Hubgenes_barplot: Degree plot of hubgenes.

In petraf01/iJRF: Integrative Joint Random Forest Network Models

Description

This function returns a degree plot for the top hub-genes in a given network.

Usage

Hubgenes_barplot(net.final,net1=NULL,net2=NULL,genes.name,
                name.net1=NULL,name.net2=NULL,num.hub=NULL)

Arguments

net.final	List object from function Derive_network. The cth element of the list contains estimated interaction for the cth class.
net1	Optional integer. The network whose hubgenes must be plot. This is basically the element of the list net.final that needs to be considered. If omitted, it is set to 1.
net2	Optional integer. This is a network used for comparison and is the element of the list net.final that needs to be considered for comparison. When net2 is not omitted, the plot shows the degree of hubgenes in net1 indicating for each of them how many edges are shared with net2 and how many edges are net1- and net2- specific.
genes.name	A vector containing gene names.
name.net1	Optional string containing the name of net1.
name.net2	Optional string containing the name of net2.
num.hub	Number of top hubgenes the function will plot.

Value

Degree plot of top num.hub hub-genes in net1. When net2 is not omitted, for each hub-gene in net1, the plot will show the number of connecting edges shared with net2 and the number of connecting edges that are net1 and net2 specific.

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<-5                   # 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")

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

  # --- Degree plot
  final.net<-Hubgenes_barplot(net.final,genes.name=genes.name)

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