JRF_permutation: Derive importance scores for permuted data.

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

Description

This function computes importance score for one permuted data set. Sample labels of target genes are randomly permuted and JRF is implemented. Resulting importance scores can be used to derive an estimate of FDR.

Usage

JRF_permutation(X, ntree, mtry,genes.name,perm)

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.
perm	integer: seed for permutation.

Value

A matrix with I rows and C columns with I being the number of total interactions and C the number of classes. Element (i,k) corresponds to the importance score for interaction i under class k.

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
 perm=1;        # set permutation seed
 
   # --- 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_permutation(list(data1,data2),mtry=round(sqrt(p-1)),ntree=1000,genes.name,perm)

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