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Path-MGCN: a pathway activity-based multi-view graph convolutional network for determining spatial domains.

Qirui Zhou1, Chaowen Li1, Chao Chen1

  • 1School of Automation, Guangdong University of Technology, No. 100 Waihuan Xi Road, Guangzhou Higher Education Mega Center, Panyu District, Guangzhou 510006, China.

Briefings in Bioinformatics
|July 23, 2025
PubMed
Summary
This summary is machine-generated.

Path-MGCN integrates pathway information into spatial transcriptomics analysis, improving tissue domain identification. This method enhances biological interpretability and accurately maps molecular pathways for potential therapeutic strategies.

Keywords:
attention mechanismmulti-view graph convolutional networkpathwayspatial domainspatial transcriptomics

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Area of Science:

  • Genomics
  • Computational Biology
  • Bioinformatics

Background:

  • Spatial transcriptomics (ST) enables gene expression profiling with spatial context.
  • Current ST analysis methods often neglect pathway-level context and face data sparsity challenges.
  • Identifying spatial domains is crucial for understanding tissue architecture.

Purpose of the Study:

  • To develop an advanced computational framework, Path-MGCN, for spatial transcriptomics data analysis.
  • To integrate pathway information and address data sparsity in ST analysis.
  • To enhance the biological interpretability of spatial transcriptomics data.

Main Methods:

  • Developed Path-MGCN, a multi-view graph convolutional network (GCN) with an attention mechanism.
  • Calculated spot-level pathway activity scores using gene set variation analysis.
  • Constructed spatial and functional proximity graphs, utilizing a Zero-inflated negative binomial decoder.

Main Results:

  • Path-MGCN demonstrated superior accuracy and robustness across diverse ST datasets (human and mouse).
  • The method outperformed existing state-of-the-art approaches, maintaining performance with various pathway databases.
  • Identified Tertiary lymphoid structure-like regions and spatially resolved metabolic heterogeneity in breast cancer.

Conclusions:

  • Path-MGCN provides an accurate and interpretable framework for dissecting tissue heterogeneity using ST data.
  • The method enables detailed spatial mapping of molecular pathways, crucial for understanding tumor progression.
  • Highlights potential targeted therapeutic strategies for synergistic anti-tumor therapies.