Instructions for generating the prostate cancer TF-miRNA network shown in Fig. 2a.

The following R libraries required by SIMBA should be installed first: mclust, extremevalues and matrixStats.

Step 1: Download SIMBA from Github: https://github.com/SIMBA2/SIMBA
Step 2: Install SIMBA: 
On Mac:
Rscript -e "install.packages('SIMBA_1.0_macosx.tgz', repos = NULL)"
On Windows: 
Rscript -e "install.packages('SIMBA.zip', repos = NULL)"
On Linux:
Rscript -e "install.packages('SIMBA_1.0_R_x86_64-pc-linux-gnu.tar.gz', repos = NULL)"
Step 3: Open R and run the following code:

# The entire process will run for approximately 20 minutes on a Core i7 desktop with Ubuntu 12.04. 
# Once done, the network can be visualized with Cytoscape. The network should be the same as the one shown in Fig. 2a in the manuscript.

library(SIMBA)
data('mRNA_exp',package='SIMBA')
data('miRNA_exp',package='SIMBA')
data('miRNA_CNV',package='SIMBA')
data('TFlist',package='SIMBA')

#calculate outliers for mRNA and miRNA
mRNA_outliers<-calc_bimodal_outliers(mRNA_exp)
miRNA_outliers<-calc_bimodal_outliers(miRNA_exp)

#filter miRNA outliers by CNV data
miRNA_outliers2<-filter_outliers_by_CNV(miRNA_CNV,miRNA_outliers)

#build an initial network for TF and miRNAs
init.net<-build_initial_network(mRNA_exp,miRNA_exp,mRNA_outliers,miRNA_outliers2,TFlist)

#build a network for TF and miRNAs with MDL
FDRcutoff<-0.05
net<-build_network(mRNA_exp,miRNA_exp,mRNA_outliers,miRNA_outliers2,TFlist,init.net,FDRcutoff)

#filter the network by binomial test
finalnet<-filter_network(net)
# finalnet then can be visualized using Cytoscape, it is the network shown in Fig. 2a in the manuscript.