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SLGCA: spatial cross-level graph contrastive autoencoder for multislice spatial domain identification and

Xin Lu1, Murong Zhou2, Guohua Wang1,3

  • 1College of Computer and Control Engineering, Northeast Forestry University, Harbin 150040, China.

Briefings in Bioinformatics
|November 3, 2025
PubMed
Summary

Spatial domain identification in spatial transcriptomics (ST) is improved by SLGCA, a novel cross-level graph contrastive learning method. SLGCA accurately identifies spatial domains across multiple tissue sections, outperforming existing approaches.

Keywords:
graph contrastive learningmultislice spatial domain identificationspatial domain identificationspatial transcriptomics

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Area of Science:

  • Genomics
  • Computational Biology
  • Bioinformatics

Background:

  • Spatial transcriptomics (ST) reveals cellular spatial organization and heterogeneity.
  • Accurate spatial domain identification is vital for ST data analysis.
  • Existing methods often fail to fully integrate local and global spatial information.

Purpose of the Study:

  • To develop a novel method for enhanced spatial domain identification in ST data.
  • To address limitations of existing methods in exploiting spatial information and integrating multi-level features.
  • To improve the accuracy and robustness of spatial domain identification across multiple tissue sections.

Main Methods:

  • Proposed SLGCA, a novel method based on cross-level graph contrastive learning.
  • Implemented a dual-channel learning mechanism combining local and global information contrastive learning.
  • Enabled integration of multiple tissue sections without pre-alignment, eliminating batch effects.

Main Results:

  • SLGCA significantly outperforms benchmark methods in spatial domain identification accuracy.
  • Demonstrated accurate identification of spatial domains across multiple ST data techniques.
  • Enabled dissection of tumor heterogeneity in breast cancer and uncovered the liver cancer microenvironment.

Conclusions:

  • SLGCA offers a significant advancement in spatial domain identification for ST data.
  • The method accurately identifies spatial domains and reveals biological insights, such as tumor heterogeneity and distinct cell subtypes.
  • SLGCA provides a robust tool for analyzing complex spatial transcriptomic data.