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Updated: Jun 13, 2026

Comprehensive Spatial Profiling of Species-agnostic Transcriptomes via Stereo-seq
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Spatial Gene Set Enrichment Analysis with Applications to Spatially Resolved Transcriptomic Data.

Zizhao Xie1, Yanghong Guo2, Qiwei Li2

  • 1Department of Biostatistics, Brown University.

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

Keywords:
Bayes factorBayesian hierarchical modelGene set enrichment analysisSpatially variable gene

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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.