This supplement to the paper Museum of Spatial Transcriptomics and the associated database of spatial transcriptomics literature is inspired by museum catalogs that provide insight and detail to further understanding of the exhibits. The results presented are based on code that can be run interactively on RStudio Cloud. We present key analyses of metadata curated for the database, and provide further analyses and results beyond what could be included here in the more_analyses directory of this repository. The markdown that generates this text is on GitHub, and is version controlled so that its development can be tracked now and in the future. Please notify us of errors, omissions, or other suggestions via submission of issues on GitHub: To submit new entries to the database, please fill out this Google Form. If the text in some figures are too small to read, then right click on the figure to open in a new tab to zoom in.

0.1 Quick stats

As of 2024-02-15, this database contains:

  • 2101 current era publications, 1363 of which are for data collection and 809 are for data analysis (see Chapter 1 for definition of prequel and current eras)
  • 277 prequel era publications
  • Current era publications from 632 institutions1 in 352 cities in 41 countries
  • 651 current era data analysis software packages whose source code is available online

0.2 Running the code

This document is built with the bookdown package from a collection of R Markdown files. How some of figures look depends on parameters that can be changed, such as size of bins when binning number of publications in time to show a trend. The source code is on RStudio Cloud. The dependencies are pre-installed in the RStudio Cloud project. By default, when the database is queried by code, the most up to date version is used, which can be newer than the rendered static version on To build the document in RStudio Cloud, run this in the R console:

bookdown::render_book("index.Rmd", output_format = "bookdown::bs4_book")

If you are cloning this repo into a fresh RStudio Cloud project or a fresh machine, install the packages required to build the book as follows:

First install remotes with install.packages("remotes"). Then use remotes:install_deps(dependencies = TRUE) to install all required packages from CRAN, Bioconductor, and GitHub. So in short,

remotes::install_deps(dependencies = TRUE)

Because many packages are installed, the installation can be sped up with the argument Ncpus in install_deps() to specify the number of CPU cores to use to install packages in parallel, such as Ncpus = 2L for 2 cores. The free plan of RStudio Cloud only has 1 core, but this argument can be used when multiple cores are available.

By default, the most up to date version of the database is downloaded for analyses in this book. However, as the museumst R package written for these analyses contains a cached version of the database, historical versions of the database can be viewed by installing older versions of museumst and setting update = FALSE when calling museumst::read_metadata() when running code from this book on RStudio Cloud or your computer. Older versions of museumst can be installed with

remotes::install_github("pachterlab/museumst", ref = "v0.0.0.9016")

where ref refers to a release. Release history of museumst can be seen here. Documentation of museumst can be seen here.