1d:T797,# Compute permutation-based FDR of importance scores and return estimated regulations. This function computes permutation-based FDR of importance scores and returns gene-gene regulations. ```r iRafNet_network(out.iRafNet,out.perm,TH) ``` ## Arguments - `out.iRafNet`: Output object from function `iRafNet`. - `out.perm`: Output object from function `Run_permutation.` - `TH`: Threshold for FDR. ## Returns List of estimated regulations. ## Examples ```r # --- 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) ``` ## References Petralia, F., Song, W.M., Tu, Z. and Wang, P. (2016). New method for joint network analysis reveals common and different coexpression patterns among genes and proteins in breast cancer. **Journal of proteome research**, 15 (3), pp.743-754. A. Liaw and M. Wiener (2002). Classification and Regression by randomForest. **R News** 2 , 18--22. Xie, Y., Pan, W. and Khodursky, A.B., 2005. A note on using permutation-based false discovery rate estimates to compare different analysis methods for microarray data. **Bioinformatics**, 21 (23), pp.4280-4288.

iRafNet_network function