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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 framework reveals spatial transcriptomics patterns in human tissue and tumors, suggesting improved domain identification.

Key Points

  • HarveST uncovers biologically meaningful spatial domains and associated marker genes, enhancing analysis in spatial transcriptomics.
  • In applications to human cortical tissue and tumors, HarveST exhibits superior gene detection capabilities across multiple platforms.
  • Analysis utilized a graph-based framework with self-supervised and partially supervised learning strategies for feature extraction.
  • This approach may enable new insights into tissue architecture and cellular interactions within normal and pathological conditions.

Cite This Study

Zhang et al. (2025) studied this question.

synapsesocial.com/papers/68af4cd8ad7bf08b1ead6444https://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. 1HarveST uses a heterogeneous graph learning framework to reveal spatial transcriptomics patterns2026
  2. 2Leveraging Spot–Gene Heterogeneous Graphs for Unified Spatially Resolved Transcriptomics Domain Detection on Single-Slice and Multi-Slice Data2026
  3. 3Heterogeneous graph contrastive learning for integration and alignment of spatial transcriptomics data2025
  4. 4SemanticST: A Scalable Multi‐Contextual Graph Learning Framework for Uncovering Spatial Niches and Robust Multi‐Sample Integration in Spatial Transcriptomics2026
  5. 5Accurately deciphering spatial domains for spatially resolved transcriptomics with stCluster2024 · 34 citations