README.md
In LiNk-NY/terraTCGAdata: OpenAccess TCGA Data on Terra as MultiAssayExperiment
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
#> `$`, `[`, `[[` - 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
LiNk-NY/terraTCGAdata documentation built on May 1, 2024, 10:43 p.m.
R Package Documentation
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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:
-
terraTCGAdataallows the creation of theMultiAssayExperimentobject from the different indicated resources. -
The
getClinicalTableandgetAssayTablefunctions allow for the discovery of datasets within the Terra data model. The column names from these tables can be provided as inputs to theterraTCGAdatafunction.
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
#> `$`, `[`, `[[` - 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
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