Metrics Paper Figures

Code and data for generating data figures used in the main and supplemental sections of the manuscript "A framework for evaluating edited cell libraries created by massively parallel genome engineering"

Overview

• Quickstart
• Organization of contents

Quickstart

• Create the cell_lib_eval_paper conda environment: conda env create -f environment.yaml
• Activate conda environment: conda activate cell_lib_eval_paper
• Generate the figures used in the main section of the paper: R -e "rmarkdown::render('Rmd/main_figures.Rmd', output_file='../main_figures.html')"
• Generate the figures used in the supplemental section of the paper: R -e "rmarkdown::render('Rmd/supp_figures.Rmd', output_file='../supp_figures.html')"

Organization

• Inputs
  • README.md - this file
  • environment.yaml - definition of cell_lib_eval_paper conda environment required to generate figures
  • Rmd/ - Rmarkdown code used to generate figures
  • R/ - common R code used by the Rmarkdown files in Rmd/
  • Data/ - data used in the generation of figures
• Outputs
  • png/ - directory with individual png files of figures and figure sub-panels
  • main_figures.html - figures used in the main section of the paper
  • supp_figures.html - figures used in the supplemental section of the paper

Deeper Dive

Figure 4 from the supplemental section is generated based on simulated data. For convenience, the simulated data has been pre-generated and stored in Data/rdata/richness_sim.RData. If desired, reproduction of the simulated data is possible by following these additional steps.

• Install additional required packages, either via conda as described below, or from CRAN or with install.packages() in an R session
  • conda install -c conda-forge r-doparallel
  • conda install -c conda-forge r-foreach
• Run the simulation to create results in Data/rdata/richness_sim.RData. This will benefit from being run on a multi-core machine - on a system with 56 cores it took just under two hours.
  • R CMD BATCH --vanilla R/generate_sim.R