Introduction

Genetic inteRaction and EssenTiality mApper (GRETTA) is an R package that leverages data generated by the Cancer Dependency Map (DepMap) project to perform in-silico genetic knockout screens and map essentiality networks. A manuscript describing this tool is available at bioinformatics (Takemon, Y. and Marra, MA., 2023).

The DepMap data used in this tutorial is version 22Q2. This version along with all versions provided in this repository were downloaded through the DepMap data portal, which was distributed and used under the terms and conditions of CC Attribution 4.0 license.

Maintainer

This repository is maintained by Yuka Takemon, research associate in Dr. Marco Marra’s laboratory at Canada’s Michael Smith Genome Sciences Centre.

Citations

When using GRETTA, please cite the manuscript describing GRETTA: Yuka Takemon, Marco A Marra, GRETTA: an R package for mapping in silico genetic interaction and essentiality networks, Bioinformatics, Volume 39, Issue 6, June 2023, btad381, https://doi.org/10.1093/bioinformatics/btad381

Please also cite the DepMap project and the appropriate data version found on https://depmap.org/portal/: Tsherniak A, Vazquez F, Montgomery PG, Weir BA, Kryukov G, Cowley GS, Gill S, Harrington WF, Pantel S, Krill-Burger JM, Meyers RM, Ali L, Goodale A, Lee Y, Jiang G, Hsiao J, Gerath WFJ, Howell S, Merkel E, Ghandi M, Garraway LA, Root DE, Golub TR, Boehm JS, Hahn WC. Defining a Cancer Dependency Map. Cell. 2017 Jul 27;170(3):564-576.

Questions

Please check the FAQ section for additional information and if you cannot find your answer there or have a request please submit an issue.

Requirements

GRETTA is supported and compatible for R versions >= 4.2.0.
12G of space to store one DepMap data set with and an additional 11G of temporary space to for .tar.gz prior to extraction.

Installation

Warning The new version of dbplyr (v2.4.0) is currently incompatable with another library used in GRETTA. If you encounter an error message like the one below. Please install the previous working version also shown below.

Error message:
Error in `collect()`:
! Failed to collect lazy table.
Caused by error in `db_collect()`:
! Arguments in `...` must be used.
✖ Problematic argument:
• ..1 = Inf
ℹ Did you misspell an argument name?
Solution:
install.packages("devtools")
devtools::install_version("dbplyr", version = "2.3.4")`

You can install the GRETTA package from GitHub with:

install.packages(c("devtools", "dplyr","forcats","ggplot2"))
devtools::install_github("ytakemon/GRETTA")

DepMap 22Q2 data and the data documentation files are provided above and can be extracted directly in terminal using the following bash code (not in R/RStudio). For other DepMap data versions please refer to the FAQ section.

# Make a new directory/folder called GRETTA_project and go into directory
mkdir GRETTA_project
cd GRETTA_project

# Download data from the web
wget https://www.bcgsc.ca/downloads/ytakemon/GRETTA/22Q2/GRETTA_DepMap_22Q2_data.tar.gz

# Extract data and data documentation
tar -zxvf GRETTA_DepMap_22Q2_data.tar.gz

A singularity container has also been provided and instructions can be found here.

Additional DepMap versions

In this example we use DepMap’s 2022 data release (22Q2). However, we also provide previous data released in 2020 (v20Q1) and 2021 (v21Q4), which are available at :https://www.bcgsc.ca/downloads/ytakemon/GRETTA/. We are hoping to make new data sets available as the are released by DepMap.

Workflows

Genetic interaction mapping

Install GRETTA and download accompanying data.
Select mutant cell lines that carry mutations in the gene of interest and control cell lines.
- (optional specifications) can be used to select cell lines based on disease type, disease subtype, or amino acid change.
Determine differential expression between mutant and control cell line groups.
- (optional but recommended).
Perform in silico genetic screen.
Visualize results.

Co-essential network mapping

Install GRETTA and download accompanying data.
Run correlation coefficient analysis.
- (optional specifications) can be used to perform analysis on cell lines of a specific disease type(s).
Calculate inflection points of negative/positive curve to determine a threshold.
Apply threshold.
Visualize results.

Example: Identifying ARID1A genetic interactions

ARID1A encodes a member of the chromatin remodeling SWItch/Sucrose Non-Fermentable (SWI/SNF) complex and is a frequently mutated gene in cancer. It is known that ARID1A and its homolog, ARID1B, are synthetic lethal to one another: The dual loss of ARID1A and its homolog, ARID1B, in a cell is lethal; however, the loss of either gene alone is not (Helming et al., 2014). This example will demonstrate how we can identify synthetic lethal interactors of ARID1A using GRETTA and predict this known interaction.

For this example you will need to call the following libraries. If you they are not installed yet use install.packages() (eg. install.packages("dplyr")).

# Load library
library(tidyverse)
#> ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
#> ✔ dplyr     1.1.4     ✔ readr     2.1.5
#> ✔ forcats   1.0.0     ✔ stringr   1.5.1
#> ✔ ggplot2   4.0.0     ✔ tibble    3.2.1
#> ✔ lubridate 1.9.3     ✔ tidyr     1.3.1
#> ✔ purrr     1.0.2     
#> ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
#> ✖ dplyr::filter() masks stats::filter()
#> ✖ dplyr::lag()    masks stats::lag()
#> ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors

library(GRETTA)
#> 
#>       _______ .______       _______ .___________.___________.    ___      
#>      /  _____||   _  \     |   ____||           |           |   /   \     
#>     |  |  __  |  |_)  |    |  |__   `---|  |----`---|  |----`  /  ^  \    
#>     |  | |_ | |      /     |   __|      |  |        |  |      /  /_\  \   
#>     |  |__| | |  |\  \----.|  |____     |  |        |  |     /  _____  \  
#>      \______| | _| `._____||_______|    |__|        |__|    /__/     \__\ 
#>     
#>     Welcome to GRETTA! The version loaded is: 4.0.0
#> The latest DepMap dataset accompanying this package is v24Q3. 
#> Please refer to our tutorial on GitHub for loading DepMap data and details: https://github.com/ytakemon/GRETTA

Download example data

A small data set has been created for this tutorial and can be downloaded using the following code.

path <- getwd()
download_example_data(path)
#> Warning in dir.create(paste0(path, "/GRETTA_example")):
#> '/projects/marralab/ytakemon_prj/DepMap/GRETTA/GRETTA_example' already exists
#> Warning in dir.create(paste0(path, "/GRETTA_example_output")):
#> '/projects/marralab/ytakemon_prj/DepMap/GRETTA/GRETTA_example_output' already
#> exists
#> Data saved to: /projects/marralab/ytakemon_prj/DepMap/GRETTA/GRETTA_example/

Then, assign variable that point to where the .rda files are stored and where result files should go.

gretta_data_dir <- paste0(path,"/GRETTA_example/")
gretta_output_dir <- paste0(path,"/GRETTA_example_output/")

Exploring cell lines

One way to explore cell lines that are available in DepMap is through their portal. However, there are some simple built-in methods in GRETTA to provide users with a way to glimpse the data using the series of list_available functions: list_mutations(), list_cancer_types(), list_cancer_subtypes()

Current DepMap data used by default is version 22Q2, which contains whole-genome sequencing or whole-exome sequencing annotations for 1771 cancer cell lines (1406 cell line

GRETTA

Install / Use

README