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.
|Author||Ali Bashashati, Reza Haffari, Jiarui Ding, Gavin Ha, Kenneth Liu, Jamie Rosner and Sohrab Shah|
|Date of publication||None|
|Maintainer||Jiarui Ding <email@example.com>|
actualEvents: Actual events covered by driver mutations
computeDrivers: Compute a list of driver mutations
computeRandomizedResult: Randomly compute lists of driver mutations
DriverNet-package: Drivernet: uncovering somatic driver mutations modulating...
DriverNetResult-class: Class '"DriverNetResult"'
drivers: List of driver mutations identified by DriverNet
getPatientOutlierMatrix: Compute the patient outlier matrix
preprocessMatrices: Remove unnecessary entries from matrices
resultSummary: Summarize result for drivers ranking.
sampleDriversList: Sample DriverNet result
sampleGeneNames: Sample gene names
sampleInfluenceGraph: Sample influence graph
samplePatientExpressionMatrix: Sample patient expression matrix
samplePatientMutationMatrix: Sample patient mutation matrix
samplePatientOutlierMatrix: Sample patient outlier matrix
sampleRandomDriversResult: Sample Result from computeRandomizedResult
totalEvents: Total number of events in the data