Plot_Modules: Derive Network Modules.

In petraf01/iJRF: Integrative Joint Random Forest Network Models

Description

This function returns the list of modules.

Usage

Plot_Modules(net.final,genes.name)

Arguments

net.final	Network to plot.
genes.name	A vector containing gene names.

Value

Return list of modules.

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 sets
 p<-40                   # number of variables (genes/proteins)
 genes.name<-paste("G",seq(1,p),sep="")   # genes/proteins name
 
 data1<-matrix(rnorm(p*n1),p,n1)       # generate data1
 data2<-matrix(rnorm(p*n2),p,n1)       # generate data2

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

  # --- Degree plot
  final.net<-Plot_Modules(out.iJRFNet[sample(dim(out.iJRFNet)[1],200),c(1,2)]
               ,genes.name=genes.name)

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