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Objective

To systematically evaluate the performance and scalability of leading GNN-based spatial transcriptomics (ST) models—STAGATE, GraphST, and SEDR—on high-density next-generation datasets such as Stereo-seq and Slide-seq, and to identify their limitations in modeling complex spatial structures.

Key Findings

• Performance Evaluation: Comprehensive assessment using ARI, AMI, NMI, and HOMO metrics revealed modest and variable clustering performance across high-density datasets.
• - SEDR achieved the highest scores on Slide-seq datasets, but overall accuracy remained low.
• - Stereo-seq results were inconsistent, with no single model consistently outperforming others.
• Scalability Limitation: Existing GNN-based ST models struggle to generalize to high-resolution data, indicating poor scalability to large, complex tissue structures.
• Benchmark Establishment: This study provides a performance baseline for evaluating future models on next-generation ST platforms.
• Research Implication: Highlights the urgent need for developing new computational frameworks that are more robust, adaptive, and scalable for analyzing dense spatial transcriptomics data.

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Objective

To overcome the limitation of fixed spatial neighborhood scales in existing spatial transcriptomics (ST) models by developing En-STAGATE, an ensemble framework that integrates multiple spatial graphs across varying neighborhood radii to capture both local and global tissue structures for improved domain identification.

Key Findings

• Multi-Scale Ensemble Design: En-STAGATE constructs and fuses representations from multiple spatial graphs at different neighborhood radii, effectively modeling both fine-grained cellular interactions and large-scale tissue organization.
• Enhanced Biological Interpretability: The multi-scale integration provides a more comprehensive and biologically meaningful latent representation of tissues.
• Empirical Validation: Achieved competitive or superior performance across 14 benchmark datasets, outperforming STAGATE and other leading methods in several key cases.
• Improved Domain Delineation: Notably higher Adjusted Rand Index (ARI) scores on datasets such as 151674, 151675, 151676, and Mouse, demonstrating robustness in complex tissue structures.
• Key Insight: Highlights that a single neighborhood scale is insufficient for spatial domain identification, establishing multi-scale analysis as a critical paradigm in spatial transcriptomics.

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Objective

To address the over-smoothing and spatial bias in current spatial transcriptomics (ST) models by developing GraphST++, a multi-modal framework that integrates spatial, gene expression, and histological features for constructing biologically faithful tissue graphs.

Key Findings

• Multi-Modal Graph Construction: GraphST++ defines inter-spot relationships using a composite score combining spatial distance, gene expression similarity, and histological morphology, reducing over-reliance on proximity-based connections.
• Improved Tissue Representation: The proposed approach captures complex biological interactions and produces a more accurate reflection of the tissue microenvironment.
• Superior Performance: Demonstrated higher Adjusted Rand Index (ARI) compared to leading models, including GraphST, across multiple benchmark datasets such as human breast cancer (BRCA) and human brain tissues.
• Robustness Across Datasets: While not universally dominant, GraphST++ consistently benefits from morphological integration, yielding more stable and biologically interpretable clustering results.
• Broader Implications: Highlights the importance of multi-modal fusion in advancing spatial transcriptomics analysis and enabling more precise tissue domain identification.

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Objective

To overcome the limitation of existing graph-based spatial transcriptomics (ST) models in capturing long-range spatial dependencies by developing STAGATE++, a hybrid GAT–Transformer architecture that jointly learns local and global tissue representations.

Key Findings

• Hybrid Architecture Advantage: STAGATE++ integrates a Graph Attention Network (GAT) for precise local feature extraction with a Transformer encoder for modeling long-range spatial dependencies.
• Enhanced Spatial Awareness: The model constructs a holistic and spatially coherent tissue representation, improving understanding of tissue organization.
• Empirical Superiority: Demonstrated state-of-the-art performance across 14 benchmark datasets, consistently outperforming existing methods in spatial domain identification.
• Validation Metric: Significant improvements in Adjusted Rand Index (ARI) confirm the importance of global context modeling in tissue delineation.
• Future Extensions: Plans include incorporating histopathological image features, applying self-supervised training for better generalization, and integrating GNN interpretability techniques for biological insight and clinical transparency.

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