FinalScore_parallel: Derive final importance scores for object of class...

In petraf01/iJRF: Integrative Joint Random Forest Network Models

Description

This function returns importance score for each gene-gene (protein-protein) interaction.

Usage

FinalScore_parallel(importance, model, genes.name)

Arguments

importance	A matrix containing importance scores. When model iRafNet is implemented, importance is a two dimensional matrix (p x p) with p being the total number of genes/proteins. When either function iJRF or ptmJRF is implemented, importance is a three dimensional matrix of importance scores (p x p x C) with p being the total number of genes/proteins and C the number of classes.
model	Variable indicating which iJRFNet model will be implemented. Takes values in c("iJRF", "iRafNet", "ptmJRF")
genes.name	Vector containing genes name. The order needs to match the rows/columns of importance.

Value

A matrix with I rows and C + 2 columns where I is the total number of gene-gene (protein-protein) interactions and C is the total number of classes. The first two columns contain gene names for each interaction while the remaining columns contain importance scores for different classes. When model iRafNet is implemented, the number of classes is 1 and therefore only three columns will be returned.

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.

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 iJRFNet 

  ## --- Run multiple jobs in parallel and combine them
  
   out.new<-array(0,c(p,p,nclasses))
   n.var=0
   for (k in 1:3){ 
      out<-iJRFNet_parallel(X=list(data1,data2),genes.name=genes.name,
      model="iJRF",parallel=c(k,2))
      
      n.target<-dim(out$importance)[2]
      
      for (c in 1:nclasses) {
      out.new[,seq(n.var+1,n.var+n.target),c]<-out$importance[,,c];}
      
      n.var=n.var+n.target
    }
     
   ## --- Derive interactions 
   FinalScore_parallel(importance=out.new,model="iJRF",genes.name=genes.name)

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