Integrating Spatial Transcriptomics Data with PRECAST

 

OVERVIEW OF TECHNOLOGY ON OFFER

An efficient data integration method for multiple spatial transcriptomics datasets with complex batch effects and/or biological effects between slides.

BRIEF DESCRIPTION

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.

POTENTIAL APPLICATIONS

With the aligned low-dimensional embedding, multiple downstream analyses can be done i.e.: performing differential expression analysis to identify genes differentially expressed across various conditions and trajectory analyses to examine how cells differentiate among cell types.

• The follow-up analyses will provide clinicians with new tools to examine tumor pathogenesis such as how tumor cells develop in their spatial neighbors
• The analyses may also facilitate research in neurological disorders as well by jointly analyzing multiple SRT datasets either in different regions of interest or across multiple samples from different conditions

KEY BENEFIT

PRECAST can resolve aligned representations, provide outstanding visualizations, and achieve higher spatial clustering accuracy which outperforms existing data integration methods.

INVENTOR BIO

Liu Jin

CONTACT

Please email us for further enquiries: cted@duke-nus.edu.sg