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Dimension-agnostic and granularity-based spatially variable gene identification using BSP

Author

Listed:
  • Juexin Wang

    (Computing, and Engineering, Indiana University Indianapolis
    University of Missouri)

  • Jinpu Li

    (University of Missouri
    University of Missouri)

  • Skyler T. Kramer

    (University of Missouri
    University of Missouri)

  • Li Su

    (University of Missouri
    University of Missouri)

  • Yuzhou Chang

    (The Ohio State University
    The James Comprehensive Cancer Center, The Ohio State University)

  • Chunhui Xu

    (University of Missouri
    University of Missouri)

  • Michael T. Eadon

    (Indiana University)

  • Krzysztof Kiryluk

    (Vagelos College of Physicians & Surgeons, Columbia University Irving Medical Center)

  • Qin Ma

    (The Ohio State University
    The James Comprehensive Cancer Center, The Ohio State University)

  • Dong Xu

    (University of Missouri
    University of Missouri
    University of Missouri)

Abstract

Identifying spatially variable genes (SVGs) is critical in linking molecular cell functions with tissue phenotypes. Spatially resolved transcriptomics captures cellular-level gene expression with corresponding spatial coordinates in two or three dimensions and can be used to infer SVGs effectively. However, current computational methods may not achieve reliable results and often cannot handle three-dimensional spatial transcriptomic data. Here we introduce BSP (big-small patch), a non-parametric model by comparing gene expression pattens at two spatial granularities to identify SVGs from two or three-dimensional spatial transcriptomics data in a fast and robust manner. This method has been extensively tested in simulations, demonstrating superior accuracy, robustness, and high efficiency. BSP is further validated by substantiated biological discoveries in cancer, neural science, rheumatoid arthritis, and kidney studies with various types of spatial transcriptomics technologies.

Suggested Citation

  • Juexin Wang & Jinpu Li & Skyler T. Kramer & Li Su & Yuzhou Chang & Chunhui Xu & Michael T. Eadon & Krzysztof Kiryluk & Qin Ma & Dong Xu, 2023. "Dimension-agnostic and granularity-based spatially variable gene identification using BSP," Nature Communications, Nature, vol. 14(1), pages 1-15, December.
    • Handle: RePEc:nat:natcom:v:14:y:2023:i:1:d:10.1038_s41467-023-43256-5
    DOI: 10.1038/s41467-023-43256-5
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    References listed on IDEAS

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