标题： 空间正则化高斯混合模型用于聚类空间转录组数据 显示英文标题

标题： Spatially Regularized Gaussian Mixtures for Clustering Spatial Transcriptomic Data

摘要： 空间转录组学在保留组织结构的同时测量组织样本中数千个基因的表达。 这一类技术使得研究基因表达的空间变异及其对特定生物过程的影响成为可能。 识别具有相似表达谱的基因至关重要，因此推动了利用空间数据结构来聚类基因的灵活方法的发展。 在此，我们提出了一种通过高斯过程对多个空间位置上测量的观测进行聚类的建模框架。 我们不将它们的协方差核指定为空间结构的函数，而是用它来指导它们精度矩阵的广义Cholesky分解。 这种方法可以避免核函数设定错误的问题，并有助于估计非平稳的空间协方差结构。 应用于空间转录组数据时，我们的模型能够识别出在包含不同细胞类型的组织区域（如肿瘤区域和间质区域）中具有独特空间相关性模式的基因簇。 显示英文摘要

摘要： Spatial transcriptomics measures the expression of thousands of genes in a tissue sample while preserving its spatial structure. This class of technologies has enabled the investigation of the spatial variation of gene expressions and their impact on specific biological processes. Identifying genes with similar expression profiles is of utmost importance, thus motivating the development of flexible methods leveraging spatial data structure to cluster genes. Here, we propose a modeling framework for clustering observations measured over numerous spatial locations via Gaussian processes. Rather than specifying their covariance kernels as a function of the spatial structure, we use it to inform a generalized Cholesky decomposition of their precision matrices. This approach prevents issues with kernel misspecification and facilitates the estimation of a non-stationarity spatial covariance structure. Applied to spatial transcriptomic data, our model identifies gene clusters with distinctive spatial correlation patterns across tissue areas comprising different cell types, like tumoral and stromal areas.