CERES

Code repository for reproducing figures in the publication describing CERES.

Meyers, Bryan, et al. Computational correction of copy number effect improves specificity of CRISPR–Cas9 essentiality screens in cancer cells. Nature Genetics. 2017. Article

Installation

We recommend running this code on a machine with at least 16GB RAM.

Install the CERES R package and all dependencies

Follow all installation instructions for the CERES R package

Clone this repository

From the command line:

git clone https://github.com/cancerdatasci/ceres-manuscript

Open an R session from this directory

This can be done a variety of ways.

cd ceres-manuscript
R

or if you use RStudio

cd ceres-manuscript
open CERES_Manuscript.Rproj

From the R console, run the Manu_Setup.R script. This will install any missing packages and download data files from the figshare record. Follow the instructions when the program asks for figshare authentication.

source("./R/Manu_Setup.R")

Run CERES

source("./R/Man_RunCERES.R")

Prepare Data For Analysis

source("./R/Manu_CacheDatasets.R")

Run Analysis Files

As you may have noticed, this R project uses a directory structure and functions from the R package, ProjectTemplate. The following command runs each R file in the src/ directory. Each script roughly corresponds to one type of analysis in the paper, and can be run individually using source(). Many of the scripts take advantage of parallelization using the package doMC. The number of threads/cores used may be edited towards the top of each script which uses them. If you are using a machine with 8GB of RAM, we recommend only using a single core for all analyses. With 16GB of RAM, we regularly use 4-6 cores.

library(ProjectTemplate)
run.project()