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STANCE: a unified statistical model to detect cell-type-specific spatially variable genes in spatial transcriptomics

Author

Listed:
  • Haohao Su

    (Michigan State University)

  • Yuesong Wu

    (Michigan State University)

  • Bin Chen

    (Michigan State University
    Michigan State University
    Michigan State University)

  • Yuehua Cui

    (Michigan State University)

Abstract

One of the major challenges in spatial transcriptomics is to detect spatially variable genes (SVGs), whose expression patterns are non-random across tissue locations. Many SVGs correlate with cell type compositions, introducing the concept of cell type-specific SVGs (ctSVGs). Existing ctSVG detection methods treat cell type-specific spatial effects as fixed effects, leading to tissue spatial rotation-dependent results. Moreover, SVGs may exhibit random spatial patterns within cell types, meaning an SVG is not always a ctSVG, and vice versa, further complicating detection. We propose STANCE, a unified statistical model for both SVGs and ctSVGs detection under a linear mixed-effect model framework that integrates gene expression, spatial location, and cell type composition information. STANCE ensures tissue rotation-invariant results, with a two-stage approach: initial SVG/ctSVG detection followed by ctSVG-specific testing. We demonstrate its performance through extensive simulations and analyses of public datasets. Downstream analyses reveal STANCE’s potential in spatial transcriptomics analysis.

Suggested Citation

  • Haohao Su & Yuesong Wu & Bin Chen & Yuehua Cui, 2025. "STANCE: a unified statistical model to detect cell-type-specific spatially variable genes in spatial transcriptomics," Nature Communications, Nature, vol. 16(1), pages 1-17, December.
    • Handle: RePEc:nat:natcom:v:16:y:2025:i:1:d:10.1038_s41467-025-57117-w
    DOI: 10.1038/s41467-025-57117-w
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    References listed on IDEAS

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    1. Lulu Shang & Xiang Zhou, 2022. "Spatially aware dimension reduction for spatial transcriptomics," Nature Communications, Nature, vol. 13(1), pages 1-22, December.
    2. Guanao Yan & Shuo Harper Hua & Jingyi Jessica Li, 2025. "Categorization of 34 computational methods to detect spatially variable genes from spatially resolved transcriptomics data," Nature Communications, Nature, vol. 16(1), pages 1-21, December.
    3. Lawrence Hubert & Phipps Arabie, 1985. "Comparing partitions," Journal of Classification, Springer;The Classification Society, vol. 2(1), pages 193-218, December.
    4. Alma Andersson & Ludvig Larsson & Linnea Stenbeck & Fredrik Salmén & Anna Ehinger & Sunny Z. Wu & Ghamdan Al-Eryani & Daniel Roden & Alex Swarbrick & Åke Borg & Jonas Frisén & Camilla Engblom & Joakim, 2021. "Spatial deconvolution of HER2-positive breast cancer delineates tumor-associated cell type interactions," Nature Communications, Nature, vol. 12(1), pages 1-14, December.
    5. Brendan F. Miller & Feiyang Huang & Lyla Atta & Arpan Sahoo & Jean Fan, 2022. "Reference-free cell type deconvolution of multi-cellular pixel-resolution spatially resolved transcriptomics data," Nature Communications, Nature, vol. 13(1), pages 1-13, December.
    6. Dawei Liu & Xihong Lin & Debashis Ghosh, 2007. "Semiparametric Regression of Multidimensional Genetic Pathway Data: Least-Squares Kernel Machines and Linear Mixed Models," Biometrics, The International Biometric Society, vol. 63(4), pages 1079-1088, December.
    7. Lukas M. Weber & Arkajyoti Saha & Abhirup Datta & Kasper D. Hansen & Stephanie C. Hicks, 2023. "nnSVG for the scalable identification of spatially variable genes using nearest-neighbor Gaussian processes," Nature Communications, Nature, vol. 14(1), pages 1-12, December.
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