Current clustering analysis of spatial transcriptomics data primarily relies on molecular information and fails to fully exploit the morphological features present in histology images, leading to compromised accuracy and interpretability. To overcome these limitations, we have developed a multi-stage statistical method called iIMPACT. It identifies and defines histology-based spatial domains based on AI-reconstructed histology images and spatial context of gene expression measurements, and detects domain-specific differentially expressed genes. Through multiple case studies, we demonstrate iIMPACT outperforms existing methods in accuracy and interpretability and provides insights into the cellular spatial organization and landscape of functional genes within spatial transcriptomics data.

中文翻译：

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iIMPACT：集成图像和分子图谱以进行空间转录组学分析


目前空间转录组学数据的聚类分析主要依赖于分子信息，未能充分利用组织学图像中存在的形态学特征，导致准确性和可解释性受到影响。为了克服这些限制，我们开发了一种称为 iIMPACT 的多阶段统计方法。它根据 AI 重建的组织学图像和基因表达测量的空间背景来识别和定义基于组织学的空间域，并检测域特异性差异表达基因。通过多个案例研究，我们证明了 iIMPACT 在准确性和可解释性方面优于现有方法，并提供了对空间转录组学数据中功能基因的细胞空间组织和景观的见解。