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August 20, 2025Open Access

HarveST: Heterogeneous Graph Learning Framework for Revealing Spatial Transcriptomics Patterns

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Authors

YZYanlin ZhangJFJunning FengTYTianwei Yu

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Overview

Heterogeneous graph model improves gene expression analysis in tissues, suggesting new pathways for spatial domain detection.

Key Points

  • HarveST reveals biologically meaningful spatial domains in various tissues, enhancing gene expression understanding.
  • It demonstrates superior performance in spatial transcriptomics, detecting spatially variable genes with precision.
  • The framework utilizes a dual learning approach for feature extraction and domain refinement in analysis.
  • Significantly advances spatial transcriptomics, merging topological and molecular data for deeper insights in studies.

Cite This Study

Zhang et al. (2025) studied this question.

synapsesocial.com/papers/68af3e49cf1dd9ea359eb62ahttps://doi.org/10.21203/rs.3.rs-7283360/v1
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Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1Heterogeneous graph contrastive learning for integration and alignment of spatial transcriptomics data2025
  2. 2FmH2ST: foundation model-based spatial transcriptomics generation from histological images2025
  3. 3Hypergraph Neural Networks Reveal Spatial Domains from Single-cell Transcriptomics Data2025
  4. 4M‐STGCN: A Position‐Aware Multimodal Graph Convolutional Framework for Joint Spatial Domain Identification and Gene Expression Denoising2026
  5. 5Reg2ST: recognizing potential patterns from gene expression for spatial transcriptomics prediction.2025