# --- Generate data sets
n<-20 # sample size
p<-5 # number of genes
genes.name<-paste("G",seq(1,p),sep="") # genes name
M=5; # number of permutations
data<-matrix(rnorm(p*n),n,p) # generate gene expression matrix
data[,1]<-data[,2] # var 1 and var 2 interact
W<-abs(matrix(rnorm(p*p),p,p)) # generate weights for regulatory relationships
# --- Standardize variables to mean 0 and variance 1
data <- (apply(data, 2, function(x) { (x - mean(x)) / sd(x) } ))
# --- Run iRafNet and obtain importance score of regulatory relationships
out.iRafNet<-iRafNet(data,W,mtry=round(sqrt(p-1)),ntree=1000,genes.name)
# --- Run iRafNet for M permuted data sets
out.perm<-Run_permutation(data,W,mtry=round(sqrt(p-1)),ntree=1000,genes.name,M)
# --- Derive final networks
final.net<-iRafNet_network(out.iRafNet,out.perm,0.001)
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