We developed the machine-learning based approach OncodriveROLE to classify cancer driver genes into to Activating or Loss of Function roles for cancer gene development. Here you can download the code of the method, and browse the results of applying OncodriveROLE to two recently published list of driver genes (HCDs and Cancer5000) in the respective tabs Plots, Gene classification and performance. You may adjust the cut-offs with the sliders to the left, download the results according to the selected cut-offs or directly download the classifier to use with your own data. For further information please refer to the manuscript.
For further information or citation please refer to the manuscript: OncodriveROLE classifies cancer driver genes in loss of function and activating mode of action. Michael P. Schroeder et al
The OncodriveROLE.RData classifier is an R object than can easily be loaded and applied on your own driver catalog and data set. The only dependencies are R itself and the R library randomForest.
1. Open an R session in order to load the randomForest library
library("randomForest")
2. Prepare the features data or download the example
feature data (HCD list)
and load it
testset <- read.delim("testset.txt")
choose the rownames you like better (sym or ensg)
rownames(testset) <- testset$sym
3. Download the classifier
OncodriveROLE.RData
, load and
use it as follows:
load("OncodriveROLE.RData")
result <- oncodriveROLE.classify(testset)
The result should look something like this:
prob.Act prob.LoF OncodriveROLE
CFH 0.918 0.082 Activating
MATK 0.8515 0.1485 Activating
CSDE1 0.2515 0.7485 Loss of Function
BRCA1 0.002 0.998 Loss of Function
HGF 0.4235 0.5765 No class
BCLAF1 0.9225 0.0775 Activating
RANBP3 0.0365 0.9635 Loss of Function
TRIO 0.9655 0.0345 Activating
CDH1 0.0035 0.9965 Loss of Function
The OncodriveROLE classifier has been constructed and applied based
on
TCGA pan-cancer 17
mutational and cna datasets including data from the following
TCGA projects:
BLCA, BRCA, COADREAD, GBM, HNSC, KIRC, LAML, LGG, LUAD, LUSC, OV, PRAD, SKCM, STAD, THCA, UCEC.
Here we provide the pre-computed features for the three classification
features used for classification. Be aware that applying the classifier
to passenger driver genes may yield
apparently
good classifications,
but are nothing else than false positives.
The feature data must contain the three following columns along with optional identifier columns:
MUTS_trunc_mutfreq MUTS_clusters_miss_VS_pam CNA_cbs_logratio_GvL 0.0580 1.079 1 0.0666 0 -1.17609 0.1739 0.7781 0.9542 0.2637 0.6020 -0.6989 0.1308 1.2430 0.6368 0.0763 1.1139 0.3521 0.1724 0.3010 -0.8450 0.0656 0.6020 1.7075 0.4107 0.1760 -1.3222
All ensembl identifiers are in version v70 of the ensembl database.
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