PRODIGY: Personalized prioritization of driver genes

#' Example run of PRODIGY
#' library(PRODIGY)
#' # Load SNV+expression data from TCGA
#' data(COAD_SNV)
#' data(COAD_Expression)
#' # Load STRING network data 
#' data(STRING_network)
#' network = STRING_network
#' # Take samples for which SNV and expression is available 
#' samples = intersect(colnames(expression_matrix),colnames(snv_matrix))[1:5]
#' # Get differentially expressed genes (DEGs) for all samples
#' expression_matrix = expression_matrix[which(rownames(expression_matrix) %in% unique(c(network[,1],network[,2]))),]
#' DEGs = get_DEGs(expression_matrix,samples,sample_origins=NULL,beta=2,gamma=0.05)
#' # Identify sample origins (tumor or normal)
#' sample_origins = rep("tumor",ncol(expression_matrix))
#' sample_origins[substr(colnames(expression_matrix),nchar(colnames(expression_matrix)[1])-1,nchar(colnames(expression_matrix)[1]))=="11"] = "normal"	
#' list_of_pathways = get_pathway_list_from_graphite(source = "reactome",minimal_number_of_nodes = 10,num_of_cores = 20)
#' # Run PRODIGY
#' all_patients_scores = PRODIGY_cohort(snv_matrix = snv_matrix,expression_matrix = expression_matrix,network=network,samples=samples,DEGs=DEGs,alpha=0.05,
#' 			pathway_list=list_of_pathways,num_of_cores=30,sample_origins=sample_origins,write_results = F, results_folder = "./",
#' 			beta=2,gamma=0.05,delta=0.05)
#' # Get driver gene rankings for all samples 
#' results = analyze_PRODIGY_results(all_patients_scores)
#' #Load gold standard drivers from CGC. Here we used only genes that were annotated with a driver SNV by CGC.
#' data(gold_standard_drivers)