Shixiang Wang

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UCSCXenaTools is an R package for downloading and exploring data from UCSC Xena data hubs, which are

a collection of UCSC-hosted public databases such as TCGA, ICGC, TARGET, GTEx, CCLE, and others. Databases are normalized so they can be combined, linked, filtered, explored and downloaded.

UCSC Xena

If you use this package in academic field, please cite:

Wang, Shixiang, et al. "The predictive power of tumor mutational burden 
    in lung cancer immunotherapy response is influenced by patients' sex." 
    International journal of cancer (2019).

Installation

Install stable release from CRAN with:

install.packages("UCSCXenaTools")

You can also install devel version of UCSCXenaTools from github with:

# install.packages('remotes')
remotes::install_github("ShixiangWang/UCSCXenaTools")

Data Hub List

All datasets are available at https://xenabrowser.net/datapages/.

Currently, UCSCXenaTools supports 10 data hubs of UCSC Xena.

If any url of data hubs are changed or a new data hub is online, please remind me by emailing to or opening an issue on GitHub.

Usage

Download UCSC Xena Datasets and load them into R by UCSCXenaTools is a workflow with generate, filter, query, download and prepare 5 steps, which are implemented as XenaGenerate, XenaFilter, XenaQuery, XenaDownload and XenaPrepare functions, respectively. They are very clear and easy to use and combine with other packages like dplyr.

To show the basic usage of UCSCXenaTools, we will download clinical data of LUNG, LUAD, LUSC from TCGA (hg19 version) data hub.

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 1.2.5
#> Project URL: https://github.com/ropensci/UCSCXenaTools
#> Usages: https://shixiangwang.github.io/home/en/tools/#ucscxenatools
#> 
#> If you use it in published research, please cite:
#> Wang, Shixiang, et al. "The predictive power of tumor mutational burden
#>     in lung cancer immunotherapy response is influenced by patients' sex."
#>     International journal of cancer (2019).
#> =========================================================================
#> 
data(XenaData)

head(XenaData)
#> # A tibble: 6 x 17
#>   XenaHosts XenaHostNames XenaCohorts XenaDatasets SampleCount DataSubtype
#>   <chr>     <chr>         <chr>       <chr>        <chr>       <chr>      
#> 1 https://… publicHub     Acute lymp… mullighan20… 30          copy number
#> 2 https://… publicHub     Acute lymp… mullighan20… 159         phenotype  
#> 3 https://… publicHub     Acute lymp… mullighan20… 129         copy number
#> 4 https://… publicHub     Breast Can… Caldas2007/… 242         phenotype  
#> 5 https://… publicHub     Breast Can… Caldas2007/… 220         copy number
#> 6 https://… publicHub     Breast Can… Caldas2007/… 135         gene expre…
#> # … with 11 more variables: Label <chr>, 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         https://pcawg.xenahubs.net 
#>                     "treehouseHub"                         "pcawgHub" 
#>       https://atacseq.xenahubs.net    https://singlecell.xenahubs.net 
#>                       "atacseqHub"                    "singlecellHub"

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
#>   https://pcawg.xenahubs.net
#>   https://atacseq.xenahubs.net
#>   https://singlecell.xenahubs.net
#> cohorts() (140 total):
#>   Acute lymphoblastic leukemia (Mullighan 2008)
#>   Breast Cancer (Caldas 2007)
#>   Breast Cancer (Chin 2006)
#>   ...
#>   human brain transcriptome (Darmanis PNAS 2015)
#>   mouse cortex and hippocampus (Zeisel Science 2015)
#> datasets() (1646 total):
#>   mullighan2008_public/mullighan2008_500K_genomicMatrix
#>   mullighan2008_public/mullighan2008_public_clinicalMatrix
#>   mullighan2008_public/mullighan2008_SNP6_genomicMatrix
#>   ...
#>   Zeisel/Zeisel_expression_mRNA_log2
#>   Zeisel/Zeisel_expression_phenotype

You can set subset argument to narrow datasets.

XenaGenerate(subset = XenaHostNames == "tcgaHub")
#> class: XenaHub 
#> hosts():
#>   https://tcga.xenahubs.net
#> cohorts() (38 total):
#>   TCGA Acute Myeloid Leukemia (LAML)
#>   TCGA Adrenocortical Cancer (ACC)
#>   TCGA Bile Duct Cancer (CHOL)
#>   ...
#>   TCGA Thyroid Cancer (THCA)
#>   TCGA Uterine Carcinosarcoma (UCS)
#> datasets() (879 total):
#>   TCGA.LAML.sampleMap/HumanMethylation27
#>   TCGA.LAML.sampleMap/HumanMethylation450
#>   TCGA.LAML.sampleMap/Gistic2_CopyNumber_Gistic2_all_data_by_genes
#>   ...
#>   TCGA.UCS.sampleMap/Pathway_Paradigm_RNASeq_And_Copy_Number
#>   TCGA.UCS.sampleMap/mutation_curated_broad

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 Acute Myeloid Leukemia (LAML)"
#> [2] "TCGA Adrenocortical Cancer (ACC)"  
#> [3] "TCGA Bile Duct Cancer (CHOL)"      
#> [4] "TCGA Bladder Cancer (BLCA)"        
#> [5] "TCGA Breast Cancer (BRCA)"         
#> [6] "TCGA Cervical Cancer (CESC)"
# get datasets
head(datasets(xe))
#> [1] "TCGA.LAML.sampleMap/HumanMethylation27"                          
#> [2] "TCGA.LAML.sampleMap/HumanMethylation450"                         
#> [3] "TCGA.LAML.sampleMap/Gistic2_CopyNumber_Gistic2_all_data_by_genes"
#> [4] "TCGA.LAML.sampleMap/mutation_wustl_hiseq"                        
#> [5] "TCGA.LAML.sampleMap/GA"                                          
#> [6] "TCGA.LAML.sampleMap/HiSeqV2_percentile"

Pipe operator %>% can also be used here.

library(dplyr)
XenaData %>% filter(XenaHostNames == "tcgaHub", grepl("BRCA", XenaCohorts), 
    grepl("Path", XenaDatasets)) %>% XenaGenerate()
#> class: XenaHub 
#> hosts():
#>   https://tcga.xenahubs.net
#> cohorts() (1 total):
#>   TCGA Breast Cancer (BRCA)
#> datasets() (4 total):
#>   TCGA.BRCA.sampleMap/Pathway_Paradigm_mRNA_And_Copy_Number
#>   TCGA.BRCA.sampleMap/Pathway_Paradigm_RNASeq
#>   TCGA.BRCA.sampleMap/Pathway_Paradigm_RNASeq_And_Copy_Number
#>   TCGA.BRCA.sampleMap/Pathway_Paradigm_mRNA

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):
#>   Acute lymphoblastic leukemia (Mullighan 2008)
#>   Connectivity Map
#>   Pediatric tumor (Khan)
#>   TARGET Acute Lymphoblastic Leukemia
#>   TCGA Acute Myeloid Leukemia (LAML)
#>   TCGA Pan-Cancer (PANCAN)
#> datasets() (34 total):
#>   mullighan2008_public/mullighan2008_500K_genomicMatrix
#>   mullighan2008_public/mullighan2008_SNP6_genomicMatrix
#>   cmap/rankMatrix_reverse
#>   ...
#>   TCGA.PANCAN.sampleMap/SNP6_genomicSegment
#>   TCGA.PANCAN.sampleMap/HiSeqV2_exon
x2 %>% XenaGenerate()
#> class: XenaHub 
#> hosts():
#>   https://tcga.xenahubs.net
#> cohorts() (1 total):
#>   TCGA Lung Cancer (LUNG)
#> datasets() (13 total):
#>   TCGA.LUNG.sampleMap/HumanMethylation27
#>   TCGA.LUNG.sampleMap/HumanMethylation450
#>   TCGA.LUNG.sampleMap/Gistic2_CopyNumber_Gistic2_all_data_by_genes
#>   ...
#>   TCGA.LUNG.sampleMap/HiSeqV2_exon
#>   TCGA.LUNG.sampleMap/AgilentG4502A_07_3

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 Acute Myeloid Leukemia (LAML)
#>   TCGA Adrenocortical Cancer (ACC)
#>   TCGA Bile Duct Cancer (CHOL)
#>   ...
#>   TCGA Thyroid Cancer (THCA)
#>   TCGA Uterine Carcinosarcoma (UCS)
#> datasets() (37 total):
#>   TCGA.LAML.sampleMap/LAML_clinicalMatrix
#>   TCGA.ACC.sampleMap/ACC_clinicalMatrix
#>   TCGA.CHOL.sampleMap/CHOL_clinicalMatrix
#>   ...
#>   TCGA.THCA.sampleMap/THCA_clinicalMatrix
#>   TCGA.UCS.sampleMap/UCS_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 Adenocarcinoma (LUAD)
#>   TCGA Lung Cancer (LUNG)
#>   TCGA Lung Squamous Cell Carcinoma (LUSC)
#> datasets() (3 total):
#>   TCGA.LUAD.sampleMap/LUAD_clinicalMatrix
#>   TCGA.LUNG.sampleMap/LUNG_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.LUAD.sampleMap/LUAD_clinicalMatrix
#> 2 https://tcga.xenahubs.net TCGA.LUNG.sampleMap/LUNG_clinicalMatrix
#> 3 https://tcga.xenahubs.net TCGA.LUSC.sampleMap/LUSC_clinicalMatrix
#>                                                                             url
#> 1 https://tcga.xenahubs.net/download/TCGA.LUAD.sampleMap/LUAD_clinicalMatrix.gz
#> 2 https://tcga.xenahubs.net/download/TCGA.LUNG.sampleMap/LUNG_clinicalMatrix.gz
#> 3 https://tcga.xenahubs.net/download/TCGA.LUSC.sampleMap/LUSC_clinicalMatrix.gz

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/mx/rfkl27z90c96wbmn3_kjk8c80000gn/T//Rtmph9opGx/test.
#> Downloading TCGA.LUAD.sampleMap__LUAD_clinicalMatrix.gz
#> Downloading TCGA.LUNG.sampleMap__LUNG_clinicalMatrix.gz
#> Downloading TCGA.LUSC.sampleMap__LUSC_clinicalMatrix.gz
#> 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.gz"
#> [2] "TCGA.LUNG.sampleMap__LUNG_clinicalMatrix.gz"
#> [3] "TCGA.LUSC.sampleMap__LUSC_clinicalMatrix.gz"
# way2: local files
cli2 = XenaPrepare(file.path(destdir, "TCGA.LUAD.sampleMap__LUAD_clinicalMatrix.gz"))
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.gz" "LUNG_clinicalMatrix.gz"

∂∂

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

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])

Download TCGA data with readable options

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
#>   <chr>     <chr>         <chr>       <chr>        <chr>       <chr>      
#> 1 https://… tcgaHub       TCGA Lung … TCGA.LUAD.s… 706         phenotype  
#> 2 https://… tcgaHub       TCGA Ocula… TCGA.UVM.sa… 80          phenotype  
#> # … with 14 more variables: Label <chr>, 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
#>   <chr>     <chr>         <chr>       <chr>        <chr>       <chr>      
#> 1 https://… tcgaHub       TCGA Lung … TCGA.LUAD.s… 706         phenotype  
#> 2 https://… tcgaHub       TCGA Ocula… TCGA.UVM.sa… 80          phenotype  
#> # … with 14 more variables: Label <chr>, 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] "LAML"     "ACC"      "CHOL"     "BLCA"     "BRCA"     "CESC"    
#>  [7] "COADREAD" "COAD"     "UCEC"     "ESCA"     "FPPP"     "GBM"     
#> [13] "HNSC"     "KICH"     "KIRC"     "KIRP"     "DLBC"     "LIHC"    
#> [19] "LGG"      "GBMLGG"   "LUAD"     "LUNG"     "LUSC"     "SKCM"    
#> [25] "MESO"     "UVM"      "OV"       "PANCAN"   "PAAD"     "PCPG"    
#> [31] "PRAD"     "READ"     "SARC"     "STAD"     "TGCT"     "THYM"    
#> [37] "THCA"     "UCS"     
#> 
#> $DataType
#>  [1] "DNA Methylation"                       
#>  [2] "Gene Level Copy Number"                
#>  [3] "Somatic Mutation"                      
#>  [4] "Gene Expression RNASeq"                
#>  [5] "miRNA Mature Strand Expression RNASeq" 
#>  [6] "Gene Somatic Non-silent Mutation"      
#>  [7] "Copy Number Segments"                  
#>  [8] "Exon Expression RNASeq"                
#>  [9] "Phenotype"                             
#> [10] "PARADIGM Pathway Activity"             
#> [11] "Protein Expression RPPA"               
#> [12] "Transcription Factor Regulatory Impact"
#> [13] "Gene Expression Array"                 
#> [14] "Signatures"                            
#> [15] "iCluster"                              
#> 
#> $FileType
#>  [1] "Methylation27K"                            
#>  [2] "Methylation450K"                           
#>  [3] "Gistic2"                                   
#>  [4] "wustl hiseq automated"                     
#>  [5] "IlluminaGA RNASeq"                         
#>  [6] "IlluminaHiSeq RNASeqV2 in percentile rank" 
#>  [7] "IlluminaHiSeq RNASeqV2 pancan normalized"  
#>  [8] "IlluminaHiSeq RNASeqV2"                    
#>  [9] "After remove germline cnv"                 
#> [10] "PANCAN AWG analyzed"                       
#> [11] "Clinical Information"                      
#> [12] "wustl automated"                           
#> [13] "Gistic2 thresholded"                       
#> [14] "Before remove germline cnv"                
#> [15] "Use only RNASeq"                           
#> [16] "Use RNASeq plus Copy Number"               
#> [17] "bcm automated"                             
#> [18] "IlluminaHiSeq RNASeq"                      
#> [19] "bcm curated"                               
#> [20] "broad curated"                             
#> [21] "RPPA"                                      
#> [22] "bsgsc automated"                           
#> [23] "broad automated"                           
#> [24] "bcgsc automated"                           
#> [25] "ucsc automated"                            
#> [26] "RABIT Use IlluminaHiSeq RNASeqV2"          
#> [27] "RABIT Use IlluminaHiSeq RNASeq"            
#> [28] "RPPA normalized by RBN"                    
#> [29] "RABIT Use Agilent 244K Microarray"         
#> [30] "wustl curated"                             
#> [31] "Use Microarray plus Copy Number"           
#> [32] "Use only Microarray"                       
#> [33] "Agilent 244K Microarray"                   
#> [34] "IlluminaGA RNASeqV2"                       
#> [35] "bcm SOLiD"                                 
#> [36] "RABIT Use IlluminaGA RNASeqV2"             
#> [37] "RABIT Use IlluminaGA RNASeq"               
#> [38] "RABIT Use Affymetrix U133A Microarray"     
#> [39] "Affymetrix U133A Microarray"               
#> [40] "MethylMix"                                 
#> [41] "bcm SOLiD curated"                         
#> [42] "Gene Expression Subtype"                   
#> [43] "Platform-corrected PANCAN12 dataset"       
#> [44] "Batch effects normalized"                  
#> [45] "MC3 Public Version"                        
#> [46] "TCGA Sample Type and Primary Disease"      
#> [47] "RPPA pancan normalized"                    
#> [48] "Tumor copy number"                         
#> [49] "Genome-wide DNA Damage Footprint HRD Score"
#> [50] "TCGA Molecular Subtype"                    
#> [51] "iCluster cluster assignments"              
#> [52] "iCluster latent variables"                 
#> [53] "RNA based StemnessScore"                   
#> [54] "DNA methylation based StemnessScore"       
#> [55] "Pancan Gene Programs"                      
#> [56] "Immune Model Based Subtype"                
#> [57] "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()

SessionInfo

sessionInfo()
#> R version 3.6.0 RC (2019-04-21 r76409)
#> Platform: x86_64-apple-darwin15.6.0 (64-bit)
#> Running under: macOS High Sierra 10.13.6
#> 
#> Matrix products: default
#> BLAS:   /Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRblas.0.dylib
#> LAPACK: /Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRlapack.dylib
#> 
#> locale:
#> [1] zh_CN.UTF-8/zh_CN.UTF-8/zh_CN.UTF-8/C/zh_CN.UTF-8/zh_CN.UTF-8
#> 
#> attached base packages:
#> [1] stats     graphics  grDevices utils     datasets  methods   base     
#> 
#> other attached packages:
#> [1] dplyr_0.8.3         UCSCXenaTools_1.2.5 pacman_0.5.1       
#> 
#> loaded via a namespace (and not attached):
#>  [1] Rcpp_1.0.1       knitr_1.23       magrittr_1.5     hms_0.5.0       
#>  [5] tidyselect_0.2.5 R6_2.4.0         rlang_0.4.0      fansi_0.4.0     
#>  [9] stringr_1.4.0    httr_1.4.0       tools_3.6.0      xfun_0.8        
#> [13] utf8_1.1.4       cli_1.1.0        htmltools_0.3.6  yaml_2.2.0      
#> [17] digest_0.6.20    assertthat_0.2.1 tibble_2.1.3     crayon_1.3.4    
#> [21] bookdown_0.12    readr_1.3.1      purrr_0.3.2      formatR_1.7     
#> [25] vctrs_0.2.0      curl_3.3         zeallot_0.1.0    glue_1.3.1      
#> [29] evaluate_0.14    rmarkdown_1.14   blogdown_0.14    stringi_1.4.3   
#> [33] compiler_3.6.0   pillar_1.4.2     backports_1.1.4  pkgconfig_2.0.2

Bug Report

I have no time to test if all conditions are right and all datasets can normally be downloaded. So if you have any question or suggestion, please open an issue on Github at https://github.com/ShixiangWang/UCSCXenaTools/issues.

Acknowledgement

This package is based on XenaR, thanks Martin Morgan for his work.

LICENSE

GPL-3

Please note, code from XenaR package under Apache 2.0 license.