Scholarlink

<p>Understanding cell activities in their spatial context is crucial for revealing spatially influenced cellular states. While single-cell RNA-seq (scRNA-seq) provides comprehensive gene expression profiles, it lacks spatial resolution. Subcellular spatial transcriptomics (SST) captures spatial information but measures only limited genes. To overcome this problem, we present VISTA, a model that predicts unmeasured gene expression in SST data by integrating scRNA-seq and SST through variational inference and geometric deep learning with uncertainty quantification. Across four datasets, VISTA achieves superior imputation accuracy, scalability, and efficiency. Its accurate imputation supports diverse downstream analyses, including identifying spatially variable genes, uncovering ligand-receptor interactions, inferring spatial RNA velocity, performing in-silico perturbations, and disentangling spatial versus intrinsic expression variations. By bridging comprehensive molecular coverage with spatial precision, VISTA enhances the interpretability and utility of SST data, advancing our understanding of tissue organization and cellular microenvironments.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

VISTA uncovers missing gene expression and spatial-induced information for spatial transcriptomic data analysis

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年1月8日

查看全文
  • Tianyu Liu,
  • Yingxin Lin,
  • Xiao Luo,
  • Yizhou Sun,
  • Hongyu Zhao

摘要

Understanding cell activities in their spatial context is crucial for revealing spatially influenced cellular states. While single-cell RNA-seq (scRNA-seq) provides comprehensive gene expression profiles, it lacks spatial resolution. Subcellular spatial transcriptomics (SST) captures spatial information but measures only limited genes. To overcome this problem, we present VISTA, a model that predicts unmeasured gene expression in SST data by integrating scRNA-seq and SST through variational inference and geometric deep learning with uncertainty quantification. Across four datasets, VISTA achieves superior imputation accuracy, scalability, and efficiency. Its accurate imputation supports diverse downstream analyses, including identifying spatially variable genes, uncovering ligand-receptor interactions, inferring spatial RNA velocity, performing in-silico perturbations, and disentangling spatial versus intrinsic expression variations. By bridging comprehensive molecular coverage with spatial precision, VISTA enhances the interpretability and utility of SST data, advancing our understanding of tissue organization and cellular microenvironments.