--- a
+++ b/R/amaretto_initialize.R
@@ -0,0 +1,198 @@
+#' CreateRegulatorData
+#'
+#' Determine potential regulator genes.
+#' @return result
+#' @keywords internal
+CreateRegulatorData <- function(MA_matrix = MA_matrix, CNV_matrix = NULL, MET_matrix = NULL, Driver_list = NULL, PvalueThreshold = 0.001, RsquareThreshold = 0.1, method = "union") {
+    if (is.null(Driver_list)) 
+        DriversList <- NULL
+    if (is.null(CNV_matrix)) 
+        CNV_matrix <- matrix(0, nrow = 0, ncol = 0)
+    if (nrow(CNV_matrix) > 1) {
+        GeneVariances = rowVars(CNV_matrix)
+        CNV_matrix = CNV_matrix[GeneVariances >= 1e-04,]
+    }
+    if (nrow(CNV_matrix) > 0) {
+        CNV_matrix = FindTranscriptionallyPredictive_CNV(MA_matrix, CNV_matrix, PvalueThreshold = PvalueThreshold, RsquareThreshold = RsquareThreshold)
+    }
+    cat("\tFound", length(rownames(CNV_matrix)), "CNV driver genes.\n")
+    if (is.null(MET_matrix)) 
+        MET_matrix <- matrix(0, nrow = 0, ncol = 0)
+    if (nrow(MET_matrix) > 1) {
+        METcounts = apply(MET_matrix, 1, function(x) length(unique(x)))
+        MET_matrix = MET_matrix[METcounts == 2, ]
+        GeneVariances = rowVars(MET_matrix)
+        MET_matrix = MET_matrix[GeneVariances >= 1e-04,]
+    }
+    MET_drivers <- intersect(rownames(MET_matrix),rownames(MA_matrix))
+    cat("\tFound", length(MET_drivers), "MethylMix driver genes.\n")
+    if (!is.null(Driver_list)) {
+        DriversList <- Driver_list
+        DriversList <- intersect(DriversList, rownames(MA_matrix))
+        cat("\tFound", length(DriversList), "driver genes from the input list.\n")
+    }
+    if (is.null(Driver_list)) 
+        Drivers <- unique(c(rownames(CNV_matrix), MET_drivers,DriversList))
+    dataset_drivers <- unique(c(rownames(CNV_matrix),MET_drivers))
+    if (!is.null(Driver_list) & method == "union") 
+        Drivers <- union(dataset_drivers, DriversList)
+    if (!is.null(Driver_list) & method == "intersect") 
+        Drivers <- intersect(dataset_drivers, DriversList)
+    cat("\tFound a total of", length(Drivers), "unique drivers with your selected method.\n")
+    if (length(Drivers) == 0) {
+        cat("AMARETTO doesn't find any driver genes.")
+        return("No driver")
+    } else {
+        if (length(Drivers) == 1) {
+            RegulatorData_temp <- matrix(0, 1, ncol(MA_matrix))
+            colnames(RegulatorData_temp) <- colnames(MA_matrix)
+            rownames(RegulatorData_temp) <- Drivers
+            RegulatorData_temp[1, ] <- MA_matrix[Drivers,]
+            RegulatorData <- RegulatorData_temp
+        } else {
+            RegulatorData = MA_matrix[Drivers, ]
+        }
+        RegulatorData = t(scale(t(RegulatorData)))
+        MET_aberrations <- matrix(0, ncol = 3, nrow = length(MET_drivers))
+        rownames(MET_aberrations) <- MET_drivers
+        colnames(MET_aberrations)<-c("Hyper-methylated","Hypo-methylated","No_change")
+        if (length(MET_drivers) > 0) {
+            MET_aberrations[, "Hyper-methylated"] <- rowSums(MET_matrix[MET_drivers,] > 0)/ncol(MET_matrix)
+            MET_aberrations[, "Hypo-methylated"] <- rowSums(MET_matrix[MET_drivers,] < 0)/ncol(MET_matrix)
+            MET_aberrations[, "No_change"] <- rowSums(MET_matrix[MET_drivers,] == 0)/ncol(MET_matrix)
+        }
+        CNV_alterations <- matrix(0, ncol = 3, nrow = nrow(CNV_matrix))
+        rownames(CNV_alterations) <- rownames(CNV_matrix)
+        colnames(CNV_alterations)<-c("Amplification","Deletion","No_change")
+        if (nrow(CNV_alterations) > 0) {
+            CNV_alterations[, "Amplification"] <- rowSums(CNV_matrix > 0)/ncol(CNV_matrix)
+            CNV_alterations[, "Deletion"] <- rowSums(CNV_matrix < 0)/ncol(CNV_matrix)
+            CNV_alterations[, "No_change"] <- rowSums(CNV_matrix == 0)/ncol(CNV_matrix)
+        }
+        driverList_alterations <- matrix(1, ncol = 1, nrow = length(DriversList))
+        rownames(driverList_alterations) <- DriversList
+        Alterations <- matrix(0, nrow = length(Drivers), ncol = 3)
+        colnames(Alterations) <- c("CNV", "MET", "Driver List")
+        rownames(Alterations) <- Drivers
+        Alterations[which(Drivers %in% rownames(CNV_matrix)), 1] <- rep(1, length(which(Drivers %in% rownames(CNV_matrix))))
+        Alterations[which(Drivers %in% rownames(MET_matrix)), 2] <- rep(1, length(which(Drivers %in% rownames(MET_matrix))))
+        Alterations[which(Drivers %in% rownames(driverList_alterations)), 3] <- rep(1, length(which(Drivers %in% rownames(driverList_alterations))))
+        Alterations <- list(MET = MET_aberrations, 
+                            CNV = CNV_alterations,
+                            Driver_list = driverList_alterations, 
+                            Summary = Alterations)
+        return(list(RegulatorData = RegulatorData, Alterations = Alterations))
+    }
+}
+
+#' FindTranscriptionallyPredictive_CNV
+#'
+#' Function to identify which genes CNV significantly predict expression of that gene.
+#' @return result
+#' @keywords internal
+FindTranscriptionallyPredictive_CNV <- function(MA_matrix, 
+    CNV_matrix, PvalueThreshold = 0.001, RsquareThreshold = 0.1) {
+    OverlapGenes = Reduce(intersect, list(rownames(MA_matrix), 
+        rownames(CNV_matrix)))
+    OverlapSamples = Reduce(intersect, list(colnames(MA_matrix), 
+        colnames(CNV_matrix)))
+    if (length(OverlapGenes) == 1) {
+        CNV_TCGA_temp = MA_matrix_temp <- matrix(0, 
+            length(OverlapGenes), length(OverlapSamples))
+        rownames(CNV_TCGA_temp) = rownames(MA_matrix_temp) <- OverlapGenes
+        colnames(CNV_TCGA_temp) = colnames(MA_matrix_temp) <- OverlapSamples
+        CNV_TCGA_temp[1, ] <- CNV_matrix[OverlapGenes, 
+            OverlapSamples]
+        MA_matrix_temp[1, ] <- MA_matrix[OverlapGenes, 
+            OverlapSamples]
+        CNV_matrix = CNV_TCGA_temp
+        MA_matrix = MA_matrix_temp
+    } else {
+        CNV_matrix = CNV_matrix[OverlapGenes, OverlapSamples]
+        MA_matrix = MA_matrix[OverlapGenes, OverlapSamples]
+    }
+    if (length(OverlapGenes) > 0 && length(OverlapSamples) > 
+        0) {
+        CNVdrivers = c()
+        for (i in 1:length(rownames(CNV_matrix))) {
+            res = lm(MA_matrix[i, ] ~ CNV_matrix[i, 
+                ])
+            res.summary = summary(res)
+            if (res$coefficients[2] > 0 & res.summary$coefficients[2, 
+                4] < PvalueThreshold & res.summary$r.squared > 
+                RsquareThreshold) {
+                CNVdrivers = c(CNVdrivers, rownames(CNV_matrix)[i])
+            }
+        }
+        if (length(CNVdrivers) == 1) {
+            CNV_matrix_temp <- matrix(0, 1, ncol(CNV_matrix))
+            colnames(CNV_matrix_temp) = colnames(CNV_matrix)
+            rownames(CNV_matrix_temp) = CNVdrivers
+            CNV_matrix_temp[1, ] <- CNV_matrix[CNVdrivers, 
+                ]
+            CNV_matrix = CNV_matrix_temp
+        } else {
+            CNV_matrix = CNV_matrix[CNVdrivers, ]
+        }
+    }
+    return(CNV_matrix = CNV_matrix)
+}
+
+#' geneFiltering
+#'
+#' Function to filter gene expression matrix
+#' @return result
+#' @keywords internal
+geneFiltering <- function(Type, MAdata, Percentage) {
+    switch(Type, Variance = {
+        GeneVariances = rowVars(MAdata)
+        tmpResult = sort(GeneVariances, decreasing = TRUE)
+        SortedGenes = names(tmpResult)
+        tmpNrGenes = round(length(rownames(MAdata)) * 
+            Percentage/100)
+        MAdata_Filtered = MAdata[SortedGenes[1:tmpNrGenes], 
+            ]
+    }, MAD = {
+        GeneVariances = rowMads(MAdata)
+        names(GeneVariances) = rownames(MAdata)
+        tmpResult = sort(GeneVariances, decreasing = TRUE)
+        SortedGenes = names(tmpResult)
+        tmpNrGenes = round(length(rownames(MAdata)) * 
+            Percentage/100)
+        MAdata_Filtered = MAdata[SortedGenes[1:tmpNrGenes], 
+            ]
+    })
+    return(MAdata_Filtered)
+}
+
+#' read_gct
+#' 
+#' Function to turn a .gct data files into a matrix format
+#'
+#' @param file_address Address of the input gct file. 
+#'
+#' @importFrom utils read.delim
+#' @return result
+#' @export
+#' @examples
+#' data_matrix<-read_gct(file_address="")
+read_gct <- function(file_address){
+  if(file_address==""){
+    print("No gct file address is provided.")
+    return(NULL)
+  }
+  else{
+    data_fr <-read.delim(file_address, skip=2, sep="\t", header=TRUE, row.names=1)
+    data_fr <- as.matrix(subset(data_fr, select=-c(Description)))
+    return(data_fr)
+  }
+}
+
+#' printf
+#'
+#' Wrapper function for C-style formatted output.
+#' @return result
+#' @keywords internal
+printf <- function(...) {
+    cat(sprintf(...))
+}