README.md

In chenlabgccri/Prep4DeepDEP: Prep4DeepDEP: an R Package to Prepare Data for DeepDEP

Prep4DeepDEP: an R package to prepare data for DeepDEP

Introduction

Prep4DeepDEP is an R package to prepare the input and output data files required by the Python package of DeepDEP. DeepDEP is a deep learning model that uses the baseline genomic profiles of cancer cell lines (CCLs) or tumors to predict their gene dependencies (i.e., the degree to which knocking out a gene inhibits the survival of cancer cells).

Prep4DeepDEP generates the genomic and gene fingerprint data tables from user's datasets. Prep4DeepDEP has two main modes: - The ‘Prediction’ mode generates data for DeepDEP to predict gene dependency scores of unscreened CCLs or tumors. It extracts and orders the required genomic features from user’s genome-wide datasets, and generates the functional fingerprints of gene dependencies of interest (DepOIs) from a user-provided list or the default 1,298 genes we studied in the paper. For the copy number alteration (CNA) data, an embedded R function (PrepCNA) converts copy-number segments to bins (every 10k bases in the genome) and calculate per-bin CNA scores. - The ‘Training’ mode generates data to train a new DeepDEP model using user's genome-wide genomic data and gene dependency scores from an in-house CRISPR screening experiment. It creates data tables of genomics and gene dependencies for all CCL-DepOI pairs (number of samples = number of CCLs x number of DepOIs). Functional fingerprints are generated based on the list of genes available in the gene dependency dataset.

Please refer to the paper and DeepDEP package about how to use the generated data tables for DeepDEP model training and prediction.

Installation from GitHub

if (!requireNamespace("devtools", quietly = TRUE))
    install.packages("devtools")

devtools::install_github("ChenLabGCCRI/Prep4DeepDEP")

Run Prep4DeepDEP

Prep4DeepDEP <- function(exp.data = NULL, mut.data = NULL,
                         meth.data = NULL,cna.data  = NULL,
                         dep.data = NULL, mode = c("Training","Prediction"),
                         filename.out = "data_out")

Examples

Please see the examples in the example.r file:

path <- system.file("examples/",package = "Prep4DeepDEP")
file.edit(paste0(path,"example.r"))

Inputs/outputs

Inputs:

• Single or multi-omic data (gene mutation, gene expression, DNA methylation, and CNA) of CCLs or tumors. Dimension: #genomic features by #CCLs/tumors.
• List of DepOIs of interest with or without corresponding gene dependency data (training: required; prediction: optional). Dimension: #DepOIs by #CCLs if screening data are available for the training mode, or #DepOIs by 1 (gene symbols of DepOIs) for the prediction mode.

Outputs:

Prediction mode - .txt file for each genomic data. Dimension: #genomic features by #CCLs/tumors. - .txt file for gene fingerprints. Dimension: #fingerprint features (3,115 chemical and genetic perturbation [CGP] gene sets) by #DepOIs.

Training mode - .txt file for each genomic data. Dimension: #genomic features by #CCL-DepOI pairs. - .txt file for gene fingerprints. Dimension: #fingerprint features (3115) by #CCL-DepOIs.

Please refer to the manual of detailed descriptions of each input and output parameter:

?Prep4DeepDEP

Flowchart

Note: examples of the output data (paths labeled in blue) are available at the DeepDEP repository.

R Version

The functionality of this package was developed and tested using R version 3.6.1 (2019-07-05) on an x86_64-pc-linux-gnu platform.

Reference

Chiu YC, Zheng S, Wang LJ, Iskra BS, Rao MK, Houghton PJ, Huang Y, Chen Y. "Predicting and characterizing a cancer dependency map of tumors with deep learning." Science Advances. 2021;7(34). Epub 2021/08/22. doi: 10.1126/sciadv.abh1275.

chenlabgccri/Prep4DeepDEP documentation built on Sept. 3, 2021, 7:16 a.m.