Skip to content

README.md

Latest commit

 

History

History
53 lines (43 loc) · 2.43 KB

README.md

File metadata and controls

53 lines (43 loc) · 2.43 KB

Overview {-}

License Lifecycle: stable

  • This repository contains RMarkdown documents to reproduce the results figures for a research publication. Each figure is self-contained with explicit inputs and library imports by using RMarkdown's knit and merge approach1.

  • Although beyond the scope of this reproducible code repository, some supplementary figures are created alongside the results figures where convenient. The workflow overview figures 1 and 4 are also beyond the scope because they do not involve code: figure 1 was created using BioRender.com and figure 4 was created using LaTeX.

  • Download the input data from DOI 10.5281/zenodo.151026212.

  • To recreate the figures code, if necessary, edit the config.R file in the R subdirectory with the path to your downloaded data directory, then run these R commands to install the dependencies and then build the RMarkdown book:

    ## Install the dependencies:
install.packages(c("BiocManager", "remotes"))
options(repos = BiocManager::repositories(), Ncpus = parallel::detectCores())
remotes::install_deps(dependencies = TRUE)

## Build the RMarkdown book:
bookdown::render_book()

    The above workflow has been tested on Ubuntu 24.10 GNU/Linux, Windows 11, and macOS 15.3.

  • An additional download contains the job submission scripts that generate the input data. However, these scripts are fairly specific to the compute clusters3 on which they were run and require over a quarter million compute hours to complete and are, therefore, intended as reference rather than being part of a practical, fully-reproducible workflow.

Footnotes

  1. Knit then merge (K-M) approach https://bookdown.org/yihui/bookdown/new-session.html ↩

  2. Simulation and processed data input for publication figures generated by the spatialmatch R package https://zenodo.org/records/15102621 ↩

  3. Purdue University's Anvil cluster (primarily), San Diego Supercomputer Center's Expanse cluster, and the University of Michigan's Lighthouse and Great Lakes clusters (these latter non-Purdue clusters were only used to split up running the 50,000 GranSim simulations). ↩