Department of Mathematical Sciences, The University of Texas at Dallas, 800 W Campbell Rd, Richardson, TX 75080, United States.
Department of Statistics, The Chinese University of Hong Kong, Ma Liu Shui, Hong Kong, China.
Brief Bioinform. 2024 Sep 23;25(6). doi: 10.1093/bib/bbae524.
The field of spatially resolved transcriptomics (SRT) has greatly advanced our understanding of cellular microenvironments by integrating spatial information with molecular data collected from multiple tissue sections or individuals. However, methods for multi-sample spatial clustering are lacking, and existing methods primarily rely on molecular information alone. This paper introduces BayeSMART, a Bayesian statistical method designed to identify spatial domains across multiple samples. BayeSMART leverages artificial intelligence (AI)-reconstructed single-cell level information from the paired histology images of multi-sample SRT datasets while simultaneously considering the spatial context of gene expression. The AI integration enables BayeSMART to effectively interpret the spatial domains. We conducted case studies using four datasets from various tissue types and SRT platforms, and compared BayeSMART with alternative multi-sample spatial clustering approaches and a number of state-of-the-art methods for single-sample SRT analysis, demonstrating that it surpasses existing methods in terms of clustering accuracy, interpretability, and computational efficiency. BayeSMART offers new insights into the spatial organization of cells in multi-sample SRT data.
空间分辨转录组学（SRT）领域通过将空间信息与从多个组织切片或个体收集的分子数据相结合，极大地提高了我们对细胞微环境的理解。然而，多样本空间聚类的方法仍然缺乏，现有的方法主要依赖于单一的分子信息。本文介绍了 BayeSMART，这是一种贝叶斯统计方法，旨在识别多个样本中的空间域。BayeSMART 利用人工智能（AI）从多样本 SRT 数据集的配对组织学图像中重建单细胞水平信息，同时考虑基因表达的空间背景。AI 集成使 BayeSMART 能够有效地解释空间域。我们使用来自不同组织类型和 SRT 平台的四个数据集进行案例研究，并将 BayeSMART 与替代的多样本空间聚类方法以及许多用于单样本 SRT 分析的最新方法进行比较，结果表明它在聚类准确性、可解释性和计算效率方面优于现有方法。BayeSMART 为多样本 SRT 数据中细胞的空间组织提供了新的见解。
