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

Abstract

MotivationSpatially resolved transcriptomics is a new set of technologies to measure gene expression for up to thousands of genes at near-single-cell, single-cell, or sub-cellular resolution, together with the spatial positions of the measurements. Analyzing combined molecular and spatial information has generated new insights about biological processes that manifest in a spatial manner within tissues. However, to efficiently analyze these data, specialized data infrastructure is required, which facilitates storage, retrieval, subsetting, and interfacing with downstream tools.ResultsHere, we describe SpatialExperiment, a new data infrastructure for storing and accessing spatially resolved transcriptomics data, implemented within the Bioconductor framework in the R programming language. SpatialExperiment extends the existing SingleCellExperiment for single-cell data from the Bioconductor framework, which brings with it advantages of modularity, interoperability, standardized operations, and comprehensive documentation. We demonstrate the structure and user interface with examples from the 10x Genomics Visium and seqFISH platforms. SpatialExperiment is extendable to alternative technological platforms measuring expression and to new types of data modalities, such as spatial immunofluorescence or proteomics, in the future. We also provide access to example datasets and visualization tools in the STexampleData, TENxVisiumData, and ggspavis packages.Availability and ImplementationSpatialExperiment is freely available from Bioconductor at https://bioconductor.org/packages/SpatialExperiment. The STexampleData, TENxVisiumData, and ggspavis packages are available from GitHub and will be submitted to Bioconductor.

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

“…As part of the Bioconductor project [22] building upon the SingleCellExperiment [25] and Spatial-Experiment [33] data classes, the imcRtools package fully integrates with a variety of single-cell and spatial analysis approaches and tools. It furthermore standardizes analysis approaches that were previously developed for highly multiplexed imaging data [4,16,28,36,37] and therefore complements other spatial data analysis tools such as giotto [21] and spatial clustering approaches including BayesSpace [26] and lisaClust [43].…”

Squidpy: a scalable framework for spatial single cell analysis

“…Using these estimates, we calculate expected means ( µ ij ) and standard deviations ( σ ij ) for each pair, and a Z-score as, The Z-score indicates if a cluster pair is over-represented or over-depleted for node-node interactions in the connectivity graph. This approach was first described (to the best of our knowledge) by Schapiro et al 10 . The analysis and visualization can be performed with the analysis code showed below.…”

Advances in spatial transcriptomic data analysis

“…Most of these packages or toolboxes are developed in independent laboratories, which results in multiple different data structures that do not necessarily share the same data format. To overcome some of these challenges, the R/Bioconductor community is engaged in the careful design of generally applicable data structures has recently published the first version of the spatialExperiment class (Righelli et al 2021). This is a new S4 class that extends the popular singleCellExperiment class (Amezquita et al 2020) and is designed to operate with several types of ST data sets, including at both multi-and subcellular resolution.…”