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  3. Gatorduo: Global-consistency Dual-graph Refinement With Pseudo-label Agreement For Spatial Transcriptomics.
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  3. Gatorduo: Global-consistency Dual-graph Refinement With Pseudo-label Agreement For Spatial Transcriptomics.

Related Experiment Video

Comprehensive Spatial Profiling of Species-agnostic Transcriptomes via Stereo-seq
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GatorDuo: Global-Consistency Dual-Graph Refinement With Pseudo-Label Agreement for Spatial Transcriptomics.

Zhenhao Zhang1, Antonio Jimeno Yepes2, Jiang Bian3

  • 1College of Life Sciences, Northwest A&F University, Yangling, Shaanxi 712100 China.

Biorxiv : the Preprint Server for Biology
|May 25, 2026

View abstract on PubMed

Summary
This summary is machine-generated.

GatorDuo improves spatial transcriptomics analysis by using dual graphs to accurately identify tissue domains. This method refines neighborhoods, enhancing domain boundary clarity and cluster separability for robust biological insights.

Keywords:
Contrastive self-supervised learningMixture-of-Experts fusionReinforcement learningSpatial domain identificationSpatial transcriptomics

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Area of Science:

  • Computational Biology
  • Genomics
  • Bioinformatics

Background:

  • Spatial transcriptomics (ST) integrates gene expression with spatial data for tissue domain identification.
  • Current graph-based methods struggle with noisy ST data, leading to inaccurate neighborhood construction and blurred domain boundaries.
  • Spurious edges in static graphs propagate misleading signals, weakening cluster separability.

Purpose of the Study:

  • To introduce GatorDuo, a novel framework for robust spatial domain identification in ST data.
  • To enhance the accuracy of neighborhood construction and stabilize graph topology for improved representation learning.
  • To adaptively determine optimal clustering granularity and generate unified embeddings for downstream analysis.

Main Methods:

  • GatorDuo employs a topology-aware dual-graph contrastive self-supervised framework.
  • It utilizes global-consistency-based graph refinement with a pseudo-label agreement mask to suppress spurious edges.
  • A contextual bandit reinforcement-learning strategy adaptively selects clustering granularity, and a Mixture-of-Experts module integrates embeddings.

    • Main Results:

    • GatorDuo consistently achieves strong and robust spatial domain identification performance across eight diverse ST benchmarks.
    • The method outperforms ten representative baselines in multiple clustering metrics.
    • It successfully suppresses cross-domain shortcut edges, stabilizing neighborhood topology for reliable representation learning.

    Conclusions:

    • GatorDuo offers a robust and accurate solution for spatial domain identification in spatial transcriptomics.
    • The framework's adaptive clustering and unified embedding generation support comprehensive downstream biological analyses.
    • This approach enhances the reliability of ST data interpretation, particularly in the presence of sparsity and noise.