Contents

1 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.

The data model for the GDC is complex, but it worth a quick overview. 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 of RESTful endpoints.

2 Quickstart

This software is in development and will likely change in response to user feedback. To report bugs or problems, either submit a new issue or submit a bug.report(package='GenomicDataCommons') from within R (which will redirect you to the new issue on GitHub).

2.1 Installation

Installation is available from GitHub as of now.

source('https://bioconductor.org/biocLite.R')
biocLite('GenomicDataCommons')
library(GenomicDataCommons)

2.2 Check basic functionality

GenomicDataCommons::status()
## $commit
## [1] "a38d9114206f253599cfcb12e454fc10582be38d"
## 
## $data_release
## [1] "Data Release 9.0 - October 24, 2017"
## 
## $status
## [1] "OK"
## 
## $tag
## [1] "1.10.0"
## 
## $version
## [1] 1

If this statement results in an error such as SSL connect error, see the troubleshooting section below.

2.3 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 HTSeq from ovarian cancer patients.

library(magrittr)
ge_manifest = files() %>% 
    filter( ~ cases.project.project_id == 'TCGA-OV' &
                type == 'gene_expression' &
                analysis.workflow_type == 'HTSeq - Counts') %>%
    manifest()

2.4 Download data

This code block downloads the 379 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.

destdir = tempdir()
fnames = lapply(ge_manifest$id[1:20],gdcdata,
                destination_dir=destdir,overwrite=TRUE,
                progress=FALSE)

If the download had included controlled-access data, the download above would have needed to include a token. Details are available in the authentication section below.

2.5 Metadata queries

expands = c("diagnoses","annotations",
             "demographic","exposures")
clinResults = cases() %>% 
    GenomicDataCommons::select(NULL) %>%
    GenomicDataCommons::expand(expands) %>% 
    results(size=50)
clinDF = as.data.frame(clinResults)
library(DT)
datatable(clinDF, extensions = 'Scroller', options = list(
  deferRender = TRUE,
  scrollY = 200,
  scrollX = TRUE,
  scroller = TRUE
))