Single-cell RNA-sequencing technologies may provide valuable insights to the understanding of the composition of different cell types and their functions within a tissue. Recent technologies such as spatial transcriptomics, enable the measurement of gene expressions at the single cell level along with the spatial locations of these cells in the tissue. Dimension-reduction and spatial clustering are two of the most common exploratory analysis strategies for spatial transcriptomic data. However, existing dimension reduction methods may lead to a loss of inherent dependency structure among genes at any spatial location in the tissue and hence do not provide insights of gene co-expression pattern. In spatial transcriptomics, the matrix-variate gene expression data, along with spatial co-ordinates of the single cells, provides information on both gene expression dependencies and cell spatial dependencies through its row and column covariances. In this work, we propose a flexible Bayesian approach to simultaneously estimate the row and column covariances for the matrix-variate spatial transcriptomic data. The posterior estimates of the row and column covariances provide data summaries for downstream exploratory analysis. We illustrate our method with simulations and two analyses of real data generated from a recent spatial transcriptomic platform. Our work elucidates gene co-expression networks as well as clear spatial clustering patterns of the cells.

翻译：单细胞RNA测序技术可为理解组织内不同细胞类型的组成及其功能提供重要见解。空间转录组学等新兴技术能够在单细胞水平上测量基因表达，同时保留这些细胞在组织中的空间位置信息。降维和空间聚类是空间转录组数据最常用的两种探索性分析策略。然而，现有降维方法可能导致组织内任意空间位置上基因间固有依存结构的丢失，因此无法揭示基因共表达模式。在空间转录组学中，矩阵型基因表达数据与单细胞空间坐标共同通过行列协方差矩阵提供了基因表达依赖性及细胞空间依赖性的信息。本研究提出一种灵活的贝叶斯方法，可同步估计矩阵型空间转录组数据的行协方差与列协方差。行列协方差的后验估计值可为下游探索性分析提供数据摘要。我们通过模拟实验及对近期空间转录组平台真实数据的两项分析验证了该方法。本工作既阐明了基因共表达网络，也揭示了细胞清晰的空间聚类模式。