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  3. Spatial Gene Set Enrichment Analysis With Applications To Spatially Resolved Transcriptomic Data.
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  3. Spatial Gene Set Enrichment Analysis With Applications To Spatially Resolved Transcriptomic Data.

Related Experiment Video

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Spatial Gene Set Enrichment Analysis with Applications to Spatially Resolved Transcriptomic Data.

Zizhao Xie, Yanghong Guo, Qiwei Li

    Biorxiv : the Preprint Server for Biology
    |June 12, 2026

    View abstract on PubMed

    Summary
    This summary is machine-generated.

    This study introduces spaGSE, a new Bayesian model for spatial pathway enrichment analysis in spatial transcriptomics. It enhances pathway detection by considering gene expression patterns within tissues, improving biological insights.

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    Area of Science:

    • Genomics
    • Computational Biology
    • Bioinformatics

    Background:

    • Spatially resolved transcriptomics reveals gene expression variations across tissues.
    • Spatially variable genes in the same pathway often share expression patterns, indicating coordinated biological functions.
    • Current gene set enrichment methods overlook spatial dependence, limiting pathway detection and interpretability.

    Purpose of the Study:

    • To develop a novel Bayesian hierarchical model, spaGSE, for spatial pathway enrichment analysis.
    • To integrate gene-level spatial expression statistics with pathway annotations.
    • To improve the detection and interpretability of spatially organized biological pathways.

    Main Methods:

    • Developed spaGSE, a Bayesian hierarchical model for spatial pathway enrichment analysis.
  • Modeled latent spatially variable gene signals using a Gaussian mixture framework.
  • Linked spatial variation to gene set membership via logistic regression with spike-and-slab priors.

    • Main Results:

    • spaGSE demonstrated scalability and superior power compared to existing methods in simulations and real data.
    • Maintained robust false positive rate control.
    • Identified biologically relevant pathways with coordinated spatial organization in cancer and developmental tissues.

    Conclusions:

    • spaGSE effectively incorporates spatial information for pathway-level inference in spatial transcriptomics.
    • The method enhances the discovery of spatially organized biological pathways.
    • spaGSE offers a powerful and interpretable approach for analyzing spatial transcriptomics data.