runFastHeinz.e: Heuristic algorithm to detect optimized subnetwork in...

In yuy113/wActNet: Detection of active subnetwork in metabolic weighted network

Heuristic algorithm to detect optimized subnetwork in correlation network

Description

Detect the optimized subnetwork in one network object based on combined node scores and edge scores in the netowrk by heuristic algorithm by maximized the weighted sum of node scores and edge scores in all possible subnetworks

Usage

runFastHeinz.e(network, node.scores, edge.scores, weightratio.edge.node = 1)

Arguments

network	An igraph or graphNEL object with nodes and edges with default node scores and node names, the edges with names and scores
node.scores	The vector of scores of the corresponding nodes in network the names of the node.scores are the names of the nodes in the network
edge.scores	The vector of scores of connecting edges of all the nodes in network the names of the edge.scores are the names of the edges in the network, which is the names of two connecting nodes in the network joined with _
weightratio.edge.node	The weight ratio of edges scores versus node scores with default value 1, reflecting the weights of the nodes and edges in the network at object function

Value

The optimized subnetwork in igraph format with attributes node and edge with both name and scores for nodes and also edges

Examples

dat1<-matrix(rnorm(20000),ncol=200,nrow=100)
colnames(dat1)<-paste("Var", as.character(1:200),sep="")

# simulate the p values for all the possible edges in the network
ind.pos.pval.edge<-rbinom(200*199/2,1,0.5)
pval.edge<-(1-ind.pos.pval.edge)*runif(200*199/2)+ind.pos.pval.edge*rbeta(200*199/2,0.1,1)
names(pval.edge)<-unlist(sapply(1:199,function(i){sapply((i+1):200, function(j){paste(paste("Var",
as.character(i),sep=""),paste("Var",as.character(j),sep=""),sep="_")})}))

# simulate p values for all the nodes in the network
ind.pos.pval.node<-rbinom(200,1,0.2)
pval.node<-(1-ind.pos.pval.node)*runif(200)+ind.pos.pval.node*rbeta(200,0.1,1)
names(pval.node)<-paste("Var", as.character(1:200),sep="")

# generate the node score-NodeScore, edge scores-EdgeScore and igraph object-Network
network.test<-uniform.beta.node.edge.score(pval.node,pval.edge,0.05,0.05,dat1)

network.test1<-network.test$Network
node.scores<-network.test$NodeScore
edge.scores<-network.test$EdgeScore

# detect the optimized subnetwork in simulated network
subnetwork.opt<-runFastHeinz.e(network.test1, node.scores,edge.scores,weightratio.edge.node=1)

yuy113/wActNet documentation built on Sept. 30, 2024, 5:50 a.m.