iRafNet {iRafNet} | R Documentation |
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 |
|
W |
|
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 |
Value
Importance score for each regulatory relationship. The first column contains gene name of regulators, the second column contains gene name of targets, and third column contains corresponding importance scores.
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
# --- Generate data sets
n<-20 # sample size
p<-5 # number of genes
genes.name<-paste("G",seq(1,p),sep="") # genes name
data<-matrix(rnorm(p*n),n,p) # generate expression matrix
W<-abs(matrix(rnorm(p*p),p,p)) # generate weights 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)