#' 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(...))
}
Datasets
Models
