iRafNet (version 0.1-1)

iRafNet: Integrative random forest for gene regulatory network inference

Description

This function fits iRafNet, a flexible unified integrative algorithm that allows information from prior data, such as protein-protein interactions and gene knock-down, to be jointly considered for gene regulatory network inference. This function takes as input only one set of sampling scores, computed considering one prior data such as protein-protein interactions or gene expression from knock-out experiments. Note that some of the functions utilized are a modified version of functions contained in the R package randomForest (A. Liaw and M. Wiener, 2002).

Usage

iRafNet(X, W, ntree, mtry,genes.name)

Arguments

X

(n x p) matrix containing expression levels for n patients and p genes.

W

(p x p) matrix containing iRafNet sampling scores. Element (i,j) contains score for regulatory relationship (i -> j). Scores must be non-negative. Larger value of sampling score corresponds to higher likelihood of gene i regulating gene j. Columns and rows of W must be in the same order as the columns of X. Sampling scores W are computed considering one prior data such as protein-protein interactions or gene expression from knock-out experiments.

ntree

numeric value: number of trees.

mtry

numeric value: number of potential regulators to be sampled at each tree node.

genes.name

vector containing gene names. The order needs to match the columns of X.

Value

out (p x p) matrix of importance scores. Element (i,j) contains importance score for regulatory relationship (i -> j).

References

Petralia, F., Wang, P., Yang, J., Tu, Z. (2015) Integrative random forest for gene regulatory network inference, Bioinformatics, 31, i197-i205.

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

Examples

 # --- Derive weighted networks via iRafNet
 
  n<-20                  # sample size for each data sets
  p<-5                   # number of genes
  genes.name<-paste("G",seq(1,p),sep="")   # genes name
 
  # --- Generate data sets
 
  data<-matrix(rnorm(p*n),n,p)       # generate expression matrix
  W<-abs(matrix(rnorm(p*p),p,p))          # generate score for regulatory relationships
 
  # --- Standardize variables to mean 0 and variance 1
   
  data <- (apply(data, 2, function(x) { (x - mean(x)) / sd(x) } ))

  # --- Run iRafNet and obtain importance score of regulatory relationships
  
  out<-iRafNet(data,W,mtry=round(sqrt(p-1)),ntree=1000,genes.name)