
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    References listed on IDEAS

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