RTCGA package

The Cancer Genome Atlas (TCGA) Data Portal provides a platform for researchers to search, download, and analyze data sets generated by TCGA. It contains clinical information, genomic characterization data, and high level sequence analysis of the tumor genomes. The key is to understand genomics to improve cancer care.

RTCGA package offers download and integration of the variety and volume of TCGA data using patient barcode key, what enables easier data possession. This may have an benefcial infuence on impact on development of science and improvement of patients’ treatment. RTCGA is an open-source R package, available to download from Bioconductor

source("http://bioconductor.org/biocLite.R")
biocLite("RTCGA")

or from github

if (!require(devtools)) {
    install.packages("devtools")
    require(devtools)
}
biocLite("MarcinKosinski/RTCGA")

Furthermore, RTCGA package transforms TCGA data to form which is convenient to use in R statistical package. Those data transformations can be a part of statistical analysis pipeline which can be more reproducible with RTCGA.

Use cases and examples are shown in RTCGA packages vignettes:

browseVignettes("RTCGA")

How to download rna-seq data to gain the same datasets as in RTCGA.rnaseq package?

There are many available date times of TCGA data releases. To see them all just type:

library(RTCGA)
checkTCGA('Dates')

Version 1.0.0 of RTCGA.rnaseq package contains rna-seq datasets from 2015-08-21. There were downloaded as follows:

Available cohorts

All cohort names can be checked using:

(cohorts <- infoTCGA() %>% 
   rownames() %>% 
   sub("-counts", "", x=.))

For all cohorts the following code downloads the rna-seq data.

Downloading tarred files

# dir.create( "data2" )
releaseDate <- "2015-08-21"
sapply( cohorts, function(element){
tryCatch({
downloadTCGA( cancerTypes = element, 
              dataSet = "rnaseqv2__illuminahiseq_rnaseqv2__unc_edu__Level_3__RSEM_genes_normalized__data.Level",
              destDir = "data2/", 
              date = releaseDate )},
error = function(cond){
   cat("Error: Maybe there weren't rnaseq data for ", element, " cancer.\n")
}
)
})

Reading downloaded rna-seq datasets

Shortening paths and directories

list.files( "data2") %>% 
   file.path( "data2", .) %>%
   file.rename( to = substr(.,start=1,stop=50))

Paths to rna-seq data

Below is the code that automatically gives the path to *rnaseqv2* files for all cohorts types downloaded to data2 folder.

sapply( cohorts, function( element ){
   folder <- grep( paste0( "(_",element,"\\.", "|","_",element,"-FFPE)", ".*rnaseqv2"), 
                   list.files("data2"),value = TRUE)
   file <- grep( paste0(element, ".*rnaseqv2"), list.files( file.path( "data2",folder ) ),
                 value = TRUE)
   path <- file.path( "data2", folder, file )
   assign( value = path, x = paste0(element, ".rnaseq.path"), envir = .GlobalEnv)
})

Reading rna-seq data

Code is below

sapply( cohorts, function(element){
   tryCatch({
    assign( value = readTCGA(get(paste0(element,".rnaseq.path"),
                                      envir = .GlobalEnv),
                          "rnaseq"),
            x = paste0(element, ".rnaseq"),
            envir = .GlobalEnv )
    },
   error=function(cond){
   cat("Error: Maybe there weren't rnaseq data for ", element, " cancer.\n")
})
})

Saving rna-seq data to RTCGA.rnaseq package

grep( "rnaseq", ls(), value = TRUE)[ -c(grep("path", grep( "rnaseq", ls(), value = TRUE)))] %>%
   cat( sep="," ) #can one to id better? as from use_data documentation:
   # ...    Unquoted names of existing objects to save
   devtools::use_data(ACC.rnaseq,BLCA.rnaseq,BRCA.rnaseq,CESC.rnaseq,CHOL.rnaseq,COAD.rnaseq,DLBC.rnaseq,ESCA.rnaseq,GBM.rnaseq,HNSC.rnaseq,KICH.rnaseq,KIPAN.rnaseq,KIRC.rnaseq,KIRP.rnaseq,LAML.rnaseq,LGG.rnaseq,LIHC.rnaseq,LUAD.rnaseq,LUSC.rnaseq,MESO.rnaseq,OV.rnaseq,PAAD.rnaseq,PCPG.rnaseq,PRAD.rnaseq,READ.rnaseq,SKCM.rnaseq,STES.rnaseq,TGCT.rnaseq,THCA.rnaseq,THYM.rnaseq,UCEC.rnaseq,UCS.rnaseq,UVM.rnaseq, overwrite = TRUE, compress = "xz")