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Spatial-ZEDNet : a unified spatial transcriptomics framework for detecting differential gene activation and

Osafu Augustine Egbon1, Komlan Atitey1, Jiaqi Li1

  • 1Biostatistics and Computational Biology Branch, National Institute of Environmental Health Sciences, 111 T.W. Alexander Drive, Research Triangle Park, Durham, NC 27709, United States.

Briefings in Bioinformatics
|April 19, 2026
PubMed
Summary
This summary is machine-generated.

Spatial-ZEDNet accurately identifies changes in gene expression and activation across tissues, even with misaligned data and excess zeros. This method enhances our understanding of how exposures remodel tissues.

Keywords:
Bayesian hierarchical modelingGaussian Markov random fielddifferential gene activation and expressionhost-pathogen interactionspatial transcriptomicszero-inflated modeling

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Area of Science:

  • Genomics
  • Computational Biology
  • Systems Biology

Background:

  • Spatial transcriptomics enables gene expression analysis within tissue context.
  • Challenges include tissue misalignment, zero inflation in single-cell data, and ignored spatial dependencies.
  • Existing methods struggle to detect differential gene activation and account for spatial information.

Purpose of the Study:

  • To develop a novel framework, Spatial-ZEDNet, for robustly detecting spatially differentially expressed genes (DEGs) and differentially activated genes (DAGs).
  • To address limitations of existing methods by modeling zero inflation and spatial dependencies.
  • To improve the integration and interpretation of spatial transcriptomic data across different biological conditions and exposures.

Main Methods:

  • Introduced Spatial-ZEDNet, a hierarchical Gaussian random field framework.
  • Jointly models zero inflation and spatial dependencies for DEG and DAG detection.
  • Employs a novel alignment strategy that does not require spatial coordinate matching between conditions.

Main Results:

  • Spatial-ZEDNet demonstrates superior power and specificity compared to standard methods in simulations and real data.
  • The method robustly distinguishes DEGs from spatially variable genes.
  • Identified spatially localized immune gene expression and activation (e.g., Mmp7, Olr1, Ifitm3, Gbp3) in colitis and Plasmodium infection models, linking to inflammatory disease loci.

Conclusions:

  • Explicitly modeling excess zeros significantly improves the detection of spatially regulated gene activation states.
  • Spatial-ZEDNet offers a statistically rigorous and interpretable framework for analyzing spatial transcriptomic data under various exposures.
  • The framework advances mechanistic understanding of exposure-induced tissue remodeling and identifies coordinated tissue-specific responses.