AI 中文总结
针对空间转录组学数据聚类方法的局限性，提出BayesClint贝叶斯方法同时进行因子分析和空间聚类，在多尺度联合聚类，还采用特征选择机制，通过模拟研究和实际应用展示了其优于其他方法的优势。
AI 中文摘要
空间转录组学的最新进展使研究人员能够在单细胞空间分辨率下分析基因表达，通常在一项研究中针对多个组织样本。每个细胞的这种高维分子图谱可用于将细胞分类为具有不同功能的细胞类型，或将组织分割为生物学相关的空间区域。尽管已开发出许多非空间和空间聚类方法来将这些细胞聚类为细胞类型或空间区域，但大多数有两个主要局限性：一是分别进行降维和聚类；二是在单一尺度上聚类细胞，而非将细胞类型和空间区域聚类视为两个不同尺度的不同任务。为克服这些局限性，我们提出BayesClint，一种贝叶斯方法，它在多个样本上同时进行因子分析和空间聚类，在单细胞和组织区域尺度联合进行聚类。为提高可解释性，我们在稀疏因子载荷矩阵估计中采用特征选择机制，检测区分细胞类型簇的活跃基因和差异表达基因。我们通过模拟研究和两个实际数据应用说明了该方法相对于其他现有先进方法的优势。
英文摘要
Recent advances in spatial transcriptomics have enabled researchers to profile gene expression at the single-cell spatial resolution, often for multiple tissue samples in a single study. This high-dimensional molecular profile for each cell can be used to sort cells into cell types with distinct functions, or segment the tissue into biologically relevant spatial domains. Although many non-spatial and spatial clustering methods have been developed to cluster these cells into cell types or spatial domains, most have two main limitations: first, they perform dimension reduction and clustering separately; second, they cluster cells at a single scale, rather than treating cell type and spatial domain clustering as distinct tasks at two different scales. To overcome these limitations, we propose BayesClint, a Bayesian method that simultaneously performs factor analysis and spatial clustering on multiple samples, where the clustering is done jointly at the single-cell and tissue regional scale. To increase interpretability, we employ a feature selection mechanism within the estimation of the sparse factor loadings matrix, which detects active genes and differentially expressed genes that discriminate between cell type clusters. We illustrate the advantages of the method over alternative state-of-the-art approaches through simulation studies and two real data applications.
Comments19 pages, 2 figures, 3 tables
