R build status


BayesSpace provides tools for clustering and enhancing the resolution of spatial gene expression experiments.

BayesSpace clusters a low-dimensional representation of the gene expression matrix, incorporating a spatial prior to encourage neighboring spots to cluster together. The method can enhance the resolution of the low-dimensional representation into “sub-spots”, for which features such as gene expression or cell type composition can be imputed.

System requirements

Operating system

BayesSpace has been built and tested on the following operating systems:

  • Linux: Ubuntu 18.04.4 LTS (Bionic Beaver)
  • macOS: 10.14.6 (Mojave), 10.15.6 (Catalina)
  • Windows: 10, Server 2012 R2 Standard

Software dependencies

BayesSpace requires R 4.0 and Bioconductor 3.11. Specific package dependencies are defined in the package DESCRIPTION and are managed by the Bioconductor and devtools installers.


BayesSpace has been submitted to Bioconductor. Until its availability there, it can be installed with devtools:

# Install devtools if necessary
if (!requireNamespace("devtools", quietly = TRUE))


Installation, including compilation, should take no more than one minute.

Installing from source on macOS

Installing from source on macOS (such as when installing via devtools::install_github()) requires Fortran to compile the Rcpp code.

Download links for the appropriate macOS versions can be found here:

Additional details on installing the R compiler tools for Rcpp on macOS can be found in this blog post.

Note about homebrew: While gfortran is available via homebrew, we’ve encountered issues linking to its libraries after installation. We recommend installing directly from the GNU Fortran repo.


For an example of typical BayesSpace usage, please see our package vignette for a demonstration and overview of the functions included in BayesSpace.

Running the entire vignette takes approximately 5m30s on a Macbook Pro with a 2.0 GHz quad-core processor and 16 GB of RAM.