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stHGC: a self-supervised graph representation learning for spatial domain recognition with hybrid graph and spatial

Runqing Wang1,2, Qiguo Dai1,2, Xiaodong Duan1,2

  • 1College of Computer Science and Engineering, Dalian Minzu University, 116600 Dalian, China.

Briefings in Bioinformatics
|December 22, 2024
PubMed
Summary
This summary is machine-generated.

We developed stHGC, a novel self-supervised graph learning framework for spatial domain identification in spatial transcriptomics (ST) data. stHGC accurately identifies tissue domains and improves downstream analyses.

Keywords:
hybrid neighbor graphself-supervised graph representation learningspatial domain identificationspatial regularizationspatial transcriptomics

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Area of Science:

  • Genomics
  • Computational Biology
  • Bioinformatics

Background:

  • Spatial transcriptomics (ST) technology provides gene expression data with spatial information, crucial for understanding tissue organization and cellular interactions.
  • Accurate identification of spatial domains is essential for interpreting ST data, but integrating spatial location and gene expression remains challenging.
  • Existing methods struggle with effectively leveraging both spatial and gene expression information for domain identification.

Purpose of the Study:

  • To propose a novel self-supervised graph representation learning framework, stHGC, for accurate spatial domain identification in ST data.
  • To address the challenge of integrating spatial location and high-dimensional gene expression data.
  • To enhance the understanding of tissue organization through improved spatial domain detection.

Main Methods:

  • Constructed a hybrid neighbor graph integrating spatial proximity and gene expression similarity metrics.
  • Developed a self-supervised graph representation learning framework utilizing graph attention for adjacent spots and distinct representations for non-neighboring spots.
  • Incorporated a spatial regularization constraint to preserve the structural information of spatial neighbors.

Main Results:

  • stHGC significantly outperforms state-of-the-art methods in identifying spatial domains across diverse ST datasets and resolutions.
  • The framework demonstrates superior performance in accurately delineating tissue structures.
  • stHGC proved beneficial for downstream tasks, including denoising and trajectory inference, highlighting its scalability.

Conclusions:

  • stHGC offers a powerful and scalable approach for spatial domain identification in spatial transcriptomics data.
  • The self-supervised graph representation learning framework effectively integrates spatial and gene expression information.
  • stHGC advancements contribute to a deeper understanding of tissue architecture and cellular heterogeneity.