| Title: | Drivernet: uncovering somatic driver mutations modulating transcriptional networks in cancer |
|---|---|
| Description: | 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. |
| Authors: | Ali Bashashati, Reza Haffari, Jiarui Ding, Gavin Ha, Kenneth Liu, Jamie Rosner and Sohrab Shah |
| Maintainer: | Jiarui Ding <[email protected]> |
| License: | GPL-3 |
| Version: | 1.47.0 |
| Built: | 2024-10-30 05:26:24 UTC |
| Source: | https://github.com/bioc/DriverNet |
Description: 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.
| Package: | DriverNet |
| Type: | Package |
| Version: | 1.0.0 |
| Date: | 2012-03-21 |
| License: | GPL-3 |
| LazyLoad: | yes |
| Depends: | methods |
Users would call preprocess_matrices on the three matrices to remove unnecessary data. The matrices returned are then be passed to other functions.
Ali Bashashati, Reza Haffari, Jiarui Ding, Gavin Ha, Kenneth Liu, Jamie Rosner and Sohrab Shah
Maintainer: Jiarui Ding <[email protected]>
Drivernet: uncovering somatic driver mutations modulating transcriptional networks in cancer, submitted.
data(samplePatientMutationMatrix) data(samplePatientOutlierMatrix) data(sampleInfluenceGraph) data(sampleGeneNames) driversList = computeDrivers(samplePatientMutationMatrix, samplePatientOutlierMatrix, sampleInfluenceGraph, outputFolder=NULL, printToConsole=FALSE) drivers(driversList)[1:10] randomDriversResult = computeRandomizedResult(patMutMatrix=samplePatientMutationMatrix, patOutMatrix=samplePatientOutlierMatrix, influenceGraph=sampleInfluenceGraph, geneNameList= sampleGeneNames, outputFolder=NULL, printToConsole=FALSE, numberOfRandomTests=20, weight=FALSE, purturbGraph=FALSE, purturbData=TRUE) res = resultSummary(driversList, randomDriversResult, samplePatientMutationMatrix, sampleInfluenceGraph, outputFolder=NULL, printToConsole=FALSE)data(samplePatientMutationMatrix) data(samplePatientOutlierMatrix) data(sampleInfluenceGraph) data(sampleGeneNames) driversList = computeDrivers(samplePatientMutationMatrix, samplePatientOutlierMatrix, sampleInfluenceGraph, outputFolder=NULL, printToConsole=FALSE) drivers(driversList)[1:10] randomDriversResult = computeRandomizedResult(patMutMatrix=samplePatientMutationMatrix, patOutMatrix=samplePatientOutlierMatrix, influenceGraph=sampleInfluenceGraph, geneNameList= sampleGeneNames, outputFolder=NULL, printToConsole=FALSE, numberOfRandomTests=20, weight=FALSE, purturbGraph=FALSE, purturbData=TRUE) res = resultSummary(driversList, randomDriversResult, samplePatientMutationMatrix, sampleInfluenceGraph, outputFolder=NULL, printToConsole=FALSE)
It gives a detailed list of events covered by the driver mutations selected by DriverNet.
actualEvents(x)actualEvents(x)
x |
computeDrivers
DriverNetResult-class
data(sampleDriversList) actualEvents(sampleDriversList)data(sampleDriversList) actualEvents(sampleDriversList)
Use a greedy algorithm to rank a list of driver mutations.
computeDrivers(patMutMatrix, patOutMatrix, influenceGraph, outputFolder = NULL, printToConsole = FALSE, weighted = FALSE)computeDrivers(patMutMatrix, patOutMatrix, influenceGraph, outputFolder = NULL, printToConsole = FALSE, weighted = FALSE)
patMutMatrix |
Patient Mutation Matrix |
patOutMatrix |
Patient Outlier Matrix |
influenceGraph |
Influence Graph Matrix |
outputFolder |
The folder to store the log. If set to NULL, no log files will be written. If set to "", the log will be written to the current folder. |
printToConsole |
If set to TRUE, progress and result of the function will be printed to the console. |
weighted |
Must be set to FALSE in this version. |
An object of DriverNetResult class that can be passed to the resultSummary method.
Ali Bashashati, Reza Haffari, Jiarui Ding, Gavin Ha, Kenneth Liu, Jamie Rosner and Sohrab Shah
Maintainer: Jiarui Ding <[email protected]>
data(samplePatientMutationMatrix) data(samplePatientOutlierMatrix) data(sampleInfluenceGraph) driversList = computeDrivers(samplePatientMutationMatrix, samplePatientOutlierMatrix, sampleInfluenceGraph, outputFolder=NULL, printToConsole=FALSE) drivers(driversList)[1:10]data(samplePatientMutationMatrix) data(samplePatientOutlierMatrix) data(sampleInfluenceGraph) driversList = computeDrivers(samplePatientMutationMatrix, samplePatientOutlierMatrix, sampleInfluenceGraph, outputFolder=NULL, printToConsole=FALSE) drivers(driversList)[1:10]
It renames the mutations in patMutMatrix with a randomized list of gene names and computes a list of driver mutations. It will repeat this process by numberOfRandomTests times. The result could be passed to the resultSummary method to calculate p-value for the driver mutations.
computeRandomizedResult(patMutMatrix, patOutMatrix, influenceGraph, geneNameList, outputFolder = NULL, printToConsole = FALSE, numberOfRandomTests = 500, weighted = FALSE, purturbGraph = FALSE, purturbData = TRUE)computeRandomizedResult(patMutMatrix, patOutMatrix, influenceGraph, geneNameList, outputFolder = NULL, printToConsole = FALSE, numberOfRandomTests = 500, weighted = FALSE, purturbGraph = FALSE, purturbData = TRUE)
patMutMatrix |
Patient Mutation Matrix |
patOutMatrix |
Patient Expression(Outlier) Matrix |
influenceGraph |
Influence Graph Matrix |
geneNameList |
A list of gene names that the new mutation names will be randomly chosen from. |
outputFolder |
The folder to store the log. If set to NULL, no log files will be written. If set to "", the log will be written to the current folder. |
printToConsole |
If set to TRUE, progress and result of the function will be printed to the console. |
numberOfRandomTests |
Number of lists of randomized driver mutations to be generated. |
weighted |
Must be set to FALSE in this version. |
purturbGraph |
Must be set to FALSE in this version. |
purturbData |
Must be set to TRUE in this version. |
A list of numeric vectors where each vector stores the number of events covered by the randomly identified drivers. The list can be passed to the resultSummary method to compute p-values.
Ali Bashashati, Reza Haffari, Jiarui Ding, Gavin Ha, Kenneth Liu, Jamie Rosner and Sohrab Shah
Maintainer: Jiarui Ding <[email protected]>
data(samplePatientMutationMatrix) data(samplePatientOutlierMatrix) data(sampleInfluenceGraph) data(sampleGeneNames) randomDriversResult = computeRandomizedResult(patMutMatrix=samplePatientMutationMatrix, patOutMatrix=samplePatientOutlierMatrix, influenceGraph=sampleInfluenceGraph, geneNameList=sampleGeneNames, outputFolder=NULL, printToConsole=FALSE, numberOfRandomTests=20, weight=FALSE, purturbGraph=FALSE, purturbData=TRUE)data(samplePatientMutationMatrix) data(samplePatientOutlierMatrix) data(sampleInfluenceGraph) data(sampleGeneNames) randomDriversResult = computeRandomizedResult(patMutMatrix=samplePatientMutationMatrix, patOutMatrix=samplePatientOutlierMatrix, influenceGraph=sampleInfluenceGraph, geneNameList=sampleGeneNames, outputFolder=NULL, printToConsole=FALSE, numberOfRandomTests=20, weight=FALSE, purturbGraph=FALSE, purturbData=TRUE)
"DriverNetResult"
A class storing the result from running the computeDrivers function. It contains the list of driver mutations found, the detailed events covered by these drivers and the total number of events in the test data.
Objects can be created by calls of the form new("DriverNetResult", drivers=...,
actualEvents=..., totalEvents=...).
drivers:Object of class "character" ~~
actualEvents:Object of class "list" ~~
totalEvents:Object of class "numeric" ~~
signature(x = "DriverNetResult"): ...
signature(x = "DriverNetResult"): ...
signature(x = "DriverNetResult"): ...
Ali Bashashati, Reza Haffari, Jiarui Ding, Gavin Ha, Kenneth Liu, Jamie Rosner and Sohrab Shah
Maintainer: Jiarui Ding <[email protected]>
data(sampleDriversList) drivers(sampleDriversList)[1:10]data(sampleDriversList) drivers(sampleDriversList)[1:10]
It gives the list of driver mutations identified by DriverNet ranked by the number of events that they cover.
drivers(x)drivers(x)
x |
computeDrivers
DriverNetResult-class
data(sampleDriversList) actualEvents(sampleDriversList)data(sampleDriversList) actualEvents(sampleDriversList)
Given a real-value patient expression matrix, compute the patient outlier matrix and remove the genes which are not expressed.
getPatientOutlierMatrix(patExpMatrix, th=2)getPatientOutlierMatrix(patExpMatrix, th=2)
patExpMatrix |
Patient Expression Matrix |
th |
The threshold, the default value is 2 ( the gene expression values outside the 2 standard deviation range are considered as outliers). |
The output patOutMatrix is an input to the computeDrivers method
Ali Bashashati, Reza Haffari, Jiarui Ding, Gavin Ha, Kenneth Liu, Jamie Rosner and Sohrab Shah
Maintainer: Jiarui Ding <[email protected]>
computeDrivers
data(samplePatientExpressionMatrix) samplePatientOutlierMatrix = getPatientOutlierMatrix(samplePatientExpressionMatrix)data(samplePatientExpressionMatrix) samplePatientOutlierMatrix = getPatientOutlierMatrix(samplePatientExpressionMatrix)
Remove patients (rows) which are not in both matrices from patMutMatrix and patOutMatrix. Remove mutations (columns) from patMutMatrix if they are not a row of influenceGraph. Remove expressions (columns) from patOutMatrix if they are not a column of influenceGraph.
preprocessMatrices(patMutMatrix, patOutMatrix, influenceGraph)preprocessMatrices(patMutMatrix, patOutMatrix, influenceGraph)
patMutMatrix |
Patient Mutation Matrix |
patOutMatrix |
Patient Outlier Matrix |
influenceGraph |
Influence Graph Matrix |
1 |
Reduced version of Patient Mutation Matrix |
2 |
Reduced version of Patient Expression(Outlier) Matrix |
3 |
Influence Graph Matrix |
The dimension of influenceGraph is not changed.
Ali Bashashati, Reza Haffari, Jiarui Ding, Gavin Ha, Kenneth Liu, Jamie Rosner and Sohrab Shah
Maintainer: Jiarui Ding <[email protected]>
data(samplePatientMutationMatrix) data(samplePatientOutlierMatrix) data(sampleInfluenceGraph) tmp = preprocessMatrices(samplePatientMutationMatrix, samplePatientOutlierMatrix, sampleInfluenceGraph) smallerPatientMutationMatrix = tmp[[1]] smallerPatientOutlierMatrix = tmp[[2]] influenceGraph = tmp[[3]]data(samplePatientMutationMatrix) data(samplePatientOutlierMatrix) data(sampleInfluenceGraph) tmp = preprocessMatrices(samplePatientMutationMatrix, samplePatientOutlierMatrix, sampleInfluenceGraph) smallerPatientMutationMatrix = tmp[[1]] smallerPatientOutlierMatrix = tmp[[2]] influenceGraph = tmp[[3]]
Ranks the drivers.
resultSummary(mainResult, randResult, patMutMatrix, influenceGraph, outputFolder = NULL, printToConsole = FALSE)resultSummary(mainResult, randResult, patMutMatrix, influenceGraph, outputFolder = NULL, printToConsole = FALSE)
mainResult |
An object of DriverNetResult class created by the computeDrivers method. |
randResult |
A list of vectors representing driver coverage generated by |
patMutMatrix |
Patient Mutation Matrix |
influenceGraph |
Influence Graph Matrix |
outputFolder |
The folder to store the summary file. If set to NULL, no files will be written. If set to "", the files will be written to the current folder. |
printToConsole |
If set to TRUE, progress and result of the function will be printed to the console. |
A matrix storing the summary result.
Ali Bashashati, Reza Haffari, Jiarui Ding, Gavin Ha, Kenneth Liu, Jamie Rosner and Sohrab Shah
Maintainer: Jiarui Ding <[email protected]>
data(sampleDriversList) data(sampleRandomDriversResult) data(samplePatientMutationMatrix) data(sampleInfluenceGraph) res = resultSummary(sampleDriversList, sampleRandomDriversResult, samplePatientMutationMatrix, sampleInfluenceGraph, outputFolder=NULL, printToConsole=FALSE)data(sampleDriversList) data(sampleRandomDriversResult) data(samplePatientMutationMatrix) data(sampleInfluenceGraph) res = resultSummary(sampleDriversList, sampleRandomDriversResult, samplePatientMutationMatrix, sampleInfluenceGraph, outputFolder=NULL, printToConsole=FALSE)
An object of DriverNetResult class that obtained from running the codecomputeDrivers function.
data(sampleDriversList)data(sampleDriversList)
Formal class ‘DriverNetResult’ with 3 slots (1) @ drivers: a character vector of the driver gene name; (2) @ actualEvents: the covered events in each mutated patient; (3) @totalEvents: the total number of outliers
computeDrivers
data(sampleDriversList) drivers(sampleDriversList)[1:10]data(sampleDriversList) drivers(sampleDriversList)[1:10]
A list of gene names used to generate permutation of names in computeRandomizedResult method.
data(sampleGeneNames)data(sampleGeneNames)
A factor of gene names
data(sampleGeneNames)data(sampleGeneNames)
A binary matrix representing influence graph between genes.
data(sampleInfluenceGraph)data(sampleInfluenceGraph)
A two-dimensional binary matrix. The row and column names are gene names. If two genes i and j are in the same pathway, sampleInfluenceGraph[i, j] = 1.
data(sampleInfluenceGraph)data(sampleInfluenceGraph)
A real-value matrix representing gene expressions in patients.
data(samplePatientExpressionMatrix)data(samplePatientExpressionMatrix)
A two-dimensional real value matrix. The row names are patients, and collumn names are genes. The entry samplePatientExpressionMatrix[i,j] is the gene expression values of gene j in patient i.
data(samplePatientExpressionMatrix)data(samplePatientExpressionMatrix)
A binary matrix representing gene mutations in patients.
data(samplePatientMutationMatrix)data(samplePatientMutationMatrix)
A two-dimensional binary matrix. The row names are patients, and collumn names are genes. If gene j is mutated in patient i, samplePatientMutationMatrix[i, j]=1.
data(samplePatientMutationMatrix)data(samplePatientMutationMatrix)
A binary matrix representing gene expressions in patients.
data(samplePatientOutlierMatrix)data(samplePatientOutlierMatrix)
A two-dimensional binary matrix. The row names are patients, and collumn names are genes. If genes j is an outlier in patient i, samplePatientOutlierMatrix[i, j] = 1.
data(samplePatientOutlierMatrix)data(samplePatientOutlierMatrix)
A sample result from running computeRandomizedResult with numberOfRandomTests = 20.
data(sampleRandomDriversResult)data(sampleRandomDriversResult)
A list of predicted drivers from different runs.
computeRandomizedResult
data(sampleRandomDriversResult)data(sampleRandomDriversResult)
Total number of events in the data
totalEvents(x)totalEvents(x)
x |
computeDrivers
DriverNetResult-class
data(sampleDriversList) totalEvents(sampleDriversList)data(sampleDriversList) totalEvents(sampleDriversList)