BayesSpace enables the robust characterization of spatial gene expression architecture in tissue sections at increased resolution
Edward Zhao,Matthew R Stone,Raphael Gottardo Fred Hutchinson Cancer Research Center
Recently developed spatial gene expression technologies such as the Spatial Transcriptomics and Visium platforms allow for comprehensive measurement of transcriptomic profiles while retaining spatial context. However, existing methods for analyzing spatial gene expression data often do not efficiently leverage the spatial information and fail to address the limited resolution of the technology. Here, we introduce BayesSpace, a Bioconductor R package implementing a fully Bayesian statistical method for clustering analysis and resolution enhancement of spatial transcriptomics data that seamlessly integrates into current transcriptomics analysis workflows. We show that BayesSpace improves the identification of distinct intratissue transcriptional profiles from spatial transcriptomics samples of the brain, melanoma, invasive ductal carcinoma, and ovarian adenocarcinoma. We use immunohistochemistry and an in silico dataset constructed from scRNA-seq data to show that BayesSpace resolves tissue structure that is not detectable at the original resolution, while identifying transcriptional heterogeneity inaccessible to histological analysis. Overall, our results illustrate BayesSpace’s utility in facilitating the discovery of biological insights from spatial transcriptomics datasets.
Keywords: spatial transcriptomics,clustering,R