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Statistical identification of cell type-specific spatially variable genes in spatial transcriptomics

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  • Lulu Shang

    (The University of Texas MD Anderson Cancer Center)

  • Peijun Wu

    (University of Michigan
    University of Michigan)

  • Xiang Zhou

    (University of Michigan
    University of Michigan)

Abstract

An essential task in spatial transcriptomics is identifying spatially variable genes (SVGs). Here, we present Celina, a statistical method for systematically detecting cell type-specific SVGs (ct-SVGs)—a subset of SVGs exhibiting distinct spatial expression patterns within specific cell types. Celina utilizes a spatially varying coefficient model to accurately capture each gene’s spatial expression pattern in relation to the distribution of cell types across tissue locations, ensuring effective type I error control and high power. Celina proves powerful compared to existing methods in single-cell resolution spatial transcriptomics and stands as the only effective solution for spot-resolution spatial transcriptomics. Applied to five real datasets, Celina uncovers ct-SVGs associated with tumor progression and patient survival in lung cancer, identifies metagenes with unique spatial patterns linked to cell proliferation and immune response in kidney cancer, and detects genes preferentially expressed near amyloid-β plaques in an Alzheimer’s model.

Suggested Citation

  • Lulu Shang & Peijun Wu & Xiang Zhou, 2025. "Statistical identification of cell type-specific spatially variable genes in spatial transcriptomics," Nature Communications, Nature, vol. 16(1), pages 1-20, December.
    • Handle: RePEc:nat:natcom:v:16:y:2025:i:1:d:10.1038_s41467-025-56280-4
    DOI: 10.1038/s41467-025-56280-4
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