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Updated: Jun 16, 2025

Spatial Profiling of Protein and RNA Expression in Tissue: An Approach to Fine-Tune Virtual Microdissection
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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, TX 77030, USA.

Biorxiv : the Preprint Server for Biology
|March 3, 2025
PubMed
Summary
This summary is machine-generated.

A new Generalized Score Test (GST) improves spatial transcriptomics analysis by accounting for spatial correlations, reducing false positives in cancer research. This robust method enhances gene expression analysis accuracy for complex tissues.

Keywords:
GEEType I errorWilcoxon rank-sum testdifferential expressiongeneralized score testspatial transcriptomics

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Area of Science:

  • Genomics and Bioinformatics
  • Computational Biology
  • Cancer Research

Background:

  • Spatial transcriptomics (ST) offers insights into gene expression within tissue architecture, crucial for understanding cancers.
  • Current ST analysis tools like Seurat often use the Wilcoxon rank-sum test, which ignores spatial correlations.
  • Ignoring spatial correlations can lead to inflated false positive rates and inaccurate biological findings in ST data.

Purpose of the Study:

  • To develop and validate a robust statistical method for differential gene expression analysis in spatial transcriptomics.
  • To address the limitations of existing methods by incorporating spatial correlations.
  • To improve the accuracy of identifying biologically relevant gene expression changes in complex tissues like tumors.

Main Methods:

  • Proposed a Generalized Score Test (GST) within the Generalized Estimating Equations (GEEs) framework.
  • Compared GST against the Wilcoxon rank-sum test and GEEs with the robust Wald test.
  • Conducted simulations and applied the methods to breast and prostate cancer ST datasets.

Main Results:

  • The GST demonstrated superior Type I error control and comparable power to existing methods in simulations.
  • GST-identified differentially expressed genes in cancer datasets were enriched in cancer progression pathways.
  • The Wilcoxon test identified genes enriched in non-cancer pathways and produced significant false positives.

Conclusions:

  • The proposed GST approach is a robust and accurate method for differential gene expression analysis in spatial transcriptomics.
  • GST effectively accounts for spatial correlations, leading to more reliable identification of cancer-related gene expression changes.
  • The R package "SpatialGEE" implements the GST method for broader application in ST data analysis.