Centre for Quantitative Medicine, Health Services & Systems Research, Duke-NUS Medical School, Singapore, Singapore.
School of Statistics, Renmin University, Beijing, China.
Nat Commun. 2023 Jan 18;14(1):296. doi: 10.1038/s41467-023-35947-w.
Spatially resolved transcriptomics involves a set of emerging technologies that enable the transcriptomic profiling of tissues with the physical location of expressions. Although a variety of methods have been developed for data integration, most of them are for single-cell RNA-seq datasets without consideration of spatial information. Thus, methods that can integrate spatial transcriptomics data from multiple tissue slides, possibly from multiple individuals, are needed. Here, we present PRECAST, a data integration method for multiple spatial transcriptomics datasets with complex batch effects and/or biological effects between slides. PRECAST unifies spatial factor analysis simultaneously with spatial clustering and embedding alignment, while requiring only partially shared cell/domain clusters across datasets. Using both simulated and four real datasets, we show improved cell/domain detection with outstanding visualization, and the estimated aligned embeddings and cell/domain labels facilitate many downstream analyses. We demonstrate that PRECAST is computationally scalable and applicable to spatial transcriptomics datasets from different platforms.
空间分辨转录组学涉及一系列新兴技术，能够对具有表达物理位置的组织进行转录组分析。尽管已经开发了多种用于数据集成的方法，但大多数方法都是针对单细胞 RNA-seq 数据集的，没有考虑空间信息。因此，需要能够整合来自多个组织切片（可能来自多个个体）的空间转录组学数据的方法。在这里，我们提出了 PRECAST，这是一种用于具有复杂批次效应和/或切片间生物学效应的多个空间转录组学数据集的数据集成方法。PRECAST 同时统一了空间因子分析与空间聚类和嵌入对齐，同时仅要求数据集之间部分共享细胞/区域聚类。使用模拟和四个真实数据集，我们展示了改进的细胞/区域检测和出色的可视化效果，并且估计的对齐嵌入和细胞/区域标签促进了许多下游分析。我们证明了 PRECAST 在计算上是可扩展的，并且适用于来自不同平台的空间转录组学数据集。
