# terraTCGAData
## Installation
``` r
if (!require("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install("terraTCGAdata")
```
# Overview
The `terraTCGAdata` R package aims to import TCGA datasets, as
[MultiAssayExperiment](http://bioconductor.org/packages/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:
1. `terraTCGAdata` allows the creation of the `MultiAssayExperiment`
object from the different indicated resources.
2. 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
``` r
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](https://bioconductor.org/packages/3.16/AnVIL)* package to
identify whether it is installed in your system.
``` r
gcloud_exists()
#> [1] TRUE
```
You can also use the `gcloud_project` to set a project name by
specifying the project argument:
``` r
gcloud_project()
#> [1] "bioconductor-rpci-anvil"
```
# Default Data Workspace
To get a table of available TCGA workspaces, use the
`selectTCGAworkspace()` function:
``` r
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.
``` r
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.
``` r
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:
``` r
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.
``` r
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`:
``` r
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.
``` r
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:
``` r
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
#> `$`, `[`, `[[` - 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
``` r
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
```
