#' AMARETTO_Download
#'
#' Downloading TCGA dataset for AMARETTO analysis
#' @param CancerSite TCGA cancer code for data download
#' @param TargetDirectory Directory path to download data
#' @return result
#' @importFrom curatedTCGAData curatedTCGAData
#' @importFrom httr GET stop_for_status
#' @importFrom limma strsplit2
#' @importFrom BiocFileCache BiocFileCache bfcadd bfcquery
#' @importFrom doParallel registerDoParallel
#' @importFrom dplyr everything mutate select
#' @importFrom foreach foreach
#' @import grDevices
#' @importFrom parallel makeCluster stopCluster
#' @importFrom readr write_tsv
#' @importFrom tibble rownames_to_column
#' @importFrom utils untar zip
#' @export
#' @examples
#' TargetDirectory <- file.path(getwd(),"Downloads/");dir.create(TargetDirectory)
#' CancerSite <- 'CHOL'
#' DataSetDirectories <- AMARETTO_Download(CancerSite,TargetDirectory = TargetDirectory)
AMARETTO_Download <- function(CancerSite = "CHOL",
TargetDirectory = TargetDirectory) {
ori.dir <- getwd()
message("Downloading Gene Expression and Copy Number Variation data for: ",
CancerSite, "\n")
Cancers = c("BLCA", "BRCA", "LUAD", "LUSC", "COADREAD",
"HNSC", "KIRC", "GBM", "OV", "LAML", "UCEC",
"COAD", "READ")
if (!(CancerSite %in% Cancers)) {
message("This TCGA cancer site/type was not tested, continue at your own risk.\n")
}
if (!file.exists(TargetDirectory))
dir.create(TargetDirectory, showWarnings = FALSE)
TCGA_acronym_uppercase = toupper(CancerSite)
assays <- c("RNASeq2GeneNorm")
MAEO <- suppressMessages(curatedTCGAData::curatedTCGAData(
CancerSite, assays, version = "1.1.38", dry.run = FALSE
))
saveRDS(MAEO, file = paste0(TargetDirectory, "/", CancerSite, "_RNASeq_MAEO.rds"))
dataType = "analyses"
dataFileTag = "CopyNumber_Gistic2.Level_4"
message("Searching CNV data for:", CancerSite,
"\n")
CNVdirectory = get_firehoseData(TargetDirectory = TargetDirectory,
TCGA_acronym_uppercase = TCGA_acronym_uppercase,
dataType = dataType, dataFileTag = dataFileTag)
on.exit(setwd(ori.dir))
return(list(CancerSite = CancerSite, MAdirectory = TargetDirectory,
CNVdirectory = CNVdirectory))
}
#' get_firehoseData
#'
#' Downloading TCGA dataset via firehose
#' @return result
#' @keywords internal
get_firehoseData <- function(TargetDirectory = "./",
TCGA_acronym_uppercase = "LUAD", dataType = "stddata",
dataFileTag = "mRNAseq_Preprocess.Level_3", FFPE = FALSE,
fileType = "tar.gz", gdacURL = "https://gdac.broadinstitute.org/runs/",
untarUngzip = TRUE, printDisease_abbr = FALSE) {
# Cases Shipped by BCR # Cases with Data* Date Last
# Updated (mm/dd/yy)
ori.dir <- getwd()
cancers <- c("Acute Myeloid Leukemia [LAML] \n",
"Adrenocortical carcinoma [ACC]\t\n", "Bladder Urothelial Carcinoma [BLCA] \n",
"Brain Lower Grade Glioma [LGG] \n", "Breast invasive carcinoma [BRCA] \n",
"Cervical squamous cell carcinoma and endocervical adenocarcinoma [CESC] \n",
"Cholangiocarcinoma [CHOL] \n", "Colon adenocarcinoma [COAD] \n",
"Esophageal carcinoma [ESCA] \n", "Glioblastoma multiforme [GBM] \n",
"Head and Neck squamous cell carcinoma [HNSC]\t\n",
"Kidney Chromophobe [KICH]\t\n", "Kidney renal clear cell carcinoma [KIRC]\t\n",
"Kidney renal papillary cell carcinoma [KIRP]\t\n",
"Liver hepatocellular carcinoma [LIHC]\t\n",
"Lung adenocarcinoma [LUAD]\t\n", "Lung squamous cell carcinoma [LUSC] \n",
"Lymphoid Neoplasm Diffuse Large B-cell Lymphoma [DLBC]\t\n",
"Mesothelioma [MESO] \n", "Ovarian serous cystadenocarcinoma [OV]\t\n",
"Pancreatic adenocarcinoma [PAAD]\t\n", "Pheochromocytoma and Paraganglioma [PCPG] \n",
"Prostate adenocarcinoma [PRAD] \n", "Rectum adenocarcinoma [READ]\t\n",
"Sarcoma [SARC]\t\n", "Skin Cutaneous Melanoma [SKCM]\t\n",
"Stomach adenocarcinoma [STAD] \n", "Testicular Germ Cell Tumors [TGCT] \n",
"Thymoma [THYM] \n", "Thyroid carcinoma [THCA]\t\n",
"Uterine Carcinosarcoma [UCS]\t \n", "Uterine Corpus Endometrial Carcinoma [UCEC]\t\n",
"Uveal Melanoma [UVM] \n")
cancers_acronyms <- c("LAML", "ACC", "BLCA", "LGG",
"BRCA", "CESC", "CHOL", "COAD", "ESCA", "GBM",
"HNSC", "KICH", "KIRC", "LIHC", "LUAD", "LUSC",
"DLBC", "MESO", "OV", "PAAD", "PCPG", "PRAD",
"READ", "SARC", "SKCM", "STAD", "TGCT", "THYM",
"THCA", "UCS", "UCEC", "UVM")
if (printDisease_abbr) {
message(cat("Here are the possible TCGA database disease acronyms. \nRe-run this function with printDisease_abbr=FALSE to then run an actual query.\n\n",
cancers))
}
if (TCGA_acronym_uppercase %in% cancers_acronyms) {
gdacURL_orig <- gdacURL
message("\t Printing gdacURL.\n")
print(gdacURL)
urlData <- web.lnk <- httr::GET(gdacURL)
#message("\t Printing urlData \n")
#print(urlData)
urlData <- limma::strsplit2(urlData, paste(dataType,
"__", sep = ""))
#message("\t Printing urlData \n")
#print(urlData)
urlData <- urlData[, 2:dim(urlData)[2]]
#message("\t Printing urlData \n")
#print(urlData)
urlData <- limma::strsplit2(urlData, "/")
#message("\t Printing urlData \n")
#print(urlData)
urlData <- urlData[, 1]
#message("\t Printing urlData \n")
#print(urlData)
urlData <- as.POSIXct(strptime(urlData, "%Y_%m_%d"))
message("\t Printing urlData \n")
print(urlData)
dateData <- as.Date(as.character(urlData[which(!is.na(urlData))]))
message("\t Printing dateData \n")
print(dateData)
#lastDate <- dateData[match(summary(dateData)[which(names(summary(dateData)) ==
# "Max.")], dateData)]
lastDate<-max(dateData)
print(summary(dateData))
print(lastDate)
lastDate <- gsub("-", "_", as.character(lastDate))
lastDateCompress <- gsub("_", "", lastDate)
gdacURL <- paste(gdacURL, dataType, "__", lastDate,
"/data/", TCGA_acronym_uppercase, "/",
lastDateCompress, "/", sep = "")
urlData <- web.lnk <- httr::GET(gdacURL)
urlData <- limma::strsplit2(urlData, "href=\\\"")
while (length(grep("was not found", urlData)) >
0) {
message(paste0("\tNOTE: the TCGA run dated ",
lastDate, " for ", TCGA_acronym_uppercase,
" isn't available for download yet. \n"))
message("\tTaking the run dated just before this one.\n")
dateData <- dateData[-which(dateData ==
(summary(dateData)[which(names(summary(dateData)) ==
"Max.")]))]
dateData<-dateData[-which(dateData==max(dateData))]
#lastDate <- dateData[match(summary(dateData)[which(names(summary(dateData)) ==
# "Max.")], dateData)]
lastDate<-max(dateData)
print(lastDate)
lastDate <- gsub("-", "_", as.character(lastDate))
lastDateCompress <- gsub("_", "", lastDate)
gdacURL <- paste(gdacURL_orig, dataType,
"__", lastDate, "/data/", TCGA_acronym_uppercase,
"/", lastDateCompress, "/", sep = "")
urlData <- web.lnk <- httr::GET(gdacURL)
urlData <- limma::strsplit2(urlData, "href=\\\"")
if (length(dateData) <= 1) {
break
}
}
httr::stop_for_status(web.lnk, task = "FALIED to download input TCGA data type")
if (FFPE) {
urlData <- urlData[grep("FFPE", urlData)]
if (length(urlData) == 0) {
stop("\nNo FFPE data found for this query. Try FFPE=FALSE.\n")
}
} else {
if (length(grep("FFPE", urlData)) > 0) {
urlData <- urlData[-grep("FFPE", urlData)]
}
if (length(urlData) == 0) {
stop("\nNo non-FFPE data found for this query. Try FFPE=TRUE.\n")
}
}
fileName <- urlData[grep(dataFileTag, urlData)]
if (length(fileName) == 0) {
warnMessage <- paste0("\nNot returning any viable url data paths after searching by date for disease ",
TCGA_acronym_uppercase, " \tfor data type ",
dataFileTag, ".No data was downloaded.\n")
warning(warnMessage)
return(NA)
}
fileName <- limma::strsplit2(fileName, "tar.gz")[1,
1]
fileName <- paste(fileName, fileType, sep = "")
gdacURL <- paste(gdacURL, fileName, sep = "")
cancer_url <- computeGisticURL(url = gdacURL)
cache_target <- cacheResource(TargetDirectory=TargetDirectory,resource = cancer_url)
utils::untar(cache_target$rpath, exdir = TargetDirectory)
DownloadedFile <- list.dirs(TargetDirectory,
full.names = TRUE)[grep(TCGA_acronym_uppercase, list.dirs(TargetDirectory,
full.names = TRUE))]
DownloadedFile <- paste0(DownloadedFile, "/")
return(DownloadedFile)
}
on.exit(setwd(ori.dir))
}
#' AMARETTO_ExportResults
#'
#' Retrieve a download of all the data linked with the run (including heatmaps)
#' @param AMARETTOinit AMARETTO initialize output
#' @param AMARETTOresults AMARETTO results output
#' @param data_address Directory to save data folder
#' @param Heatmaps Output heatmaps as pdf
#' @param CNV_matrix CNV_matrix
#' @param MET_matrix MET_matrix
#' @return result
#' @examples
#' data('ProcessedDataLIHC')
#' TargetDirectory <- file.path(getwd(),"Downloads/");dir.create(TargetDirectory)
#' AMARETTOinit <- AMARETTO_Initialize(ProcessedData = ProcessedDataLIHC,
#' NrModules = 2, VarPercentage = 50)
#'
#' AMARETTOresults <- AMARETTO_Run(AMARETTOinit)
#' AMARETTO_ExportResults(AMARETTOinit,AMARETTOresults,TargetDirectory,Heatmaps = FALSE)
#' @export
AMARETTO_ExportResults <- function(AMARETTOinit, AMARETTOresults,
data_address, Heatmaps = TRUE, CNV_matrix = NULL,
MET_matrix = NULL) {
`%dopar%` <- foreach::`%dopar%`
if (!dir.exists(data_address)) {
stop("Output directory is not existing.")
}
# add a date stamp to the output directory
output_dir <- paste0("AMARETTOresults_", gsub("-|:",
"", gsub(" ", "_", Sys.time())))
dir.create(file.path(data_address, output_dir))
NrCores <- AMARETTOinit$NrCores
NrModules <- AMARETTOresults$NrModules
ModuleNr <- NULL
# parallelize the heatmap production
# cluster <- parallel::makeCluster(c(rep("localhost",
# NrCores)), type = "SOCK")
# doParallel::registerDoParallel(cluster, cores = NrCores)
if (Heatmaps == TRUE) {
foreach::foreach(ModuleNr = 1:NrModules, .packages = c("AMARETTO")) %dopar%
{
pdf(file = file.path(data_address,
output_dir, paste0("Module_", as.character(ModuleNr),
".pdf")))
AMARETTO_VisualizeModule(AMARETTOinit,
AMARETTOresults, CNV_matrix, MET_matrix,
ModuleNr = ModuleNr)
dev.off()
}
}
# parallel::stopCluster(cluster)
# save rdata files for AMARETTO_Run and
# AMARETTO_Initialize output
save(AMARETTOresults, file = file.path(data_address,
output_dir, "/amarettoResults.RData"))
save(AMARETTOinit, file = file.path(data_address,
output_dir, "/amarettoInit.RData"))
# save some tables that might be useful for further
# analysis
write_gct(AMARETTOresults$ModuleData, file.path(data_address,
output_dir, "/ModuleData_amaretto.gct"))
write_gct(AMARETTOresults$ModuleMembership, file.path(data_address,
output_dir, "/ModuleMembership_amaretto.gct"))
write_gct(AMARETTOresults$RegulatoryProgramData,
file.path(data_address, output_dir, "/RegulatoryProgramData_amaretto.gct"))
write_gct(AMARETTOresults$RegulatoryPrograms, file.path(data_address,
output_dir, "/RegulatoryPrograms_amaretto.gct"))
readr::write_tsv(as.data.frame(AMARETTOresults$AllGenes),
file.path(data_address, output_dir, "/AllGenes_amaretto.tsv"))
readr::write_tsv(as.data.frame(AMARETTOresults$AllRegulators),
file.path(data_address, output_dir, "/AllRegulators_amaretto.tsv"))
readr::write_tsv(as.data.frame(AMARETTOresults$NrModules),
file.path(data_address, output_dir, "/NrModules_amaretto.tsv"))
# zip the file
utils::zip(zipfile = file.path(data_address, output_dir),
files = file.path(data_address, output_dir))
}
#' write_gct
#'
#' @return result
#' @keywords internal
write_gct <- function(data_in, file_address) {
Name <- Description <- NULL
header_gct <- paste0("#1.2\n", nrow(data_in), "\t",
ncol(data_in))
data_in <- tibble::rownames_to_column(as.data.frame(data_in),
"Name") %>% dplyr::mutate(Description = Name) %>%
dplyr::select(Name, Description, dplyr::everything())
write(header_gct, file = file_address, append = FALSE)
readr::write_tsv(data_in, file_address, append = TRUE,
col_names = TRUE)
}
#' computeGisticURL
#'
#' @return result
#' @keywords internal
computeGisticURL <- function(url = NULL, acronym = "CHOL") {
if (!is.null(url))
return(url)
sprintf("http://gdac.broadinstitute.org/runs/analyses__2016_01_28/data/%s/20160128/gdac.broadinstitute.org_%s-TP.CopyNumber_Gistic2.Level_4.2016012800.0.0.tar.gz",
acronym, acronym)
}
#' cacheResource
#'
#' @return result
#' @keywords internal
cacheResource <- function(TargetDirectory=TargetDirectory,
resource = resource) {
cache = BiocFileCache::BiocFileCache(TargetDirectory)
chk = bfcquery(cache, resource)
if (nrow(chk) == 0) {
message("downloading ", resource)
BiocFileCache::bfcadd(cache, resource)
return(bfcquery(cache, resource))
}
chk
}
Datasets
Models
