# Using `RTCGA` package to download mRNA data that are included in `RTCGA.mRNA` package

### Date of datasets release: 2015-11-01

#### Witold Chodor

#### 2025-10-24

# 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 a benefcial infuence on development of science and improvement of patients’ treatment. `RTCGA` is an open-source R package, available to download from Bioconductor

    if (!requireNamespace("BiocManager", quietly=TRUE))
        install.packages("BiocManager")
    BiocManager::install("RTCGA")

or use below code to download the development version which is like to be more bug-free than the release version on Bioconductor:

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

Furthermore, `RTCGA` package transforms TCGA data into 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 mRNA data to gain the same datasets as in RTCGA.mRNA package?

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

    library(RTCGA)
    checkTCGA('Dates')

Version 1.0 of `RTCGA.mRNA` package contains mRNA datasets which were released `2015-11-01`. They were downloaded in the following way (which is mainly copied from http://rtcga.github.io/RTCGA/:

## Available cohorts

All cohort names can be checked using:

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

For all cohorts the following code downloads the mRNA data.

## Downloading tarred files

    # dir.create( "data2" )
    releaseDate <- "2015-11-01"
    sapply( cohorts, function(element){
    tryCatch({
    downloadTCGA( cancerTypes = element,
                  dataSet = "Merge_transcriptome__agilentg4502a_07_3__unc_edu__Level_3__unc_lowess_normalization_gene_level__data.Level_3",
                  destDir = "data2",
                  date = releaseDate )},
    error = function(cond){
       cat("Error: Maybe there weren't mutations data for ", element, " cancer.\n")
    }
    )
    })

## Reading downloaded mRNA dataset

### Shortening paths and directories

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

### Removing `NA` files from data2

There were not mRNA data for these cohorts.

    list.files( "data2") %>%
       file.path( "data2", .) %>%
       sapply(function(x){
          if (x == "data2/NA")
             file.remove(x)
       })

### Paths to mRNA data

Below is the code that removes unneeded “MANIFEST.txt” file from each mRNA cohort folder.

    list.files( "data2") %>%
       file.path( "data2", .) %>%
       sapply(function(x){
          file.path(x, list.files(x)) %>%
             grep(pattern = "MANIFEST.txt", x = ., value=TRUE) %>%
             file.remove()
          })

Below is the code that automatically gives the path to files for all available mRNA cohorts types downloaded to `data2` folder.

    list.files("data2") %>%
       file.path("data2", .) %>%
       sapply(function(y){
          file.path(y, list.files(y)) %>%
          assign( value = .,
                  x = paste0(list.files(y) %>%
                                gsub(x = .,
                                     pattern = "\\..*",
                                     replacement = "") %>%
                                gsub(x=., pattern="-", replacement = "_"),
                             ".mRNA.path"),
                  envir = .GlobalEnv)
       })

### Reading mRNA data using `readTCGA`

Because of the fact that mRNA data are transposed in downloaded files, there has been prepared special function `readTCGA` to read and transpose data automatically. Code is below

    ls() %>%
       grep("mRNA\\.path", x = ., value = TRUE) %>%
       sapply(function(element){
          tryCatch({
             readTCGA(get(element, envir = .GlobalEnv),
                   dataType = "mRNA") %>%
             assign(value = .,
                    x = sub("\\.path", "", x = element),
                    envir = .GlobalEnv )
          }, error = function(cond){
             cat(element)
          })
         invisible(NULL)
        }
    )

# Saving mRNA data to `RTCGA.mRNA` package

    grep( "mRNA", ls(), value = TRUE) %>%
       grep("path", x=., value = TRUE, invert = TRUE) %>%
       cat( sep="," ) #can one to id better? as from use_data documentation:
       # ...    Unquoted names of existing objects to save
       devtools::use_data(BRCA.mRNA,COAD.mRNA,COADREAD.mRNA,GBMLGG.mRNA,
                          KIPAN.mRNA,KIRC.mRNA,KIRP.mRNA,LGG.mRNA,LUAD.mRNA,
                          LUSC.mRNA,OV.mRNA,READ.mRNA,UCEC.mRNA,
                          overwrite = TRUE,
                          compress="xz")