roc_curve: Plot receiver operating characteristic (ROC) curve for weighted network generated by iRafNet

Description

This function uses R package ROCR to plot ROC curves for iRafNet object.

Usage

roc_curve(out, truth)

Arguments

out

Output from iRafNet.

truth

Matrix of true regulations. Rows correspond to different regulations and match rows of out. First column contains name of regulators, second column contains name of targets and third column contains a binary variable equal 1 in case of regulation and 0 otherwise.

Value

Plot ROC curve and return area under ROC curve.

References

Petralia, F., Wang, P., Yang, J., Tu, Z. (2015) Integrative random forest for gene regulatory network inference, Bioinformatics, 31, i197-i205.

Sing, Tobias, et al. (2005) ROCR: visualizing classifier performance in R, Bioinformatics, 21, 3940-3941.

Examples

Run this code


  # --- Generate data sets
  n<-20                  # sample size
  p<-5                   # number of genes
  genes.name<-paste("G",seq(1,p),sep="")   # genes name
  data<-matrix(rnorm(p*n),n,p)    # generate expression matrix
  data[,1]<-data[,2]              # var 1 and 2 interact
  W<-abs(matrix(rnorm(p*p),p,p))  # generate score 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(data,W,mtry=round(sqrt(p-1)),ntree=1000,genes.name)
  
  # --- Matrix of true regulations
  truth<-out[,seq(1,2)]
  truth<-cbind(as.character(truth[,1]),as.character(truth[,2])
  ,as.data.frame(rep(0,,dim(out)[1])));
  truth[(truth[,1]=="G2" & truth[,2]=="G1") | (truth[,1]=="G1" & truth[,2]=="G2"),3]<-1 

  # --- Plot ROC curve and compute AUC
  auc<-roc_curve(out,truth)

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