#' 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(...))

}