UCSCXenaTools: A R package download and explore data from UCSC Xena data hubs

Current Version: 0.2.4

UCSCXenaTools is a R package download and explore 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

Installation

Install stable release from CRAN with:

install.packages("UCSCXenaTools")

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

# install.packages("devtools")
devtools::install_github("ShixiangWang/UCSCXenaTools", build_vignettes = TRUE)

Read this vignettes.

browseVignettes("UCSCXenaTools")
# or
??UCSCXenaTools

Data Hub List

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

Currently, UCSCXenaTools support all 7 data hubs of UCSC Xena.

• UCSC Public Hub: https://ucscpublic.xenahubs.net
• TCGA Hub: https://tcga.xenahubs.net
• GDC Xena Hub: https://gdc.xenahubs.net
• ICGC Xena Hub: https://icgc.xenahubs.net
• Pan-Cancer Atlas Hub: https://pancanatlas.xenahubs.net
• GA4GH (TOIL) Hub: https://toil.xenahubs.net
• Treehouse Hub: https://xena.treehouse.gi.ucsc.edu

If the API changed, please remind me by email to w_shixiang@163.com or open an issue on GitHub.

Usage

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

The following use clinical data download of LUNG, LUAD, LUSC from TCGA (hg19 version) as an example.

XenaData data.frame

Begin from version 0.2.0, UCSCXenaTools use a data.frame object (built in package) XenaData to generate an instance of XenaHub class, which communicate with API of UCSC Xena Data Hubs.

You can load XenaData after loading UCSCXenaTools into R.

library(UCSCXenaTools)
data(XenaData)

head(XenaData)
#>                         XenaHosts XenaHostNames
#> 1 https://ucscpublic.xenahubs.net   UCSC_Public
#> 2 https://ucscpublic.xenahubs.net   UCSC_Public
#> 3 https://ucscpublic.xenahubs.net   UCSC_Public
#> 4 https://ucscpublic.xenahubs.net   UCSC_Public
#> 5 https://ucscpublic.xenahubs.net   UCSC_Public
#> 6 https://ucscpublic.xenahubs.net   UCSC_Public
#>                                     XenaCohorts
#> 1                                  1000_genomes
#> 2                                  1000_genomes
#> 3 Acute lymphoblastic leukemia (Mullighan 2008)
#> 4 Acute lymphoblastic leukemia (Mullighan 2008)
#> 5 Acute lymphoblastic leukemia (Mullighan 2008)
#> 6                          B cells (Basso 2005)
#>                                               XenaDatasets
#> 1                                       1000_genomes/BRCA2
#> 2                                       1000_genomes/BRCA1
#> 3    mullighan2008_public/mullighan2008_500K_genomicMatrix
#> 4 mullighan2008_public/mullighan2008_public_clinicalMatrix
#> 5    mullighan2008_public/mullighan2008_SNP6_genomicMatrix
#> 6         basso2005_public/basso2005_public_clinicalMatrix

Generate a XenaHub object

This can be implemented by XenaGenerate function, which generate 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
#> cohorts() (134 total):
#>   1000_genomes
#>   Acute lymphoblastic leukemia (Mullighan 2008)
#>   B cells (Basso 2005)
#>   ...
#>   Treehouse PED v8
#>   Treehouse public expression dataset (July 2017)
#> datasets() (1549 total):
#>   1000_genomes/BRCA2
#>   1000_genomes/BRCA1
#>   mullighan2008_public/mullighan2008_500K_genomicMatrix
#>   ...
#>   treehouse_public_samples_unique_ensembl_expected_count.2017-09-11.tsv
#>   treehouse_public_samples_unique_hugo_log2_tpm_plus_1.2017-09-11.tsv

We can set subset argument to narrow datasets.

XenaGenerate(subset = XenaHostNames=="TCGA")
#> 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 use XenaHub() to generate a XenaHub object for API communication, but it is not recommended.

It's possible to explore hosts(), cohorts() and datasets().

xe = XenaGenerate(subset = XenaHostNames=="TCGA")
# 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(tidyverse)
> XenaData %>% filter(XenaHostNames == "TCGA", 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

Filter

There are too many datasets, we filter them by XenaFilter function.

Regular expression can be used to filter XenaHub object to what we want.

(XenaFilter(xe, filterDatasets = "clinical") -> xe2)
#> class: XenaHub 
#> hosts():
#>   https://tcga.xenahubs.net
#> cohorts() (39 total):
#>   (unassigned)
#>   TCGA Acute Myeloid Leukemia (LAML)
#>   TCGA Adrenocortical Cancer (ACC)
#>   ...
#>   TCGA Thyroid Cancer (THCA)
#>   TCGA Uterine Carcinosarcoma (UCS)
#> datasets() (37 total):
#>   TCGA.OV.sampleMap/OV_clinicalMatrix
#>   TCGA.DLBC.sampleMap/DLBC_clinicalMatrix
#>   TCGA.KIRC.sampleMap/KIRC_clinicalMatrix
#>   ...
#>   TCGA.READ.sampleMap/READ_clinicalMatrix
#>   TCGA.MESO.sampleMap/MESO_clinicalMatrix

Then select LUAD, LUSC and LUNG 3 datasets.

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

Pipe can be used here.

suppressMessages(require(dplyr))

xe %>% 
    XenaFilter(filterDatasets = "clinical") %>% 
    XenaFilter(filterDatasets = "luad|lusc|lung")
#> class: XenaHub 
#> hosts():
#>   https://tcga.xenahubs.net
#> cohorts() (39 total):
#>   (unassigned)
#>   TCGA Acute Myeloid Leukemia (LAML)
#>   TCGA Adrenocortical Cancer (ACC)
#>   ...
#>   TCGA Thyroid Cancer (THCA)
#>   TCGA Uterine Carcinosarcoma (UCS)
#> datasets() (3 total):
#>   TCGA.LUSC.sampleMap/LUSC_clinicalMatrix
#>   TCGA.LUNG.sampleMap/LUNG_clinicalMatrix
#>   TCGA.LUAD.sampleMap/LUAD_clinicalMatrix

Query

Create a query before download data

xe2_query = XenaQuery(xe2)
xe2_query
#>                       hosts                                datasets
#> 1 https://tcga.xenahubs.net TCGA.LUSC.sampleMap/LUSC_clinicalMatrix
#> 2 https://tcga.xenahubs.net TCGA.LUNG.sampleMap/LUNG_clinicalMatrix
#> 3 https://tcga.xenahubs.net TCGA.LUAD.sampleMap/LUAD_clinicalMatrix
#>                                                                             url
#> 1 https://tcga.xenahubs.net/download/TCGA.LUSC.sampleMap/LUSC_clinicalMatrix.gz
#> 2 https://tcga.xenahubs.net/download/TCGA.LUNG.sampleMap/LUNG_clinicalMatrix.gz
#> 3 https://tcga.xenahubs.net/download/TCGA.LUAD.sampleMap/LUAD_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.

xe2_download = XenaDownload(xe2_query)
#> We will download files to directory /var/folders/mx/rfkl27z90c96wbmn3_kjk8c80000gn/T//RtmpirJQdQ.
#> Downloading TCGA.LUSC.sampleMap__LUSC_clinicalMatrix.gz
#> Downloading TCGA.LUNG.sampleMap__LUNG_clinicalMatrix.gz
#> Downloading TCGA.LUAD.sampleMap__LUAD_clinicalMatrix.gz
#> Note fileNames transfromed from datasets name and / chracter all changed to __ character.
## not run
#xe2_download = XenaDownload(xe2_query, destdir = "E:/Github/XenaData/test/")

Note fileNames transfromed from datasets name and / chracter all changed to __ character.

Prepare

There are 4 ways to prepare data to R.

# way1:  directory
cli1 = XenaPrepare("E:/Github/XenaData/test/")
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("E:/Github/XenaData/test/TCGA.LUAD.sampleMap__LUAD_clinicalMatrix.gz")
class(cli2)
## [1] "tbl_df"     "tbl"        "data.frame"

cli2 = XenaPrepare(c("E:/Github/XenaData/test/TCGA.LUAD.sampleMap__LUAD_clinicalMatrix.gz",
                     "E:/Github/XenaData/test/TCGA.LUNG.sampleMap__LUNG_clinicalMatrix.gz"))
class(cli2)
## [1] "list"
names(cli2)
## [1] "TCGA.LUAD.sampleMap__LUAD_clinicalMatrix.gz"
## [2] "TCGA.LUNG.sampleMap__LUNG_clinicalMatrix.gz"

# 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.LUSC.sampleMap__LUSC_clinicalMatrix.gz"
#> [2] "TCGA.LUNG.sampleMap__LUNG_clinicalMatrix.gz"
#> [3] "TCGA.LUAD.sampleMap__LUAD_clinicalMatrix.gz"

TCGA Common Data Easy Download

getTCGAdata

getTCGAdata provide a quite easy download way for TCGA datasets, user can specify multiple options to select which data and corresponding file type want to download. Default this function will return a list include XenaHub object and selective datasets information. Once you are sure the datasets is 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 = c("normalized", "pancan normalized", 
#>         "percentile"), 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
#>                   XenaHosts XenaHostNames                     XenaCohorts
#> 1 https://tcga.xenahubs.net          TCGA TCGA Lung Adenocarcinoma (LUAD)
#> 2 https://tcga.xenahubs.net          TCGA     TCGA Ocular melanomas (UVM)
#>                              XenaDatasets ProjectID  DataType
#> 1 TCGA.LUAD.sampleMap/LUAD_clinicalMatrix      LUAD Phenotype
#> 2   TCGA.UVM.sampleMap/UVM_clinicalMatrix       UVM Phenotype
#>               FileType
#> 1 Clinical Information
#> 2 Clinical Information

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
#>                   XenaHosts XenaHostNames                     XenaCohorts
#> 1 https://tcga.xenahubs.net          TCGA TCGA Lung Adenocarcinoma (LUAD)
#> 2 https://tcga.xenahubs.net          TCGA     TCGA Ocular melanomas (UVM)
#>                              XenaDatasets ProjectID  DataType
#> 1 TCGA.LUAD.sampleMap/LUAD_clinicalMatrix      LUAD Phenotype
#> 2   TCGA.UVM.sampleMap/UVM_clinicalMatrix       UVM Phenotype
#>               FileType
#> 1 Clinical Information
#> 2 Clinical Information

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)
#> We will download files to directory /var/folders/mx/rfkl27z90c96wbmn3_kjk8c80000gn/T//RtmpirJQdQ.
#> /var/folders/mx/rfkl27z90c96wbmn3_kjk8c80000gn/T//RtmpirJQdQ/TCGA.LUAD.sampleMap__LUAD_clinicalMatrix.gz, the file has been download!
#> Downloading TCGA.UVM.sampleMap__UVM_clinicalMatrix.gz
#> Note fileNames transfromed from datasets name and / chracter all changed to __ character.

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 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()

Download by shiny is under consideration, I am try learning more about how to operate shiny.

SessionInfo

sessionInfo()
#> R version 3.5.1 (2018-07-02)
#> 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/3.5/Resources/lib/libRblas.0.dylib
#> LAPACK: /Library/Frameworks/R.framework/Versions/3.5/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] bindrcpp_0.2.2      dplyr_0.7.6         UCSCXenaTools_0.2.4
#> [4] pacman_0.4.6       
#> 
#> loaded via a namespace (and not attached):
#>  [1] Rcpp_0.12.18         knitr_1.20           bindr_0.1.1         
#>  [4] magrittr_1.5         hms_0.4.2            tidyselect_0.2.4    
#>  [7] xtable_1.8-2         R6_2.2.2             rlang_0.2.2         
#> [10] httr_1.3.1           stringr_1.3.1        tools_3.5.1         
#> [13] shinydashboard_0.7.0 htmltools_0.3.6      yaml_2.2.0          
#> [16] rprojroot_1.3-2      digest_0.6.16        assertthat_0.2.0    
#> [19] tibble_1.4.2         crayon_1.3.4         shiny_1.1.0         
#> [22] readr_1.1.1          later_0.7.3          purrr_0.2.5         
#> [25] promises_1.0.1       curl_3.2             mime_0.5            
#> [28] glue_1.3.0           evaluate_0.11        rmarkdown_1.10      
#> [31] stringi_1.2.4        compiler_3.5.1       pillar_1.3.0        
#> [34] backports_1.1.2      jsonlite_1.5         httpuv_1.4.5        
#> [37] pkgconfig_2.0.2

Bug Report

I have no time to test if all condition 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.

ToDo

• Shinny and more

Code of conduct

Please note that this project is released with a Contributor Code of Conduct. By participating in this project you agree to abide by its terms.