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

Wei Liu, Xu Liao, Ziye Luo, Yi Yang, Mai Chan Lau, Yuling Jiao, Xingjie Shi, Weiwei Zhai, Hongkai Ji, Joe Yeong and Jin Liu ()
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Wei Liu: Duke-NUS Medical School
Xu Liao: Duke-NUS Medical School
Ziye Luo: Duke-NUS Medical School
Yi Yang: Duke-NUS Medical School
Mai Chan Lau: Technology and Research (A*STAR)
Yuling Jiao: Wuhan University
Xingjie Shi: East China Normal University
Weiwei Zhai: Chinese Academy of Sciences
Hongkai Ji: Johns Hopkins Bloomberg School of Public Health
Joe Yeong: Technology and Research (A*STAR)
Jin Liu: Duke-NUS Medical School

Nature Communications, 2023, vol. 14, issue 1, 1-18

Abstract: Abstract 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.

Date: 2023
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DOI: 10.1038/s41467-023-35947-w

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