terraTCGAData

Installation

if (!require("BiocManager", quietly = TRUE))
    install.packages("BiocManager")

BiocManager::install("terraTCGAdata")

Overview

The terraTCGAdata R package aims to import TCGA datasets, as MultiAssayExperiment, available on the Terra platform. The package provides a set of functions that allow the discovery of relevant datasets. It provides one main function and two helper functions:

terraTCGAdata allows the creation of the MultiAssayExperiment object from the different indicated resources.
The getClinicalTable and getAssayTable functions allow for the discovery of datasets within the Terra data model. The column names from these tables can be provided as inputs to the terraTCGAdata function.

Data

Some public Terra workspaces come pre-packaged with TCGA data (i.e., cloud data resources are linked within the data model). Particularly the workspaces that are labelled OpenAccess_V1-0. Datasets harmonized to the hg38 genome, such as those from the Genomic Data Commons data repository, use a different data model / workflow and are not compatible with the functions in this package. For those that are, we make use of the Terra data model and represent the data as MultiAssayExperiment.

For more information on MultiAssayExperiment, please see the vignette in that package.

Requirements

Loading packages

library(AnVIL)
library(terraTCGAdata)

gcloud sdk installation

A valid GCloud SDK installation is required to use the package. To get set up, see the Bioconductor tutorials for running RStudio on Terra. Use the gcloud_exists() function from the AnVIL package to identify whether it is installed in your system.

gcloud_exists()
#> [1] TRUE

You can also use the gcloud_project to set a project name by specifying the project argument:

gcloud_project()
#> [1] "bioconductor-rpci-anvil"

Default Data Workspace

To get a table of available TCGA workspaces, use the selectTCGAworkspace() function:

selectTCGAworkspace()
#> [1] "TCGA_COAD_OpenAccess_V1-0_DATA"

You can also set the package-wide option with the terraTCGAworkspace function and check the setting with getOption('terraTCGAdata.workspace') or by running terraTCGAworkspace function.

terraTCGAworkspace("TCGA_COAD_OpenAccess_V1-0_DATA")
#> [1] "TCGA_COAD_OpenAccess_V1-0_DATA"
getOption("terraTCGAdata.workspace")
#> [1] "TCGA_COAD_OpenAccess_V1-0_DATA"

Clinical data resources

In order to determine what datasets to download, use the getClinicalTable function to list all of the columns that correspond to clinical data from the different collection centers.

ct <- getClinicalTable(workspace = "TCGA_COAD_OpenAccess_V1-0_DATA")
#> Using namespace/workspace: broad-firecloud-tcga/TCGA_COAD_OpenAccess_V1-0_DATA
ct
#> # A tibble: 960 × 6
#>    clin__bio__nationwidechildrens_org__Level_1__biospecimen__clin                                                                                            clin_…¹ clin_…² clin_…³ clin_…⁴ clin_…⁵
#>    <chr>                                                                                                                                                     <chr>   <chr>   <chr>   <chr>   <chr>  
#>  1 gs://firecloud-tcga-open-access/tcga/dcc/coad/clin__bio__nationwidechildrens_org__Level_1__biospecimen__clin/nationwidechildrens.org_COAD.bio.Level_1.38… gs://f… gs://f… <NA>    <NA>    <NA>   
#>  2 gs://firecloud-tcga-open-access/tcga/dcc/coad/clin__bio__nationwidechildrens_org__Level_1__biospecimen__clin/nationwidechildrens.org_COAD.bio.Level_1.38… gs://f… gs://f… <NA>    <NA>    <NA>   
#>  3 gs://firecloud-tcga-open-access/tcga/dcc/coad/clin__bio__nationwidechildrens_org__Level_1__biospecimen__clin/nationwidechildrens.org_COAD.bio.Level_1.38… gs://f… gs://f… <NA>    <NA>    <NA>   
#>  4 gs://firecloud-tcga-open-access/tcga/dcc/coad/clin__bio__nationwidechildrens_org__Level_1__biospecimen__clin/nationwidechildrens.org_COAD.bio.Level_1.38… gs://f… gs://f… <NA>    <NA>    <NA>   
#>  5 gs://firecloud-tcga-open-access/tcga/dcc/coad/clin__bio__nationwidechildrens_org__Level_1__biospecimen__clin/nationwidechildrens.org_COAD.bio.Level_1.42… gs://f… gs://f… <NA>    <NA>    <NA>   
#>  6 gs://firecloud-tcga-open-access/tcga/dcc/coad/clin__bio__nationwidechildrens_org__Level_1__biospecimen__clin/nationwidechildrens.org_COAD.bio.Level_1.42… gs://f… gs://f… <NA>    <NA>    <NA>   
#>  7 gs://firecloud-tcga-open-access/tcga/dcc/coad/clin__bio__nationwidechildrens_org__Level_1__biospecimen__clin/nationwidechildrens.org_COAD.bio.Level_1.42… gs://f… gs://f… <NA>    <NA>    <NA>   
#>  8 gs://firecloud-tcga-open-access/tcga/dcc/coad/clin__bio__nationwidechildrens_org__Level_1__biospecimen__clin/nationwidechildrens.org_COAD.bio.Level_1.42… gs://f… gs://f… <NA>    <NA>    <NA>   
#>  9 gs://firecloud-tcga-open-access/tcga/dcc/coad/clin__bio__nationwidechildrens_org__Level_1__biospecimen__clin/nationwidechildrens.org_COAD.bio.Level_1.42… gs://f… <NA>    <NA>    <NA>    <NA>   
#> 10 gs://firecloud-tcga-open-access/tcga/dcc/coad/clin__bio__nationwidechildrens_org__Level_1__biospecimen__clin/nationwidechildrens.org_COAD.bio.Level_1.42… gs://f… <NA>    <NA>    <NA>    <NA>   
#> # … with 950 more rows, and abbreviated variable names ¹clin__bio__nationwidechildrens_org__Level_1__auxiliary__clin, ²clin__bio__nationwidechildrens_org__Level_1__clinical__clin,
#> #   ³clin__bio__intgen_org__Level_1__auxiliary__clin, ⁴clin__bio__intgen_org__Level_1__clinical__clin, ⁵clin__bio__intgen_org__Level_1__biospecimen__clin
names(ct)
#> [1] "clin__bio__nationwidechildrens_org__Level_1__biospecimen__clin" "clin__bio__nationwidechildrens_org__Level_1__auxiliary__clin"  
#> [3] "clin__bio__nationwidechildrens_org__Level_1__clinical__clin"    "clin__bio__intgen_org__Level_1__auxiliary__clin"               
#> [5] "clin__bio__intgen_org__Level_1__clinical__clin"                 "clin__bio__intgen_org__Level_1__biospecimen__clin"

Clinical data download

After picking the column in the getClinicalTable output, use the column name as input to the getClinical function to obtain the data:

column_name <- "clin__bio__nationwidechildrens_org__Level_1__biospecimen__clin"
clin <- getClinical(
    columnName = column_name,
    participants = TRUE,
    workspace = "TCGA_COAD_OpenAccess_V1-0_DATA"
)
#> Using namespace/workspace: broad-firecloud-tcga/TCGA_COAD_OpenAccess_V1-0_DATA
#> 
#> ── Column specification ────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
#> cols(
#>   .default = col_character(),
#>   admin.day_of_dcc_upload = col_double(),
#>   admin.month_of_dcc_upload = col_double(),
#>   admin.year_of_dcc_upload = col_double(),
#>   patient.additional_studies = col_logical(),
#>   patient.days_to_index = col_double(),
#>   patient.samples.sample.additional_studies = col_logical(),
#>   patient.samples.sample.biospecimen_sequence = col_logical(),
#>   patient.samples.sample.longest_dimension = col_double(),
#>   patient.samples.sample.intermediate_dimension = col_double(),
#>   patient.samples.sample.shortest_dimension = col_double(),
#>   patient.samples.sample.initial_weight = col_double(),
#>   patient.samples.sample.current_weight = col_logical(),
#>   patient.samples.sample.freezing_method = col_logical(),
#>   patient.samples.sample.oct_embedded = col_logical(),
#>   patient.samples.sample.preservation_method = col_logical(),
#>   patient.samples.sample.tissue_type = col_logical(),
#>   patient.samples.sample.composition = col_logical(),
#>   patient.samples.sample.tumor_descriptor = col_logical(),
#>   patient.samples.sample.days_to_collection = col_double(),
#>   patient.samples.sample.time_between_clamping_and_freezing = col_logical()
#>   # ... with 1225 more columns
#> )
#> ℹ Use `spec()` for the full column specifications.
clin[, 1:6]
#> # A tibble: 460 × 6
#>    admin.bcr                      admin.file_uuid                      admin.batch_number admin.project_code admin.disease_code admin.day_of_dcc_upload
#>    <chr>                          <chr>                                <chr>              <chr>              <chr>                                <dbl>
#>  1 nationwide children's hospital a93e6bbe-80de-41a1-9cc6-41fd0f56a4e9 385.38.0           tcga               coad                                     1
#>  2 nationwide children's hospital 8b055cbc-b2ff-4c62-a07c-ccfa44964937 385.38.0           tcga               coad                                     1
#>  3 nationwide children's hospital 61f5baab-8b35-45f4-a188-7d4f3d1a2a8b 422.33.0           tcga               coad                                     1
#>  4 nationwide children's hospital fbad35cb-8be3-4b36-a05d-e93aee1c3975 422.33.0           tcga               coad                                     1
#>  5 nationwide children's hospital 5620a991-2a62-446a-a26e-41ad5c1a92c7 422.33.0           tcga               coad                                     1
#>  6 nationwide children's hospital d7563bda-caea-473f-82fd-905c2bee66ea 422.33.0           tcga               coad                                     1
#>  7 nationwide children's hospital ef41a4ba-feb2-47c2-9292-a0a0680cf9f6 422.33.0           tcga               coad                                     1
#>  8 nationwide children's hospital 96b2bc07-30bf-4e67-b776-371a791249c0 422.33.0           tcga               coad                                     1
#>  9 nationwide children's hospital d1fedef8-53a4-42ff-9cf7-194fd92c004b 76.73.0            tcga               coad                                     1
#> 10 nationwide children's hospital 51c274ce-f952-45da-a0b3-285559d5c361 29.77.0            tcga               coad                                     1
#> # … with 450 more rows
dim(clin)
#> [1]  460 2376

Assay data resources

We use the same approach for assay data. We first produce a list of assays from the getAssayTable and then we select one along with any sample codes of interest.

at <- getAssayTable(workspace = "TCGA_COAD_OpenAccess_V1-0_DATA")
at
#> # A tibble: 960 × 29
#>    snp__ge…¹ snp__…² snp__…³ snp__…⁴ rnase…⁵ rnase…⁶ rnase…⁷ prote…⁸ rnase…⁹ rnase…˟ methy…˟ rnase…˟ cna__…˟ trans…˟ rnase…˟ mirna…˟ rnase…˟ mirna…˟ rnase…˟ rnase…˟ rnase…˟ rnase…˟ methy…˟ rnase…˟
#>    <chr>     <chr>   <chr>   <chr>   <chr>   <chr>   <chr>   <chr>   <chr>   <chr>   <chr>   <chr>   <chr>   <chr>   <chr>   <chr>   <chr>   <chr>   <chr>   <chr>   <chr>   <chr>   <chr>   <chr>  
#>  1 gs://fir… gs://f… gs://f… gs://f… <NA>    <NA>    <NA>    <NA>    <NA>    <NA>    <NA>    <NA>    <NA>    <NA>    <NA>    <NA>    <NA>    <NA>    <NA>    <NA>    <NA>    <NA>    <NA>    <NA>   
#>  2 gs://fir… gs://f… gs://f… gs://f… gs://f… gs://f… gs://f… gs://f… gs://f… gs://f… gs://f… gs://f… <NA>    <NA>    <NA>    <NA>    <NA>    <NA>    <NA>    <NA>    <NA>    <NA>    <NA>    <NA>   
#>  3 gs://fir… gs://f… gs://f… gs://f… <NA>    <NA>    <NA>    <NA>    <NA>    <NA>    <NA>    <NA>    <NA>    <NA>    <NA>    <NA>    <NA>    <NA>    <NA>    <NA>    <NA>    <NA>    <NA>    <NA>   
#>  4 gs://fir… gs://f… gs://f… gs://f… gs://f… gs://f… gs://f… gs://f… gs://f… gs://f… gs://f… gs://f… <NA>    <NA>    <NA>    <NA>    <NA>    <NA>    <NA>    <NA>    <NA>    <NA>    <NA>    <NA>   
#>  5 gs://fir… gs://f… gs://f… gs://f… <NA>    <NA>    <NA>    <NA>    <NA>    <NA>    <NA>    <NA>    <NA>    <NA>    <NA>    <NA>    <NA>    <NA>    <NA>    <NA>    <NA>    <NA>    <NA>    <NA>   
#>  6 gs://fir… gs://f… gs://f… gs://f… gs://f… gs://f… gs://f… gs://f… gs://f… gs://f… gs://f… gs://f… <NA>    <NA>    <NA>    <NA>    <NA>    <NA>    <NA>    <NA>    <NA>    <NA>    <NA>    <NA>   
#>  7 gs://fir… gs://f… gs://f… gs://f… <NA>    <NA>    <NA>    <NA>    <NA>    <NA>    <NA>    <NA>    <NA>    <NA>    <NA>    <NA>    <NA>    <NA>    <NA>    <NA>    <NA>    <NA>    <NA>    <NA>   
#>  8 gs://fir… gs://f… gs://f… gs://f… gs://f… gs://f… gs://f… <NA>    gs://f… gs://f… gs://f… gs://f… <NA>    <NA>    <NA>    <NA>    <NA>    <NA>    <NA>    <NA>    <NA>    <NA>    <NA>    <NA>   
#>  9 gs://fir… gs://f… gs://f… gs://f… <NA>    <NA>    <NA>    <NA>    <NA>    <NA>    <NA>    <NA>    <NA>    <NA>    <NA>    <NA>    <NA>    <NA>    <NA>    <NA>    <NA>    <NA>    <NA>    <NA>   
#> 10 gs://fir… gs://f… gs://f… gs://f… gs://f… gs://f… gs://f… gs://f… gs://f… gs://f… gs://f… gs://f… <NA>    <NA>    <NA>    <NA>    <NA>    <NA>    <NA>    <NA>    <NA>    <NA>    <NA>    <NA>   
#> # … with 950 more rows, 5 more variables: mirnaseq__illuminahiseq_mirnaseq__bcgsc_ca__Level_3__miR_isoform_expression__data <chr>,
#> #   mirnaseq__illuminahiseq_mirnaseq__bcgsc_ca__Level_3__miR_gene_expression__data <chr>, rnaseq__illuminaga_rnaseq__unc_edu__Level_3__gene_expression__data <chr>,
#> #   rnaseq__illuminaga_rnaseq__unc_edu__Level_3__exon_expression__data <chr>, rnaseq__illuminaga_rnaseq__unc_edu__Level_3__splice_junction_expression__data <chr>, and abbreviated variable names
#> #   ¹snp__genome_wide_snp_6__broad_mit_edu__Level_3__segmented_scna_minus_germline_cnv_hg18__seg, ²snp__genome_wide_snp_6__broad_mit_edu__Level_3__segmented_scna_hg18__seg,
#> #   ³snp__genome_wide_snp_6__broad_mit_edu__Level_3__segmented_scna_hg19__seg, ⁴snp__genome_wide_snp_6__broad_mit_edu__Level_3__segmented_scna_minus_germline_cnv_hg19__seg,
#> #   ⁵rnaseqv2__illuminahiseq_rnaseqv2__unc_edu__Level_3__RSEM_genes__data, ⁶rnaseqv2__illuminahiseq_rnaseqv2__unc_edu__Level_3__RSEM_genes_normalized__data,
#> #   ⁷rnaseqv2__illuminahiseq_rnaseqv2__unc_edu__Level_3__RSEM_isoforms_normalized__data, ⁸protein_exp__mda_rppa_core__mdanderson_org__Level_3__protein_normalization__data, …
names(at)
#>  [1] "snp__genome_wide_snp_6__broad_mit_edu__Level_3__segmented_scna_minus_germline_cnv_hg18__seg"    
#>  [2] "snp__genome_wide_snp_6__broad_mit_edu__Level_3__segmented_scna_hg18__seg"                       
#>  [3] "snp__genome_wide_snp_6__broad_mit_edu__Level_3__segmented_scna_hg19__seg"                       
#>  [4] "snp__genome_wide_snp_6__broad_mit_edu__Level_3__segmented_scna_minus_germline_cnv_hg19__seg"    
#>  [5] "rnaseqv2__illuminahiseq_rnaseqv2__unc_edu__Level_3__RSEM_genes__data"                           
#>  [6] "rnaseqv2__illuminahiseq_rnaseqv2__unc_edu__Level_3__RSEM_genes_normalized__data"                
#>  [7] "rnaseqv2__illuminahiseq_rnaseqv2__unc_edu__Level_3__RSEM_isoforms_normalized__data"             
#>  [8] "protein_exp__mda_rppa_core__mdanderson_org__Level_3__protein_normalization__data"               
#>  [9] "rnaseqv2__illuminahiseq_rnaseqv2__unc_edu__Level_3__exon_quantification__data"                  
#> [10] "rnaseqv2__illuminahiseq_rnaseqv2__unc_edu__Level_3__junction_quantification__data"              
#> [11] "methylation__humanmethylation450__jhu_usc_edu__Level_3__within_bioassay_data_set_function__data"
#> [12] "rnaseqv2__illuminahiseq_rnaseqv2__unc_edu__Level_3__RSEM_isoforms__data"                        
#> [13] "cna__illuminahiseq_dnaseqc__hms_harvard_edu__Level_3__segmentation__seg"                        
#> [14] "transcriptome__agilentg4502a_07_3__unc_edu__Level_3__unc_lowess_normalization_gene_level__data" 
#> [15] "rnaseqv2__illuminaga_rnaseqv2__unc_edu__Level_3__RSEM_isoforms_normalized__data"                
#> [16] "mirnaseq__illuminaga_mirnaseq__bcgsc_ca__Level_3__miR_isoform_expression__data"                 
#> [17] "rnaseqv2__illuminaga_rnaseqv2__unc_edu__Level_3__RSEM_genes_normalized__data"                   
#> [18] "mirnaseq__illuminaga_mirnaseq__bcgsc_ca__Level_3__miR_gene_expression__data"                    
#> [19] "rnaseqv2__illuminaga_rnaseqv2__unc_edu__Level_3__junction_quantification__data"                 
#> [20] "rnaseqv2__illuminaga_rnaseqv2__unc_edu__Level_3__RSEM_genes__data"                              
#> [21] "rnaseqv2__illuminaga_rnaseqv2__unc_edu__Level_3__RSEM_isoforms__data"                           
#> [22] "rnaseqv2__illuminaga_rnaseqv2__unc_edu__Level_3__exon_quantification__data"                     
#> [23] "methylation__humanmethylation27__jhu_usc_edu__Level_3__within_bioassay_data_set_function__data" 
#> [24] "rnaseq__illuminaga_rnaseq__unc_edu__Level_3__coverage__data"                                    
#> [25] "mirnaseq__illuminahiseq_mirnaseq__bcgsc_ca__Level_3__miR_isoform_expression__data"              
#> [26] "mirnaseq__illuminahiseq_mirnaseq__bcgsc_ca__Level_3__miR_gene_expression__data"                 
#> [27] "rnaseq__illuminaga_rnaseq__unc_edu__Level_3__gene_expression__data"                             
#> [28] "rnaseq__illuminaga_rnaseq__unc_edu__Level_3__exon_expression__data"                             
#> [29] "rnaseq__illuminaga_rnaseq__unc_edu__Level_3__splice_junction_expression__data"

Summary of sample types in the data

You can get a summary table of all the samples in the adata by using the sampleTypesTable:

sampleTypesTable(workspace = "TCGA_COAD_OpenAccess_V1-0_DATA")
#> Using namespace/workspace: broad-firecloud-tcga/TCGA_COAD_OpenAccess_V1-0_DATA
#> # A tibble: 5 × 4
#>   Code  Definition            Short.Letter.Code Frequency
#>   <chr> <chr>                 <chr>                 <dbl>
#> 1 10    Blood Derived Normal  NB                      406
#> 2 11    Solid Tissue Normal   NT                       92
#> 3 06    Metastatic            TM                        1
#> 4 01    Primary Solid Tumor   TP                      460
#> 5 02    Recurrent Solid Tumor TR                        1

Intermediate function for obtaining only the data

Note that if you have the package-wide option set, the workspace argument is not needed in the function call.

prot <- getAssayData(
    assayName = "protein_exp__mda_rppa_core__mdanderson_org__Level_3__protein_normalization__data",
    sampleCode = c("01", "10"),
    workspace = "TCGA_COAD_OpenAccess_V1-0_DATA",
    sampleIdx = 1:4
)
head(prot)
#>                     TCGA-3L-AA1B-01A-21-A45F-20 TCGA-4N-A93T-01A-21-A45F-20 TCGA-4T-AA8H-01A-21-A45F-20 TCGA-5M-AAT5-01A-11-A45F-20
#> 14-3-3_beta-R-V                    -0.080527936                 -0.15754027                  -0.3840605                 -0.08742583
#> 14-3-3_epsilon-M-C                  0.055408025                  0.05978939                   0.1628557                 -0.15276783
#> 14-3-3_zeta-R-V                    -0.002073837                 -0.13374613                   0.2685011                 -0.09958612
#> 4E-BP1-R-V                         -0.026154748                 -0.35821838                   0.3263404                 -0.15502503
#> 4E-BP1_pS65-R-V                    -0.110226155                 -0.15277484                  -0.1381699                 -0.09373361
#> 4E-BP1_pT37_T46-R-V                -0.202870876                 -0.17585007                  -0.1931612                  0.34677646

MultiAssayExperiment

Finally, once you have collected all the relevant column names, these can be inputs to the main terraTCGAdata function:

mae <- terraTCGAdata(
    clinicalName = "clin__bio__nationwidechildrens_org__Level_1__biospecimen__clin",
    assays =
        c("protein_exp__mda_rppa_core__mdanderson_org__Level_3__protein_normalization__data",
        "rnaseqv2__illuminahiseq_rnaseqv2__unc_edu__Level_3__RSEM_genes_normalized__data"),
    sampleCode = NULL,
    split = FALSE,
    sampleIdx = 1:4,
    workspace = "TCGA_COAD_OpenAccess_V1-0_DATA"
)
#> Using namespace/workspace: broad-firecloud-tcga/TCGA_COAD_OpenAccess_V1-0_DATA
#> Using namespace/workspace: broad-firecloud-tcga/TCGA_COAD_OpenAccess_V1-0_DATA
#> Warning in .checkBarcodes(barcodes): Inconsistent barcode lengths: 27, 28
#> Using namespace/workspace: broad-firecloud-tcga/TCGA_COAD_OpenAccess_V1-0_DATA
#> 
#> ── Column specification ────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
#> cols(
#>   .default = col_character(),
#>   admin.day_of_dcc_upload = col_double(),
#>   admin.month_of_dcc_upload = col_double(),
#>   admin.year_of_dcc_upload = col_double(),
#>   patient.additional_studies = col_logical(),
#>   patient.days_to_index = col_double(),
#>   patient.samples.sample.additional_studies = col_logical(),
#>   patient.samples.sample.biospecimen_sequence = col_logical(),
#>   patient.samples.sample.longest_dimension = col_double(),
#>   patient.samples.sample.intermediate_dimension = col_double(),
#>   patient.samples.sample.shortest_dimension = col_double(),
#>   patient.samples.sample.initial_weight = col_double(),
#>   patient.samples.sample.current_weight = col_logical(),
#>   patient.samples.sample.freezing_method = col_logical(),
#>   patient.samples.sample.oct_embedded = col_logical(),
#>   patient.samples.sample.preservation_method = col_logical(),
#>   patient.samples.sample.tissue_type = col_logical(),
#>   patient.samples.sample.composition = col_logical(),
#>   patient.samples.sample.tumor_descriptor = col_logical(),
#>   patient.samples.sample.days_to_collection = col_double(),
#>   patient.samples.sample.time_between_clamping_and_freezing = col_logical()
#>   # ... with 1225 more columns
#> )
#> ℹ Use `spec()` for the full column specifications.
#> Warning in .checkBarcodes(barcodes): Inconsistent barcode lengths: 27, 28
#> harmonizing input:
#>   removing 455 colData rownames not in sampleMap 'primary'
mae
#> A MultiAssayExperiment object of 2 listed
#>  experiments with user-defined names and respective classes.
#>  Containing an ExperimentList class object of length 2:
#>  [1] protein_exp__mda_rppa_core__mdanderson_org__Level_3__protein_normalization__data: matrix with 200 rows and 4 columns
#>  [2] rnaseqv2__illuminahiseq_rnaseqv2__unc_edu__Level_3__RSEM_genes_normalized__data: matrix with 20531 rows and 4 columns
#> Functionality:
#>  experiments() - obtain the ExperimentList instance
#>  colData() - the primary/phenotype DataFrame
#>  sampleMap() - the sample coordination DataFrame
#>  `NSCCN/bioconductor-terratcgadata：提供TCGA数据的R包，用于生物信息学分析, `[`, `[[` - extract colData columns, subset, or experiment
#>  *Format() - convert into a long or wide DataFrame
#>  assays() - convert ExperimentList to a SimpleList of matrices
#>  exportClass() - save data to flat files

We expect that most OpenAccess_V1-0 cancer datasets follow this data model. If you encounter any errors, please provide a minimally reproducible example at https://github.com/waldronlab/terraTCGAdata.

Session Info

sessionInfo()
#> R version 4.2.1 Patched (2022-09-26 r82921)
#> Platform: x86_64-pc-linux-gnu (64-bit)
#> Running under: Ubuntu 22.04.1 LTS
#> 
#> Matrix products: default
#> BLAS:   /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.10.0
#> LAPACK: /home/mr148/src/svn/r-4-2/R/lib/R/lib/libRlapack.so
#> 
#> locale:
#>  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C               LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8     LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8    LC_PAPER=en_US.UTF-8      
#>  [8] LC_NAME=C                  LC_ADDRESS=C               LC_TELEPHONE=C             LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       
#> 
#> attached base packages:
#> [1] stats4    stats     graphics  grDevices utils     datasets  methods   base     
#> 
#> other attached packages:
#>  [1] terraTCGAdata_1.1.1         shiny_1.7.2                 testthat_3.1.4              MultiAssayExperiment_1.23.9 SummarizedExperiment_1.27.3 Biobase_2.57.1             
#>  [7] GenomicRanges_1.49.1        GenomeInfoDb_1.33.7         IRanges_2.31.2              S4Vectors_0.35.4            BiocGenerics_0.43.4         MatrixGenerics_1.9.1       
#> [13] matrixStats_0.62.0          AnVIL_1.9.9                 dplyr_1.0.10               
#> 
#> loaded via a namespace (and not attached):
#>  [1] rjson_0.2.21              ellipsis_0.3.2            futile.logger_1.4.3       XVector_0.37.1            rstudioapi_0.14           DT_0.25                   bit64_4.0.5              
#>  [8] AnnotationDbi_1.59.1      fansi_1.0.3               xml2_1.3.3                codetools_0.2-18          cachem_1.0.6              knitr_1.40                pkgload_1.3.0            
#> [15] jsonlite_1.8.0            Rsamtools_2.13.4          dbplyr_2.2.1              png_0.1-7                 BiocManager_1.30.18       readr_2.1.2               compiler_4.2.1           
#> [22] httr_1.4.4                assertthat_0.2.1          Matrix_1.5-1              fastmap_1.1.0             cli_3.4.1                 later_1.3.0               formatR_1.12             
#> [29] htmltools_0.5.3           prettyunits_1.1.1         tools_4.2.1               glue_1.6.2                GenomeInfoDbData_1.2.9    rappdirs_0.3.3            Rcpp_1.0.9               
#> [36] rapiclient_0.1.3          vctrs_0.4.2               Biostrings_2.65.6         rtracklayer_1.57.0        xfun_0.33                 stringr_1.4.1             brio_1.1.3               
#> [43] rvest_1.0.3               mime_0.12                 miniUI_0.1.1.1            lifecycle_1.0.2           restfulr_0.0.15           XML_3.99-0.10             zlibbioc_1.43.0          
#> [50] BiocStyle_2.25.0          vroom_1.5.7               hms_1.1.2                 promises_1.2.0.1          parallel_4.2.1            lambda.r_1.2.4            yaml_2.3.5               
#> [57] curl_4.3.2                memoise_2.0.1             biomaRt_2.53.2            stringi_1.7.8             RSQLite_2.2.17            BiocIO_1.7.1              GenomicDataCommons_1.21.4
#> [64] GenomicFeatures_1.49.7    filelock_1.0.2            BiocParallel_1.31.12      rlang_1.0.6               pkgconfig_2.0.3           bitops_1.0-7              evaluate_0.16            
#> [71] lattice_0.20-45           purrr_0.3.4               GenomicAlignments_1.33.1  htmlwidgets_1.5.4         bit_4.0.4                 tidyselect_1.1.2          magrittr_2.0.3           
#> [78] R6_2.5.1                  generics_0.1.3            DelayedArray_0.23.2       DBI_1.1.3                 pillar_1.8.1              KEGGREST_1.37.3           RCurl_1.98-1.8           
#> [85] tibble_3.1.8              crayon_1.5.1              futile.options_1.0.1      utf8_1.2.2                BiocFileCache_2.5.0       tzdb_0.3.0                rmarkdown_2.16           
#> [92] progress_1.2.2            grid_4.2.1                blob_1.2.3                digest_0.6.29             xtable_1.8-4              tidyr_1.2.1               httpuv_1.6.6             
#> [99] TCGAutils_1.17.3