Spatial transcriptomics at subspot resolution with BayesSpace

Abstract

Recently developed spatial gene expression technologies such as the SpatialTranscriptomics 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 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 transcriptionally distinct tissues from spatial transcriptomics samples of the brain, of melanoma, and of squamous cell carcinoma. In particular, BayesSpace's improved resolution allows the identification of tissue structure that is not detectable at the original resolution and thus not recovered by other methods. Using an in silico dataset constructed from scRNA-seq, we demonstrate that BayesSpace can spatially resolve expression patterns to near single-cell resolution without the need for external single-cell sequencing data.In all, our results illustrate the utility BayesSpace has in facilitating the discovery of biological insights from a variety of spatial transcriptomics datasets.

Define and visualize pathological architectures of human tissues from spatially resolved transcriptomics using deep learning

“…1b and Supplementary Tables 1-2). Our results demonstrate RESEPT outperforms six existing tools, namely Seurat 5 , BayesSpace 7 , SpaGCN 9 , stLearn 8 , STUtility 12 , and Giotto 6 on tissue architecture identification in terms of Adjusted Rand Index (ARI) (Fig. 1c).…”

“…Recent advances in spatially resolved technologies such as 10x Genomics Visium provide spatial context together with high-throughput gene expression for exploring tissue domains, cell types, cell-cell communications, and their biological consequences 4 . Some graph-based clustering methods ( e.g ., Seurat 5 and Giotto 6 ), statistical methods ( e.g ., BayesSpace 7 ), or deep learning-based methods ( e.g. , stLearn 8 and SpaGCN 9 ) can identify spatial architecture and interpret spatial heterogeneity.…”

Maximizing the Utility of Transcriptomics Data in Inflammatory Skin Diseases

“…Spatial transcriptomics techniques can assay cells in their native tissue context, which enables spatial characterization of transcriptional activities. Multiple computational tools have been developed to analyze spatial transcriptomics data, such as Seurat, BayesSpace (193), Giotto (194), stLearn (195). Integrative analysis of scRNA-seq and spatial transcriptomics data will help to precisely decode intercellular communications in specific tissue locations.…”

An end-to-end workflow for multiplexed image processing and analysis

“…In contrast to stand-alone tools, the Bioconductor project offers interoperability among diverse analysis packages by relying on standardized data classes [22]. An example of such is the SingleCellExperiment class that supports general single-cell analyses including clustering of cells and dimensionality reduction [23][24][25], spatial clustering [26], and visualization of multiplexed imaging data [27]. Here, we present a modular and interoperable computational workflow to process and analyze multiplexed imaging data.…”