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Mining Spatial Transcriptomics Datasets using DeepSpaceDB
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A comparative study of statistical methods for identifying differentially expressed genes in spatial transcriptomics.

Yishan Wang1,2, Chenxuan Zang1, Ziyi Li1

  • 1Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, Texas, United States of America.

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|February 11, 2026
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Summary
This summary is machine-generated.

A new statistical method using Generalized Estimating Equations (GEE) improves spatial transcriptomics analysis by controlling false positives. The Independent GEE test offers more accurate gene expression identification in cancer research.

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Area of Science:

  • Genomics and Bioinformatics
  • Computational Biology
  • Cancer Research

Background:

  • Spatial transcriptomics (ST) enables gene expression analysis with spatial context, crucial for understanding tissue architecture, especially in cancers.
  • The popular Seurat tool for ST data analysis defaults to the Wilcoxon rank-sum test, which ignores spatial correlations.
  • Ignoring spatial correlations can lead to inflated false positive rates and unreliable findings in ST differential gene expression analysis.

Purpose of the Study:

  • To develop a robust statistical framework for differential gene expression analysis in spatial transcriptomics that accounts for spatial correlations.
  • To compare the performance of the proposed framework against existing methods, including the Wilcoxon rank-sum test and z-test.

Main Methods:

  • Proposed a Generalized Estimating Equations (GEE) framework for differential gene expression analysis in spatial transcriptomics.
  • Conducted extensive simulations to compare the GEE-based tests (specifically Independent GEE with robust standard error) against existing methods.
  • Applied the methods to real spatial transcriptomics datasets from breast and prostate cancer.

Main Results:

  • Simulations demonstrated that the Independent GEE test offers superior Type I error control and comparable power to other methods by accounting for spatial correlations.
  • Analysis of breast and prostate cancer ST datasets revealed poor p-value calibration and potential false positives with the Wilcoxon rank-sum test.
  • The Independent GEE test showed better performance in real data applications, suggesting more accurate identification of gene expression changes.

Conclusions:

  • The Independent GEE test provides a robust and accurate approach for differential gene expression analysis in spatial transcriptomics data.
  • This method effectively addresses the limitations of non-parametric tests like Wilcoxon by incorporating spatial correlations.
  • The proposed method, implemented in the R package "SpatialGEE", complements existing tools and enhances the reliability of ST data interpretation in cancer research.