R/readTCGA.R
In RTCGA/RTCGA: The Cancer Genome Atlas Data Integration
Defines functions
read.rnaseq
read.methylation
read.clinical
readTCGA
Documented in
readTCGA
## RTCGA package for R
#' @title Read TCGA data to the tidy Format
#'
#' @description \code{readTCGA} function allows to read unzipped files:
#' \itemize{
#' \item clinical data - \code{Merge_Clinical.Level_1}
#' \item rnaseq data (genes' expressions) - \code{rnaseqv2__illuminahiseq_rnaseqv2}
#' \item genes' mutations data - \code{Mutation_Packager_Calls.Level}
#' \item Reverse phase protein array data (RPPA) - \code{protein_normalization__data.Level_3}
#' \item Merge transcriptome agilent data (mRNA) -
#' \code{Merge_transcriptome__agilentg4502a_07_3__unc_edu__Level_3__unc_lowess_normalization_gene_level__data.Level_3}
#' \item miRNASeq data -
#' \code{Merge_mirnaseq__illuminaga_mirnaseq__bcgsc_ca__Level_3__miR_gene_expression__data.Level_3} or
#' \code{"Merge_mirnaseq__illuminahiseq_mirnaseq__bcgsc_ca__Level_3__miR_gene_expression__data.Level_3"}
#' \item methylation data -
#' \code{Merge_methylation__humanmethylation27}
#' \item isoforms data -
#' \code{Merge_rnaseqv2__illuminahiseq_rnaseqv2__unc_edu__Level_3__RSEM_isoforms_normalized__data.Level_3}
#' \item CNV data - \code{segmented_scna_minus_germline_cnv_hg19}
#' }
#' from TCGA project. Those files can be easily downloded with \link{downloadTCGA} function. See examples.
#'
#' @param path See details and examples.
#'
#' @param dataType One of \code{'clinical', 'rnaseq', 'mutations', 'RPPA', 'mRNA', 'miRNASeq', 'methylation', 'isoforms', 'CNV'} depending on which type of data user is trying to read in the tidy format.
#' @param ... Further arguments passed to the \link{as.data.frame}.
#'
#' @return
#' An output is a \code{data.frame} with \code{dataType} data.
#'
#' @details
#' All cohort names can be checked using: \code{ sub( x = names( infoTCGA() ), '-counts', '')}.
#'
#' Parameter \code{path} specification:
#' \itemize{
#' \item If \code{dataType = 'clinical'} a path to a \code{cancerType.clin.merged.txt} file.
#' \item If \code{dataType = 'mutations'} a path to the unzziped folder \code{Mutation_Packager_Calls.Level} containing \code{.maf} files.
#' \item If \code{dataType = 'rnaseq'} a path to the uzziped file \code{rnaseqv2__illuminahiseq_rnaseqv2__unc_edu__Level_3__RSEM_genes_normalized__data.Level}.
#' \item If \code{dataType = 'RPPA'} a path to the unzipped file in folder \code{protein_normalization__data.Level_3}.
#' \item If \code{dataType = 'mRNA'} a path to the unzipped file \code{cancerType.transcriptome__agilentg4502a_07_3__unc_edu__Level_3__unc_lowess_normalization_gene_level__data.data.txt}.
#' \item If \code{dataType = 'miRNASeq'} a path to unzipped files \code{cancerType.mirnaseq__illuminahiseq_mirnaseq__bcgsc_ca__Level_3__miR_gene_expression__data.data.txt} or \code{cancerType.mirnaseq__illuminaga_mirnaseq__bcgsc_ca__Level_3__miR_gene_expression__data.data.txt}
#' \item If \code{dataType = 'methylation'} a path to unzipped files \code{cancerType.methylation__humanmethylation27__jhu_usc_edu__Level_3__within_bioassay_data_set_function__data.data.txt}.
#' \item If \code{dataType = 'isoforms'} a path to unzipped files \code{cancerType.rnaseqv2__illuminahiseq_rnaseqv2__unc_edu__Level_3__RSEM_isoforms_normalized__data.data.txt}.
#' \item If \code{dataType = 'CNV'} a path to unzipped files \code{cancerType.Merge_snp__genome_wide_snp_6__broad_mit_edu__Level_3__segmented_scna_minus_germline_cnv_hg18__seg.Level_3.txt}.
#' }
#'
#'
#' @section Issues:
#'
#' If you have any problems, issues or think that something is missing or is not
#' clear please post an issue on
#' \href{https://github.com/RTCGA/RTCGA/issues}{https://github.com/RTCGA/RTCGA/issues}.
#'
#' @author
#' Marcin Kosinski, \email{m.p.kosinski@@gmail.com}
#'
#' Witold Chodor, \email{witoldchodor@@gmail.com}
#'
#' @seealso
#'
#' \pkg{RTCGA} website \href{http://rtcga.github.io/RTCGA/articles/Data_Download.html}{http://rtcga.github.io/RTCGA/articles/Data_Download.html}.
#'
#' @examples
#'
#' \dontrun{
#'
#' ##############
#' ##### clinical
#' ##############
#'
#' dir.create('data')
#'
#' # downloading clinical data
#' # dataset = "clinical" is default parameter so we may omit it
#' downloadTCGA(cancerTypes = c('BRCA', 'OV'),
#' destDir = 'data' )
#' # shorten paths so that they are shorter than 256 signs - windows issue
#' list.files("data", full.names = TRUE) %>%
#' file.rename(to = substr(., start = 1, stop = 50))
#'
#' # reading datasets
#' sapply(c('BRCA', 'OV'), function(element){
#' path <- list.files('data', recursive = TRUE,
#' full.names = TRUE,
#' patten = "clin.merged.txt")
#' assign(value = readTCGA( path, 'clinical' ),
#' x = paste0(element, '.clin.data'),
#' envir = .GlobalEnv)})
#'
#' ############
#' ##### rnaseq
#' ############
#'
#' dir.create('data2')
#'
#' # downloading rnaseq data
#' downloadTCGA(cancerTypes = 'BRCA',
#' dataSet = 'Level_3__RSEM_genes_normalized',
#' destDir = 'data2')
#'
#' # shorten paths so that they are shorter than 256 signs - windows issue
#' list.files("data2", full.names = TRUE) %>%
#' file.rename(to = substr(., start = 1, stop = 50))
#'
#' path_rnaseq <- list.files('data2', recursive = TRUE,
#' full.names = TRUE,
#' patten = 'illuminahiseq')
#' readTCGA(path = pathRNA, dataType = 'rnaseq') -> rnaseq_data
#'
#'
#' ###############
#' ##### mutations
#' ###############
#'
#' # Example directory in which untarred data will be stored
#' dir.create('data3')
#'
#'
#' downloadTCGA(cancerTypes = 'OV',
#' dataSet = 'Mutation_Packager_Calls.Level',
#' destDir = 'data3')
#'
#' # reading data
#' list.files('data3', recursive = TRUE) -> directory
#'
#' readTCGA(directory, 'mutations') -> mut_file
#'
#' #################
#' ##### methylation
#' #################
#'
#' # Example directory in which untarred data will be stored
#' dir.create('data4')
#'
#' # Download KIRP methylation data and store it in data4 folder
#' cancerType = "KIRP"
#' downloadTCGA(cancerTypes = cancerType,
#' dataSet = "Merge_methylation__humanmethylation27",
#' destDir = "data4")
#'
#' # Shorten path of subdirectory with KIRP methylation data
#' list.files(path = "data4", full.names = TRUE) %>%
#' file.rename(to = file.path("data4", paste0(cancerType, ".methylation")))
#'
#' # Remove manifest.txt file
#' list.files(path = "data4", full.names = TRUE,
#' recursive = TRUE, pattern = "MANIFEST") %>%
#' file.remove()
#'
#' # Read KIRP methylation data
#' path <- list.files(path = "data4", full.names = TRUE, recursive = TRUE)
#' KIRP.methylation <- readTCGA(path, dataType = "methylation")
#'
#'
#' ##########
#' ##### RPPA
#' ##########
#'
#' # Directory in which untarred data will be stored
#' dir.create('data5')
#'
#' # Download BRCA RPPA data and store it in data5 folder
#' cancerType = "BRCA"
#' downloadTCGA(cancerTypes = cancerType,
#' dataSet = "protein_normalization__data.Level_3",
#' destDir = "data5")
#'
#' # Shorten path of subdirectory with BRCA RPPA data
#' list.files(path = "data5", full.names = TRUE) %>%
#' file.rename(from = ., to = file.path("data5", paste0(cancerType, ".RPPA")))
#'
#' # Remove manifest.txt file
#' list.files(path = "data5", full.names = TRUE,
#' recursive = TRUE, pattern = "MANIFEST") %>%
#' file.remove()
#'
#' # Read BRCA RPPA data
#' path <- list.files(path = "data5", full.names = TRUE, recursive = TRUE)
#' BRCA.RPPA <- readTCGA(path, dataType = "RPPA")
#'
#'
#' ##########
#' ##### mRNA
#' ##########
#'
#' # Directory in which untarred data will be stored
#' dir.create('data6')
#'
#' # Download UCEC mRNA data and store it in data6 folder
#' cancerType = "UCEC"
#' downloadTCGA(cancerTypes = cancerType,
#' dataSet = "agilentg4502a_07_3__unc_edu__Level_3",
#' destDir = "data6")
#'
#' # Shorten path of subdirectory with UCEC mRNA data
#' list.files(path = "data6", full.names = TRUE) %>%
#' file.rename(from = ., to = file.path("data6",paste0(cancerType, ".mRNA")))
#'
#' # Remove manifest.txt file
#' list.files(path = "data6", full.names = TRUE,
#' recursive = TRUE, pattern = "MANIFEST") %>%
#' file.remove()
#'
#' # Read UCEC mRNA data
#' path <- list.files(path = "data6", full.names = TRUE, recursive = TRUE)
#' UCEC.mRNA <- readTCGA(path, dataType = "mRNA")
#'
#' ##############
#' ##### miRNASeq
#' ##############
#'
#' # Directory in which untarred data will be stored
#' dir.create('data7')
#'
#' # Download BRCA miRNASeq data and store it in data7 folder
#' # Remember that miRNASeq data are produced by two machines:
#' # Illumina Genome Analyzer and Illumina HiSeq 2000 machines
#' cancerType <- "BRCA"
#' downloadTCGA(cancerTypes = cancerType,
#' dataSet = paste0("Merge_mirnaseq__illuminaga_mirnaseq__bcgsc",
#' "_ca__Level_3__miR_gene_expression__data.Level_3"),
#' destDir = "data7")
#'
#' downloadTCGA(cancerTypes = cancerType,
#' dataSet = paste0("Merge_mirnaseq__illuminahiseq_mirnaseq__",
#' "bcgsc_ca__Level_3__miR_gene_expression__data.Level_3"),
#' destDir = "data7")
#'
#' # Shorten path of subdirectory with BRCA miRNASeq data
#' list.files(path = "data7", full.names = TRUE) %>%
#' sapply(function(path){
#' if (grepl(pattern = "illuminaga", path)){
#' file.rename(from = grep(pattern = "illuminaga", path, value = TRUE),
#' to = file.path("data7",paste0(cancerType, ".miRNASeq.illuminaga")))
#' } else if (grepl(pattern = "illuminahiseq", path)){
#' file.rename(from = grep(pattern = "illuminahiseq", path, value = TRUE),
#' to = file.path("data7",paste0(cancerType, ".miRNASeq.illuminahiseq")))
#' }
#' })
#'
#' # Remove manifest.txt file
#' list.files(path = "data6", full.names = TRUE,
#' recursive = TRUE, pattern = "MANIFEST") %>%
#' file.remove()
#'
#' # Read BRCA miRNASeq data
#' path <- list.files(path = "data7", full.names = TRUE, recursive = TRUE)
#' path_illuminaga <- grep("illuminaga", path, fixed = TRUE, value = TRUE)
#' path_illuminahiseq <- grep("illuminahiseq", path, fixed = TRUE, value = TRUE)
#'
#' BRCA.miRNASeq.illuminaga <- readTCGA(path_illuminaga, dataType = "miRNASeq")
#' BRCA.miRNASeq.illuminahiseq <- readTCGA(path_illuminahiseq, dataType = "miRNASeq")
#'
#' BRCA.miRNASeq.illuminaga <- cbind(machine = "Illumina Genome Analyzer",
#' BRCA.miRNASeq.illuminaga)
#' BRCA.miRNASeq.illuminahiseq <- cbind(machine = "Illumina HiSeq 2000",
#' BRCA.miRNASeq.illuminahiseq)
#'
#' BRCA.miRNASeq <- rbind(BRCA.miRNASeq.illuminaga, BRCA.miRNASeq.illuminahiseq)
#'
#' ##############
#' ##### isoforms
#' ##############
#'
#' # Directory in which untarred data will be stored
#' dir.create('data8')
#'
#' # Download ACC isoforms data and store it in data8 folder
#' cancerType = "ACC"
#' downloadTCGA(cancerTypes = cancerType,
#' dataSet = paste0("Merge_rnaseqv2__illuminahiseq_rnaseqv2__unc",
#' "_edu__Level_3__RSEM_isoforms_normalized__data.Level_3"),
#' destDir = "data8")
#'
#' # Shorten path of subdirectory with ACC isoforms data
#' list.files(path = "data8", full.names = TRUE) %>%
#' file.rename(from = ., to = file.path("data8",paste0(cancerType, ".isoforms")))
#'
#' # Remove manifest.txt file
#' list.files(path = "data6", full.names = TRUE,
#' recursive = TRUE, pattern = "MANIFEST") %>%
#' file.remove()
#'
#' # Read ACC isoforms data
#' path <- list.files(path = "data8", full.names = TRUE, recursive = TRUE)
#' ACC.isoforms <- readTCGA(path, dataType = "isoforms")
#'
#' }
#'
#' @family RTCGA
#' @rdname readTCGA
#' @export
readTCGA <- function(path, dataType, ...) {
assertthat::assert_that(is.character(path) & length(path) == 1)
assertthat::assert_that(is.character(dataType) & length(dataType) == 1)
assertthat::assert_that(dataType %in% c("clinical", "rnaseq", "mutations", "RPPA", "mRNA",
"miRNASeq", "methylation", "isoforms", "CNV"))
if (dataType %in% c("clinical", "miRNASeq")) {
return(read.clinical(path, ...))
}
if (dataType %in% c("methylation")) {
return(read.methylation(path, ...))
}
if (dataType %in% c("rnaseq", "RPPA", "mRNA", "isoforms")) {
return(read.rnaseq(path, ...))
}
if (dataType == "mutations") {
return(read.mutations(path, ...))
}
if (dataType == "CNV") {
return(fread(path, data.table = FALSE, ...))
}
}
read.clinical <- function(clinicalDir, ...) {
comboClinical <- fread(clinicalDir, data.table = FALSE)
colNames <- comboClinical[, 1]
comboClinical <- as.data.frame(t(comboClinical[, -1]), ...)
names(comboClinical) <- colNames
return(comboClinical)
}
read.methylation <- function(methylationDir, ...){
methylationData <- fread(methylationDir, data.table = FALSE)
first_row <- methylationData[1,]
Beta_value_column <- which(first_row == "Beta_value")
methylationData <- methylationData[,c(1, 3, 4, 5, Beta_value_column)]
colNames <- methylationData[,1]
methylationData <- as.data.frame(t(methylationData[,-1]), ...)
names(methylationData) <- colNames
row.names(methylationData)[1:3] <- c("TCGA-05.1", "TCGA-05.2", "TCGA-05.3")
x <- row.names(methylationData)[4]
row.names(methylationData)[4] <- sub("\\.3$", "", x)
methylationDataNumeric <- methylationData[-c(1:3), -1]
apply(methylationDataNumeric, MARGIN = 2, function(x){
as.numeric(as.character(x))
}) -> methylationDataNumeric
methylationDataCodes <- cbind(bcr_patient_barcode = row.names(methylationData)[-c(1:3)],
as.data.frame(methylationDataNumeric))
#attr(methylationDataCodes, "info") <- methylationData[c(1:3), -1]
return(methylationDataCodes)
}
read.rnaseq <- function(rnaseqDir, ...) {
rnaseqData <- fread(rnaseqDir, data.table = FALSE) %>% t()
colnames(rnaseqData) <- rnaseqData[1, ]
barcodes <- rownames(rnaseqData)
rnaseqData <- rnaseqData[-1, -1] %>%
apply(2, function(x) as.numeric(as.character(x))) %>%
as.data.frame(x = .) %>%
cbind(bcr_patient_barcode = barcodes[-1], .)
return(rnaseqData)
}
read.mutations <- function(mutationsDir, ...) {
element <- mutationsDir
maf_files <- list.files(element) %>% file.path(element, .) %>% grep(x = ., pattern = "TCGA", value = TRUE)
# there are extra manifest.txt files
barcodes <- list.files(element) %>% grep(x = ., pattern = "TCGA", value = TRUE) %>% substr(start = 1, stop = 15)
pb <- txtProgressBar(min = 0, max = length(maf_files), style = 3)
tmp <- tempfile()
for (i in seq_along(maf_files)) {
maf_data <- read.delim(maf_files[i])
n_col <- ncol(maf_data) + 1
maf_data[, n_col] <- barcodes[i]
names(maf_data)[n_col] <- "bcr_patient_barcode"
invisible(write.table(x = maf_data, sep = "\t", file = tmp, append = TRUE, col.names = i==1, quote = FALSE, row.names = FALSE))
setTxtProgressBar(pb, i)
}
close(pb)
mutations_file <- read.delim(tmp)
file.remove(tmp)
return(mutations_file)
}
RTCGA/RTCGA documentation built on Nov. 1, 2022, 8:15 p.m.
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Defines functions read.rnaseq read.methylation read.clinical readTCGA
Documented in readTCGA
## RTCGA package for R
#' @title Read TCGA data to the tidy Format
#'
#' @description \code{readTCGA} function allows to read unzipped files:
#' \itemize{
#' \item clinical data - \code{Merge_Clinical.Level_1}
#' \item rnaseq data (genes' expressions) - \code{rnaseqv2__illuminahiseq_rnaseqv2}
#' \item genes' mutations data - \code{Mutation_Packager_Calls.Level}
#' \item Reverse phase protein array data (RPPA) - \code{protein_normalization__data.Level_3}
#' \item Merge transcriptome agilent data (mRNA) -
#' \code{Merge_transcriptome__agilentg4502a_07_3__unc_edu__Level_3__unc_lowess_normalization_gene_level__data.Level_3}
#' \item miRNASeq data -
#' \code{Merge_mirnaseq__illuminaga_mirnaseq__bcgsc_ca__Level_3__miR_gene_expression__data.Level_3} or
#' \code{"Merge_mirnaseq__illuminahiseq_mirnaseq__bcgsc_ca__Level_3__miR_gene_expression__data.Level_3"}
#' \item methylation data -
#' \code{Merge_methylation__humanmethylation27}
#' \item isoforms data -
#' \code{Merge_rnaseqv2__illuminahiseq_rnaseqv2__unc_edu__Level_3__RSEM_isoforms_normalized__data.Level_3}
#' \item CNV data - \code{segmented_scna_minus_germline_cnv_hg19}
#' }
#' from TCGA project. Those files can be easily downloded with \link{downloadTCGA} function. See examples.
#'
#' @param path See details and examples.
#'
#' @param dataType One of \code{'clinical', 'rnaseq', 'mutations', 'RPPA', 'mRNA', 'miRNASeq', 'methylation', 'isoforms', 'CNV'} depending on which type of data user is trying to read in the tidy format.
#' @param ... Further arguments passed to the \link{as.data.frame}.
#'
#' @return
#' An output is a \code{data.frame} with \code{dataType} data.
#'
#' @details
#' All cohort names can be checked using: \code{ sub( x = names( infoTCGA() ), '-counts', '')}.
#'
#' Parameter \code{path} specification:
#' \itemize{
#' \item If \code{dataType = 'clinical'} a path to a \code{cancerType.clin.merged.txt} file.
#' \item If \code{dataType = 'mutations'} a path to the unzziped folder \code{Mutation_Packager_Calls.Level} containing \code{.maf} files.
#' \item If \code{dataType = 'rnaseq'} a path to the uzziped file \code{rnaseqv2__illuminahiseq_rnaseqv2__unc_edu__Level_3__RSEM_genes_normalized__data.Level}.
#' \item If \code{dataType = 'RPPA'} a path to the unzipped file in folder \code{protein_normalization__data.Level_3}.
#' \item If \code{dataType = 'mRNA'} a path to the unzipped file \code{cancerType.transcriptome__agilentg4502a_07_3__unc_edu__Level_3__unc_lowess_normalization_gene_level__data.data.txt}.
#' \item If \code{dataType = 'miRNASeq'} a path to unzipped files \code{cancerType.mirnaseq__illuminahiseq_mirnaseq__bcgsc_ca__Level_3__miR_gene_expression__data.data.txt} or \code{cancerType.mirnaseq__illuminaga_mirnaseq__bcgsc_ca__Level_3__miR_gene_expression__data.data.txt}
#' \item If \code{dataType = 'methylation'} a path to unzipped files \code{cancerType.methylation__humanmethylation27__jhu_usc_edu__Level_3__within_bioassay_data_set_function__data.data.txt}.
#' \item If \code{dataType = 'isoforms'} a path to unzipped files \code{cancerType.rnaseqv2__illuminahiseq_rnaseqv2__unc_edu__Level_3__RSEM_isoforms_normalized__data.data.txt}.
#' \item If \code{dataType = 'CNV'} a path to unzipped files \code{cancerType.Merge_snp__genome_wide_snp_6__broad_mit_edu__Level_3__segmented_scna_minus_germline_cnv_hg18__seg.Level_3.txt}.
#' }
#'
#'
#' @section Issues:
#'
#' If you have any problems, issues or think that something is missing or is not
#' clear please post an issue on
#' \href{https://github.com/RTCGA/RTCGA/issues}{https://github.com/RTCGA/RTCGA/issues}.
#'
#' @author
#' Marcin Kosinski, \email{m.p.kosinski@@gmail.com}
#'
#' Witold Chodor, \email{witoldchodor@@gmail.com}
#'
#' @seealso
#'
#' \pkg{RTCGA} website \href{http://rtcga.github.io/RTCGA/articles/Data_Download.html}{http://rtcga.github.io/RTCGA/articles/Data_Download.html}.
#'
#' @examples
#'
#' \dontrun{
#'
#' ##############
#' ##### clinical
#' ##############
#'
#' dir.create('data')
#'
#' # downloading clinical data
#' # dataset = "clinical" is default parameter so we may omit it
#' downloadTCGA(cancerTypes = c('BRCA', 'OV'),
#' destDir = 'data' )
#' # shorten paths so that they are shorter than 256 signs - windows issue
#' list.files("data", full.names = TRUE) %>%
#' file.rename(to = substr(., start = 1, stop = 50))
#'
#' # reading datasets
#' sapply(c('BRCA', 'OV'), function(element){
#' path <- list.files('data', recursive = TRUE,
#' full.names = TRUE,
#' patten = "clin.merged.txt")
#' assign(value = readTCGA( path, 'clinical' ),
#' x = paste0(element, '.clin.data'),
#' envir = .GlobalEnv)})
#'
#' ############
#' ##### rnaseq
#' ############
#'
#' dir.create('data2')
#'
#' # downloading rnaseq data
#' downloadTCGA(cancerTypes = 'BRCA',
#' dataSet = 'Level_3__RSEM_genes_normalized',
#' destDir = 'data2')
#'
#' # shorten paths so that they are shorter than 256 signs - windows issue
#' list.files("data2", full.names = TRUE) %>%
#' file.rename(to = substr(., start = 1, stop = 50))
#'
#' path_rnaseq <- list.files('data2', recursive = TRUE,
#' full.names = TRUE,
#' patten = 'illuminahiseq')
#' readTCGA(path = pathRNA, dataType = 'rnaseq') -> rnaseq_data
#'
#'
#' ###############
#' ##### mutations
#' ###############
#'
#' # Example directory in which untarred data will be stored
#' dir.create('data3')
#'
#'
#' downloadTCGA(cancerTypes = 'OV',
#' dataSet = 'Mutation_Packager_Calls.Level',
#' destDir = 'data3')
#'
#' # reading data
#' list.files('data3', recursive = TRUE) -> directory
#'
#' readTCGA(directory, 'mutations') -> mut_file
#'
#' #################
#' ##### methylation
#' #################
#'
#' # Example directory in which untarred data will be stored
#' dir.create('data4')
#'
#' # Download KIRP methylation data and store it in data4 folder
#' cancerType = "KIRP"
#' downloadTCGA(cancerTypes = cancerType,
#' dataSet = "Merge_methylation__humanmethylation27",
#' destDir = "data4")
#'
#' # Shorten path of subdirectory with KIRP methylation data
#' list.files(path = "data4", full.names = TRUE) %>%
#' file.rename(to = file.path("data4", paste0(cancerType, ".methylation")))
#'
#' # Remove manifest.txt file
#' list.files(path = "data4", full.names = TRUE,
#' recursive = TRUE, pattern = "MANIFEST") %>%
#' file.remove()
#'
#' # Read KIRP methylation data
#' path <- list.files(path = "data4", full.names = TRUE, recursive = TRUE)
#' KIRP.methylation <- readTCGA(path, dataType = "methylation")
#'
#'
#' ##########
#' ##### RPPA
#' ##########
#'
#' # Directory in which untarred data will be stored
#' dir.create('data5')
#'
#' # Download BRCA RPPA data and store it in data5 folder
#' cancerType = "BRCA"
#' downloadTCGA(cancerTypes = cancerType,
#' dataSet = "protein_normalization__data.Level_3",
#' destDir = "data5")
#'
#' # Shorten path of subdirectory with BRCA RPPA data
#' list.files(path = "data5", full.names = TRUE) %>%
#' file.rename(from = ., to = file.path("data5", paste0(cancerType, ".RPPA")))
#'
#' # Remove manifest.txt file
#' list.files(path = "data5", full.names = TRUE,
#' recursive = TRUE, pattern = "MANIFEST") %>%
#' file.remove()
#'
#' # Read BRCA RPPA data
#' path <- list.files(path = "data5", full.names = TRUE, recursive = TRUE)
#' BRCA.RPPA <- readTCGA(path, dataType = "RPPA")
#'
#'
#' ##########
#' ##### mRNA
#' ##########
#'
#' # Directory in which untarred data will be stored
#' dir.create('data6')
#'
#' # Download UCEC mRNA data and store it in data6 folder
#' cancerType = "UCEC"
#' downloadTCGA(cancerTypes = cancerType,
#' dataSet = "agilentg4502a_07_3__unc_edu__Level_3",
#' destDir = "data6")
#'
#' # Shorten path of subdirectory with UCEC mRNA data
#' list.files(path = "data6", full.names = TRUE) %>%
#' file.rename(from = ., to = file.path("data6",paste0(cancerType, ".mRNA")))
#'
#' # Remove manifest.txt file
#' list.files(path = "data6", full.names = TRUE,
#' recursive = TRUE, pattern = "MANIFEST") %>%
#' file.remove()
#'
#' # Read UCEC mRNA data
#' path <- list.files(path = "data6", full.names = TRUE, recursive = TRUE)
#' UCEC.mRNA <- readTCGA(path, dataType = "mRNA")
#'
#' ##############
#' ##### miRNASeq
#' ##############
#'
#' # Directory in which untarred data will be stored
#' dir.create('data7')
#'
#' # Download BRCA miRNASeq data and store it in data7 folder
#' # Remember that miRNASeq data are produced by two machines:
#' # Illumina Genome Analyzer and Illumina HiSeq 2000 machines
#' cancerType <- "BRCA"
#' downloadTCGA(cancerTypes = cancerType,
#' dataSet = paste0("Merge_mirnaseq__illuminaga_mirnaseq__bcgsc",
#' "_ca__Level_3__miR_gene_expression__data.Level_3"),
#' destDir = "data7")
#'
#' downloadTCGA(cancerTypes = cancerType,
#' dataSet = paste0("Merge_mirnaseq__illuminahiseq_mirnaseq__",
#' "bcgsc_ca__Level_3__miR_gene_expression__data.Level_3"),
#' destDir = "data7")
#'
#' # Shorten path of subdirectory with BRCA miRNASeq data
#' list.files(path = "data7", full.names = TRUE) %>%
#' sapply(function(path){
#' if (grepl(pattern = "illuminaga", path)){
#' file.rename(from = grep(pattern = "illuminaga", path, value = TRUE),
#' to = file.path("data7",paste0(cancerType, ".miRNASeq.illuminaga")))
#' } else if (grepl(pattern = "illuminahiseq", path)){
#' file.rename(from = grep(pattern = "illuminahiseq", path, value = TRUE),
#' to = file.path("data7",paste0(cancerType, ".miRNASeq.illuminahiseq")))
#' }
#' })
#'
#' # Remove manifest.txt file
#' list.files(path = "data6", full.names = TRUE,
#' recursive = TRUE, pattern = "MANIFEST") %>%
#' file.remove()
#'
#' # Read BRCA miRNASeq data
#' path <- list.files(path = "data7", full.names = TRUE, recursive = TRUE)
#' path_illuminaga <- grep("illuminaga", path, fixed = TRUE, value = TRUE)
#' path_illuminahiseq <- grep("illuminahiseq", path, fixed = TRUE, value = TRUE)
#'
#' BRCA.miRNASeq.illuminaga <- readTCGA(path_illuminaga, dataType = "miRNASeq")
#' BRCA.miRNASeq.illuminahiseq <- readTCGA(path_illuminahiseq, dataType = "miRNASeq")
#'
#' BRCA.miRNASeq.illuminaga <- cbind(machine = "Illumina Genome Analyzer",
#' BRCA.miRNASeq.illuminaga)
#' BRCA.miRNASeq.illuminahiseq <- cbind(machine = "Illumina HiSeq 2000",
#' BRCA.miRNASeq.illuminahiseq)
#'
#' BRCA.miRNASeq <- rbind(BRCA.miRNASeq.illuminaga, BRCA.miRNASeq.illuminahiseq)
#'
#' ##############
#' ##### isoforms
#' ##############
#'
#' # Directory in which untarred data will be stored
#' dir.create('data8')
#'
#' # Download ACC isoforms data and store it in data8 folder
#' cancerType = "ACC"
#' downloadTCGA(cancerTypes = cancerType,
#' dataSet = paste0("Merge_rnaseqv2__illuminahiseq_rnaseqv2__unc",
#' "_edu__Level_3__RSEM_isoforms_normalized__data.Level_3"),
#' destDir = "data8")
#'
#' # Shorten path of subdirectory with ACC isoforms data
#' list.files(path = "data8", full.names = TRUE) %>%
#' file.rename(from = ., to = file.path("data8",paste0(cancerType, ".isoforms")))
#'
#' # Remove manifest.txt file
#' list.files(path = "data6", full.names = TRUE,
#' recursive = TRUE, pattern = "MANIFEST") %>%
#' file.remove()
#'
#' # Read ACC isoforms data
#' path <- list.files(path = "data8", full.names = TRUE, recursive = TRUE)
#' ACC.isoforms <- readTCGA(path, dataType = "isoforms")
#'
#' }
#'
#' @family RTCGA
#' @rdname readTCGA
#' @export
readTCGA <- function(path, dataType, ...) {
assertthat::assert_that(is.character(path) & length(path) == 1)
assertthat::assert_that(is.character(dataType) & length(dataType) == 1)
assertthat::assert_that(dataType %in% c("clinical", "rnaseq", "mutations", "RPPA", "mRNA",
"miRNASeq", "methylation", "isoforms", "CNV"))
if (dataType %in% c("clinical", "miRNASeq")) {
return(read.clinical(path, ...))
}
if (dataType %in% c("methylation")) {
return(read.methylation(path, ...))
}
if (dataType %in% c("rnaseq", "RPPA", "mRNA", "isoforms")) {
return(read.rnaseq(path, ...))
}
if (dataType == "mutations") {
return(read.mutations(path, ...))
}
if (dataType == "CNV") {
return(fread(path, data.table = FALSE, ...))
}
}
read.clinical <- function(clinicalDir, ...) {
comboClinical <- fread(clinicalDir, data.table = FALSE)
colNames <- comboClinical[, 1]
comboClinical <- as.data.frame(t(comboClinical[, -1]), ...)
names(comboClinical) <- colNames
return(comboClinical)
}
read.methylation <- function(methylationDir, ...){
methylationData <- fread(methylationDir, data.table = FALSE)
first_row <- methylationData[1,]
Beta_value_column <- which(first_row == "Beta_value")
methylationData <- methylationData[,c(1, 3, 4, 5, Beta_value_column)]
colNames <- methylationData[,1]
methylationData <- as.data.frame(t(methylationData[,-1]), ...)
names(methylationData) <- colNames
row.names(methylationData)[1:3] <- c("TCGA-05.1", "TCGA-05.2", "TCGA-05.3")
x <- row.names(methylationData)[4]
row.names(methylationData)[4] <- sub("\\.3$", "", x)
methylationDataNumeric <- methylationData[-c(1:3), -1]
apply(methylationDataNumeric, MARGIN = 2, function(x){
as.numeric(as.character(x))
}) -> methylationDataNumeric
methylationDataCodes <- cbind(bcr_patient_barcode = row.names(methylationData)[-c(1:3)],
as.data.frame(methylationDataNumeric))
#attr(methylationDataCodes, "info") <- methylationData[c(1:3), -1]
return(methylationDataCodes)
}
read.rnaseq <- function(rnaseqDir, ...) {
rnaseqData <- fread(rnaseqDir, data.table = FALSE) %>% t()
colnames(rnaseqData) <- rnaseqData[1, ]
barcodes <- rownames(rnaseqData)
rnaseqData <- rnaseqData[-1, -1] %>%
apply(2, function(x) as.numeric(as.character(x))) %>%
as.data.frame(x = .) %>%
cbind(bcr_patient_barcode = barcodes[-1], .)
return(rnaseqData)
}
read.mutations <- function(mutationsDir, ...) {
element <- mutationsDir
maf_files <- list.files(element) %>% file.path(element, .) %>% grep(x = ., pattern = "TCGA", value = TRUE)
# there are extra manifest.txt files
barcodes <- list.files(element) %>% grep(x = ., pattern = "TCGA", value = TRUE) %>% substr(start = 1, stop = 15)
pb <- txtProgressBar(min = 0, max = length(maf_files), style = 3)
tmp <- tempfile()
for (i in seq_along(maf_files)) {
maf_data <- read.delim(maf_files[i])
n_col <- ncol(maf_data) + 1
maf_data[, n_col] <- barcodes[i]
names(maf_data)[n_col] <- "bcr_patient_barcode"
invisible(write.table(x = maf_data, sep = "\t", file = tmp, append = TRUE, col.names = i==1, quote = FALSE, row.names = FALSE))
setTxtProgressBar(pb, i)
}
close(pb)
mutations_file <- read.delim(tmp)
file.remove(tmp)
return(mutations_file)
}
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