Run_permutation: Derive importance scores for M permuted data sets.

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

Description

This function computes importance score for M permuted data sets. 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

Run_permutation(X, ntree, mtry,genes.name,M)

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. Rows need to be the same across objects, while samples can vary. 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.
M	integer: total number of permutations.

Value

A three dimensional matrix (I x M x C) with I being the number of total interactions, M the number of permutations and C the number of classes. Element (i,j,k) corresponds to the importance score for interaction i, permuted data j and 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
 M=5; 
 
   # --- 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<-Run_permutation(list(data1,data2),mtry=round(sqrt(p-1)),ntree=1000,genes.name,M)

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