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HEARTSVG: a fast and accurate method for identifying spatially variable genes in large-scale spatial transcriptomics

Author

Listed:
  • Xin Yuan

    (Shanghai Jiao Tong University
    Shanghai Jiao Tong University)

  • Yanran Ma

    (Shanghai Jiao Tong University)

  • Ruitian Gao

    (Shanghai Jiao Tong University)

  • Shuya Cui

    (Shanghai Jiao Tong University
    Shanghai Jiao Tong University)

  • Yifan Wang

    (Shanghai Jiao Tong University)

  • Botao Fa

    (Xi’an Jiaotong University)

  • Shiyang Ma

    (Shanghai Jiao Tong University School of Medicine)

  • Ting Wei

    (Shanghai Jiao Tong University)

  • Shuangge Ma

    (Shanghai Jiao Tong University
    Yale University)

  • Zhangsheng Yu

    (Shanghai Jiao Tong University
    Shanghai Jiao Tong University
    Shanghai Jiao Tong University School of Medicine
    Shanghai Jiao Tong University School of Medicine)

Abstract

Identifying spatially variable genes (SVGs) is crucial for understanding the spatiotemporal characteristics of diseases and tissue structures, posing a distinctive challenge in spatial transcriptomics research. We propose HEARTSVG, a distribution-free, test-based method for fast and accurately identifying spatially variable genes in large-scale spatial transcriptomic data. Extensive simulations demonstrate that HEARTSVG outperforms state-of-the-art methods with higher $${F}_{1}$$ F 1 scores (average $${F}_{1}$$ F 1 Score=0.948), improved computational efficiency, scalability, and reduced false positives (FPs). Through analysis of twelve real datasets from various spatial transcriptomic technologies, HEARTSVG identifies a greater number of biologically significant SVGs (average AUC = 0.792) than other comparative methods without prespecifying spatial patterns. Furthermore, by clustering SVGs, we uncover two distinct tumor spatial domains characterized by unique spatial expression patterns, spatial-temporal locations, and biological functions in human colorectal cancer data, unraveling the complexity of tumors.

Suggested Citation

  • Xin Yuan & Yanran Ma & Ruitian Gao & Shuya Cui & Yifan Wang & Botao Fa & Shiyang Ma & Ting Wei & Shuangge Ma & Zhangsheng Yu, 2024. "HEARTSVG: a fast and accurate method for identifying spatially variable genes in large-scale spatial transcriptomics," Nature Communications, Nature, vol. 15(1), pages 1-14, December.
  • Handle: RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-49846-1
    DOI: 10.1038/s41467-024-49846-1
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    References listed on IDEAS

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