ptmJRF: Joint Random Forest for the simultaneous estimation of...
In petraf01/iJRF: Integrative Joint Random Forest Network Models

Description Usage Arguments Value References Examples

Algorithm for the simultaneous estimation of multiple related networks. Some of the functions utilized are a modified version of functions contained in the R package randomForest (A. Liaw and M. Wiener, 2002).

1	ptmJRF(X, ntree=NULL, mtry=NULL,genes.name,ptm.name)

`X`	List object containing expression data for each class, `X=list(x_1,x_2, ... )` where `x_1` is a `(F x n_j)` matrix with rows corresponding to post translational modification sites and columns to samples, while `x_j` for `j > 1` is a `(p x n_j)` matrix with rows corresponding to proteins and columns to samples. For `x_2, x_3, ...` Rows need to be the same corresponding to the same proteins, while samples can vary. Missing values are not allowed. Rows of object `x_1` does not need to be ordered in a specific way.
`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`.
`ptm.name`	List of post translational modification variables in protein domain. This list must be ordered as rows of `X[[1]]`.

A matrix with I rows and C + 2 columns where I is the total number of gene-gene interactions and C is the number of classes. The first two columns contain gene names for each interaction while the remaining columns contain importance scores for different classes.

Petralia, F., Song, W.M., Tu, Z. and Wang, P. (2016). New method for joint network analysis reveals common and different coexpression patterns among genes and proteins in breast cancer. Journal of proteome research, 15(3), pp.743-754.

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

 # --- Generate data sets
 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
 ptm.name<-c("G1","G2","G3","G3","G4","G5","G1")   # ptm name
 p.ptm<-length(ptm.name)
 
 data1<-matrix(rnorm(p.ptm*n2),p.ptm,n1)       # generate PTM data
 data2<-matrix(rnorm(p*n1),p,n1)       # generate global proteomics data
 
 # --- Run JRF and obtain importance score of interactions for each class
  out<-ptmJRF(X=list(data1,data2),genes.name=genes.name,ptm.name=ptm.name)