1 Prerequisites

Methods from two packages hosted on GitHub are used in this vignette, the packages are installed as follows.

BiocInstaller::biocLite("LiNk-NY/RTCGAToolbox")
BiocInstaller::biocLite("waldronlab/BiocInterfaces")

These and other packages available in Bioconductor or CRAN are loaded as follows.

library(MultiAssayExperiment)
library(RTCGAToolbox)
library(BiocInterfaces)
library(readr)

2 Argument Definitions

The RTCGAToolbox package provides the getFirehoseDatasets() method for obtaining the names of all 33 cohorts contained within the TCGA data. Beyond the 33 cohorts, there are 5 additional “pan” cohorts where data of multiple cohorts was merged - information about the cohorts is available via the TCGA website. Additionally, the getFirehoseRunningDates() and getFirehoseAnalyzeDates() methods are used to obtain the most recent running and analysis dates. Finally, a character vector dd is created to specify the location of the data directory where output should be saved.

ds <- getFirehoseDatasets()[27]
rd <- getFirehoseRunningDates()[1]
ad <- getFirehoseAnalyzeDates()[1]
dd <- "data"

3 Function Definition

A function, newMAEO(), is defined as shown below for the purpose of creating a new MultiAssayExperiment object with a single line of code. It accepts the arguments defined in the previous chunk and is capable of accepting multiple cohort names (e.g. ds <- getFirehoseDatasets()[1:5]). Even though the implementation is not parallel, low-level operations remain vectorized regardless of the for loop.

In the first part of the function, the existence of the data directory is checked and it is created if necessary. Then a cohort object is either loaded or serialized from the getFirehoseData() method and saved to the data directory. Once serialized, pData is extracted from the clinical slot and the rownames are cleaned by gsub() and type_convert() functions.

A named list of extraction targets is then created from the slot names of the cohort object and the TCGAextract() method is used within a try statement. The try statement is necessary because each cohort will have some variation in the slots that contain data. Once filtering is done, the TCGAcleanExpList() method is used to remove samples that do not have matching pData and the output can be passed to generateMap() which will generate a sample map.

Finally, the named list of extracted targets (of class ExperimentList), the pData, and the generated sample map can be passed to the MultiAssayExperiment() constructor function. A MultiAssayExperiment will be created, serialized and saved to the data directory, making it easier to return to in the future.

newMAEO <- function(ds, rd, ad, dd) {
  if(!dir.exists(dd)) {
    dir.create(dd)
  }
  for(i in ds) {
    cn <- tolower(i)
    fp <- file.path(dd, paste0(cn, ".rds"))
    if(file.exists(fp)) {
      co <- readRDS(fp)
    } else {
      co <- getFirehoseData(i, runDate = rd, gistic2_Date = ad,
                            RNAseq_Gene = TRUE,
                            Clinic = TRUE,
                            miRNASeq_Gene = TRUE,
                            RNAseq2_Gene_Norm = TRUE,
                            CNA_SNP = TRUE,
                            CNV_SNP = TRUE,
                            CNA_Seq = TRUE,
                            CNA_CGH = TRUE,
                            Methylation = TRUE,
                            Mutation = TRUE,
                            mRNA_Array = TRUE,
                            miRNA_Array = TRUE,
                            RPPA_Array = TRUE,
                            RNAseqNorm = "raw_counts",
                            RNAseq2Norm = "normalized_count",
                            forceDownload = FALSE,
                            destdir = "./tmp",
                            fileSizeLimit = 500000,
                            getUUIDs = FALSE)
      saveRDS(co, file = fp, compress = "bzip2")
    }
    pd <- Clinical(co)
    rownames(pd) <- toupper(gsub("\\.", "-", rownames(pd)))
    pd <- type_convert(pd)
    targets <- c(slotNames(co)[c(5:16)], "gistica", "gistict")
    names(targets) <- targets
    dataList <- lapply(targets, function(x) {try(TCGAextract(co, x))})
    dataFull <- Filter(function(x){class(x)!="try-error"}, dataList)
    ExpList <- experiments(dataFull)
    NewExpList <- TCGAcleanExpList(ExpList, pd)
    NewMap <- generateMap(NewExpList, pd, TCGAbarcode)
    MAEO <- MultiAssayExperiment(NewExpList, pd, NewMap)
    saveRDS(MAEO, file = file.path(dd, paste0(cn, "MAEO.rds")),
            compress = "bzip2")
  }
}

4 Function Call

Lastly, it is necessary to call the newMAEO() function defined above and pass it the arguments defined using the RTCGAToolbox package. Using this function, a MultiAssayExperiment object for the prostate adenocarcinoma cohort is created with a single call.

newMAEO(ds, rd, ad, dd)