MinNetRank: MinNetRank

In weitinging/MinNetRank: An easy and efficient network-based method of integrating multi-omics data for cancer genes discovery

Description

Discovery of candidate cancer genes by network-based integration of multi-omics data

Usage

MinNetRank(Network = "AdjacencyMatrix", SNP = FALSE, TumorExpression = FALSE, NormalExpression = FALSE, CGC = KnownGenes, beta = 0.4841825)

Arguments

Network	the interaction network
SNP	the mutation matrix
TumorExpression	the tumor sample expresion
NormalExpression	the normal sample expresion
CGC	the known cancer genes
beta	the restart probability

Details

MinNetRank utilized minimum strategy to prioritize genes both the mutation relevance score and expression relevance score are high. MinNetRank was a single sample network diffusion approach that could detect personalized driver genes. MinNetRank combined the ranking of genes for individual samples into a robust population-level gene ranking.

Author(s)

Ting Wei <[e-mail]>; Zhangsheng Yu Maintainer: Ting Wei <[e-mail]>

Examples

library(MinNetRank)

#load the adjacency network
data("AdjacencyMatrix")

#load the known cancer genes
data("KnownGenes")

#load the mutation data
data("LihcMutation")

#load the tumor expression data
data("LihcTumorExpression")

#load the normal expression data
data("LihcNormalExpression")

#Using AdjacencyMatrix
##Using the mutation and expression data
Network = "AdjacencyMatrix"
LihcMinNetRank = MinNetRank(Network, SNP=LihcMutation, TumorExpression=LihcTumorExpression, NormalExpression=LihcNormalExpression, CGC=KnownGenes, beta = 0.4841825)
write.table(LihcMinNetRank, file='TCGA-LIHC.MinNetRank.Result.xls', quote =F, sep="\t", row.names = FALSE)

weitinging/MinNetRank documentation built on Dec. 29, 2019, 12:26 a.m.