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DescriptionAssociating multi-omic data for accurate prediction of driver mutations in cancer
Development Information
Current version0.0.1
LicenseGPL v3
Last updated2017/07/07
Citation Wang, Ng, Chen, et al. Cancer driver mutation prediction through Bayesian integration of multi-omic data, PLoS One. 2018; 13(5): e0196939. 
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Contact Ken Chen 


Identification of cancer driver mutations is critical for advancing cancer research and personalized medicine. Due to inter-tumor genetic heterogeneity, many driver mutations occur at low frequencies, which make it challenging to distinguish them from passenger mutations. Here, we show that a novel Bayesian hierarchical modeling approach, named RDriver can achieve enhanced prediction accuracy by identifying mutations that not only have high functional impact scores but also are associated with systemic variation in gene expression levels. In examining 3,080 tumor samples from 8 cancer types in The Cancer Genome Atlas, RDriver predicted 1,389 driver mutations. Compared with existing tools, RDriver identified more low frequency mutations associated with lineage specific functional properties, timing of occurrence and patient survival. Evaluation of RDriver predictions using engineered cell-line models resulted in a positive predictive value of 0.94. Our study highlights the importance of integrating multi-omic data in predicting cancer driver mutations and provides a statistically rigorous solution for cancer target discovery and development.

How to setup RDriver and obtain the results?

Download and unpack the rDriver package. Run matlab Load example file: rDriver_example_run.m

example_dataset.mat includes the input data:

x: mutation matrix (number of mutations by number of samples)
y: expression matrix (number of genes by number of samples)
mf: mutation feature cell array (number of mutations by 1; each cell has 2 functional impact scores)
params: a struct array containing the running parameters


rDriver Source Code (Matlab) Package

Example data are in whole or part based upon data generated by the TCGA Research Network: .


This software is for educational and research purposes only.
Before using TCGA data, please read TCGA guidelines for publication and moratoriums