slidr

An R package for identifying synthetic lethal pairs from large-scale perturbation screens.

Data

The entire dataset used in the paper is big and cannot be stored on Github. The raw shRNA data has already been published as a part of project DRIVE (https://data.mendeley.com/datasets/y3ds55n88r/4 ) and all the mutation and copynumber data from CCLE are available at https://portals.broadinstitute.org/ccle. The MutSig 2CV v3.1 MAF file for each cancer type is available at http://firebrowse.org/. If you wish to use the processed data, please contact us and we'd be happy to share them. The code for processing the data, running both pan-cancer and cancer-type specific analyses, performing causal inference, and plotting the results are available under Scripts.

SLIdR usage

You can install SLIdR using devtools.

install.packages("devtools") 
library(devtools) 
install_github("cbg-ethz/slidr")

To run SLIdR, specify a path to store the results and use the identifySLHits function. An example dataset for liver cancer is available in the package under LiverData.

library(slidr)
library(dplyr)

data(LiverData)

# Path for results
path_results <- "~/Downloads/"
# Threshold for significance in WT cell lines
thresh <-  0.1

hits <- slidr::identifySLHits(canc_data = LiverData, 
                              path_results = path_results, 
                              WT_pval_thresh = thresh)
                      
# Filtering significant hits in WT cell lines
hits <- hits %>% 
        dplyr::filter(WT_pvalue >= thresh)

The resulting variable hits is a dataframe of all the SL pairs in liver cancer as reported in the paper. SLIdR creates two folders in the specified output directory:

• Hit_List - including .txt file with all the hits before filtering by WT_pval_thresh value
• Plots - Boxplots of all the significant SL pairs after filtering by WT_pval_thresh value

Contributions

Sumana Srivatsa
Hesam Montazeri

Contact

If you have any questions, please contact
Sumana Srivatsa