TCGAbiolinks has provided a few functions to search GDC database. This section starts by explaining the different GDC sources (Harmonized and Legacy Archive), followed by some examples how to access them.

Useful information

Different sources: Legacy vs Harmonized

There are two available sources to download GDC data using TCGAbiolinks: - GDC Legacy Archive : provides access to an unmodified copy of data that was previously stored in CGHub and in the TCGA Data Portal hosted by the TCGA Data Coordinating Center (DCC), in which uses as references GRCh37 (hg19) and GRCh36 (hg18). - GDC harmonized database: data available was harmonized against GRCh38 (hg38) using GDC Bioinformatics Pipelines which provides methods to the standardization of biospecimen and clinical data.

Understanding the barcode

A TCGA barcode is composed of a collection of identifiers. Each specifically identifies a TCGA data element. Refer to the following figure for an illustration of how metadata identifiers comprise a barcode. An aliquot barcode contains the highest number of identifiers.

Example:

  • Aliquot barcode: TCGA-G4-6317-02A-11D-2064-05
  • Participant: TCGA-G4-6317
  • Sample: TCGA-G4-6317-02

For more information check TCGA wiki

Searching arguments

You can easily search GDC data using the GDCquery function.

Using a summary of filters as used in the TCGA portal, the function works with the following arguments:

  • project A list of valid project (it can be more than one) (see table below)
  • data.category A valid project (see list with getProjectSummary(project))
  • data.type A data type to filter the files to download
  • sample.type A sample type to filter the files to download (See table below)
  • workflow.type GDC workflow type
  • barcode A list of barcodes to filter the files to download (can be partial barcodes)
  • legacy Access legacy archive data (hg19 and hg18 data) instead of harmonized data? Default: FALSE
  • platform Experimental data platform (HumanMethylation450, AgilentG4502A_07 etc). Used only for legacy repository
  • file.type A string to filter files, based on its names. Used only for legacy repository

project options

The options for the field project are below:

sample.type options

The options for the field sample.type are below:

The other fields (data.category, data.type, workflow.type, platform, file.type) can be found below. Please, not that these tables are still incomplete.

Harmonized data options (legacy = FALSE)

data.category data.type workflow.type platform
Transcriptome Profiling Gene Expression Quantification HTSeq - Counts
HTSeq - FPKM-UQ
HTSeq - FPKM
Isoform Expression Quantification -
miRNA Expression Quantification -
Copy Number Variation Copy Number Segment
Masked Copy Number Segment
Simple Nucleotide Variation
Raw Sequencing Data
Biospecimen
Clinical
DNA Methylation Illumina Human Methylation 450
Illumina Human Methylation 27

Legacy archive data options (legacy = TRUE)

data.category data.type platform file.type
Copy number variation - Affymetrix SNP Array 6.0 nocnv_hg18.seg
- Affymetrix SNP Array 6.0 hg18.seg
- Affymetrix SNP Array 6.0 nocnv_hg19.seg
- Affymetrix SNP Array 6.0 hg19.seg
- Illumina HiSeq -
Simple nucleotide variation Simple somatic mutation
Raw sequencing data
Biospecimen
Clinical
Protein expression MDA RPPA Core -
Gene expression Gene expression quantification Illumina HiSeq normalized_results
Illumina HiSeq results
HT_HG-U133A -
AgilentG4502A_07_2 -
AgilentG4502A_07_1 -
HuEx-1_0-st-v2 FIRMA.txt
gene.txt
Isoform expression quantification
miRNA gene quantification hg19.mirna
hg19.mirbase20
mirna
Exon junction quantification
Exon quantification
miRNA isoform quantification hg19.isoform
isoform
DNA methylation Illumina Human Methylation 450 -
Illumina Human Methylation 27 -
Illumina DNA Methylation OMA003 CPI -
Illumina DNA Methylation OMA002 CPI -
Illumina Hi Seq
Raw microarray data Raw intensities Illumina Human Methylation 450 idat
Illumina Human Methylation 27 idat
Other

Harmonized database examples

DNA methylation data: Recurrent tumor samples

In this example we will access the harmonized database (legacy = FALSE) and search for all DNA methylation data for recurrent glioblastoma multiform (GBM) and low grade gliomas (LGG) samples.

query <- GDCquery(project = c("TCGA-GBM", "TCGA-LGG"),
                  data.category = "DNA Methylation",
                  legacy = FALSE,
                  platform = c("Illumina Human Methylation 450"),
                  sample.type = "Recurrent Solid Tumor"
)
datatable(getResults(query), 
              filter = 'top',
              options = list(scrollX = TRUE, keys = TRUE, pageLength = 5), 
              rownames = FALSE)

Samples with DNA methylation and gene expression data

In this example we will access the harmonized database (legacy = FALSE) and search for all patients with DNA methylation (platform HumanMethylation450k) and gene expression data for Colon Adenocarcinoma tumor (TCGA-COAD).

query.met <- GDCquery(project = "TCGA-COAD",
                  data.category = "DNA Methylation",
                  legacy = FALSE,
                  platform = c("Illumina Human Methylation 450"))
query.exp <- GDCquery(project = "TCGA-COAD",
                  data.category = "Transcriptome Profiling",
                  data.type = "Gene Expression Quantification", 
                  workflow.type = "HTSeq - FPKM-UQ")

# Get all patients that have DNA methylation and gene expression.
common.patients <- intersect(substr(getResults(query.met, cols = "cases"), 1, 12),
                             substr(getResults(query.exp, cols = "cases"), 1, 12))

# Only seelct the first 5 patients
query.met <- GDCquery(project = "TCGA-COAD",
                  data.category = "DNA Methylation",
                  legacy = FALSE,
                  platform = c("Illumina Human Methylation 450"),
                  barcode = common.patients[1:5])
query.exp <- GDCquery(project = "TCGA-COAD",
                  data.category = "Transcriptome Profiling",
                  data.type = "Gene Expression Quantification", 
                  workflow.type = "HTSeq - FPKM-UQ",
                  barcode = common.patients[1:5])
datatable(getResults(query.met, cols = c("data_type","cases")),
              filter = 'top',
              options = list(scrollX = TRUE, keys = TRUE, pageLength = 5), 
              rownames = FALSE)
datatable(getResults(query.exp, cols = c("data_type","cases")), 
              filter = 'top',
              options = list(scrollX = TRUE, keys = TRUE, pageLength = 5), 
              rownames = FALSE)

Raw Sequencing Data: Finding the match between file names and barcode for Controlled data.

This exmaple shows how the user can search for breast cancer Raw Sequencing Data (“Controlled”) and verify the name of the files and the barcodes associated with it.

query <- GDCquery(project = c("TCGA-BRCA"),
                  data.category = "Raw Sequencing Data",  
                  sample.type = "Primary solid Tumor")
# Only first 100 to make render faster
datatable(getResults(query, rows = 1:100,cols = c("file_name","cases")), 
              filter = 'top',
              options = list(scrollX = TRUE, keys = TRUE, pageLength = 5), 
              rownames = FALSE)

Legacy archive examples

DNA methylation

This exmaple shows how the user can search for glioblastoma multiform (GBM) and low grade gliomas (LGG) DNA methylation data for platform Illumina Human Methylation 450 and Illumina Human Methylation 27.

query <- GDCquery(project = c("TCGA-GBM","TCGA-LGG"),
                      legacy = TRUE,
                      data.category = "DNA methylation",
                      platform = c("Illumina Human Methylation 450", "Illumina Human Methylation 27"))
datatable(getResults(query, rows = 1:100), 
              filter = 'top',
              options = list(scrollX = TRUE, keys = TRUE, pageLength = 5), 
              rownames = FALSE)

Gene expression

This exmaple shows how the user can search for glioblastoma multiform (GBM) gene expression data with the normalized results for expression of a gene. For more information check rnaseqV2 TCGA wiki

# Gene expression aligned against hg19.
query.exp.hg19 <- GDCquery(project = "TCGA-GBM",
                  data.category = "Gene expression",
                  data.type = "Gene expression quantification",
                  platform = "Illumina HiSeq", 
                  file.type  = "normalized_results",
                  experimental.strategy = "RNA-Seq",
                  barcode = c("TCGA-14-0736-02A-01R-2005-01", "TCGA-06-0211-02A-02R-2005-01"),
                  legacy = TRUE)
datatable(getResults(query.exp.hg19), 
              filter = 'top',
              options = list(scrollX = TRUE, keys = TRUE, pageLength = 5), 
              rownames = FALSE)