Description Usage Arguments Details Author(s) Examples
Discovery of candidate cancer genes by network-based integration of multi-omics data
1 | MinNetRank(Network = "AdjacencyMatrix", SNP = FALSE, TumorExpression = FALSE, NormalExpression = FALSE, CGC = KnownGenes, beta = 0.4841825)
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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 |
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.
Ting Wei <weitinging@sjtu.edu.cn>; Zhangsheng Yu Maintainer: Ting Wei <weitinging@sjtu.edu.cn>
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 | 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)
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