iRafNet_network {iRafNet}

Compute permutation-based FDR of importance scores and return estimated regulations.

Description

This function computes permutation-based FDR of importance scores and returns gene-gene regulations.

Usage

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.

Value

List of estimated regulations.

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.

Examples

  # --- 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)