XenaData data.frame

Begin from version 0.2.0, UCSCXenaTools uses a data.frame object (built in package, someone may call it tibble) XenaData to generate an instance of XenaHub class to record general information of all datasets of UCSC Xena Data Hubs.

You can load XenaData after loading UCSCXenaTools into R.

library(UCSCXenaTools)
#> =========================================================================================
#> UCSCXenaTools version 1.3.3
#> Project URL: https://github.com/ropensci/UCSCXenaTools
#> Usages: https://cran.r-project.org/web/packages/UCSCXenaTools/vignettes/USCSXenaTools.html
#> 
#> If you use it in published research, please cite:
#> Wang et al., (2019). The UCSCXenaTools R package: a toolkit for accessing genomics data
#>   from UCSC Xena platform, from cancer multi-omics to single-cell RNA-seq.
#>   Journal of Open Source Software, 4(40), 1627, https://doi.org/10.21105/joss.01627
#> =========================================================================================
#>                               --Enjoy it--
data(XenaData)

head(XenaData)
#> # A tibble: 6 x 17
#>   XenaHosts XenaHostNames XenaCohorts XenaDatasets SampleCount DataSubtype Label
#>   <chr>     <chr>         <chr>       <chr>              <int> <chr>       <chr>
#> 1 https://… publicHub     Breast Can… ucsfNeve_pu…          51 gene expre… Neve…
#> 2 https://… publicHub     Breast Can… ucsfNeve_pu…          57 phenotype   Phen…
#> 3 https://… publicHub     Glioma (Ko… kotliarov20…         194 copy number Kotl…
#> 4 https://… publicHub     Glioma (Ko… kotliarov20…         194 phenotype   Phen…
#> 5 https://… publicHub     Lung Cance… weir2007_pu…         383 copy number CGH  
#> 6 https://… publicHub     Lung Cance… weir2007_pu…         383 phenotype   Phen…
#> # … with 10 more variables: Type <chr>, AnatomicalOrigin <chr>,
#> #   SampleType <chr>, Tags <chr>, ProbeMap <chr>, LongTitle <chr>,
#> #   Citation <chr>, Version <chr>, Unit <chr>, Platform <chr>

Names of all hub names/urls can be accessed by object .xena_hosts:

UCSCXenaTools:::.xena_hosts
#>        https://ucscpublic.xenahubs.net              https://tcga.xenahubs.net 
#>                            "publicHub"                              "tcgaHub" 
#>               https://gdc.xenahubs.net              https://icgc.xenahubs.net 
#>                               "gdcHub"                              "icgcHub" 
#>              https://toil.xenahubs.net       https://pancanatlas.xenahubs.net 
#>                              "toilHub"                       "pancanAtlasHub" 
#> https://xena.treehouse.gi.ucsc.edu:443             https://pcawg.xenahubs.net 
#>                         "treehouseHub"                             "pcawgHub" 
#>           https://atacseq.xenahubs.net     https://singlecellnew.xenahubs.net 
#>                           "atacseqHub"                        "singlecellHub" 
#>         https://kidsfirst.xenahubs.net 
#>                         "kidsfirstHub"

Generate a XenaHub object

This can be implemented by XenaGenerate function, which generates XenaHub object from XenaData data frame.

XenaGenerate()
#> class: XenaHub 
#> hosts():
#>   https://ucscpublic.xenahubs.net
#>   https://tcga.xenahubs.net
#>   https://gdc.xenahubs.net
#>   https://icgc.xenahubs.net
#>   https://toil.xenahubs.net
#>   https://pancanatlas.xenahubs.net
#>   https://xena.treehouse.gi.ucsc.edu:443
#>   https://pcawg.xenahubs.net
#>   https://atacseq.xenahubs.net
#>   https://singlecellnew.xenahubs.net
#>   https://kidsfirst.xenahubs.net
#> cohorts() (151 total):
#>   Breast Cancer Cell Lines (Neve 2006)
#>   Glioma (Kotliarov 2006)
#>   Lung Cancer CGH (Weir 2007)
#>   ...
#>   Pediatric Brain Tumor Atlas: PNOC
#>   Pediatric Brain Tumor Atlas: CBTTC
#> datasets() (1641 total):
#>   ucsfNeve_public/ucsfNeveExp_genomicMatrix
#>   ucsfNeve_public/ucsfNeve_public_clinicalMatrix
#>   kotliarov2006_public/kotliarov2006_genomicMatrix
#>   ...
#>   CBTTC/Participants.txt
#>   CBTTC/survival.txt

You can set subset argument to narrow datasets.

XenaGenerate(subset = XenaHostNames == "tcgaHub")
#> class: XenaHub 
#> hosts():
#>   https://tcga.xenahubs.net
#> cohorts() (38 total):
#>   TCGA Ovarian Cancer (OV)
#>   TCGA Kidney Clear Cell Carcinoma (KIRC)
#>   TCGA Lower Grade Glioma (LGG)
#>   ...
#>   TCGA Colon Cancer (COAD)
#>   TCGA Formalin Fixed Paraffin-Embedded Pilot Phase II (FPPP)
#> datasets() (715 total):
#>   TCGA.OV.sampleMap/HumanMethylation27
#>   TCGA.OV.sampleMap/HumanMethylation450
#>   TCGA.OV.sampleMap/Gistic2_CopyNumber_Gistic2_all_data_by_genes
#>   ...
#>   TCGA.FPPP.sampleMap/miRNA_HiSeq_gene
#>   TCGA.FPPP.sampleMap/FPPP_clinicalMatrix

You can also use XenaHub() to generate a XenaHub object for API communication, but it is not recommended.

It’s possible to extract info from XenaHub object by hosts(), cohorts() and datasets().

xe = XenaGenerate(subset = XenaHostNames == "tcgaHub")
# get hosts
hosts(xe)
#> [1] "https://tcga.xenahubs.net"
# get cohorts
head(cohorts(xe))
#> [1] "TCGA Ovarian Cancer (OV)"                   
#> [2] "TCGA Kidney Clear Cell Carcinoma (KIRC)"    
#> [3] "TCGA Lower Grade Glioma (LGG)"              
#> [4] "TCGA Kidney Papillary Cell Carcinoma (KIRP)"
#> [5] "TCGA Pan-Cancer (PANCAN)"                   
#> [6] "TCGA Bile Duct Cancer (CHOL)"
# get datasets
head(datasets(xe))
#> [1] "TCGA.OV.sampleMap/HumanMethylation27"                          
#> [2] "TCGA.OV.sampleMap/HumanMethylation450"                         
#> [3] "TCGA.OV.sampleMap/Gistic2_CopyNumber_Gistic2_all_data_by_genes"
#> [4] "TCGA.OV.sampleMap/OV_clinicalMatrix"                           
#> [5] "TCGA.OV.sampleMap/RPPA_RBN"                                    
#> [6] "TCGA.OV.sampleMap/HT_HG-U133A"

Pipe operator %>% can also be used here.

library(dplyr)
XenaData %>% filter(XenaHostNames == "tcgaHub", grepl("BRCA", XenaCohorts), grepl("Path", 
    XenaDatasets)) %>% XenaGenerate()
#> class: XenaHub 
#> hosts():
#>   
#> cohorts() (0 total):
#>   
#> datasets() (0 total):
#> 

Sometimes we only know some keywords, XenaScan() can be used to scan all rows to detect if the keywords exist in XenaData.

x1 = XenaScan(pattern = "Blood")
x2 = XenaScan(pattern = "LUNG", ignore.case = FALSE)

x1 %>% XenaGenerate()
#> class: XenaHub 
#> hosts():
#>   https://ucscpublic.xenahubs.net
#>   https://tcga.xenahubs.net
#> cohorts() (6 total):
#>   Connectivity Map
#>   TARGET Acute Lymphoblastic Leukemia
#>   Pediatric tumor (Khan)
#>   Acute lymphoblastic leukemia (Mullighan 2008)
#>   TCGA Pan-Cancer (PANCAN)
#>   TCGA Acute Myeloid Leukemia (LAML)
#> datasets() (28 total):
#>   cmap/rankMatrix_reverse
#>   TARGET_ALL/TARGETcnv_genomicMatrix
#>   TARGET_ALL/TARGETexp_genomicMatrix
#>   ...
#>   TCGA.LAML.sampleMap/mutation_wustl
#>   TCGA.LAML.sampleMap/mutation_wustl_gene
x2 %>% XenaGenerate()
#> class: XenaHub 
#> hosts():
#>   https://tcga.xenahubs.net
#> cohorts() (1 total):
#>   TCGA Lung Cancer (LUNG)
#> datasets() (15 total):
#>   TCGA.LUNG.sampleMap/HumanMethylation27
#>   TCGA.LUNG.sampleMap/HumanMethylation450
#>   TCGA.LUNG.sampleMap/Gistic2_CopyNumber_Gistic2_all_data_by_genes
#>   ...
#>   mc3/LUNG_mc3.txt
#>   mc3_gene_level/LUNG_mc3_gene_level.txt

Filter

There are too many datasets in xe, you can filter them by XenaFilter function. Regular expression can be used here.

(xe2 <- XenaFilter(xe, filterDatasets = "clinical"))
#> class: XenaHub 
#> hosts():
#>   https://tcga.xenahubs.net
#> cohorts() (37 total):
#>   TCGA Ovarian Cancer (OV)
#>   TCGA Kidney Clear Cell Carcinoma (KIRC)
#>   TCGA Lower Grade Glioma (LGG)
#>   ...
#>   TCGA Colon Cancer (COAD)
#>   TCGA Formalin Fixed Paraffin-Embedded Pilot Phase II (FPPP)
#> datasets() (37 total):
#>   TCGA.OV.sampleMap/OV_clinicalMatrix
#>   TCGA.KIRC.sampleMap/KIRC_clinicalMatrix
#>   TCGA.LGG.sampleMap/LGG_clinicalMatrix
#>   ...
#>   TCGA.COAD.sampleMap/COAD_clinicalMatrix
#>   TCGA.FPPP.sampleMap/FPPP_clinicalMatrix

Then select LUAD, LUSC and LUNG 3 datasets.

xe2 <- XenaFilter(xe2, filterDatasets = "LUAD|LUSC|LUNG")

Pipe can be used here.

xe %>% XenaFilter(filterDatasets = "clinical") %>% XenaFilter(filterDatasets = "luad|lusc|lung")
#> class: XenaHub 
#> hosts():
#>   https://tcga.xenahubs.net
#> cohorts() (3 total):
#>   TCGA Lung Cancer (LUNG)
#>   TCGA Lung Adenocarcinoma (LUAD)
#>   TCGA Lung Squamous Cell Carcinoma (LUSC)
#> datasets() (3 total):
#>   TCGA.LUNG.sampleMap/LUNG_clinicalMatrix
#>   TCGA.LUAD.sampleMap/LUAD_clinicalMatrix
#>   TCGA.LUSC.sampleMap/LUSC_clinicalMatrix

Browse datasets

Sometimes, you may want to check data before you query and download data. A new feature XenaBrowse is implemented in UCSCXenaTools.

Create two XenaHub objects:

• to_browse - a XenaHub object contains a cohort and a dataset.
• to_browse2 - a XenaHub object contains 2 cohorts and 2 datasets.

to_browse <- XenaGenerate(subset = XenaHostNames == "tcgaHub") %>% XenaFilter(filterDatasets = "clinical") %>% 
    XenaFilter(filterDatasets = "LUAD")

to_browse
#> class: XenaHub 
#> hosts():
#>   https://tcga.xenahubs.net
#> cohorts() (1 total):
#>   TCGA Lung Adenocarcinoma (LUAD)
#> datasets() (1 total):
#>   TCGA.LUAD.sampleMap/LUAD_clinicalMatrix

to_browse2 <- XenaGenerate(subset = XenaHostNames == "tcgaHub") %>% XenaFilter(filterDatasets = "clinical") %>% 
    XenaFilter(filterDatasets = "LUAD|LUSC")

to_browse2
#> class: XenaHub 
#> hosts():
#>   https://tcga.xenahubs.net
#> cohorts() (2 total):
#>   TCGA Lung Adenocarcinoma (LUAD)
#>   TCGA Lung Squamous Cell Carcinoma (LUSC)
#> datasets() (2 total):
#>   TCGA.LUAD.sampleMap/LUAD_clinicalMatrix
#>   TCGA.LUSC.sampleMap/LUSC_clinicalMatrix

XenaBrowse() function can be used to browse dataset/cohort links using your default web browser. At default, this function limit one dataset/cohort for preventing user to open too many links at once.

# This will open you web browser
XenaBrowse(to_browse)

XenaBrowse(to_browse, type = "cohort")

# This will throw error
XenaBrowse(to_browse2)
#> Error in XenaBrowse(to_browse2): This function limite 1 dataset to browse.
#>  Set multiple to TRUE if you want to browse multiple links.

XenaBrowse(to_browse2, type = "cohort")
#> Error in XenaBrowse(to_browse2, type = "cohort"): This function limite 1 cohort to browse. 
#>  Set multiple to TRUE if you want to browse multiple links.

When you make sure you want to open multiple links, you can set multiple option to TRUE.

XenaBrowse(to_browse2, multiple = TRUE)
XenaBrowse(to_browse2, type = "cohort", multiple = TRUE)

Query

Create a query before downloading data.

xe2_query = XenaQuery(xe2)
#> This will check url status, please be patient.
xe2_query
#>                       hosts                                datasets
#> 1 https://tcga.xenahubs.net TCGA.LUNG.sampleMap/LUNG_clinicalMatrix
#> 2 https://tcga.xenahubs.net TCGA.LUAD.sampleMap/LUAD_clinicalMatrix
#> 3 https://tcga.xenahubs.net TCGA.LUSC.sampleMap/LUSC_clinicalMatrix
#>                                                                          url
#> 1 https://tcga.xenahubs.net/download/TCGA.LUNG.sampleMap/LUNG_clinicalMatrix
#> 2 https://tcga.xenahubs.net/download/TCGA.LUAD.sampleMap/LUAD_clinicalMatrix
#> 3 https://tcga.xenahubs.net/download/TCGA.LUSC.sampleMap/LUSC_clinicalMatrix

Download

Default, data will be downloaded to system temp directory. You can specify the path.

If the data exists, command will not run to download them, but you can force it by force option.

destdir = file.path(tempdir(), "test")
xe2_download = XenaDownload(xe2_query, destdir = destdir, trans_slash = TRUE)
#> All downloaded files will under directory /var/folders/bj/nw1w4g1j37ddpgb6zmh3sfh80000gn/T//RtmpG4580z/test.
#> Downloading TCGA.LUNG.sampleMap__LUNG_clinicalMatrix
#> Downloading TCGA.LUAD.sampleMap__LUAD_clinicalMatrix
#> Downloading TCGA.LUSC.sampleMap__LUSC_clinicalMatrix
#> Note file names inherit from names in datasets column
#>   and '/' all changed to '__'.

Of note, at default, the downloaded files will keep same directory structure as Xena. You can set trans_slash to TRUE, it will transform / in dataset id to __, this will make all downloaded files are under same directory.

Prepare

There are 4 ways to prepare data to R.

# way1: directory
cli1 = XenaPrepare(destdir)
names(cli1)
#> [1] "TCGA.LUAD.sampleMap__LUAD_clinicalMatrix"
#> [2] "TCGA.LUNG.sampleMap__LUNG_clinicalMatrix"
#> [3] "TCGA.LUSC.sampleMap__LUSC_clinicalMatrix"

# way2: local files
cli2 = XenaPrepare(file.path(destdir, "/TCGA.LUAD.sampleMap__LUAD_clinicalMatrix"))
class(cli2)
#> [1] "spec_tbl_df" "tbl_df"      "tbl"         "data.frame"

# way3: urls
cli3 = XenaPrepare(xe2_download$url[1:2])
names(cli3)
## [1] "LUSC_clinicalMatrix" "LUNG_clinicalMatrix"

# way4: xenadownload object
cli4 = XenaPrepare(xe2_download)
names(cli4)
#> [1] "TCGA.LUNG.sampleMap__LUNG_clinicalMatrix"
#> [2] "TCGA.LUAD.sampleMap__LUAD_clinicalMatrix"
#> [3] "TCGA.LUSC.sampleMap__LUSC_clinicalMatrix"

From v0.2.6, XenaPrepare() can enable chunk feature when file is too big and user only need subset of file.

Following code show how to subset some rows or columns of files, sample is the name of the first column, user can directly use it in logical expression, x can be a representation of data frame user wanna do subset operation. More custom operation can be set as a function and pass to callback option.

# select rows which sample (gene symbol here) in "HIF3A" or "RNF17"
testRNA = UCSCXenaTools::XenaPrepare("~/Download/HiSeqV2.gz", use_chunk = TRUE, subset_rows = sample %in% c("HIF3A", "RNF17"))
# only keep 1 to 3 columns
testRNA = UCSCXenaTools::XenaPrepare("~/Download/HiSeqV2.gz", use_chunk = TRUE, select_cols = colnames(x)[1:3])

getTCGAdata

getTCGAdata provides a more readable way for downloading TCGA (hg19 version, different from gdcHub) datasets, user can specify multiple options to select data and corresponding file type to download. Default this function will return a list include XenaHub object and selected datasets information. Once you are sure the datasets are exactly what you want, download can be set to TRUE to download the data.

Check arguments of getTCGAdata:

args(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, 
#>     ...) 
#> NULL

# or run ??getTCGAdata to read documentation

Select one or more projects, default will select only clinical datasets:

getTCGAdata(c("UVM", "LUAD"))
#> $Xena
#> class: XenaHub 
#> hosts():
#>   https://tcga.xenahubs.net
#> cohorts() (2 total):
#>   TCGA Lung Adenocarcinoma (LUAD)
#>   TCGA Ocular melanomas (UVM)
#> datasets() (2 total):
#>   TCGA.LUAD.sampleMap/LUAD_clinicalMatrix
#>   TCGA.UVM.sampleMap/UVM_clinicalMatrix
#> 
#> $DataInfo
#> # A tibble: 2 x 20
#>   XenaHosts XenaHostNames XenaCohorts XenaDatasets SampleCount DataSubtype Label
#>   <chr>     <chr>         <chr>       <chr>              <int> <chr>       <chr>
#> 1 https://… tcgaHub       TCGA Lung … TCGA.LUAD.s…         706 phenotype   Phen…
#> 2 https://… tcgaHub       TCGA Ocula… TCGA.UVM.sa…          80 phenotype   Phen…
#> # … with 13 more variables: Type <chr>, AnatomicalOrigin <chr>,
#> #   SampleType <chr>, Tags <chr>, ProbeMap <chr>, LongTitle <chr>,
#> #   Citation <chr>, Version <chr>, Unit <chr>, Platform <chr>, ProjectID <chr>,
#> #   DataType <chr>, FileType <chr>

tcga_data = getTCGAdata(c("UVM", "LUAD"))

# only return XenaHub object
tcga_data$Xena
#> class: XenaHub 
#> hosts():
#>   https://tcga.xenahubs.net
#> cohorts() (2 total):
#>   TCGA Lung Adenocarcinoma (LUAD)
#>   TCGA Ocular melanomas (UVM)
#> datasets() (2 total):
#>   TCGA.LUAD.sampleMap/LUAD_clinicalMatrix
#>   TCGA.UVM.sampleMap/UVM_clinicalMatrix

# only return datasets information
tcga_data$DataInfo
#> # A tibble: 2 x 20
#>   XenaHosts XenaHostNames XenaCohorts XenaDatasets SampleCount DataSubtype Label
#>   <chr>     <chr>         <chr>       <chr>              <int> <chr>       <chr>
#> 1 https://… tcgaHub       TCGA Lung … TCGA.LUAD.s…         706 phenotype   Phen…
#> 2 https://… tcgaHub       TCGA Ocula… TCGA.UVM.sa…          80 phenotype   Phen…
#> # … with 13 more variables: Type <chr>, AnatomicalOrigin <chr>,
#> #   SampleType <chr>, Tags <chr>, ProbeMap <chr>, LongTitle <chr>,
#> #   Citation <chr>, Version <chr>, Unit <chr>, Platform <chr>, ProjectID <chr>,
#> #   DataType <chr>, FileType <chr>

Set download=TRUE to download data, default data will be downloaded to system temp directory (you can specify the path with destdir option):

# only download clinical data
getTCGAdata(c("UVM", "LUAD"), download = TRUE)

Support Data Type and Options:

• clinical information: clinical
• mRNA Sequencing: mRNASeq
• mRNA microarray: mRNAArray
• miRNA Sequencing: miRNASeq
• exon Sequencing: exonRNASeq
• RPPA array: RPPAArray
• DNA Methylation: Methylation
• Gene mutation: GeneMutation
• Somatic mutation: SomaticMutation
• Gistic2 Copy Number: GisticCopyNumber
• Copy Number Segment: CopyNumberSegment

other data type supported by Xena cannot download use this function. Please refer to downloadTCGA function or XenaGenerate function.

NOTE: Sequencing data are all based on Illumina Hiseq platform, other platform (Illumina GA) data supported by Xena cannot download using this function. This is for building consistent data download flow. Mutation use broad automated version (except PANCAN use MC3 Public Version). If you wan to download other datasets, please refer to downloadTCGA function or XenaGenerate function.

Download any TCGA data by datatypes and filetypes

downloadTCGA function can be used to download any TCGA data supported by Xena, but in a way different from getTCGAdata function.

# download RNASeq data (use UVM as an example)
downloadTCGA(project = "UVM", data_type = "Gene Expression RNASeq", file_type = "IlluminaHiSeq RNASeqV2")

See the arguments:

args(downloadTCGA)
#> function (project = NULL, data_type = NULL, file_type = NULL, 
#>     destdir = tempdir(), force = FALSE, ...) 
#> NULL

Except destdir option, you only need to select three arguments for downloading data. Even throught the number is far less than getTCGAdata, it is more complex than the latter.

Before you download data, you need spare some time to figure out what data type and file type available and what your datasets have.

availTCGA can return all information you need:

availTCGA()
#> Note not all projects have listed data types and file types, you can use showTCGA function to check if exist
#> $ProjectID
#>  [1] "OV"       "KIRC"     "LGG"      "KIRP"     "PANCAN"   "CHOL"    
#>  [7] "COADREAD" "ACC"      "CESC"     "READ"     "SARC"     "DLBC"    
#> [13] "PRAD"     "LUNG"     "LIHC"     "KICH"     "HNSC"     "PCPG"    
#> [19] "ESCA"     "THCA"     "LUAD"     "LAML"     "BLCA"     "SKCM"    
#> [25] "LUSC"     "TGCT"     "PAAD"     "GBM"      "STAD"     "MESO"    
#> [31] "UVM"      "GBMLGG"   "THYM"     "UCEC"     "BRCA"     "UCS"     
#> [37] "COAD"     "FPPP"    
#> 
#> $DataType
#>  [1] "DNA Methylation"                       
#>  [2] "Gene Level Copy Number"                
#>  [3] "Phenotype"                             
#>  [4] "Protein Expression RPPA"               
#>  [5] "Gene Expression Array"                 
#>  [6] "Gene Expression RNASeq"                
#>  [7] "Copy Number Segments"                  
#>  [8] "miRNA Mature Strand Expression RNASeq" 
#>  [9] "Exon Expression RNASeq"                
#> [10] "Transcription Factor Regulatory Impact"
#> [11] "PARADIGM Pathway Activity"             
#> [12] NA                                      
#> [13] "Gene Somatic Non-silent Mutation"      
#> [14] "Somatic Mutation"                      
#> [15] "Signatures"                            
#> [16] "iCluster"                              
#> 
#> $FileType
#>  [1] "Methylation27K"                            
#>  [2] "Methylation450K"                           
#>  [3] "Gistic2"                                   
#>  [4] "Clinical Information"                      
#>  [5] "RPPA normalized by RBN"                    
#>  [6] "Affymetrix U133A Microarray"               
#>  [7] "IlluminaHiSeq RNASeqV2 in percentile rank" 
#>  [8] "IlluminaHiSeq RNASeqV2 pancan normalized"  
#>  [9] "IlluminaHiSeq RNASeqV2"                    
#> [10] "IlluminaHiSeq RNASeq"                      
#> [11] "After remove germline cnv"                 
#> [12] "Agilent 244K Microarray"                   
#> [13] "Gistic2 thresholded"                       
#> [14] "RPPA"                                      
#> [15] "Before remove germline cnv"                
#> [16] "Gene Expression Subtype"                   
#> [17] "RABIT Use Agilent 244K Microarray"         
#> [18] "RABIT Use Affymetrix U133A Microarray"     
#> [19] "Platform-corrected PANCAN12 dataset"       
#> [20] NA                                          
#> [21] "IlluminaGA RNASeq"                         
#> [22] "RABIT Use IlluminaHiSeq RNASeqV2"          
#> [23] "RABIT Use IlluminaHiSeq RNASeq"            
#> [24] "MethylMix"                                 
#> [25] "IlluminaGA RNASeqV2"                       
#> [26] "broad automated"                           
#> [27] "wustl automated"                           
#> [28] "RABIT Use IlluminaGA RNASeqV2"             
#> [29] "RABIT Use IlluminaGA RNASeq"               
#> [30] "Batch effects normalized"                  
#> [31] "MC3 Public Version"                        
#> [32] "TCGA Sample Type and Primary Disease"      
#> [33] "RPPA pancan normalized"                    
#> [34] "Tumor copy number"                         
#> [35] "Genome-wide DNA Damage Footprint HRD Score"
#> [36] "TCGA Molecular Subtype"                    
#> [37] "iCluster cluster assignments"              
#> [38] "iCluster latent variables"                 
#> [39] "RNA based StemnessScore"                   
#> [40] "DNA methylation based StemnessScore"       
#> [41] "Pancan Gene Programs"                      
#> [42] "Immune Model Based Subtype"                
#> [43] "Immune Signature Scores"

Note not all datasets have these property, showTCGA can help you to check it. It will return all data in TCGA, you can use following code in RStudio and search your data.

View(showTCGA())

OR you can use shiny app provided by UCSCXenaTools to search.

Run shiny by:

UCSCXenaTools::XenaShiny()