空间转录组学(spatial transcriptomics)在识别特定基因表达模式、发现新的细胞类型标志物,以及揭示细胞自组织和共协作方面发挥重要作用。本文系统分类并回顾了近年来基于人工智能理论和技术开发的空间转录组学数据分析方法,这些方法各有特点,适用于不同的研究场景。通过深入分析这些方法,本文提供一个全面的视角,以了解空间转录组学领域的前沿分析进展,并推动这些方法在生物医学研究中的应用,为解析复杂组织中细胞空间异质性和生态位提供工具支持。

Spatial transcriptomics plays a crucial role in identifying specific gene expression patterns, discovering novel cell type markers, and revealing cellular self-organization and cooperation. This article systematically classifies and reviews spatial transcriptomics data analysis methods developed in recent years based on artificial intelligence theories and techniques, each with distinct characteristics suited to different research scenarios. Through in-depth analysis of these methods, it offers a comprehensive perspective on understanding the cutting-edge analytical technologies in the field of spatial transcriptomics, promotes their application in biomedical research, and aids in exploring the spatial heterogeneity and ecological niches of cells within complex tissues.