# simplify TCGA data download workflow

## ------------------------------------

# Typical Cohorts Structure

# Given GBM as an example: <https://xenabrowser.net/datapages/?cohort=TCGA%20Glioblastoma%20(GBM)>

# Cohorts

#       Copy Number

#           gistic2

#           gistic2 thresholded

#       Copy Number Segments

#           After remove germline cnv

#           Before remove germline cnv

#       DNA Methylation

#           Methylation27k

#           Methylation450k

#       Exon Expression RNASeq

#           IlluminaHiSeq

#       Gene Expression Array

#           AffyU133a (always change)

#       Gene Expression RNASeq

#           IlluminaHiSeq

#           IlluminaHiSeq pancan normalized

#           IlluminaHiSeq percentile

#       miRNA Mature Strand Expression RNASeq

#           IlluminaHiseq

#       PARADIGM Pathway Activity

#           expression

#           expression (array) + CNV

#           expression + CNV

#           exprssion (array)

#       Phenotype

#           Phenotypes

#       Protein Expression RPPA

#           RPPA

#           RPPA (replicate-base normalization)

#       Somatic Mutation (SNPs and small INDELs)

#           broad

#           ucsc automated

#       Somatic non-silent mutation (gene-level)

#           broad

#           PANCAN AWG

#           ucsc automated

#       Transcription factor regulatory impact

#           Agilent, by RABIT

#           HiSeqV2, by RABIT

#           U133A, by RABIT

# compiler::setCompilerOptions(suppressAll = TRUE)

# suppress Binding Notes

# suppressBindingNotes <- function(variablesMentionedInNotes) {

#     for(variable in variablesMentionedInNotes) {

#         assign(variable, NULL, envir = .GlobalEnv)

#     }

# }

# suppressBindingNotes(c("XenaHostNames","XenaCohorts", "ProjectID", "DataType", "FileType"))

##' @title Get TCGA Common Data Sets by Project ID and Property

##' @description This is the most useful function for user to download common

##' TCGA datasets, it is similar to `getFirehoseData` function in `RTCGAToolbox`

##'  package.

##' @details TCGA Common Data Sets are frequently used for biological analysis.

##' To make easier to achieve these data, this function provide really easy

##' options to choose datasets and behavior. All availble information about

##' datasets of TCGA can access vis `availTCGA()` and check with `showTCGA()`.

##' @author Shixiang Wang <w_shixiang@163.com>

##' @inheritParams downloadTCGA

##' @param clinical logical. if `TRUE`, download clinical information. Default is `TRUE`.

##' @param download logical. if `TRUE`, download data, otherwise return a result list include data

##' information. Default is `FALSE`. You can set this to `FALSE` if you want to check what you will download or

##' use other function provided by `UCSCXenaTools` to filter result datasets you want to download.

##' @param forceDownload logical. if `TRUE`, force to download files no matter if exist. Default is `FALSE`.

##' @param mRNASeq logical. if `TRUE`, download mRNASeq data. Default is `FALSE`.

##' @param mRNAArray logical. if `TRUE`, download mRNA microarray data. Default is `FALSE`.

##' @param mRNASeqType character vector. Can be one, two or three

##' in `c("normalized", "pancan normalized", "percentile")`.

##' @param miRNASeq logical. if `TRUE`, download miRNASeq data. Default is `FALSE`.

##' @param exonRNASeq logical. if `TRUE`, download exon RNASeq data. Default is `FALSE`.

##' @param RPPAArray logical. if `TRUE`, download RPPA data. Default is `FALSE`.

##' @param ReplicateBaseNormalization logical. if `TRUE`, download RPPA data by Replicate Base

##' Normalization (RBN). Default is `FALSE`.

##' @param Methylation logical. if `TRUE`, download DNA Methylation data. Default is `FALSE`.

##' @param MethylationType character vector. Can be one or two in `c("27K", "450K")`.

##' @param GeneMutation logical. if `TRUE`, download gene mutation data. Default is `FALSE`.

##' @param SomaticMutation logical. if `TRUE`, download somatic mutation data. Default is `FALSE`.

##' @param GisticCopyNumber logical. if `TRUE`, download Gistic2 Copy Number data. Default is `FALSE`.

##' @param Gistic2Threshold logical. if `TRUE`, download Threshold Gistic2 data. Default is `TRUE`.

##' @param CopyNumberSegment logical. if `TRUE`, download Copy Number Segment data. Default is `FALSE`.

##' @param RemoveGermlineCNV logical. if `TRUE`, download Copy Number Segment data which has removed

##' germline copy number variation. Default is `TRUE`.

##' @return if `download=TRUE`, return `data.frame` from `XenaDownload`,

##' otherwise return a list including `XenaHub` object and datasets information

##' @export

##' @examples

##' ###### get data, but not download

##'

##' # 1 choose project and data types you wanna download

##' getTCGAdata(project = "LUAD", mRNASeq = TRUE, mRNAArray = TRUE,

##' mRNASeqType = "normalized", miRNASeq = TRUE, exonRNASeq = TRUE,

##' RPPAArray = TRUE, Methylation = TRUE, MethylationType = "450K",

##' GeneMutation = TRUE, SomaticMutation = TRUE)

##'

##' # 2 only choose 'LUAD' and its clinical data

##' getTCGAdata(project = "LUAD")

##' \dontrun{

##' ###### download datasets

##'

##' # 3 download clinical datasets of LUAD and LUSC

##' getTCGAdata(project = c("LUAD", "LUSC"), clinical = TRUE, download = TRUE)

##'

##' # 4 download clinical, RPPA and gene mutation datasets of LUAD and LUSC

##' # getTCGAdata(project = c("LUAD", "LUSC"), clinical = TRUE, RPPAArray = TRUE, GeneMutation = TRUE)

##' }

getTCGAdata <- function(project = NULL,

                        clinical = TRUE,

                        download = FALSE,

                        forceDownload = FALSE,

                        destdir = tempdir(),

                        mRNASeq = FALSE,

                        mRNAArray = FALSE,

                        mRNASeqType = "normalized",

                        miRNASeq = FALSE,

                        exonRNASeq = FALSE,

                        RPPAArray = FALSE,

                        ReplicateBaseNormalization = FALSE,

                        Methylation = FALSE,

                        MethylationType = c("27K", "450K"),

                        GeneMutation = FALSE,

                        SomaticMutation = FALSE,

                        GisticCopyNumber = FALSE,

                        Gistic2Threshold = TRUE,

                        CopyNumberSegment = FALSE,

                        RemoveGermlineCNV = TRUE,

                        ...) {

  #----- check data type of input

  stopifnot(!is.null(project))

  stopifnot(is.logical(

    c(

      clinical,

      mRNASeq,

      mRNAArray,

      miRNASeq,

      RPPAArray,

      ReplicateBaseNormalization,

      Methylation,

      GeneMutation,

      SomaticMutation,

      GisticCopyNumber,

      Gistic2Threshold,

      CopyNumberSegment,

      RemoveGermlineCNV,

      download,

      forceDownload

    )

  ))

  projects <- c(

    "LAML",

    "ACC",

    "CHOL",

    "BLCA",

    "BRCA",

    "CESC",

    "COADREAD",

    "COAD",

    "UCEC",

    "ESCA",

    "FPPP",

    "GBM",

    "HNSC",

    "KICH",

    "KIRC",

    "KIRP",

    "DLBC",

    "LIHC",

    "LGG",

    "GBMLGG",

    "LUAD",

    "LUNG",

    "LUSC",

    "SKCM",

    "MESO",

    "UVM",

    "OV",

    "PANCAN",

    "PAAD",

    "PCPG",

    "PRAD",

    "READ",

    "SARC",

    "STAD",

    "TGCT",

    "THYM",

    "THCA",

    "UCS"

  )

  if (!all(project %in% projects)) {

    message("Only following Project valid:")

    print(project[project %in% projects])

    stop("Invaild Input!")

  }

  tcga_all <- .decodeDataType(Target = "tcgaHub")

  # tcga_all %>%

  #     filter(ProjectID %in% project) %>% # select project

  #     filter()

  res <- subset(tcga_all, ProjectID %in% project)

  res %>%

    filter(

      DataType != "Transcription Factor Regulatory Impact",

      DataType != "Signatures",

      DataType != "PARADIGM Pathway Activity",

      DataType != "iCluster"

    ) -> res

  if (clinical) {

    quo_cli <- dplyr::quo((FileType == "Clinical Information"))

  } else {

    quo_cli <- dplyr::quo((FALSE))

  }

  if (mRNASeq) {

    if (!all(mRNASeqType %in% c("normalized", "pancan normalized", "percentile"))) {

      message("Available mRNASeqType values are:")

      print(c("normalized", "pancan normalized", "percentile"))

      stop("Not Vaild Input!")

    }

    RNA <- c(

      "IlluminaHiSeq RNASeqV2",

      "IlluminaHiSeq RNASeqV2 pancan normalized",

      "IlluminaHiSeq RNASeqV2 in percentile rank"

    )

    names(RNA) <- c("normalized", "pancan normalized", "percentile")

    RNA_select <- c(RNA[mRNASeqType], "Batch effects normalized")

    quo_RNA <- dplyr::quo((

      DataType == "Gene Expression RNASeq" & FileType %in% RNA_select

    ))

  } else {

    quo_RNA <- dplyr::quo((FALSE))

  }

  if (mRNAArray) {

    quo_RNAa <- dplyr::quo((DataType == "Gene Expression Array"))

  } else {

    quo_RNAa <- dplyr::quo((FALSE))

  }

  if (miRNASeq) {

    miRNA_select <- c("IlluminaHiSeq RNASeq", "Batch effects normalized")

    quo_miRNA <- dplyr::quo((

      DataType == "miRNA Mature Strand Expression RNASeq" &

        FileType %in% miRNA_select

    ))

  } else {

    quo_miRNA <- dplyr::quo((FALSE))

  }

  if (exonRNASeq) {

    quo_exon <- dplyr::quo((

      DataType == "Exon Expression RNASeq" &

        FileType == "IlluminaHiSeq RNASeqV2"

    ))

  } else {

    quo_exon <- dplyr::quo((FALSE))

  }

  # Have no miRNA Array? Need Check

  # if(miRNAArray){

  #

  # }

  if (RPPAArray) {

    if (ReplicateBaseNormalization) {

      RPPA_select <- "RPPA normalized by RBN"

    } else {

      RPPA_select <- "RPPA"

    }

    quo_RPPA <- dplyr::quo((

      DataType == "Protein Expression RPPA" &

        FileType %in% c(RPPA_select, "RPPA pancan normalized")

    ))

  } else {

    quo_RPPA <- dplyr::quo((FALSE))

  }

  if (Methylation) {

    if (!all(MethylationType %in% c("27K", "450K"))) {

      message("Available MethylationType values are:")

      print(c("27K", "450K"))

      stop("Not Vaild Input!")

    }

    Methy <- c("Methylation27K", "Methylation450K")

    names(Methy) <- c("27K", "450K")

    Methy_select <- Methy[MethylationType]

    quo_Methy <- dplyr::quo((

      DataType == "DNA Methylation" & FileType %in% Methy_select

    ))

  } else {

    quo_Methy <- dplyr::quo((FALSE))

  }

  if (GeneMutation) {

    quo_genMutation <- dplyr::quo((

      DataType == "Gene Somatic Non-silent Mutation" &

        FileType %in% c("broad automated", "MC3 Public Version")

    ))

  } else {

    quo_genMutation <- dplyr::quo((FALSE))

  }

  if (SomaticMutation) {

    quo_somaticMutation <- dplyr::quo((

      DataType == "Somatic Mutation" &

        FileType %in% c("broad automated", "MC3 Public Version")

    ))

  } else {

    quo_somaticMutation <- dplyr::quo((FALSE))

  }

  if (GisticCopyNumber) {

    if (Gistic2Threshold) {

      gistic_select <- "Gistic2 thresholded"

    } else {

      gistic_select <- "Gistic2"

    }

    quo_gistic <- dplyr::quo((

      DataType == "Gene Level Copy Number" & FileType == gistic_select

    ))

  } else {

    quo_gistic <- dplyr::quo((FALSE))

  }

  if (CopyNumberSegment) {

    if (RemoveGermlineCNV) {

      cns_select <- "After remove germline cnv"

    } else {

      cns_select <- "Before remove germline cnv"

    }

    quo_cns <- dplyr::quo((

      DataType == "Copy Number Segments" & FileType == cns_select

    ))

  } else {

    quo_cns <- dplyr::quo((FALSE))

  }

  cond_select <- dplyr::quo(

    !!quo_cli |

      !!quo_RNA |

      !!quo_RNAa |

      !!quo_miRNA |

      !!quo_exon |

      !!quo_RPPA |

      !!quo_Methy |

      !!quo_genMutation |

      !!quo_somaticMutation | !!quo_gistic | !!quo_cns

  )

  res <- filter(res, !!cond_select)

  if (download) {

    res %>%

      XenaGenerate() %>%

      XenaQuery() %>%

      XenaDownload(destdir = destdir, force = forceDownload, ...)

  } else {

    xe <- res %>% XenaGenerate()

    list(Xena = xe, DataInfo = res)

  }

}

##' @title Easily Download TCGA Data by Several Options

##' @description TCGA is a very useful database and here we provide this function to

##' download TCGA (include TCGA Pancan) datasets in human-friendly way. Users who are not

##' familiar with R operation will benefit from this.

##' @details All availble information about datasets of TCGA can access vis `availTCGA()` and

##' check with `showTCGA()`.

##' @author Shixiang Wang <w_shixiang@163.com>

##' @param project default is `NULL`. Should be one or more of TCGA project id (character vector) provided by Xena.

##' See all available project id, please use `availTCGA("ProjectID")`.

##' @param data_type default is `NULL`. Should be a character vector specify data type.

##' See all available data types by `availTCGA("DataType")`.

##' @param file_type default is `NULL`. Should be a character vector specify file type.

##' See all available file types by `availTCGA("FileType")`.

##' @inheritParams XenaDownload

##' @return same as `XenaDownload()` function result.

##' @export

##' @examples

##' \dontrun{

##' # download RNASeq data (use UVM as example)

##' downloadTCGA(project = "UVM",

##'                  data_type = "Gene Expression RNASeq",

##'                  file_type = "IlluminaHiSeq RNASeqV2")

##' }

##' @seealso [UCSCXenaTools::XenaQuery()],

##' [UCSCXenaTools::XenaFilter()],

##' [UCSCXenaTools::XenaDownload()],

##' [UCSCXenaTools::XenaPrepare()],

##' [UCSCXenaTools::availTCGA()],

##' [UCSCXenaTools::showTCGA()]

downloadTCGA <- function(project = NULL,

                         data_type = NULL,

                         file_type = NULL,

                         destdir = tempdir(),

                         force = FALSE,

                         ...) {

  stopifnot(

    !is.null(project),

    !is.null(data_type),

    !is.null(file_type)

  )

  tcga_all <- .decodeDataType(Target = "tcgaHub")

  tcga_projects <- unique(tcga_all$ProjectID)

  # suppress binding notes

  ProjectID <- DataType <- FileType <- NULL

  if (!all(project %in% tcga_projects)) {

    message(

      project,

      " are not (all) valid, please select one or more of following valid project ID:"

    )

    print(tcga_projects, quote = FALSE)

    return(invisible(NULL))

  }

  res <- tcga_all %>%

    filter(

      ProjectID %in% project,

      DataType %in% data_type,

      FileType %in% file_type

    )

  if (nrow(res) == 0) { # nocov start

    message("Find nothing about your input, please check it.")

    message("availTCGA and showTCGA function may help you.")

    return(invisible(NULL))

  } # nocov end

  res %>%

    XenaGenerate() %>%

    XenaQuery() %>%

    XenaDownload(destdir = destdir, force = force, ...)

}

##' @title Get or Check TCGA Available ProjectID, DataType and FileType

##' @param which a character of `c("All", "ProjectID", "DataType", "FileType")`

##' @author Shixiang Wang <w_shixiang@163.com>

##' @export

##' @examples

##' \donttest{

##' availTCGA("all")

##' }

availTCGA <- function(which = c("all", "ProjectID", "DataType", "FileType")) {

  which <- match.arg(which)

  tcga_all <- .decodeDataType(Target = "tcgaHub")

  tcga_projects <- unique(tcga_all$ProjectID)

  tcga_datatype <- unique(tcga_all$DataType)

  tcga_filetype <- unique(tcga_all$FileType)

  if (which == "all") {

    message(

      "Note not all projects have listed data types and file types, you can use showTCGA function to check if exist"

    )

    return(

      list(

        ProjectID = tcga_projects,

        DataType = tcga_datatype,

        FileType = tcga_filetype

      )

    )

  }

  if (which == "ProjectID") {

    return(tcga_projects)

  }

  if (which == "DataType") {

    return(tcga_datatype)

  }

  if (which == "FileType") {

    return(tcga_filetype)

  }

}

##' @title Show TCGA data structure by Project ID or ALL

##' @description This can used to check if data type or file type exist in one or more projects by hand.

##' @param project a character vector. Can be "all" or one or more of TCGA Project IDs.

##' @return a `data.frame` including project data structure information.

##' @author Shixiang Wang <w_shixiang@163.com>

##' @export

##' @examples

##' \donttest{

##' showTCGA("all")

##' }

##' @seealso [UCSCXenaTools::availTCGA()]

showTCGA <- function(project = "all") {

  # suppress binding notes

  ProjectID <- DataType <- FileType <- NULL

  tcga_all <- .decodeDataType(Target = "tcgaHub")

  if (project == "all") {

    # res = data.table::data.table(tcga_all)

    # res = res[, .(ProjectID, DataType, FileType)]

    res <- tcga_all %>% select(ProjectID, DataType, FileType)

  } else {

    res <- tcga_all %>%

      filter(ProjectID %in% project) %>%

      select(ProjectID, DataType, FileType)

    # res = data.table::data.table(tcga_all)

    # res = res[ProjectID %in% project, .(ProjectID, DataType, FileType)]

  }

  if (nrow(res) == 0) { # nocov start

    message("Something is wrong in your input, NULL will be returned, please check.")

    return(NULL)

  } # nocov end

  return(res)

}

# Only works for TCGA

.decodeDataType <- function(XenaData = UCSCXenaTools::XenaData,

                            Target = "tcgaHub") {

  # This TCGA include TCGA PANCAN dataset

  if ("tcgaHub" %in% Target) {

    Target <- c(Target, "pancanAtlasHub")

  }

  # supress binding notes

  XenaHostNames <- XenaCohorts <- NULL

  ob <- XenaData %>% filter(XenaHostNames %in% Target)

  if ("tcgaHub" %in% Target) {

    # decode project id

    ob %>% mutate(ProjectID = sub(".*\\((.*)\\)", "\\1", XenaCohorts)) -> ob

    # decode DataType

    ob %>%

      mutate(

        DataType = dplyr::case_when(

          grepl("Gistic2_CopyNumber_Gistic2", XenaDatasets) ~ "Gene Level Copy Number",

          grepl(

            "PANCAN_Genome_Wide_SNP_6_whitelisted.gene.xena",

            XenaDatasets

          ) ~ "Gene Level Copy Number",

          # pancan

          grepl("SNP6", XenaDatasets) ~ "Copy Number Segments",

          grepl(

            "PANCAN_Genome_Wide_SNP_6_whitelisted.xena",

            XenaDatasets

          ) ~ "Copy Number Segments",

          # pancan

          grepl("HumanMethylation", XenaDatasets) ~ "DNA Methylation",

          grepl("MethylMix", XenaDatasets) ~ "DNA Methylation",

          grepl("HiSeq.*_exon", XenaDatasets) ~ "Exon Expression RNASeq",

          grepl("GA_exon", XenaDatasets) ~ "Exon Expression RNASeq",

          grepl("GAV2_exon", XenaDatasets) ~ "Exon Expression RNASeq",

          grepl("AgilentG", XenaDatasets) ~ "Gene Expression Array",

          grepl("HT_HG-U133A", XenaDatasets) ~ "Gene Expression Array",

          grepl("GA$", XenaDatasets) &

            !grepl("RABIT", XenaDatasets) ~ "Gene Expression RNASeq",

          grepl("GAV2$", XenaDatasets) &

            !grepl("RABIT", XenaDatasets) ~ "Gene Expression RNASeq",

          grepl("HiSeq$", XenaDatasets) &

            !grepl("RABIT", XenaDatasets) ~ "Gene Expression RNASeq",

          grepl("HiSeqV2$", XenaDatasets) &

            !grepl("RABIT", XenaDatasets) ~ "Gene Expression RNASeq",

          grepl("HiSeqV2_PANCAN$", XenaDatasets) ~ "Gene Expression RNASeq",

          grepl("HiSeqV2_percentile$", XenaDatasets) ~ "Gene Expression RNASeq",

          grepl(

            "EB\\+\\+AdjustPANCAN_IlluminaHiSeq_RNASeqV2.geneExp.xena",

            XenaDatasets

          ) ~ "Gene Expression RNASeq",

          # pancan

          grepl("miRNA", XenaDatasets) ~ "miRNA Mature Strand Expression RNASeq",

          grepl(

            "pancanMiRs_EBadjOnProtocolPlatformWithoutRepsWithUnCorrectMiRs",

            XenaDatasets

          ) ~ "miRNA Mature Strand Expression RNASeq",

          # pancan

          grepl("Pathway_Paradigm", XenaDatasets) ~ "PARADIGM Pathway Activity",

          grepl("erge_merged_reals", XenaDatasets) ~ "PARADIGM Pathway Activity",

          # pancan

          grepl("clinicalMatrix", XenaDatasets) ~ "Phenotype",

          grepl(

            "Survival_SupplementalTable_S1_20171025_xena_sp",

            XenaDatasets

          ) ~ "Phenotype",

          # pancan

          grepl("Subtype_Immune_Model_Based.txt", XenaDatasets) ~ "Phenotype",

          # pancan

          grepl("TCGASubtype.20170308.tsv", XenaDatasets) ~ "Phenotype",

          # pancan

          grepl(

            "TCGA_phenotype_denseDataOnlyDownload.tsv",

            XenaDatasets

          ) ~ "Phenotype",

          # pancan

          grepl("gene_expression_subtype", XenaDatasets) ~ "Phenotype",

          # OV

          grepl("RPPA", XenaDatasets) ~ "Protein Expression RPPA",

          grepl("mutation_", XenaDatasets) &

            !endsWith(XenaDatasets, "gene") ~ "Somatic Mutation",

          grepl("mc3.v0.2.8.PUBLIC.xena", XenaDatasets) ~ "Somatic Mutation",

          # pancan

          grepl("mutation($|(.*_gene$))", XenaDatasets) ~ "Gene Somatic Non-silent Mutation",

          grepl(

            "mc3.v0.2.8.PUBLIC.nonsilentGene.xena",

            XenaDatasets

          ) ~ "Gene Somatic Non-silent Mutation",

          # pancan

          grepl("RABIT", XenaDatasets) ~ "Transcription Factor Regulatory Impact",

          grepl("iCluster", XenaDatasets) ~ "iCluster",

          grepl(

            "Pancan12_GenePrograms_drugTargetCanon_in_Pancan33.tsv",

            XenaDatasets

          ) ~ "Signatures",

          # pancan

          grepl("TCGA.HRD_withSampleID.txt", XenaDatasets) ~ "Signatures",

          # pancan

          grepl(

            "TCGA_pancancer_10852whitelistsamples_68ImmuneSigs.xena",

            XenaDatasets

          ) ~ "Signatures",

          # pancan

          grepl("StemnessScores_DNAmeth_20170210.tsv", XenaDatasets) ~ "Signatures",

          # pancan

          grepl(

            "StemnessScores_RNAexp_20170127.2.tsv",

            XenaDatasets

          ) ~ "Signatures" # pancan

        )

      ) -> ob

    # decode file type

    ob %>%

      mutate(

        FileType = dplyr::case_when(

          DataType == "Gene Level Copy Number" &

            grepl("Gistic2_all_data_by_genes", XenaDatasets) ~ "Gistic2",

          DataType == "Gene Level Copy Number" &

            grepl("Gistic2_all_thresholded.by_genes", XenaDatasets) ~ "Gistic2 thresholded",

          DataType == "Gene Level Copy Number" &

            grepl(

              "PANCAN_Genome_Wide_SNP_6_whitelisted.gene.xena",

              XenaDatasets

            ) ~ "Tumor copy number",

          DataType == "Copy Number Segments" &

            grepl("SNP6_genomicSegment", XenaDatasets) ~ "Before remove germline cnv",

          DataType == "Copy Number Segments" &

            grepl("SNP6_nocnv_genomicSegment", XenaDatasets) ~ "After remove germline cnv",

          DataType == "Copy Number Segments" &

            grepl(

              "PANCAN_Genome_Wide_SNP_6_whitelisted.xena",

              XenaDatasets

            ) ~ "After remove germline cnv",

          DataType == "DNA Methylation" &

            grepl("HumanMethylation27", XenaDatasets) ~ "Methylation27K",

          DataType == "DNA Methylation" &

            grepl("HumanMethylation450", XenaDatasets) ~ "Methylation450K",

          DataType == "DNA Methylation" &

            grepl("oneoff_TCGA_LGG_MethylMix", XenaDatasets) ~ "MethylMix",

          DataType == "Exon Expression RNASeq" &

            grepl("GA_exon", XenaDatasets) ~ "IlluminaGA RNASeq",

          DataType == "Exon Expression RNASeq" &

            grepl("GAV2_exon", XenaDatasets) ~ "IlluminaGA RNASeqV2",

          DataType == "Exon Expression RNASeq" &

            grepl("HiSeq_exon", XenaDatasets) ~ "IlluminaHiSeq RNASeq",

          DataType == "Exon Expression RNASeq" &

            grepl("HiSeqV2_exon", XenaDatasets) ~ "IlluminaHiSeq RNASeqV2",

          DataType == "Gene Expression Array" &

            grepl("AgilentG4502A", XenaDatasets) ~ "Agilent 244K Microarray",

          DataType == "Gene Expression Array" &

            grepl("HT_HG-U133A", XenaDatasets) ~ "Affymetrix U133A Microarray",

          DataType == "Gene Expression RNASeq" &

            endsWith(XenaDatasets, "GA") ~ "IlluminaGA RNASeq",

          DataType == "Gene Expression RNASeq" &

            endsWith(XenaDatasets, "GAV2") ~ "IlluminaGA RNASeqV2",

          DataType == "Gene Expression RNASeq" &

            endsWith(XenaDatasets, "HiSeq") ~ "IlluminaHiSeq RNASeq",

          DataType == "Gene Expression RNASeq" &

            endsWith(XenaDatasets, "HiSeqV2") ~ "IlluminaHiSeq RNASeqV2",

          DataType == "Gene Expression RNASeq" &

            endsWith(XenaDatasets, "HiSeqV2_PANCAN") ~ "IlluminaHiSeq RNASeqV2 pancan normalized",

          DataType == "Gene Expression RNASeq" &

            endsWith(XenaDatasets, "HiSeqV2_percentile") ~ "IlluminaHiSeq RNASeqV2 in percentile rank",

          DataType == "Gene Expression RNASeq" &

            grepl("AdjustPANCAN_IlluminaHiSeq_RNASeqV2", XenaDatasets) ~ "Batch effects normalized",

          DataType == "miRNA Mature Strand Expression RNASeq" &

            endsWith(XenaDatasets, "miRNA_GA_gene") ~ "IlluminaGA RNASeq",

          DataType == "miRNA Mature Strand Expression RNASeq" &

            endsWith(XenaDatasets, "miRNA_HiSeq_gene") ~ "IlluminaHiSeq RNASeq",

          DataType == "miRNA Mature Strand Expression RNASeq" &

            grepl(

              "pancanMiRs_EBadjOnProtocolPlatformWithoutRepsWithU",

              XenaDatasets

            ) ~ "Batch effects normalized",

          DataType == "PARADIGM Pathway Activity" &

            grepl("merge_merged_reals", XenaDatasets) ~ "Platform-corrected PANCAN12 dataset",

          DataType == "PARADIGM Pathway Activity" &

            endsWith(XenaDatasets, "Pathway_Paradigm_mRNA") ~ "Use only Microarray",

          DataType == "PARADIGM Pathway Activity" &

            endsWith(

              XenaDatasets,

              "Pathway_Paradigm_mRNA_And_Copy_Number"

            ) ~ "Use Microarray plus Copy Number",

          DataType == "PARADIGM Pathway Activity" &

            endsWith(XenaDatasets, "Pathway_Paradigm_RNASeq") ~ "Use only RNASeq",

          DataType == "PARADIGM Pathway Activity" &

            endsWith(

              XenaDatasets,

              "Pathway_Paradigm_RNASeq_And_Copy_Number"

            ) ~ "Use RNASeq plus Copy Number",

          DataType == "Phenotype" &

            endsWith(XenaDatasets, "clinicalMatrix") ~ "Clinical Information",

          DataType == "Phenotype" &

            grepl(

              "Survival_SupplementalTable_S1_20171025_xena_sp",

              XenaDatasets

            ) ~ "Clinical Information",

          DataType == "Phenotype" &

            grepl("gene_expression_subtype", XenaDatasets) ~ "Gene Expression Subtype",

          DataType == "Phenotype" &

            grepl("Subtype_Immune_Model_Based", XenaDatasets) ~ "Immune Model Based Subtype",

          DataType == "Phenotype" &

            grepl("TCGASubtype", XenaDatasets) ~ "TCGA Molecular Subtype",

          DataType == "Phenotype" &

            grepl(

              "TCGA_phenotype_denseDataOnlyDownload",

              XenaDatasets

            ) ~ "TCGA Sample Type and Primary Disease",

          DataType == "Protein Expression RPPA" &

            endsWith(XenaDatasets, "RPPA") ~ "RPPA",

          DataType == "Protein Expression RPPA" &

            endsWith(XenaDatasets, "RPPA_RBN") ~ "RPPA normalized by RBN",

          DataType == "Protein Expression RPPA" &

            grepl("TCGA-RPPA-pancan-clean", XenaDatasets) ~ "RPPA pancan normalized",

          DataType == "Somatic Mutation" &

            grepl("mc3.v0.2.8.PUBLIC.xena", XenaDatasets) ~ "MC3 Public Version",

          DataType == "Somatic Mutation" &

            endsWith(XenaDatasets, "mutation_bcgsc") ~ "bcgsc automated",

          DataType == "Somatic Mutation" &

            endsWith(XenaDatasets, "mutation_bcm") ~ "bcm automated",

          DataType == "Somatic Mutation" &

            endsWith(XenaDatasets, "mutation_bcm_solid") ~ "bcm SOLiD",

          DataType == "Somatic Mutation" &

            endsWith(XenaDatasets, "mutation_broad") ~ "broad automated",

          DataType == "Somatic Mutation" &

            endsWith(XenaDatasets, "mutation_curated_bcm") ~ "bcm curated",

          DataType == "Somatic Mutation" &

            endsWith(XenaDatasets, "mutation_curated_bcm_solid") ~ "bcm SOLiD curated",

          DataType == "Somatic Mutation" &

            endsWith(XenaDatasets, "mutation_curated_broad") ~ "broad curated",

          DataType == "Somatic Mutation" &

            endsWith(XenaDatasets, "mutation_curated_wustl") ~ "wustl curated",

          DataType == "Somatic Mutation" &

            endsWith(XenaDatasets, "mutation_ucsc_maf") ~ "ucsc automated",

          DataType == "Somatic Mutation" &

            endsWith(XenaDatasets, "mutation_wustl") ~ "wustl automated",

          DataType == "Somatic Mutation" &

            endsWith(XenaDatasets, "mutation_wustl_hiseq") ~ "wustl hiseq automated",

          DataType == "Gene Somatic Non-silent Mutation" &

            grepl(

              "mc3.v0.2.8.PUBLIC.nonsilentGene.xena",

              XenaDatasets

            ) ~ "MC3 Public Version",

          DataType == "Gene Somatic Non-silent Mutation" &

            endsWith(XenaDatasets, "mutation") ~ "PANCAN AWG analyzed",

          DataType == "Gene Somatic Non-silent Mutation" &

            endsWith(XenaDatasets, "mutation_bcgsc_gene") ~ "bsgsc automated",

          DataType == "Gene Somatic Non-silent Mutation" &

            endsWith(XenaDatasets, "mutation_bcm_gene") ~ "bcm automated",

          DataType == "Gene Somatic Non-silent Mutation" &

            endsWith(XenaDatasets, "mutation_bcm_solid_gene") ~ "bcm SOLiD",

          DataType == "Gene Somatic Non-silent Mutation" &

            endsWith(XenaDatasets, "mutation_broad_gene") ~ "broad automated",

          DataType == "Gene Somatic Non-silent Mutation" &

            endsWith(XenaDatasets, "mutation_curated_bcm_gene") ~ "bcm curated",

          DataType == "Gene Somatic Non-silent Mutation" &

            endsWith(XenaDatasets, "mutation_curated_bcm_solid_gene") ~ "bcm SOLiD curated",

          DataType == "Gene Somatic Non-silent Mutation" &

            endsWith(XenaDatasets, "mutation_curated_broad_gene") ~ "broad curated",

          DataType == "Gene Somatic Non-silent Mutation" &

            endsWith(XenaDatasets, "mutation_curated_wustl_gene") ~ "wustl curated",

          DataType == "Gene Somatic Non-silent Mutation" &

            endsWith(XenaDatasets, "mutation_ucsc_maf_gene") ~ "ucsc automated",

          DataType == "Gene Somatic Non-silent Mutation" &

            endsWith(XenaDatasets, "mutation_wustl_gene") ~ "wustl automated",

          DataType == "Gene Somatic Non-silent Mutation" &

            endsWith(XenaDatasets, "mutation_wustl_hiseq_gene") ~ "wustl hiseq automated",

          DataType == "Transcription Factor Regulatory Impact" &

            grepl("HiSeq.V2$", XenaDatasets) ~ "RABIT Use IlluminaHiSeq RNASeqV2",

          DataType == "Transcription Factor Regulatory Impact" &

            grepl("HiSeq$", XenaDatasets) ~ "RABIT Use IlluminaHiSeq RNASeq",

          DataType == "Transcription Factor Regulatory Impact" &

            grepl("GA.V2$", XenaDatasets) ~ "RABIT Use IlluminaGA RNASeqV2",

          DataType == "Transcription Factor Regulatory Impact" &

            grepl("GA$", XenaDatasets) ~ "RABIT Use IlluminaGA RNASeq",

          DataType == "Transcription Factor Regulatory Impact" &

            grepl("Agilent$", XenaDatasets) ~ "RABIT Use Agilent 244K Microarray",

          DataType == "Transcription Factor Regulatory Impact" &

            grepl("U133A$", XenaDatasets) ~ "RABIT Use Affymetrix U133A Microarray",

          DataType == "iCluster" &

            grepl("TCGA_PanCan33_iCluster_k28_tumor", XenaDatasets) ~ "iCluster cluster assignments",

          DataType == "iCluster" &

            grepl("lat.vars.iCluster.redo.tumor", XenaDatasets) ~ "iCluster latent variables",

          DataType == "Signatures" &

            grepl(

              "Pancan12_GenePrograms_drugTargetCanon",

              XenaDatasets

            ) ~ "Pancan Gene Programs",

          DataType == "Signatures" &

            grepl("StemnessScores_DNAmeth_", XenaDatasets) ~ "DNA methylation based StemnessScore",

          DataType == "Signatures" &

            grepl("StemnessScores_RNAexp", XenaDatasets) ~ "RNA based StemnessScore",

          DataType == "Signatures" &

            grepl(

              "TCGA_pancancer_10852whitelistsamples_68ImmuneSigs",

              XenaDatasets

            ) ~ "Immune Signature Scores",

          DataType == "Signatures" &

            grepl("TCGA.HRD_withSampleID.txt", XenaDatasets) ~ "Genome-wide DNA Damage Footprint HRD Score"

        )

      ) -> ob_tcga

  }

  ob_tcga

}

# grep unique pattern

# ob1 = sub("TCGA.*/(.*)", "\\1", ob$XenaDatasets) %>% table() %>% names() -> uniqueDatasets

# ob1 = tibble(XenaDatasets = uniqueDatasets)

# grep("gene_expression_subtype", ob$XenaDatasets, value = TRUE)

utils::globalVariables(c("DataType", "FileType", "ProjectID"))