PPubMed28 Apr 2022Cited 139×

SpatialExperiment: infrastructure for spatially-resolved transcriptomics data in R using Bioconductor

Dario Righelli, Lukas M Weber, Helena L Crowell, Brenda Pardo, Leonardo Collado-Torres, Shila Ghazanfar, Aaron T L Lun, Stephanie C Hicks, Davide Risso, Valentina Boeva

Abstract

Spatially-resolved transcriptomics (SRT) refers to a new set of high-throughput technologies, which measure up to transcriptome-wide gene expression along with the spatial coordinates of the measurements. Technological platforms differ in terms of the number of measured genes (from hundreds to full transcriptome) and spatial resolution (from multiple cells per coordinate to approximately single-cell to sub-cellular). Examples of SRT platforms include Spatial Transcriptomics (), 10x Genomics Visium (), Slide-seq (), Slide-seqV2 (), sci-Space (), seqFISH (; ), seqFISH+ (), osmFISH () and MERFISH

A figure from SpatialExperiment: infrastructure for spatially-resolved transcriptomics data in R using Bioconductor — fig. from the paper

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SpatialExperiment provides a Bioconductor class that standardizes storage and analysis of spatial transcriptomics data, enabling interoperable, modular workflows.

What’s new

i.Developed the SpatialExperiment class extending SingleCellExperiment for spot- and molecule-level spatial data
ii.Created companion packages STexampleData and TENxVisiumData with example datasets
iii.Implemented ggspavis for visualization of spatial transcriptomics data
iv.Integrated the infrastructure with Bioconductor principles of modularity, interoperability, and continuous testing
v.Provided documentation, tutorials, and testing to facilitate community adoption

How they did it

The authors designed a new S4 class within the Bioconductor framework that adds spatial coordinate slots to the existing SingleCellExperiment structure, implemented supporting packages for example data and visualization, and released the code with extensive documentation and continuous integration testing.

What they found

A new SpatialExperiment class is available on Bioconductor, extending SingleCellExperiment for spatial data
Example data packages (STexampleData, TENxVisiumData) and the ggspavis visualization tools were released alongside the class
The infrastructure enables seamless use of existing SingleCellExperiment‑based analysis tools on spatial datasets

Caveats

✕Initially supports only spot‑level or assigned‑molecule data, leaving unassigned molecules unrepresented
✕Tied to the R/Bioconductor ecosystem, limiting direct use from other programming environments
✕Adoption requires users to convert existing data to the new class, which may involve workflow changes

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