BayeSMART: Bayesian clustering of multi-sample spatially resolved transcriptomics data.

IF 6.8 2区 生物学 Q1 BIOCHEMICAL RESEARCH METHODS
Yanghong Guo, Bencong Zhu, Chen Tang, Ruichen Rong, Ying Ma, Guanghua Xiao, Lin Xu, Qiwei Li
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引用次数: 0

Abstract

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.

BayeSMART:多样本空间解析转录组学数据的贝叶斯聚类。
空间分辨转录组学(SRT)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,极大地推动了我们对细胞微环境的了解。然而,目前还缺乏多样本空间聚类的方法,现有的方法主要依赖于单独的分子信息。本文介绍 BayeSMART,这是一种贝叶斯统计方法,旨在识别多个样本的空间域。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。人工智能集成使 BayeSMART 能够有效解释空间域。我们使用来自不同组织类型和 SRT 平台的四个数据集进行了案例研究,并将 BayeSMART 与其他多样本空间聚类方法和一些用于单样本 SRT 分析的最先进方法进行了比较,结果表明它在聚类准确性、可解释性和计算效率方面都超过了现有方法。BayeSMART 为多样本 SRT 数据中的细胞空间组织提供了新的见解。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Briefings in bioinformatics
Briefings in bioinformatics 生物-生化研究方法
CiteScore
13.20
自引率
13.70%
发文量
549
审稿时长
6 months
期刊介绍: Briefings in Bioinformatics is an international journal serving as a platform for researchers and educators in the life sciences. It also appeals to mathematicians, statisticians, and computer scientists applying their expertise to biological challenges. The journal focuses on reviews tailored for users of databases and analytical tools in contemporary genetics, molecular and systems biology. It stands out by offering practical assistance and guidance to non-specialists in computerized methodologies. Covering a wide range from introductory concepts to specific protocols and analyses, the papers address bacterial, plant, fungal, animal, and human data. The journal's detailed subject areas include genetic studies of phenotypes and genotypes, mapping, DNA sequencing, expression profiling, gene expression studies, microarrays, alignment methods, protein profiles and HMMs, lipids, metabolic and signaling pathways, structure determination and function prediction, phylogenetic studies, and education and training.
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