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Module_B.R
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# =============================================================================
# TCGA-Assembler version 2
#
# Copyright (C) <2017> <Yitan Zhu>
# This file is part of TCGA-Assembler.
#
# TCGA-Assembler is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# TCGA-Assembler is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with TCGA-Assembler. If not, see <http://www.gnu.org/licenses/>.
# =============================================================================
# =============================================================================
# TCGA-Assembler Version 2 Module B
# =============================================================================
# =============================================================================
# variable prefix example
# =============================================================================
# s : string
# n : number
# v : vector
# d : data.frame
# m : matrix
# l : list
# lv : list of vector
# ld : list of data.frame
# =============================================================================
# library
# =============================================================================
library(RCurl)
library(httr)
library(stringr)
library(HGNChelper)
library(rjson)
# =============================================================================
# Main Functions of Module B
# =============================================================================
CombineMultiPlatformData <- function(inputDataList,
combineStyle = "Intersect") {
options(warn = -1)
vl <- inputDataList
for (i in 1:length(vl)) {
# Keep only one sample for a tissue type of a patient. # Usually, there is only one sample of a tissue type of a patient existing in data.
vl[[i]]$Data <- ToPatientData(vl[[i]]$Data)
if (i == 1) {
sampleId <- colnames(vl[[i]]$Data)
}
# For each genomic feature, keep only one row of data.
lResult <- CombineRedundantFeature(Data = vl[[i]]$Data, Des = vl[[i]]$Des)
vl[[i]]$Des <- lResult$Des
vl[[i]]$Data <- lResult$Data
if (combineStyle == "Intersect") {
sampleId <- sort(intersect(sampleId, colnames(vl[[i]]$Data)))
}
if (combineStyle == "Union") {
sampleId <- sort(union(sampleId, colnames(vl[[i]]$Data)))
}
if (dim(vl[[i]]$Des)[2] == 1) {
vl[[i]]$Des <- cbind(vl[[i]]$Des, Description = cbind(rep("", dim(vl[[i]]$Des)[1])))
}
else {
if ((dim(vl[[i]]$Des)[2] == 3) && (vl[[i]]$dataType == "copyNumber")) {
vl[[i]]$Des <- cbind(vl[[i]]$Des[, 1, drop = FALSE],
Description = paste(vl[[i]]$Des[, 2], vl[[i]]$Des[, 3], sep = ""))
}
}
if (vl[[i]]$dataType == "miRNAExp") {
for (kk in 1:dim(vl[[i]]$Des)[1]) {
vl[[i]]$Des[kk, 1] <- paste(substr(vl[[i]]$Des[kk, 1], 5, 7), substr(vl[[i]]$Des[kk, 1], 9, 100), sep = "")
}
vl[[i]]$Des[, 1] <- toupper(vl[[i]]$Des[, 1])
}
# for combining methylation data at CpG site level.
if (vl[[i]]$dataType == "methylation") {
if (sum(toupper(c("REF", "GeneSymbol", "ChromosomeID", "CoordinateID")) %in% toupper(colnames(vl[[i]]$Des))) == 4) {
vl[[i]]$Des <- cbind(vl[[i]]$Des[, "GeneSymbol", drop = FALSE],
Description = paste(vl[[i]]$Des[, "REF"], vl[[i]]$Des[, "ChromosomeID"],
vl[[i]]$Des[, "CoordinateID"], sep = "|"))
vl[[i]]$Des[, "Description"] <- gsub(" ", "", vl[[i]]$Des[, "Description"])
}
}
vl[[i]]$Des <- switch(vl[[i]]$dataType,
methylation = cbind(vl[[i]]$Des[, "GeneSymbol", drop = FALSE], Platform = rep("methylation" , dim(vl[[i]]$Des)[1]), Description = vl[[i]]$Des[, 2]),
copyNumber = cbind(vl[[i]]$Des[, "GeneSymbol", drop = FALSE], Platform = rep("copyNumber" , dim(vl[[i]]$Des)[1]), Description = vl[[i]]$Des[, 2]),
somaticMutation = cbind(vl[[i]]$Des[, "GeneSymbol", drop = FALSE], Platform = rep("somaticMutation", dim(vl[[i]]$Des)[1]), Description = vl[[i]]$Des[, 2]),
geneExp = cbind(vl[[i]]$Des[, "GeneSymbol", drop = FALSE], Platform = rep("geneExp" , dim(vl[[i]]$Des)[1]), Description = vl[[i]]$Des[, 2]),
miRNAExp = cbind(vl[[i]]$Des[, "GeneSymbol", drop = FALSE], Platform = rep("miRNAExp" , dim(vl[[i]]$Des)[1]), Description = vl[[i]]$Des[, 2]),
protein_RPPA = cbind(vl[[i]]$Des[, "GeneSymbol", drop = FALSE], Platform = rep("protein_RPPA" , dim(vl[[i]]$Des)[1]), Description = vl[[i]]$Des[, 2]),
protein_iTRAQ = cbind(vl[[i]]$Des[, "GeneSymbol", drop = FALSE], Platform = rep("protein_iTRAQ" , dim(vl[[i]]$Des)[1]), Description = vl[[i]]$Des[, 2]))
# check NA gene. which(vl[[i]]$Des[, "GeneSymbol"] != "NA")
ID <- intersect(which(!is.na(vl[[i]]$Des[, "GeneSymbol"])),
which(vl[[i]]$Des[, "GeneSymbol"] != "?"))
vl[[i]]$Des <- vl[[i]]$Des[ID, , drop = FALSE]
vl[[i]]$Data <- vl[[i]]$Data[ID, , drop = FALSE]
}
for (i in 1:length(vl)) {
# Get the data of samples that should be kept
if (combineStyle == "Intersect") {
vl[[i]]$Data <- vl[[i]]$Data[, sampleId, drop = FALSE]
}
if (combineStyle == "Union") {
tempData <- matrix(NA, dim(vl[[i]]$Data)[1], length(sampleId))
colnames(tempData) <- sampleId
tempData[, colnames(vl[[i]]$Data)] <- vl[[i]]$Data
vl[[i]]$Data <- tempData
}
rownames(vl[[i]]$Data) <- NULL
rownames(vl[[i]]$Des) <- NULL
}
# Combine the datasets into matrix format
Data <- vl[[1]]$Data
Des <- vl[[1]]$Des
for (i in 2:length(vl)) {
Data <- rbind(Data, vl[[i]]$Data)
Des <- rbind(Des, vl[[i]]$Des)
}
OrderID <- order(as.character(Des[, "GeneSymbol"]), as.character(Des[, "Platform"]),
as.character(Des[, "Description"]), na.last = TRUE, decreasing = FALSE)
Data <- Data[OrderID, , drop = FALSE]
Des <- Des[OrderID, , drop = FALSE]
rownames(Data) <- NULL
rownames(Des) <- NULL
# filter the all zero line of mutation
vbDes <- Des[, "Platform"] == "somaticMutation"
vbData <- apply(Data, 1, function(x){all(unique(x) == 0)})
vb <- !(vbDes & vbData)
Data <- Data[vb, , drop = FALSE]
Des <- Des[vb, , drop = FALSE]
vbDataRowNA <- apply(Data, 1, function(x){all(is.na(x))})
Data <- Data[!vbDataRowNA, , drop = FALSE]
Des <- Des[!vbDataRowNA, , drop = FALSE]
lResult <- list(Des = Des, Data = Data)
options(warn = 0)
lResult
}
ExtractTissueSpecificSamples <- function(inputData, tissueType, singleSampleFlag, sampleTypeFile = "./SupportingFiles/TCGASampleType.txt") {
options(warn = -1)
TCGABarcode <- colnames(inputData)
TCGABarcode <- sapply(strsplit(TCGABarcode, split = "-"), function(x){
Str = paste(x[1], x[2], x[3], substr(x[4], 1, 2), sep = "-")
Str
})
if (singleSampleFlag == TRUE) {
DuplicatedLabel <- duplicated(TCGABarcode)
ID <- which(DuplicatedLabel == FALSE)
inputData <- inputData[, ID, drop = FALSE]
TCGABarcode <- TCGABarcode[ID]
}
TCGADataLabel <- sapply(strsplit(TCGABarcode, split = "-"), function(x)x[4])
TCGADataLabelValue <- as.numeric(substr(TCGADataLabel, 1, 2))
sampleType <- read.table(file = sampleTypeFile, header = TRUE, sep = "\t", na.string = "", quote = "", stringsAsFactors = FALSE)
Code <- sampleType[which(sampleType[, "Options"] %in% tissueType), "Code"]
if (length(Code) == 0) {
writeLines("Error: no valid tissue or cell type specified")
inputData <- NULL
}
else {
ID <- which(TCGADataLabelValue %in% Code)
if (length(ID) == 0) {
writeLines(paste("There is no sample of ", paste(tissueType, collapse = ", "), sep = ""))
inputData <- NULL
}
else {
inputData <- inputData[, ID, drop = FALSE]
writeLines(paste(length(ID), " samples of ", paste(tissueType, collapse = ", "), " are extracted.", sep = ""))
}
}
options(warn = 0)
inputData
}
CalculateSingleValueMethylationData <- function(input, regionOption, DHSOption, outputFileName, outputFileFolder, chipAnnotationFile = "./SupportingFiles/MethylationChipAnnotation.rda") {
options(warn = -1)
dir.create(path = outputFileFolder, recursive = TRUE)
if (sum(! regionOption %in% c("TSS1500", "TSS200", "5'UTR", "1stExon", "Body", "3'UTR", "All")) > 0) {
stop("Error: invalid regionOption.")
}
if (sum(! DHSOption %in% c("DHS", "notDHS", "Both")) > 0) {
stop("Error: invalid DHSOption")
}
regionOptionO <- regionOption
# if ("TSS1500" %in% regionOption)
# {
# regionOption <- unique(c(regionOption, "TSS200"))
# }
#
if ((!("All" %in% regionOption)) || (DHSOption != "Both")) {
load(chipAnnotationFile)
ID <- 1:dim(MethylAnno)[1]
if (!("All" %in% regionOption)) {
ID <- sort(unique(which(MethylAnno[, "UCSC_RefGene_Group"] %in% regionOption)))
}
if (DHSOption == "DHS") {
ID <- sort(intersect(ID, which(MethylAnno[, "DHS"] == TRUE)))
}
else {
if (DHSOption == "notDHS") {
ID <- sort(intersect(ID, setdiff(1:dim(MethylAnno)[1], which(MethylAnno[, "DHS"] == TRUE))))
}
}
MethylAnno <- MethylAnno[ID, , drop = FALSE]
ID <- which(input$Des[, "REF"] %in% MethylAnno[, "IlmnID"])
input$Des <- input$Des[ID, , drop = FALSE]
input$Data <- input$Data[ID, , drop = FALSE]
}
# Check whether conflict with NA gene
ID <- sort(intersect(which(!is.na(input$Des[, "GeneSymbol"])), which(input$Des[, "GeneSymbol"] != "")), decreasing = FALSE)
input$Des <- input$Des[ID, , drop = FALSE]
input$Data <- input$Data[ID, , drop = FALSE]
UniGene <- unique(input$Des[, "GeneSymbol"])
Data <- matrix(NA, length(UniGene), dim(input$Data)[2])
for (i in 1:length(UniGene)) {
if ((i %% 2000 == 0) || (i == length(UniGene))) {
writeLines(paste("Calculation ", round(i/length(UniGene)*100, digits = 2), "% done.", sep = ""))
}
Data[i, ] <- colMeans(input$Data[which(input$Des[, "GeneSymbol"] == UniGene[i]), , drop = FALSE], na.rm = TRUE)
}
colnames(Data) <- colnames(input$Data)
Des <- cbind(GeneSymbol = UniGene, SingleValueType = rep(paste(paste(regionOptionO, collapse = "-"), DHSOption, sep = "|"), length(UniGene)))
# draw and save a box plot of data
png(filename = paste(outputFileFolder, "/", outputFileName, "__", paste(regionOptionO, collapse = "-"), "__", DHSOption, "__boxplot.png", sep = ""), width = 30*dim(Data)[2]+300, height = 1500, units = "px")
par(las = 2, mai = c(4.5, 1, 0.5, 1), cex = 1.5)
boxplot(Data)
dev.off()
# save data files.
rownames(Data) <- NULL
rownames(Des) <- NULL
ID <- which(is.nan(Data))
Data[ID] <- NA
save(Des, Data, file = paste(outputFileFolder, "/", outputFileName, "__", paste(regionOptionO, collapse = "-"), "__", DHSOption, ".rda", sep = ""))
write.table(cbind(Des, Data), file = paste(outputFileFolder, "/", outputFileName, "__", paste(regionOptionO, collapse = "-"), "__", DHSOption, ".txt", sep = ""),
quote = FALSE, sep = "\t", na = "", col.names = TRUE, row.names = FALSE)
options(warn = 0)
return(list(Data = Data, Des = Des))
}
MergeMethylationData <- function(input1, input2, outputFileName, outputFileFolder) {
options(warn = -1)
dir.create(path = outputFileFolder, recursive = TRUE)
input1_O <- input1
input2_O <- input2
rownames(input1$Des) <- paste(input1$Des[, "REF"], input1$Des[, "GeneSymbol"], sep = "||")
rownames(input1$Data) <- paste(input1$Des[, "REF"], input1$Des[, "GeneSymbol"], sep = "||")
rownames(input2$Des) <- paste(input2$Des[, "REF"], input2$Des[, "GeneSymbol"], sep = "||")
rownames(input2$Data) <- paste(input2$Des[, "REF"], input2$Des[, "GeneSymbol"], sep = "||")
CommonID <- intersect(rownames(input1$Des), rownames(input2$Des))
input1$Des <- input1$Des[CommonID, , drop = FALSE]
input1$Data <- input1$Data[CommonID, , drop = FALSE]
input2$Des <- input2$Des[CommonID, , drop = FALSE]
input2$Data <- input2$Data[CommonID, , drop = FALSE]
if (dim(input1_O$Des)[1] >= dim(input2_O$Des)[1]) {
Des <- input1$Des
}
else {
Des <- input2$Des
}
Data <- cbind(input1$Data, input2$Data)
# draw and save a box plot of combined data before normalization
png(filename = paste(outputFileFolder, "/", outputFileName, "__BeforeNormalizationBoxplot.png", sep = ""), width = 30*dim(Data)[2]+300, height = 1500, units = "px")
par(las = 2, mai = c(4.5, 1, 0.5, 1), cex = 1.5)
boxplot(Data)
dev.off()
Data <- Normalize.quantiles(Data)
# draw and save a box plot of combined data after normalization
png(filename = paste(outputFileFolder, "/", outputFileName, "__AfterNormalizationBoxplot.png", sep = ""), width = 30*dim(Data)[2]+300, height = 1500, units = "px")
par(las = 2, mai = c(4.5, 1, 0.5, 1), cex = 1.5)
boxplot(Data)
dev.off()
# save data files.
rownames(Data) <- NULL
rownames(Des) <- NULL
save(Des, Data, file = paste(outputFileFolder, "/", outputFileName, ".rda", sep = ""))
write.table(cbind(Des, Data), file = paste(outputFileFolder, "/", outputFileName, ".txt", sep = ""), quote = FALSE,
sep = "\t", na = "", col.names = TRUE, row.names = FALSE)
options(warn = 0)
return(list(Data = Data, Des = Des))
}
ProcessCNAData <- function(inputFilePath, outputFileName, outputFileFolder, refGenomeFile) {
options(warn = -1)
dir.create(path = outputFileFolder, recursive = TRUE)
# Load reference genomes, which give the genomic locations of genes
RefGenome <- read.table(file = refGenomeFile, header = TRUE, sep = "\t", na.string = "", quote = "", stringsAsFactors = FALSE)
NumGene <- dim(RefGenome)[1]
mode(RefGenome[, "txStarts"]) <- "numeric"
mode(RefGenome[, "txEnds"]) <- "numeric"
# Load copy number data
InData <- read.table(file = inputFilePath, header = TRUE, sep = "\t", na.string = "", quote = "", stringsAsFactors = FALSE, check.names = FALSE)
InData[, "Sample"] <- toupper(InData[, "Sample"])
TCGAID <- InData[, "Sample"]
UniqueTCGAID <- unique(TCGAID)
InData[, "Chromosome"] <- as.character(InData[, "Chromosome"])
InData[, "Chromosome"] <- paste("CHR", InData[, "Chromosome"], sep = "")
IDID <- which(InData[, "Chromosome"] == "CHR23")
InData[IDID, "Chromosome"] <- "CHRX"
IDID <- which(InData[, "Chromosome"] == "CHR24")
InData[IDID, "Chromosome"] <- "CHRY"
InData[, "Start"] <- as.numeric(InData[, "Start"])
InData[, "End"] <- as.numeric(InData[, "End"])
InData[, "Segment_Mean"] <- as.numeric(InData[, "Segment_Mean"])
# Calculate gene-level copy number
Data <- matrix(NA, NumGene, length(UniqueTCGAID))
for (i in 1:length(UniqueTCGAID)) {
IDi <- which(InData[, "Sample"] == UniqueTCGAID[i])
InDatai <- InData[IDi, c("Chromosome", "Start", "End", "Segment_Mean"), drop = FALSE]
for (j in 1:NumGene) {
IDj <- which(InDatai[, "Chromosome"] == RefGenome[j, "Chromosome"])
if (length(IDj) > 0) {
StartP <- InDatai[IDj, "Start"]
EndP <- InDatai[IDj, "End"]
Ratio <- InDatai[IDj, "Segment_Mean"]
Overlap <- ifelse((EndP >= RefGenome[j, "txStarts"]) & (RefGenome[j, "txEnds"] >= StartP), pmin(EndP, RefGenome[j, "txEnds"]) - pmax(StartP, RefGenome[j, "txStarts"]) + 1, 0)
IDNonNA <- which(!is.na(Ratio))
Ratio <- Ratio[IDNonNA]
Overlap <- Overlap[IDNonNA]
if (sum(Overlap, na.rm = TRUE) > 0) {
Data[j, i] = sum(Overlap * Ratio, na.rm = TRUE)/sum(Overlap, na.rm = TRUE)
}
}
}
if ((i %% 20 == 0) & (i < length(UniqueTCGAID))) {
writeLines(paste("Calculating gene copy number, ", round(i/length(UniqueTCGAID)*100, digits = 2), "% done.", sep = ""))
}
}
writeLines("Calculating gene copy number, 100% done.")
colnames(Data) <- UniqueTCGAID
Des <- as.matrix(RefGenome[, 1:3, drop = FALSE])
#Check and Correct gene symbol
Des <- CheckGeneSymbol(Des)
lResult <- CombineRedundantFeature(Data = Data, Des = Des)
Data <- lResult$Data
Des <- lResult$Des
# Draw and save a box plot
png(filename = paste(outputFileFolder, "/", outputFileName, "__boxplot.png", sep = ""), width = 30*dim(Data)[2]+300, height = 1500, units = "px")
par(las = 2, mai = c(4.5, 1, 0.5, 1), cex = 1.5)
boxplot(Data)
dev.off()
# save data files.
rownames(Data) <- NULL
rownames(Des) <- NULL
save(Des, Data, file = paste(outputFileFolder, "/", outputFileName, ".rda", sep = ""))
write.table(cbind(Des, Data), file = paste(outputFileFolder, "/", outputFileName, ".txt", sep = ""), quote = FALSE,
sep = "\t", na = "", col.names = TRUE, row.names = FALSE)
options(warn = 0)
return(list(Data = Data, Des = Des))
}
ProcessRNASeqData <- function(inputFilePath, outputFileName, outputFileFolder, dataType, verType = "RNASeqV2") {
options(warn = -1)
dir.create(path = outputFileFolder, recursive = TRUE)
# Read in data.
InData <- read.table(file = inputFilePath, header = TRUE, sep = "\t", na.string = "", stringsAsFactors = FALSE, quote = "", check.names = FALSE)
InData <- InData[2:dim(InData)[1], , drop = FALSE]
if ((dataType == "geneExp") & (verType == "RNASeqV1")) {
REF <- InData[, 1]
RPKM <- as.matrix(InData[, seq(4, dim(InData)[2], 3), drop = FALSE])
mode(RPKM) <- "numeric"
GeneSymbol <- sapply(strsplit(REF, split = "\\|"), function(x)x[1])
EntrezID <- sapply(strsplit(REF, split = "\\|"), function(x)x[2])
Des <- cbind(GeneSymbol = GeneSymbol, EntrezID = EntrezID)
Data <- RPKM
#Check and Correct gene symbol
Des <- CheckGeneSymbol(Des)
}
if (dataType == "exonExp") {
REF <- InData[, 1]
# RPKM <- as.matrix(InData[, seq(4, dim(InData)[2], 3), drop = FALSE])
RPKM <- as.matrix(InData[, 2:dim(InData)[2], drop = FALSE])
mode(RPKM) <- "numeric"
Des <- cbind(ExonID = REF)
Data <- RPKM
}
if ((dataType == "geneExp") & (verType == "RNASeqV2")) {
REF <- InData[, 1]
NormalizedCount <- as.matrix(InData[, 2:dim(InData)[2], drop = FALSE])
mode(NormalizedCount) <- "numeric"
GeneSymbol <- sapply(strsplit(REF, split = "\\|"), function(x)x[1])
EntrezID <- sapply(strsplit(REF, split = "\\|"), function(x)x[2])
Des <- cbind(GeneSymbol = GeneSymbol, EntrezID = EntrezID)
Data <- NormalizedCount
#Check and Correct gene symbol
Des <- CheckGeneSymbol(Des)
}
if (verType == "Microarray") {
dataType <- "geneExp"
REF <- InData[, 1]
Data <- as.matrix(InData[, 2:dim(InData)[2], drop = FALSE])
mode(Data) <- "numeric"
Des <- cbind(GeneSymbol = REF, EntrezID = rep("", length(REF)))
#Check and Correct gene symbol
Des <- CheckGeneSymbol(Des)
}
lResult <- CombineRedundantFeature(Data = Data, Des = Des)
Data <- lResult$Data
Des <- lResult$Des
if (dataType == "geneExp") {
# Draw and save a box plot
png(filename = paste(outputFileFolder, "/", outputFileName, "__boxplot.png", sep = ""), width = 30*dim(Data)[2]+300, height = 1500, units = "px")
par(las = 2, mai = c(4.5, 1, 0.5, 1), cex = 1.5)
nonNaID <- which(!is.na(Data))
if ((sum(Data[nonNaID]<0) == 0) && (max(Data[nonNaID])>50)) {
boxplot(log2(Data), main = "Boxplot drawn based on log2 tranformed data.")
} else {
boxplot(Data)
}
dev.off()
}
# save data files
rownames(Data) <- NULL
rownames(Des) <- NULL
save(Des, Data, file = paste(outputFileFolder, "/", outputFileName, ".rda", sep = ""))
write.table(cbind(Des, Data), file = paste(outputFileFolder, "/", outputFileName, ".txt", sep = ""), quote = FALSE,
sep = "\t", na = "", col.names = TRUE, row.names = FALSE)
options(warn = 0)
return(list(Data = Data, Des = Des))
}
ProcessmiRNASeqData <- function(inputFilePath, outputFileName, outputFileFolder, fileSource = "TCGA-Assembler") {
options(warn = -1)
dir.create(path = outputFileFolder, recursive = TRUE)
# Read in data.
InData <- read.table(file = inputFilePath, header = TRUE, sep = "\t", na.string = "", quote = "", stringsAsFactors = FALSE, check.names = FALSE)
InData <- InData[2:dim(InData)[1], , drop = FALSE]
# divide read cout data and RPM data
REF <- InData[, 1]
if (fileSource == "Firehose") {
Count <- as.matrix(InData[, seq(2, dim(InData)[2], 3), drop = FALSE])
RPM <- as.matrix(InData[, seq(3, dim(InData)[2], 3), drop = FALSE])
}
if (fileSource == "TCGA-Assembler") {
Count <- as.matrix(InData[, seq(2, dim(InData)[2], 2), drop = FALSE])
RPM <- as.matrix(InData[, seq(3, dim(InData)[2], 2), drop = FALSE])
}
# rownames(Count) <- REF
mode(Count) <- "numeric"
# rownames(RPM) <- REF
mode(RPM) <- "numeric"
# # Draw and save box plot
# png(filename = paste(outputFileFolder, "/", outputFileName, "__ReadCount.boxplot.png", sep = ""), width = 30*dim(Count)[2]+300, height = 1500, units = "px")
# par(las = 2, mai = c(4.5, 1, 0.5, 1), cex = 1.5)
# boxplot(log2(Count))
# dev.off()
# png(filename = paste(outputFileFolder, "/", outputFileName, "__RPM.boxplot.png", sep = ""), width = 30*dim(RPM)[2]+300, height = 1500, units = "px")
# par(las = 2, mai = c(4.5, 1, 0.5, 1), cex = 1.5)
# boxplot(log2(RPM))
# dev.off()
#save data files
Des <- cbind(GeneSymbol = REF)
Data <- Count
lResult <- CombineRedundantFeature(Data = Data, Des = Des)
Data <- lResult$Data
Des <- lResult$Des
ID <- which(is.na(Des[, "GeneSymbol"]))
Des[ID, "GeneSymbol"] <- ""
rownames(Data) <- NULL
rownames(Des) <- NULL
save(Des, Data, file = paste(outputFileFolder, "/", outputFileName, "__ReadCount.rda", sep = ""))
write.table(cbind(Des, Data), file = paste(outputFileFolder, "/", outputFileName, "__ReadCount.txt", sep = ""), quote = FALSE,
sep = "\t", na = "", col.names = TRUE, row.names = FALSE)
Des <- cbind(GeneSymbol = REF)
Data <- RPM
lResult <- CombineRedundantFeature(Data = Data, Des = Des)
Data <- lResult$Data
Des <- lResult$Des
ID <- which(is.na(Des[, "GeneSymbol"]))
Des[ID, "GeneSymbol"] <- ""
rownames(Data) <- NULL
rownames(Des) <- NULL
save(Des, Data, file = paste(outputFileFolder, "/", outputFileName, "__RPM.rda", sep = ""))
write.table(cbind(Des, Data), file = paste(outputFileFolder, "/", outputFileName, "__RPM.txt", sep = ""), quote = FALSE,
sep = "\t", na = "", col.names = TRUE, row.names = FALSE)
options(warn = 0)
return(list(Data = Data, Des = Des))
}
ProcessMethylation450Data <- function(inputFilePath, outputFileName, outputFileFolder, fileSource = "TCGA-Assembler") {
options(warn = -1)
dir.create(path = outputFileFolder, recursive = TRUE)
writeLines("Loading data.")
if (fileSource == "Firehose") {
# read in data
GetMethylation450Data(inputFilePath, outputFileFolder, outputFileName)
load(paste(outputFileFolder, "/", outputFileName, ".TempFiles/", outputFileName, ".TempData.rda", sep = ""))
unlink(x = paste(outputFileFolder, "/", outputFileName, ".TempFiles", sep = ""), recursive = TRUE, force = TRUE)
Methy450Des <- as.matrix(Methy450Des)
}
if (fileSource == "TCGA-Assembler") {
# read in data
InData <- read.table(file = inputFilePath, header = TRUE, sep = "\t", na.string = "", quote = "", stringsAsFactors = FALSE, check.names = FALSE)
Methy450Des <- as.matrix(InData[, 1:4, drop = FALSE])
colnames(Methy450Des) <- c("REF", "GeneSymbol", "ChromosomeID", "CoordinateID")
ID <- which(Methy450Des[, "GeneSymbol"] == "DISCONTUNUED")
Methy450Des[ID, "GeneSymbol"] <- ""
Methy450Data <- as.matrix(InData[, 5:dim(InData)[2], drop = FALSE])
mode(Methy450Data) <- "numeric"
rm("InData")
}
# some probes have more than one gene symbols, collpase the data
# AddDes <- matrix("", dim(Methy450Des)[1], 4)
AddDes <- matrix("", 100000, 4)
colnames(AddDes) <- colnames(Methy450Des)
# AddData <- matrix(NA, dim(Methy450Data)[1], dim(Methy450Data)[2])
AddData <- matrix(NA, 100000, dim(Methy450Data)[2])
colnames(AddData) <- colnames(Methy450Data)
AddIndex <- 0
for (ProbeID in 1:dim(Methy450Des)[1]) {
GrepID <- grep(";", Methy450Des[ProbeID, "GeneSymbol"])
if ((ProbeID %% 50000 == 0) || (ProbeID == dim(Methy450Des)[1])) {
writeLines(paste("Process CpG sites corresponding to multiple genes, ", ceiling(ProbeID/dim(Methy450Des)[1]*100), "% done.", sep = ""))
}
if (length(GrepID) > 0) {
TempStr <- strsplit(Methy450Des[ProbeID, "GeneSymbol"], split = ";")
Methy450Des[ProbeID, "GeneSymbol"] <- TempStr[[1]][1]
for (TempStri in 2:length(TempStr[[1]])) {
TempAddDes <- Methy450Des[ProbeID, , drop = FALSE]
TempAddDes[1, "GeneSymbol"] <- TempStr[[1]][TempStri]
AddIndex <- AddIndex + 1
AddDes[AddIndex, ] <- TempAddDes
AddData[AddIndex, ] <- Methy450Data[ProbeID, ]
}
}
}
# writeLines(paste("Number of added rows is ", AddIndex, sep = ""))
Methy450Des <- rbind(Methy450Des, AddDes[1:AddIndex, , drop = FALSE])
Methy450Data <- rbind(Methy450Data, AddData[1:AddIndex, , drop = FALSE])
Methy450Des[, "ChromosomeID"] <- gsub(" ", "", Methy450Des[, "ChromosomeID"])
Methy450Des[, "CoordinateID"] <- gsub(" ", "", Methy450Des[, "CoordinateID"])
OrderID <- order(as.numeric(Methy450Des[, "ChromosomeID"]), as.numeric(Methy450Des[, "CoordinateID"]), Methy450Des[, "GeneSymbol"], na.last = TRUE, decreasing = FALSE)
Des <- Methy450Des[OrderID, , drop = FALSE]
Data <- Methy450Data[OrderID, , drop = FALSE]
#Check and Correct gene symbol
Des <- CheckGeneSymbol(Des)
lResult <- CombineRedundantFeature(Data = Data, Des = Des)
Data <- lResult$Data
Des <- lResult$Des
# Draw and save box plot
png(filename = paste(outputFileFolder, "/", outputFileName, "__boxplot.png", sep = ""), width = 30*dim(Data)[2]+300, height = 1500, units = "px")
par(las = 2, mai = c(4.5, 1, 0.5, 1), cex = 1.5)
boxplot(Data)
dev.off()
# save output data files
rownames(Data) <- NULL
rownames(Des) <- NULL
save(Data, Des, file = paste(outputFileFolder, "/", outputFileName, ".rda", sep = ""))
write.table(cbind(Des, Data), file = paste(outputFileFolder, "/", outputFileName, ".txt", sep = ""),
quote = FALSE, sep = "\t", na = "", col.names = TRUE, row.names = FALSE)
options(warn = 0)
return(list(Data = Data, Des = Des))
}
CheckGeneSymbol <- function(Des) {
data(hgnc.table)
hgnc.table <- rbind(hgnc.table, c("13-SEP", "SEPT7P2"))
rID <- intersect(which(toupper(hgnc.table[, "Symbol"]) == toupper("NCRNA00185")),
which(toupper(hgnc.table[, "Approved.Symbol"]) == toupper("TTTY14")))
hgnc.table <- hgnc.table[sort(setdiff(1:dim(hgnc.table)[1], rID)), , drop = FALSE]
hgnc.table <- hgnc.table[which(!is.na(hgnc.table[, "Approved.Symbol"])), , drop = FALSE]
regex <- "[0-9]\\-(JAN|FEB|MAR|APR|MAY|JUN|JUL|AUG|SEP|OCT|NOV|DEC)|[0-9]\\.[0-9][0-9]E\\+[[0-9][0-9]"
MonthID <- grep(pattern = regex, hgnc.table[, 1], ignore.case = TRUE)
MonthMappingTable <- hgnc.table[MonthID, , drop = FALSE]
Des[, "GeneSymbol"] <- sub("(.*C[0-9XY]+)ORF(.+)", "\\1orf\\2", Des[, "GeneSymbol"])
ID <- intersect(which(!Des[, "GeneSymbol"] %in% hgnc.table[, "Approved.Symbol"]), which(Des[, "GeneSymbol"] %in% hgnc.table[, "Symbol"]))
DesOrg <- Des
if (length(ID) > 0) {
Des[ID, "GeneSymbol"] <- sapply(Des[ID, "GeneSymbol"], function(x)paste(hgnc.table[hgnc.table[, "Symbol"] == x, "Approved.Symbol"], collapse = "___"))
}
# writeLines("Changed genes are")
# print(cbind(DesOrg[ID, "GeneSymbol"], Des[ID, "GeneSymbol"]))
ID <- intersect(which(!Des[, "GeneSymbol"] %in% hgnc.table[, "Approved.Symbol"]), which(toupper(Des[, "GeneSymbol"]) %in% toupper(MonthMappingTable[, "Symbol"])))
if (length(ID) > 0) {
Des[ID, "GeneSymbol"] <- sapply(Des[ID, "GeneSymbol"], function(x)paste(MonthMappingTable[toupper(MonthMappingTable[, "Symbol"]) == toupper(x), "Approved.Symbol"], collapse = "___"))
}
# writeLines("Changed genes are")
# print(cbind(DesOrg[ID, "GeneSymbol"], Des[ID, "GeneSymbol"]))
ID <- which(is.na(Des[, "GeneSymbol"]))
Des[ID, "GeneSymbol"] <- ""
Des
}
ProcessRPPADataWithGeneAnnotation <- function(inputFilePath, outputFileName, outputFileFolder) {
options(warn = -1)
dir.create(path = outputFileFolder, recursive = TRUE)
# Read in data
InData <- read.table(file = inputFilePath, header = TRUE, sep = "\t", na.string = "", quote = "", stringsAsFactors = FALSE, check.names = FALSE)
OutData <- matrix(NA, 0, dim(InData)[2]-1)
# Split the gene symbol and protein antibody into two separate columns
# Duplicate rows for proteins coded by multiple genes.
Gene <- c()
Antibody <- c()
for (i in 1:dim(InData)[1]) {
GeneAntibodyStr <- InData[i, 1]
GeneStr <- strsplit(GeneAntibodyStr, split = "\\|")[[1]][1]
AntibodyStr <- strsplit(GeneAntibodyStr, split = "\\|")[[1]][2]
GeneStr <- unlist(strsplit(GeneStr, split = " "))
Gene <- c(Gene, GeneStr)
Antibody <- c(Antibody, rep(AntibodyStr, length = length(GeneStr)))
OutData <- rbind(OutData, InData[rep(i, length = length(GeneStr)), 2:dim(InData)[2], drop = FALSE])
}
colnames(OutData) <- colnames(InData)[2:dim(InData)[2]]
Des <- cbind(GeneSymbol = Gene, ProteinAntibody = Antibody)
# check gene symbol
Des <- CheckGeneSymbol(Des)
Data <- as.matrix(OutData)
mode(Data) <- "numeric"
lResult <- CombineRedundantFeature(Data = Data, Des = Des)
Data <- lResult$Data
Des <- lResult$Des
# save output data files
rownames(Data) <- NULL
rownames(Des) <- NULL
save(Data, Des, file = paste(outputFileFolder, "/", outputFileName, ".rda", sep = ""))
write.table(cbind(Des, Data), file = paste(outputFileFolder, "/", outputFileName, ".txt", sep = ""),
quote = FALSE, sep = "\t", na = "", col.names = TRUE, row.names = FALSE)
# draw and save boxplot
png(filename = paste(outputFileFolder, "/", outputFileName, "__boxplot.png", sep = ""), width = 30*dim(Data)[2]+300, height = 1500, units = "px")
par(las = 2, mai = c(4.5, 1, 0.5, 1), cex = 1.5)
boxplot(Data)
dev.off()
options(warn = 0)
return(list(Data = Data, Des = Des))
}
ProcessMethylation27Data <- function(inputFilePath, outputFileName, outputFileFolder, fileSource = "TCGA-Assembler") {
options(warn = -1)
dir.create(path = outputFileFolder, recursive = TRUE)
if (fileSource == "TCGA-Assembler") {
# read in data
InData <- read.table(file = inputFilePath, header = TRUE, sep = "\t", na.string = "", quote = "", stringsAsFactors = FALSE, check.names = FALSE)
Methy27Des <- as.matrix(InData[, 1:4, drop = FALSE])
colnames(Methy27Des) <- c("REF", "GeneSymbol", "ChromosomeID", "CoordinateID")
ID <- which(Methy27Des[, "GeneSymbol"] == "DISCONTUNUED")
Methy27Des[ID, "GeneSymbol"] <- ""
Methy27Data <- as.matrix(InData[, 5:dim(InData)[2], drop = FALSE])
mode(Methy27Data) <- "numeric"
}
if (fileSource == "Firehose") {
# read in data
InData <- read.table(file = inputFilePath, header = TRUE, sep = "\t", na.string = "", quote = "", stringsAsFactors = FALSE, check.names = FALSE)
InData <- InData[2:dim(InData)[1], , drop = FALSE]
# remove redudant columns of CpG site descriptions
GeneSymbol <- as.matrix(InData[, seq(3, dim(InData)[2], 4), drop = FALSE])
ID <- which(GeneSymbol == "DISCONTUNUED")
GeneSymbol[ID] <- ""
GeneSymbol <- t(unique(t(GeneSymbol)))
ChromID <- as.matrix(InData[, seq(4, dim(InData)[2], 4), drop = FALSE])
ChromID <- t(unique(t(ChromID)))
CoordID <- as.matrix(InData[, seq(5, dim(InData)[2], 4), drop = FALSE])
CoordID <- t(unique(t(CoordID)))
Methy27Data <- as.matrix(InData[, seq(2, dim(InData)[2], 4), drop = FALSE])
mode(Methy27Data) <- "numeric"
REF <- InData[, 1]
# Check whether the probe information is the same between samples
if (dim(GeneSymbol)[2] != 1) {
stop("Some probe's gene symbols are not consistent between samples")
}
if (dim(ChromID)[2] != 1) {
stop("Some probe's chromosome IDs are not consistent between samples")
}
if (dim(CoordID)[2] != 1) {
stop("Some probe's genmoe coordinates are not consistent between samples")
}
ChromID <- toupper(ChromID)
Methy27Des <- cbind(REF, GeneSymbol, ChromID, CoordID)
colnames(Methy27Des) <- c("REF", "GeneSymbol", "ChromosomeID", "CoordinateID")
}
# some probes have more than one gene symbols, collpase the data
AddGeneDes <- matrix("", dim(Methy27Des)[1]*3, 4)
AddData <- matrix(NA, dim(Methy27Data)[1]*3, dim(Methy27Data)[2])
AddIndex <- 0
for (ProbeID in 1:dim(Methy27Des)[1]) {
GrepID <- grep(";", Methy27Des[ProbeID, "GeneSymbol"])
if (length(GrepID) > 0) {
TempStr <- strsplit(Methy27Des[ProbeID, "GeneSymbol"], split = ";")
Methy27Des[ProbeID, "GeneSymbol"] <- TempStr[[1]][1]
for (TempStri in 2:length(TempStr[[1]])) {
TempAddGeneDes <- c(Methy27Des[ProbeID, "REF"], TempStr[[1]][TempStri], Methy27Des[ProbeID, "ChromosomeID"], Methy27Des[ProbeID, "CoordinateID"])
AddIndex <- AddIndex + 1
AddGeneDes[AddIndex, ] <- TempAddGeneDes
AddData[AddIndex, ] <- Methy27Data[ProbeID, ]
}
}
}
colnames(AddGeneDes) <- colnames(Methy27Des)
colnames(AddData) <- colnames(Methy27Data)
Methy27Des <- rbind(Methy27Des, AddGeneDes[1:AddIndex, , drop = FALSE])
Methy27Data <- rbind(Methy27Data, AddData[1:AddIndex, , drop = FALSE])
Methy27Des[, "ChromosomeID"] <- gsub(" ", "", Methy27Des[, "ChromosomeID"])
Methy27Des[, "CoordinateID"] <- gsub(" ", "", Methy27Des[, "CoordinateID"])
OrderID <- order(as.numeric(Methy27Des[, "ChromosomeID"]), as.numeric(Methy27Des[, "CoordinateID"]), Methy27Des[, "GeneSymbol"], na.last = TRUE, decreasing = FALSE)
Methy27Des <- Methy27Des[OrderID, , drop = FALSE]
Methy27Data <- Methy27Data[OrderID, , drop = FALSE]
Data <- Methy27Data
Des <- Methy27Des
# check gene symbol
Des <- CheckGeneSymbol(Des)
lResult <- CombineRedundantFeature(Data = Data, Des = Des)
Data <- lResult$Data
Des <- lResult$Des
# Draw box plot
png(filename = paste(outputFileFolder, "/", outputFileName, "__boxplot.png", sep = ""), width = 30*dim(Data)[2]+300, height = 1500, units = "px")
par(las = 2, mai = c(4.5, 1, 0.5, 1), cex = 1.5)
boxplot(Data)
dev.off()
# save output data files
rownames(Data) <- NULL
rownames(Des) <- NULL
save(Data, Des, file = paste(outputFileFolder, "/", outputFileName, ".rda", sep = ""))
write.table(cbind(Des, Data), file = paste(outputFileFolder, "/", outputFileName, ".txt", sep = ""),
quote = FALSE, sep = "\t", na = "", col.names = TRUE, row.names = FALSE)
options(warn = 0)
return(list(Data = Data, Des = Des))
}
ProcessSomaticMutationData <- function(inputFilePath, outputFileName, outputFileFolder) {
options(warn = -1)
dir.create(path = outputFileFolder, recursive = TRUE)
# Read in data
InData <- read.table(file = inputFilePath, header = TRUE, sep = "\t", na.string = "", quote = "", stringsAsFactors = FALSE, check.names = FALSE)
InputData <- InData
InputData[, "Tumor_Sample_Barcode"] <- paste(InputData[, "Tumor_Sample_Barcode"], InputData[, "Matched_Norm_Sample_Barcode"], sep = "/")
colnames(InputData)[16] <- "Sample_Pair_Barcode"
InputData <- InputData[InputData[, "Mutation_Status"] == "Somatic", c("Hugo_Symbol",
"Entrez_Gene_Id",
"NCBI_Build",
"Chromosome",
"Start_Position",
"End_Position",
"Strand",
"Variant_Classification",
"Variant_Type",
"Reference_Allele",
"Tumor_Seq_Allele1",
"Tumor_Seq_Allele2",
"dbSNP_RS",
"dbSNP_Val_Status",
"Sample_Pair_Barcode",
"Match_Norm_Seq_Allele1",
"Match_Norm_Seq_Allele2",
"Tumor_Validation_Allele1",
"Tumor_Validation_Allele2",
"Match_Norm_Validation_Allele1",
"Match_Norm_Validation_Allele2",
"Verification_Status",
"Validation_Status")]
samplePair <- unique(InputData[, "Sample_Pair_Barcode"])
TumorAllele1 <- "Tumor_Seq_Allele1"
TumorAllele2 <- "Tumor_Seq_Allele2"
MatchNormAllele1 <- "Match_Norm_Seq_Allele1"
MatchNormAllele2 <- "Match_Norm_Seq_Allele2"
Input <- InputData[, c("Hugo_Symbol",
"NCBI_Build",
"Chromosome",
"Start_Position",
"End_Position",
"Strand",
"Variant_Classification",
"Variant_Type",
"Reference_Allele",
TumorAllele1,
TumorAllele2,
MatchNormAllele1,
MatchNormAllele2)]
IDLarge <- which(Input[, TumorAllele1] > Input[, TumorAllele2])
Y <- Input[IDLarge, TumorAllele1]
Input[IDLarge, TumorAllele1] <- Input[IDLarge, TumorAllele2]
Input[IDLarge, TumorAllele2] <- Y
rm(IDLarge)
IDLarge <- which(Input[, MatchNormAllele1] > Input[, MatchNormAllele2])
Y <- Input[IDLarge, MatchNormAllele1]
Input[IDLarge, MatchNormAllele1] <- Input[IDLarge, MatchNormAllele2]
Input[IDLarge, MatchNormAllele2] <- Y
rm(IDLarge)
A <- apply(Input, 1, paste, collapse = "|||")
DupID <- duplicated(A)
UniqueA <- A[sort(which(DupID == FALSE))]
Feature <- InputData[sort(which(DupID == FALSE)), -which(colnames(InputData) == "Sample_Pair_Barcode")]
FeatureMatrix <- matrix(0, length(UniqueA), length(samplePair))
colnames(FeatureMatrix) <- samplePair
for (i in 1:length(A)) {
RowIDi <- which(UniqueA == A[i])
ColIDi <- which(samplePair == InputData[i, "Sample_Pair_Barcode"])
FeatureMatrix[RowIDi, ColIDi] <- FeatureMatrix[RowIDi, ColIDi] + 1
}
colnames(Feature)[1] <- "GeneSymbol"
# check gene symbol
Des <- CheckGeneSymbol(Feature)
Des <- cbind(Des[, 1, drop = FALSE],
Description = apply(Des, 1, function(x){paste("chr",
x[4],
":",
as.numeric(x[5]),
"-",
as.numeric(x[6]),
",",
x[7],
",",
x[8],
",",
x[9],
",",
x[10],
x[10],
">",
x[11],
x[12],
sep = "")}),
Des[, seq(2, ncol(Des))])
Data <- FeatureMatrix
lResult <- CombineRedundantFeature(Data = Data, Des = Des)
Data <- lResult$Data
Des <- lResult$Des
# save output data files
rownames(Data) <- NULL
rownames(Des) <- NULL
save(Data, Des, file = paste(outputFileFolder, "/", outputFileName, "_mutationLevel.rda", sep = ""))
write.table(cbind(Des, Data), file = paste(outputFileFolder, "/", outputFileName, "_mutationLevel.txt", sep = ""),
quote = FALSE, sep = "\t", na = "", col.names = TRUE, row.names = FALSE)
# # draw and save boxplot
# png(filename = paste(outputFileFolder, "/", outputFileName, "__boxplot.png", sep = ""), width = 30*dim(Data)[2]+300, height = 1500, units = "px")
# par(las = 2, mai = c(4.5, 1, 0.5, 1), cex = 1.5)
# boxplot(Data)
# dev.off()
# output data files geneLevel (each row is a gene)
m_Data_geneLevel <- sapply(apply(Data, 2, function(x){split(x, Des$GeneSymbol)}), function(x){sapply(x, sum)})
v_variant <- sapply(split(Des$Variant_Classification, Des$GeneSymbol), function(x){paste(sort(unique(x)), collapse = '|')})
Data <- m_Data_geneLevel
Des <- cbind('GeneSymbol' = rownames(m_Data_geneLevel), 'Variant_Classification' = v_variant)
save(Data, Des, file = paste(outputFileFolder, "/", outputFileName, "_geneLevel.rda", sep = ""))
write.table(cbind(Des, Data), file = paste(outputFileFolder, "/", outputFileName, "_geneLevel.txt", sep = ""),
quote = FALSE, sep = "\t", na = "", col.names = T, row.names = F)
# # draw and save boxplot
# png(filename = paste(outputFileFolder, "/", outputFileName, "__boxplot_geneLevel.png", sep = ""), width = 30*dim(Data)[2]+300, height = 1500, units = "px")
# par(las = 2, mai = c(4.5, 1, 0.5, 1), cex = 1.5)
# boxplot(Data)
# dev.off()
options(warn = 0)
return(list(Data = Data, Des = Des))
}
ProcessCPTACData <- function(inputFilePath, outputFileName, outputFileFolder) {
options(warn = -1)
dir.create(path = outputFileFolder, recursive = TRUE)
###
saveFolderName <- outputFileFolder
out <- inputFilePath
d <- read.csv(out,
skip = 0,
sep = "\t",
as.is = T,
na.strings = "")
colnames(d)[1] <- "GeneSymbol"
vColNote <- c("GeneSymbol", "Description", "Organism", "Chromosome", "Locus")
mInfo <- as.matrix(d[, vColNote])
mData <- as.matrix(d[, setdiff(colnames(d), vColNote)])
colnames(mData) <- gsub("\\.", "-", colnames(mData))
colnames(mData) <- gsub("-Spectral-Counts", "-Log-Ratio", colnames(mData))
for (n in grep("^OVARIAN-CONTROL-", colnames(mData))) {
substr(colnames(mData)[n], 16, 16) <- '.'
}
for (n in grep("-[0-9][0-9]*-(Log|Unshared|Spectral)", colnames(mData))) {
substr(colnames(mData)[n], 17, 17) <- '.'
}
vnUnsh <- grep("-Unshared-Log-Ratio", colnames(mData))
vnShUn <- grep("[^d]-Log-Ratio", colnames(mData))
mDataShUn <- mData[, vnShUn] # barcode.Log-Ratio
mDataUnsh <- mData[, vnUnsh] # barcode.Unshared-Log-Ratio
colnames(mDataShUn) <- unlist(strsplit(colnames(mDataShUn),
split = "-Log-Ratio"))
colnames(mDataUnsh) <- unlist(strsplit(colnames(mDataUnsh),
split = "-Unshared-Log-Ratio"))
mDataShUn <- mDataShUn[, colnames(mDataShUn)]
mDataUnsh <- mDataUnsh[, colnames(mDataUnsh)]
stopifnot(all(colnames(mDataUnsh) == colnames(mDataShUn)))
# check gene symbol
Des <- CheckGeneSymbol(mInfo)
mode(mDataShUn) <- "numeric"
lResult <- CombineRedundantFeature(Data = mDataShUn, Des = Des)
Data <- lResult$Data
Des <- lResult$Des
# save output data files
rownames(Data) <- NULL
rownames(Des) <- NULL
save(Data, Des, file = paste(outputFileFolder, "/", outputFileName, "_allPeptides.rda", sep = ""))
write.table(cbind(Des, Data), file = paste(outputFileFolder, "/", outputFileName, "_allPeptides.txt", sep = ""),
quote = FALSE, sep = "\t", na = "", col.names = TRUE, row.names = FALSE)
# draw and save boxplot
png(filename = paste(outputFileFolder, "/", outputFileName, "_allPeptides__boxplot.png", sep = ""), width = 30*dim(Data)[2]+300, height = 1500, units = "px")
par(las = 2, mai = c(4.5, 1, 0.5, 1), cex = 1.5)
boxplot(Data)
dev.off()
lShUn <- list(Data = Data, Des = Des)
# check gene symbol
Des <- CheckGeneSymbol(mInfo)
mode(mDataUnsh) <- "numeric"
lResult <- CombineRedundantFeature(Data = mDataUnsh, Des = Des)
Data <- lResult$Data
Des <- lResult$Des
# save output data files
rownames(Data) <- NULL
rownames(Des) <- NULL
save(Data, Des, file = paste(outputFileFolder, "/", outputFileName, "_unsharedPeptides.rda", sep = ""))
write.table(cbind(Des, Data), file = paste(outputFileFolder, "/", outputFileName, "_unsharedPeptides.txt", sep = ""),
quote = FALSE, sep = "\t", na = "", col.names = TRUE, row.names = FALSE)
# draw and save boxplot
png(filename = paste(outputFileFolder, "/", outputFileName, "_unsharedPeptides__boxplot.png", sep = ""), width = 30*dim(Data)[2]+300, height = 1500, units = "px")
par(las = 2, mai = c(4.5, 1, 0.5, 1), cex = 1.5)
boxplot(Data)
dev.off()
lUnsh <- list(Data = Data, Des = Des)
options(warn = 0)
return(list(allPeptides = lShUn, unsharedPeptides = lUnsh))
}
# =============================================================================
# Auxiliary Functions of Module B
# =============================================================================
ToPatientData <- function(TCGAData) {
TCGABarcode <- colnames(TCGAData)
TCGABarcode <- sapply(strsplit(TCGABarcode, split = "-"), function(x){
Str = paste(x[1], x[2], x[3], substr(x[4], 1, 2), sep = "-")
Str
})
DuplicatedLabel <- duplicated(TCGABarcode)
ID <- which(DuplicatedLabel == FALSE)
TCGAData <- TCGAData[, ID, drop = FALSE]
TCGABarcode <- TCGABarcode[ID]
colnames(TCGAData) <- TCGABarcode
TCGAData
}
CombineRedundantFeature <- function(Data, Des) {
RowName <- Des[, 1]
if (dim(Des)[2] > 1) {
for (i in 2:dim(Des)[2]) {
RowName <- paste(RowName, Des[, i], sep = "||")
}
}
UniqueRowName <- unique(RowName)
if (length(RowName) == length(UniqueRowName)) {
lResult <- list(Data = Data, Des = Des)
}
else {
dupRowName <- unique(RowName[which(duplicated(RowName))])
IDKeep <- which(!duplicated(RowName))
for (i in 1:length(dupRowName)) {
IDi <- which(RowName == dupRowName[i])
IDKeepi <- intersect(IDKeep, IDi)
Data[IDKeepi, ] <- apply(Data[IDi, , drop = FALSE], 2, mean, na.rm = TRUE)
}
IDKeep <- sort(IDKeep)
lResult <- list(Data = Data[IDKeep, , drop = FALSE], Des = Des[IDKeep, , drop = FALSE])
}
lResult
}
Normalize.quantiles <- function(Data) {
VectorData <- as.vector(Data)
VectorData <- VectorData[!is.na(VectorData)]
VectorData <- sort(VectorData, decreasing = FALSE)
NumVectorData <- length(VectorData)
NormData <- matrix(NA, dim(Data)[1], dim(Data)[2])
for (i in 1:dim(Data)[2]) {
if ((i %% 10 == 0) || (i == dim(Data)[2])) {
writeLines(paste("Normalizing data, ", round(i/dim(Data)[2]*100, digits = 2), "% done.", sep = ""))
}
IDNonNA <- which(!is.na(Data[, i]))
RankNonNA <- rank(Data[IDNonNA, i], ties.method = "average")
NumNonNA <- length(IDNonNA)
Sep <- round(seq(1, NumVectorData, (NumVectorData-1)/NumNonNA))
NormData[IDNonNA, i] <- sapply(RankNonNA, function(x) {
NumTie = sum(RankNonNA == x)
mean(VectorData[(Sep[ceiling(x-NumTie/2)]+1):Sep[floor(x+NumTie/2)+1]])
})
}
rownames(NormData) <- rownames(Data)
colnames(NormData) <- colnames(Data)
NormData
}
GetMethylation450Data <- function(InputFileName, OutputFileFolder, outputFileName) {
OriginalOutputFileFolder <- OutputFileFolder