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“Everything is related to everything else. But near things are more related than distant things.” - Tobler’s first law of geography

This package brings the tradition of geospatial statistics to spatial omics by wrapping classical geospatial packages such as spdep and gstat to be used with the SpatialFeatureExperiment class, which extends SpatialExperiment with sf. This is the Voyager R documentation website. Documentation for the Python implementation is available here. Questions, suggestions, or problems should be submitted as GitHub issues.

Voyager is a package that facilitates exploratory spatial data analysis and visualization for spatial genomics data represented by SpatialFeatureExperiment objects.

Voyager and SpatialFeatureExperiment were developed within the Bioconductor ecosystem, and build on several existing objects and tools. Single cell RNA-seq data and metadata can be represented with SingleCellExperiment S4 class objects, and these can be utilized for exploratory data analysis and visualization using the scater, scran, or scuttle packages. The SpatialExperiment class extends SingleCellExperiments to allow for representation of spatial genomics data. SpatialFeatureExperiment extends SpatialExperiment with Simple Features from sf.

Thus, Voyager : SpatialFeatureExperiment is as scater / scran / scuttle : SingleCellExperiment. Voyager also builds on the geospatial tradition, especially the spdep package, which is one of the main R packages for spatial dependence analyses. Specifically, Voyager focuses on spatial autocorrelation, which measures the extent of similarity or dissimilarity of spatially proximal regions, and that can be quantified in terms of length scale, and variation in space.


SpatialFeatureExperiment and Voyager can be installed from Bioconductor version 3.16 or higher:

if (!requireNamespace("BiocManager")) install.packages("BiocManager")
BiocManager::install(version = "3.17") # Or a higher version in the future


Voyager: exploratory single-cell genomics data analysis with geospatial statistics Lambda Moses, Pétur Helgi Einarsson, Kayla Jackson, Laura Luebbert, A. Sina Booeshaghi, Sindri Antonsson, Páll Melsted, Lior Pachter bioRxiv 2023.07.20.549945; doi: