Spatial localization of gene expression and histological images in spatial transcriptomics present many opportunities unavailable to non-spatial single cell RNA-seq (scRNA-seq). There has been decades of research in spatial data analysis prior to the rise of spatial transcriptomics, mostly in geographical space. Some existing data analysis packages for spatial transcriptomics have been inspired by this tradition, such as Moran’s I, Ripley’s K, and Gaussian process regression. To bring more of the geospatial tradition to spatial -omics, we developed SpatialFeatureExperiment (SFE), which extends the existing Bioconductor data structures SingleCellExperiment (SCE) and SpatialExperiment (SPE) with Simple Features to represent the geometries of Visium spots and cell segmentation and perform geometric operations. We developed the Voyager package that performs exploratory spatial data analysis (ESDA) on SFE objects. Currently Voyager utilizes univariate global and local geospatial statistics, such as Moran’s I, Geary’s C, Moran plot, correlogram, local Moran, Getis-Ord Gi*, and local spatial heteroscedasticity. Voyager also implements plotting functions for SFE objects and for the spatial analysis results. We plan to add bivariate and multivariate ESDA methods for the next Bioconductor release.

SFE package: https://bioconductor.org/packages/release/bioc/html/SpatialFeatureExperiment.html

Voyager package: https://bioconductor.org/packages/release/bioc/html/Voyager.html

SFEData package, with example datasets for Voyager in the SFE format from Visium, Slide-seq, CosMX, Xenium, and MERFISH: https://bioconductor.org/packages/release/data/experiment/html/SFEData.html

Voyager documentation and vignettes: https://pachterlab.github.io/voyager/index.html

Museum of Spatial Transcriptomics: https://pachterlab.github.io/LP_2021/index.html