The GenomicDataCommons Package (original) (raw)
What is the GDC?
From the Genomic Data Commons (GDC) website:
The National Cancer Institute’s (NCI’s) Genomic Data Commons (GDC) is a data sharing platform that promotes precision medicine in oncology. It is not just a database or a tool; it is an expandable knowledge network supporting the import and standardization of genomic and clinical data from cancer research programs. The GDC contains NCI-generated data from some of the largest and most comprehensive cancer genomic datasets, including The Cancer Genome Atlas (TCGA) and Therapeutically Applicable Research to Generate Effective Therapies (TARGET). For the first time, these datasets have been harmonized using a common set of bioinformatics pipelines, so that the data can be directly compared. As a growing knowledge system for cancer, the GDC also enables researchers to submit data, and harmonizes these data for import into the GDC. As more researchers add clinical and genomic data to the GDC, it will become an even more powerful tool for making discoveries about the molecular basis of cancer that may lead to better care for patients.
Thedata model for the GDC is complex, but it worth a quick overview and a graphical representation is included here.
The data model is encoded as a so-called property graph. Nodes represent entities such as Projects, Cases, Diagnoses, Files (various kinds), and Annotations. The relationships between these entities are maintained as edges. Both nodes and edges may have Properties that supply instance details.
The GDC API exposes these nodes and edges in a somewhat simplified set ofRESTful endpoints.
Quickstart
This quickstart section is just meant to show basic functionality. More details of functionality are included further on in this vignette and in function-specific help.
This software is available at Bioconductor.org and can be downloaded viaBiocManager::install.
To report bugs or problems, eithersubmit a new issueor submit a bug.report(package='GenomicDataCommons') from within R (which will redirect you to the new issue on GitHub).
Installation
Installation can be achieved via Bioconductor’s BiocManager package.
if (!require("BiocManager"))
install.packages("BiocManager")
BiocManager::install('GenomicDataCommons')library(GenomicDataCommons)Check connectivity and status
The GenomicDataCommons package relies on having network connectivity. In addition, the NCI GDC API must also be operational and not under maintenance. Checking status can be used to check this connectivity and functionality.
GenomicDataCommons::status()## $commit
## [1] "4bb408881e6dc67eca93ff9fd913629a8f2d11c2"
##
## $data_release
## [1] "Data Release 42.0 - January 30, 2025"
##
## $data_release_version
## <span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>d</mi><mi>a</mi><mi>t</mi><msub><mi>a</mi><mi>r</mi></msub><mi>e</mi><mi>l</mi><mi>e</mi><mi>a</mi><mi>s</mi><msub><mi>e</mi><mi>v</mi></msub><mi>e</mi><mi>r</mi><mi>s</mi><mi>i</mi><mi>o</mi><mi>n</mi></mrow><annotation encoding="application/x-tex">data_release_version</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.8444em;vertical-align:-0.15em;"></span><span class="mord mathnormal">d</span><span class="mord mathnormal">a</span><span class="mord mathnormal">t</span><span class="mord"><span class="mord mathnormal">a</span><span class="msupsub"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:0.1514em;"><span style="top:-2.55em;margin-left:0em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mathnormal mtight" style="margin-right:0.02778em;">r</span></span></span></span><span class="vlist-s"></span></span><span class="vlist-r"><span class="vlist" style="height:0.15em;"><span></span></span></span></span></span></span><span class="mord mathnormal">e</span><span class="mord mathnormal" style="margin-right:0.01968em;">l</span><span class="mord mathnormal">e</span><span class="mord mathnormal">a</span><span class="mord mathnormal">s</span><span class="mord"><span class="mord mathnormal">e</span><span class="msupsub"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:0.1514em;"><span style="top:-2.55em;margin-left:0em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mathnormal mtight" style="margin-right:0.03588em;">v</span></span></span></span><span class="vlist-s"></span></span><span class="vlist-r"><span class="vlist" style="height:0.15em;"><span></span></span></span></span></span></span><span class="mord mathnormal">ers</span><span class="mord mathnormal">i</span><span class="mord mathnormal">o</span><span class="mord mathnormal">n</span></span></span></span>major
## [1] 42
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## [1] 0
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## [1] "2025-01-30"
##
##
## $status
## [1] "OK"
##
## $tag
## [1] "7.8.5"
##
## $version
## [1] 1And to check the status in code:
stopifnot(GenomicDataCommons::status()$status=="OK")Find data
The following code builds a manifest that can be used to guide the download of raw data. Here, filtering finds gene expression files quantified as raw counts using STAR from ovarian cancer patients.
ge_manifest <- files() |>
filter( cases.project.project_id == 'TCGA-OV') |>
filter( type == 'gene_expression' ) |>
filter( analysis.workflow_type == 'STAR - Counts') |>
manifest()
head(ge_manifest)Download data
After the 858 gene expression files specified in the query above. Using multiple processes to do the download very significantly speeds up the transfer in many cases. On a standard 1Gb connection, the following completes in about 30 seconds. The first time the data are downloaded, R will ask to create a cache directory (see ?gdc_cachefor details of setting and interacting with the cache). Resulting downloaded files will be stored in the cache directory. Future access to the same files will be directly from the cache, alleviating multiple downloads.
fnames <- lapply(ge_manifest$id[1:20], gdcdata)If the download had included controlled-access data, the download above would have needed to include a token. Details are available inthe authentication section below.
Basic design
This package design is meant to have some similarities to the “hadleyverse” approach of dplyr. Roughly, the functionality for finding and accessing files and metadata can be divided into:
- Simple query constructors based on GDC API endpoints.
- A set of verbs that when applied, adjust filtering, field selection, and faceting (fields for aggregation) and result in a new query object (an endomorphism)
- A set of verbs that take a query and return results from the GDC
In addition, there are exhiliary functions for asking the GDC API for information about available and default fields, slicing BAM files, and downloading actual data files. Here is an overview of functionality111 See individual function and methods documentation for specific details..
- Creating a query
projects()cases()files()annotations()
- Manipulating a query
filter()facet()select()
- Introspection on the GDC API fields
mapping()available_fields()default_fields()grep_fields()available_values()available_expand()
- Executing an API call to retrieve query results
results()count()response()
- Raw data file downloads
gdcdata()transfer()gdc_client()
- Summarizing and aggregating field values (faceting)
aggregations()
- Authentication
gdc_token()
- BAM file slicing
slicing()
Usage
There are two main classes of operations when working with the NCI GDC.
- Querying metadata and finding data files (e.g., finding all gene expression quantifications data files for all colon cancer patients).
- Transferring raw or processed data from the GDC to another computer (e.g., downloading raw or processed data)
Both classes of operation are reviewed in detail in the following sections.
Authentication
[ GDC authentication documentation ]
The GDC offers both “controlled-access” and “open” data. As of this writing, only data stored as files is “controlled-access”; that is, metadata accessible via the GDC is all “open” data and some files are “open” and some are “controlled-access”. Controlled-access data are only available aftergoing through the process of obtaining access.
After controlled-access to one or more datasets has been granted, logging into the GDC web portal will allow you toaccess a GDC authentication token, which can be downloaded and then used to access available controlled-access data via the GenomicDataCommons package.
The GenomicDataCommons uses authentication tokens only for downloading data (see transfer and gdcdata documentation). The package includes a helper function, gdc_token, that looks for the token to be stored in one of three ways (resolved in this order):
- As a string stored in the environment variable,
GDC_TOKEN - As a file, stored in the file named by the environment variable,
GDC_TOKEN_FILE - In a file in the user home directory, called
.gdc_token
As a concrete example:
token = gdc_token()
transfer(...,token=token)
# or
transfer(...,token=get_token())Datafile access and download
Data downloads via the GDC API
The gdcdata function takes a character vector of one or more file ids. A simple way of producing such a vector is to produce amanifest data frame and then pass in the first column, which will contain file ids.
fnames = gdcdata(manifest_df$id[1:2],progress=FALSE)Note that for controlled-access data, a GDC authentication token is required. Using theBiocParallel package may be useful for downloading in parallel, particularly for large numbers of smallish files.
Bulk downloads
The bulk download functionality is only efficient (as of v1.2.0 of the GDC Data Transfer Tool) for relatively large files, so use this approach only when transferring BAM files or larger VCF files, for example. Otherwise, consider using the approach shown above, perhaps in parallel.
# Requires gcd_client command-line utility to be isntalled
# separately.
fnames = gdcdata(manifest_df$id[3:10], access_method = 'client')BAM slicing
Use Cases
Cases
How many cases are there per project_id?
res = cases() |> facet("project.project_id") |> aggregations()
head(res)## $project.project_id
## doc_count key
## 1 18004 FM-AD
## 2 2492 TARGET-AML
## 3 1587 TARGET-ALL-P2
## 4 1510 MP2PRT-ALL
## 5 1345 CPTAC-3
## 6 1132 TARGET-NBL
## 7 1098 TCGA-BRCA
## 8 995 MMRF-COMMPASS
## 9 826 BEATAML1.0-COHORT
## 10 652 TARGET-WT
## 11 617 TCGA-GBM
## 12 608 TCGA-OV
## 13 585 TCGA-LUAD
## 14 560 TCGA-UCEC
## 15 537 TCGA-KIRC
## 16 528 TCGA-HNSC
## 17 516 TCGA-LGG
## 18 507 TCGA-THCA
## 19 504 TCGA-LUSC
## 20 500 TCGA-PRAD
## 21 489 NCICCR-DLBCL
## 22 470 TCGA-SKCM
## 23 461 TCGA-COAD
## 24 449 REBC-THYR
## 25 443 TCGA-STAD
## 26 412 TCGA-BLCA
## 27 383 TARGET-OS
## 28 377 TCGA-LIHC
## 29 342 CPTAC-2
## 30 339 TRIO-CRU
## 31 324 CGCI-BLGSP
## 32 307 TCGA-CESC
## 33 291 TCGA-KIRP
## 34 278 HCMI-CMDC
## 35 263 TCGA-TGCT
## 36 261 TCGA-SARC
## 37 212 CGCI-HTMCP-CC
## 38 200 CMI-MBC
## 39 200 TCGA-LAML
## 40 191 TARGET-ALL-P3
## 41 185 TCGA-ESCA
## 42 185 TCGA-PAAD
## 43 179 TCGA-PCPG
## 44 176 OHSU-CNL
## 45 172 TCGA-READ
## 46 124 TCGA-THYM
## 47 113 TCGA-KICH
## 48 101 WCDT-MCRPC
## 49 92 TCGA-ACC
## 50 87 APOLLO-LUAD
## 51 87 TCGA-MESO
## 52 84 EXCEPTIONAL_RESPONDERS-ER
## 53 80 TCGA-UVM
## 54 70 CGCI-HTMCP-DLBCL
## 55 70 ORGANOID-PANCREATIC
## 56 69 TARGET-RT
## 57 63 CMI-MPC
## 58 60 MATCH-I
## 59 58 TCGA-DLBC
## 60 57 TCGA-UCS
## 61 56 BEATAML1.0-CRENOLANIB
## 62 52 MP2PRT-WT
## 63 51 TCGA-CHOL
## 64 50 CDDP_EAGLE-1
## 65 45 CTSP-DLBCL1
## 66 45 MATCH-W
## 67 45 MATCH-Z1A
## 68 41 MATCH-S1
## 69 39 CGCI-HTMCP-LC
## 70 36 CMI-ASC
## 71 36 MATCH-Z1D
## 72 35 MATCH-Q
## 73 33 MATCH-B
## 74 31 MATCH-Y
## 75 29 MATCH-Z1B
## 76 28 MATCH-P
## 77 28 MATCH-R
## 78 26 MATCH-Z1I
## 79 24 TARGET-ALL-P1
## 80 23 MATCH-U
## 81 21 MATCH-H
## 82 21 MATCH-N
## 83 13 TARGET-CCSK
## 84 11 MATCH-C1
## 85 7 VAREPOP-APOLLO
## 86 3 MATCH-S2library(ggplot2)
ggplot(res$project.project_id,aes(x = key, y = doc_count)) +
geom_bar(stat='identity') +
theme(axis.text.x = element_text(angle = 45, hjust = 1))How many cases are included in all TARGET projects?
cases() |> filter(~ project.program.name=='TARGET') |> count()## [1] 6543How many cases are included in all TCGA projects?
cases() |> filter(~ project.program.name=='TCGA') |> count()## [1] 11428What is the breakdown of sample types in TCGA-BRCA?
# The need to do the "&" here is a requirement of the
# current version of the GDC API. I have filed a feature
# request to remove this requirement.
resp = cases() |> filter(~ project.project_id=='TCGA-BRCA' &
project.project_id=='TCGA-BRCA' ) |>
facet('samples.sample_type') |> aggregations()
resp$samples.sample_typeFetch all samples in TCGA-BRCA that use “Solid Tissue” as a normal.
# The need to do the "&" here is a requirement of the
# current version of the GDC API. I have filed a feature
# request to remove this requirement.
resp = cases() |> filter(~ project.project_id=='TCGA-BRCA' &
samples.sample_type=='Solid Tissue Normal') |>
GenomicDataCommons::select(c(default_fields(cases()),'samples.sample_type')) |>
response_all()
count(resp)## [1] 162res = resp |> results()
str(res[1],list.len=6)## List of 1
## $ id: chr [1:162] "2021ed1f-dc75-4701-b8b8-1386466e4802" "20e8106b-1290-4735-abe4-7621e08e3dc8" "214a4507-d974-4b3e-8525-7408fccc6a0f" "21ef1730-e5a7-47ce-b419-d000bb59ae15" ...head(ids(resp))## [1] "2021ed1f-dc75-4701-b8b8-1386466e4802"
## [2] "20e8106b-1290-4735-abe4-7621e08e3dc8"
## [3] "214a4507-d974-4b3e-8525-7408fccc6a0f"
## [4] "21ef1730-e5a7-47ce-b419-d000bb59ae15"
## [5] "233b02f3-c4f0-4a67-9db5-e68d5cdaccb6"
## [6] "a2efe7e1-aca3-440f-825f-ed621edca69f"Get all TCGA case ids that are female
cases() |>
GenomicDataCommons::filter(~ project.program.name == 'TCGA' &
"cases.demographic.gender" %in% "female") |>
GenomicDataCommons::results(size = 4) |>
ids()## [1] "4298ccdb-2e6d-4267-822d-75b021364084"
## [2] "439794a8-51bd-4c70-968c-34cf26b90148"
## [3] "305eaef4-4644-46e3-a696-d2e4a972f691"
## [4] "ec3b2a30-fcf6-45ef-bd9d-e6089a237c0f"Get all TCGA-COAD case ids that are NOT female
cases() |>
GenomicDataCommons::filter(~ project.project_id == 'TCGA-COAD' &
"cases.demographic.gender" %exclude% "female") |>
GenomicDataCommons::results(size = 4) |>
ids()## [1] "265d7b06-65fe-42c5-ad21-e6b160e94718"
## [2] "d655bbf6-c710-411d-aff5-ceb0fb6e6680"
## [3] "4f601d7b-8db1-4c6d-9374-21dcd804980d"
## [4] "c085da47-d634-491a-80ea-514e5a231f70"Get all TCGA cases that are missing gender
cases() |>
GenomicDataCommons::filter(~ project.program.name == 'TCGA' &
missing("cases.demographic.gender")) |>
GenomicDataCommons::results(size = 4) |>
ids()## [1] "a94de778-9c21-410d-8b9d-0f9240036bb8"
## [2] "f8360744-6bc8-4f53-b1fc-a133789455a8"
## [3] "1ec1e2c4-ba2c-40fc-b5e1-e8f6e38caec6"
## [4] "24506980-2857-4069-9af3-79ce4527eb00"Get all TCGA cases that are NOT missing gender
cases() |>
GenomicDataCommons::filter(~ project.program.name == 'TCGA' &
!missing("cases.demographic.gender")) |>
GenomicDataCommons::results(size = 4) |>
ids()## [1] "4298ccdb-2e6d-4267-822d-75b021364084"
## [2] "a2663a86-a006-4867-9e88-2b523df48303"
## [3] "439794a8-51bd-4c70-968c-34cf26b90148"
## [4] "e865d40a-9989-436c-8426-88cc84c863e8"Files
How many of each type of file are available?
res = files() |> facet('type') |> aggregations()
res$typeggplot(res$type,aes(x = key,y = doc_count)) + geom_bar(stat='identity') +
theme(axis.text.x = element_text(angle = 45, hjust = 1))Find gene-level RNA-seq quantification files for GBM
q = files() |>
GenomicDataCommons::select(available_fields('files')) |>
filter(~ cases.project.project_id=='TCGA-GBM' &
data_type=='Gene Expression Quantification')
q |> facet('analysis.workflow_type') |> aggregations()## list()# so need to add another filter
file_ids = q |> filter(~ cases.project.project_id=='TCGA-GBM' &
data_type=='Gene Expression Quantification' &
analysis.workflow_type == 'STAR - Counts') |>
GenomicDataCommons::select('file_id') |>
response_all() |>
ids()Slicing
Get all BAM file ids from TCGA-GBM
I need to figure out how to do slicing reproducibly in a testing environment and for vignette building.
q = files() |>
GenomicDataCommons::select(available_fields('files')) |>
filter(~ cases.project.project_id == 'TCGA-GBM' &
data_type == 'Aligned Reads' &
experimental_strategy == 'RNA-Seq' &
data_format == 'BAM')
file_ids = q |> response_all() |> ids()bamfile = slicing(file_ids[1],regions="chr12:6534405-6538375",token=gdc_token())
library(GenomicAlignments)
aligns = readGAlignments(bamfile)Troubleshooting
SSL connection errors
- Symptom: Trying to connect to the API results in:
Error in curl::curl_fetch_memory(url, handle = handle) :
SSL connect error- Possible solutions: The issue is that the GDC supports only recent security Transport Layer Security (TLS), so the only known fix is to upgrade the system
opensslto version 1.0.1 or later.- [Mac OS],
- [Ubuntu]
- [Centos/RHEL]. After upgrading
openssl, reinstall the Rcurlandhttrpackages.
sessionInfo()
sessionInfo()## R version 4.5.0 RC (2025-04-04 r88126)
## Platform: x86_64-pc-linux-gnu
## Running under: Ubuntu 24.04.2 LTS
##
## Matrix products: default
## BLAS: /home/biocbuild/bbs-3.21-bioc/R/lib/libRblas.so
## LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.12.0 LAPACK version 3.12.0
##
## locale:
## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
## [3] LC_TIME=en_GB LC_COLLATE=C
## [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
## [7] LC_PAPER=en_US.UTF-8 LC_NAME=C
## [9] LC_ADDRESS=C LC_TELEPHONE=C
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
##
## time zone: America/New_York
## tzcode source: system (glibc)
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] ggplot2_3.5.2 GenomicDataCommons_1.32.0
## [3] knitr_1.50 BiocStyle_2.36.0
##
## loaded via a namespace (and not attached):
## [1] rappdirs_0.3.3 sass_0.4.10 generics_0.1.3
## [4] tidyr_1.3.1 xml2_1.3.8 hms_1.1.3
## [7] digest_0.6.37 magrittr_2.0.3 evaluate_1.0.3
## [10] grid_4.5.0 bookdown_0.43 fastmap_1.2.0
## [13] jsonlite_2.0.0 GenomeInfoDb_1.44.0 tinytex_0.57
## [16] BiocManager_1.30.25 httr_1.4.7 purrr_1.0.4
## [19] UCSC.utils_1.4.0 scales_1.3.0 jquerylib_0.1.4
## [22] cli_3.6.4 rlang_1.1.6 crayon_1.5.3
## [25] XVector_0.48.0 munsell_0.5.1 withr_3.0.2
## [28] cachem_1.1.0 yaml_2.3.10 tools_4.5.0
## [31] tzdb_0.5.0 dplyr_1.1.4 colorspace_2.1-1
## [34] GenomeInfoDbData_1.2.14 BiocGenerics_0.54.0 curl_6.2.2
## [37] vctrs_0.6.5 R6_2.6.1 magick_2.8.6
## [40] stats4_4.5.0 lifecycle_1.0.4 S4Vectors_0.46.0
## [43] IRanges_2.42.0 pkgconfig_2.0.3 pillar_1.10.2
## [46] bslib_0.9.0 gtable_0.3.6 Rcpp_1.0.14
## [49] glue_1.8.0 xfun_0.52 tibble_3.2.1
## [52] GenomicRanges_1.60.0 tidyselect_1.2.1 farver_2.1.2
## [55] htmltools_0.5.8.1 labeling_0.4.3 rmarkdown_2.29
## [58] readr_2.1.5 compiler_4.5.0Developer notes
- The
S3object-oriented programming paradigm is used. - We have adopted a functional programming style with functions and methods that often take an “object” as the first argument. This style lends itself to pipeline-style programming.
- The GenomicDataCommons package uses thealternative request format (POST)to allow very large request bodies.