iRafNet: Integrative random forest for co-expression network inference
In petraf01/iJRF: Integrative Joint Random Forest Network Models

Description Usage Arguments Value References Examples

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).

1	iRafNet(X, W, ntree=NULL, mtry=NULL,genes.name)

`X`	`(n x p)` Matrix containing expression levels for `n` samples and `p` genes.
`W`	`(p x p)` Symmatrix matrix containing iRafNet sampling scores. Element `(i,j)` contains score for interaction `(i - j)`. Scores must be non-negative. Larger value of sampling score corresponds to higher likelihood of gene `i` interacting with 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.
`ntree`	Numeric value: number of trees. If omitted, 1000 trees are considered.
`mtry`	Numeric value: number of predictors to be sampled at each node. If omitted, `mtry` is set to the square root of the number of variables.
`genes.name`	Vector containing genes name. The order needs to match the rows of `x_j`.

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.

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.

  # --- 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),p,n)      # generate expression matrix
  W<-abs(matrix(rnorm(p*p),p,p))    # generate weights for interactions
 
  # --- Run iRafNet and obtain importance score of interactions
  out<-iRafNet(X=list(data),W=W,genes.name=genes.name)