DriverNet



DriverNet is a package to predict functional important driver genes in cancer by integrating genome data (mutation and copy number variation data) and transcriptome data (gene expression data). The different kinds of data are combined by an influence graph, which is a gene-gene interaction network deduced from pathway data. A greedy algorithm is used to find the possible driver genes, which may mutated in a larger number of patients and these mutations will push the gene expression values of the connected genes to some extreme values.

DriverNet has been tested on Mac OS version 10.6.8, Linux CentOS release 5.5 and Windows7 systems.

If you have any questions regarding the software, feel free to contact Jiarui Ding ( jiaruid at cs dot ubc dot ca)

Publications

A. Bashashati, G. Haffari, J. Ding, G. Ha, K. Lui, J. Rosner, D. Huntsman, C. Caldas, S. Aparicio, S. P. Shah. DriverNet: uncovering the impact of somatic driver mutations on transcriptional networks in cancer. Genome Biology 2012, 13:R124

Download

Request for Software Download here.

Installation

  1. Download DriverNet_1.0.0.tar.gz into the desired folder <$tmp_dir> , open a shell (in windows just open cmd) and type:
    1. cd <$tmp_dir>
    2. R CMD INSTALL DriverNet_1.0.0.tar.gz

Usage

First open R, in Linux and Mac, just type R in shell. In R, type the following commands:

library(DriverNet)

data(samplePatientMutationMatrix)
data(samplePatientOutlierMatrix)
data(sampleInfluenceGraph)
data(sampleGeneNames)

# The main function to compute drivers
driversList = computeDrivers(samplePatientMutationMatrix, samplePatientOutlierMatrix,
sampleInfluenceGraph, outputFolder=NULL, printToConsole=FALSE)

drivers(driversList)[1:10]

# random permute the gene labels to compute p-values
randomDriversResult = computeRandomizedResult(patMutMatrix=samplePatientMutationMatrix,
patOutMatrix=samplePatientOutlierMatrix, influenceGraph=sampleInfluenceGraph,
geneNameList= sampleGeneNames, outputFolder=NULL, printToConsole=FALSE,
numberOfRandomTests=20, weight=FALSE, purturbGraph=FALSE, purturbData=TRUE)

# Summarize the results
res = resultSummary(driversList, randomDriversResult, samplePatientMutationMatrix,
sampleInfluenceGraph, outputFolder=NULL, printToConsole=FALSE)

Reproduce the paper results

First download the data file paperData.tar.gz and decompress it to the desired folder.
Open R, and load the influence graph
load("influenceGraph.rda")
Then load one of the data file, e.g.,
load("GBM_data.rda")
and run
driversList = computeDrivers(patMutMatrix, patOutMatrix,
influenceGraph, outputFolder=NULL, printToConsole=FALSE)

will give you the rank list of genes.