Microenvironmental Control of Hematopoietic Stem Cell Fate via CXCL8 and Protein Kinase C

This is an R project that will allow you to reproduce key figures from the article referenced above (PMID: 37209097). It must be used with the processed data package, pkc.cxcl8.datapkg (see below for instructions).

Steps to Reproduce Manuscript Figures

1. System Requirements

• R v4.2 or greater
• Rstudio
• This software has been tested on Linux Ubuntu 18.04.6 and Windows 10
• Loading the complete dataset occupies approximately 7 GB memory.

2. Installation

• download the package tarball to your system. Do not clone this repository since it does not contain the data. You have to get it from zenodo: https://zenodo.org/communities/blaserlab/
• clone the analysis project to your computer using git clone https://github.com/blaserlab/pkc_cxcl8.git
• open the R project by double-clicking on the pkc_cxcl8.Rproj file
• a list of the packages required for the project can be found in library_catalogs/blas02_pkc_cxcl8.tsv. Filter for packages with status == "active". Install these packages.
• install custom packages from our R Universe repository using these commands:
  • install.packages('blaseRtools', repos = c('<https://blaserlab.r-universe.dev>', '<https://cloud.r-project.org>'))
  • install.packages('blaseRtemplates', repos = c('<https://blaserlab.r-universe.dev>', '<https://cloud.r-project.org>'))
  • install.packages('blaseRdata', repos = c('<https://blaserlab.r-universe.dev>', '<https://cloud.r-project.org>'))
• edit and source R/dependencies.R
• typical time required for the first installation and data loading is approximately 15 minutes. This excludes the time required to download the data package.

3. Instructions for use after installing and configuring

• source R/dependencies.R
• source R/configs.R
• source R/make_all_figs.R. This will generate all computationally-derived figures in the manuscript.
• source R/supplemental_tables.R. This will generate all supplementary tables in the manuscript.
• open Rmd/stats.Rmd. Click on the knit dropdown menu and ensure knit directory is set to "Project Directory". Click "knit" to generate a detailed pdf statistics report to accompany the figures.
• If properly configured, these scripts should run to completion in 1-2 minutes.

4. Each computationally-generated figure panel is associated with processed data and code for visualization. Each processed data object has its own help manual and associated processing code within the data package. To access these resources do the following:

• find the variable name for the panel you wish to review in the appropriate figure composition file in R/figs/composition.
• search for that variable name in R/figs/staging
• find the original data object used to generate that panel in the code
• type ?data_object_name to get the help manual
• to review processing code, go to the installed location of pkc.cxcl8.datapkg on your system, enter the data-raw directory and run grep --include=*.R -rnw '.' -e "data_object_name"