JRF: Joint Random Forest for the simultaneous estimation of...

In JRF: Joint Random Forest (JRF) for the Simultaneous Estimation of Multiple Related Networks

Description

Algorithm for the simultaneous estimation of multiple related networks. Some of the functions utilized are a modified version of functions contained in the R package randomForest (A. Liaw and M. Wiener, 2002).

Usage

JRF(X, ntree, mtry,genes.name)

Arguments

X	List object containing expression data for each class, X=list(x_1,x_2, ... ) where x_j is a (p x n_j) matrix with rows corresponding to genes and columns to samples. Missing values are not allowed.
ntree	numeric value: number of trees.
mtry	numeric value: number of predictors to be sampled at each node.
genes.name	vector containing genes name. The order needs to match the rows of x_j.

Value

A matrix with I rows and C + 2 columns where I is the total number of gene-gene interactions and C is the number of classes. The first two columns contain gene names for each interaction while the remaining columns contain importance scores for different classes.

References

Petralia, F., Song, WM., Tu, Z. and Wang, P., A New Method for Joint Network Analysis Reveals Common and Different Co-Expression Patterns Among Genes and Proteins in Breast Cancer, submitted

A. Liaw and M. Wiener (2002). Classification and Regression by randomForest. R News 2, 18–22.

Examples

 # --- Derive weighted networks via JRF
 
 nclasses=2               # number of data sets / classes
 n1<-n2<-20               # sample size for each data sets
 p<-5                   # number of variables (genes)
 genes.name<-paste("G",seq(1,p),sep="")   # genes name
 
   # --- Generate data sets
 
 data1<-matrix(rnorm(p*n1),p,n1)       # generate data1
 data2<-matrix(rnorm(p*n2),p,n1)       # generate data2
 
   # --- Standardize variables to mean 0 and variance 1
   
  data1 <- t(apply(data1, 1, function(x) { (x - mean(x)) / sd(x) } ))
  data2 <- t(apply(data2, 1, function(x) { (x - mean(x)) / sd(x) } ))

   # --- Run JRF and obtain importance score of interactions for each class
   
  out<-JRF(list(data1,data2),mtry=round(sqrt(p-1)),ntree=1000,genes.name)

JRF documentation built on May 2, 2019, 12:21 p.m.