Derive_network: Compute permutation-based FDR of importance scores and return...

In petraf01/iJRF: Integrative Joint Random Forest Network Models

Description

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

Usage

Derive_network(out.iJRFNet,out.perm,TH)

Arguments

out.iJRFNet	Output from object of class iJRFNet.
out.perm	Output from object of class Run_permutation.
TH	Threshold for FDR.

Value

List of estimated interactions.

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
 nclasses=2               # number of data sets / classes
 n1<-n2<-20               # sample size for each data set
 p<-5                   # number of variables (genes/proteins)
 genes.name<-paste("G",seq(1,p),sep="")   # genes name

 data1<-matrix(rnorm(p*n1),p,n1)       # generate data1
 data2<-matrix(rnorm(p*n2),p,n1)       # generate data2

  # --- Run iJRFNet and obtain importance score of interactions
  out.iJRFNet<-iJRFNet(X=list(data1,data2),genes.name=genes.name,model="iJRF")

 # --- Obtain importance scores for M permuted data sets
  out.perm<-iJRFNet_permutation(X=list(data1,data2), ntree=1000,mtry=sqrt(5),
  genes.name=genes.name,M=5,model="iJRF")

  # --- Derive final networks
  final.net<-Derive_network(out.iJRFNet,out.perm,0.001)

petraf01/iJRF documentation built on Dec. 22, 2021, 7:46 a.m.