Probabilistic embedding, clustering, and alignment for integrating spatial transcriptomics data with PRECAST

Spatially resolved transcriptomics (SRT) 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 any consideration of spatial information. Thus, methods that are capable of integrating spatial transcriptomics data from multiple tissue slides, possibly from multiple individuals, are sincerely needed. Here, we present PRECAST, an efficient 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 spatial transcriptomics datasets from the 10x Visium, ST, and Slide-seqV2 platforms, we showed improved cell/domain detection with outstanding visualization, and the estimated aligned embeddings of cellular biological effects and cell/domain labels facilitate many downstream analyses. Furthermore, the slide-specific embeddings estimated by PRECAST can be utilized to study the spatial impact of neighboring microenvironments. PRECAST is computationally scalable and applicable to SRT datasets from different platforms.