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<p>Spatial transcriptomics (ST) integrates spatial information into genomics, yet methods for generating spatially-informed gene representations are limited and computationally intensive. We present SIGEL, a cost-effective framework that derives gene manifolds from ST data by exploiting spatial genomic context. The resulting SIGEL-generated gene representations (SGRs) are context-aware, biologically meaningful, and robust across samples, making them highly effective for key downstream tasks, including imputing missing genes, detecting spatial expression patterns, identifying disease-related genes and interactions, and improving spatial clustering. Extensive experiments across diverse ST datasets validate SIGEL’s effectiveness and highlight its potential in advancing spatial genomics research.</p>

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SIGEL: a context-aware genomic representation learning framework for spatial genomics analysis

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年9月22日

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  • Wenlin Li,
  • Maocheng Zhu,
  • Yucheng Xu,
  • Mengqian Huang,
  • Ziyi Wang,
  • Jing Chen,
  • Hao Wu,
  • Xiaobo Sun

摘要

Spatial transcriptomics (ST) integrates spatial information into genomics, yet methods for generating spatially-informed gene representations are limited and computationally intensive. We present SIGEL, a cost-effective framework that derives gene manifolds from ST data by exploiting spatial genomic context. The resulting SIGEL-generated gene representations (SGRs) are context-aware, biologically meaningful, and robust across samples, making them highly effective for key downstream tasks, including imputing missing genes, detecting spatial expression patterns, identifying disease-related genes and interactions, and improving spatial clustering. Extensive experiments across diverse ST datasets validate SIGEL’s effectiveness and highlight its potential in advancing spatial genomics research.