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Updated: Jul 22, 2025

Spatial Profiling of Protein and RNA Expression in Tissue: An Approach to Fine-Tune Virtual Microdissection
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A Gene Correlation Measurement Method for Spatial Transcriptome Data Based on Partitioning and Distribution.

Xiaoshu Zhu1,2, Liyuan Pang2, Xiaojun Ding1

  • 1School of Computer Science and Engineering, Yulin Normal University, Yulin, China.

Journal of Computational Biology : a Journal of Computational Molecular Cell Biology
|July 20, 2023
PubMed
Summary
This summary is machine-generated.

A new method, STgcor, accurately measures gene correlations in spatial transcriptome (ST) data. This approach identifies novel gene modules and cancer pathways missed by traditional methods like Pearson and Spearman correlation coefficients.

Keywords:
distribution of vertexesgene co-expression networkgene correlation measurementgene module identificationspatial transcriptome technologyvertexes partitioning strategy

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Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Spatial transcriptome (ST) technology enables simultaneous acquisition of spatial location and transcriptional profiles.
  • Gene regulatory networks are crucial for understanding biological processes like cell communication.
  • Existing correlation methods (PCC, SPCC) are inadequate for noisy and sparse ST data.

Purpose of the Study:

  • To develop a novel gene correlation measurement method, STgcor, optimized for spatial transcriptome data.
  • To address the challenges of high noise and sparsity inherent in ST data.
  • To identify gene modules and biological pathways relevant to cancer.

Main Methods:

  • STgcor defines spots as vertices in a 2D plane, using Gaussian distribution to identify and remove outliers.
  • The method incorporates vertex distribution degree, trend, and location to measure gene pair correlation, overcoming sparsity.
  • STgcor was compared against PCC and SPCC using weighted coexpression network analysis on breast and prostate cancer ST datasets.

Main Results:

  • STgcor demonstrated superior performance in identifying gene modules compared to PCC and SPCC.
  • The method successfully detected unique gene modules and cancer-related pathways not identified by conventional methods.
  • Analysis of ST datasets from breast and prostate cancers highlighted STgcor's effectiveness.

Conclusions:

  • STgcor is a robust and effective method for gene correlation analysis in spatial transcriptomics.
  • This novel approach enhances the discovery of biologically significant gene modules and pathways in complex tissues.
  • STgcor offers a valuable tool for advancing cancer research and understanding spatial gene regulation.