TCGAbiolinks: Searching GDC database

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.

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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 GDC TCGA barcodes

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 (see table below)]
data.category	A valid project (see list with TCGAbiolinks:::getProjectSummary(project))
data.type	A data type to filter the files to download
workflow.type	GDC workflow type
legacy	Search in the legacy repository
access	Filter by access type. Possible values: controlled, open
platform	Example:
	CGH- 1x1M_G4447A	IlluminaGA_RNASeqV2
	AgilentG4502A_07	IlluminaGA_mRNA_DGE
	Human1MDuo	HumanMethylation450
	HG-CGH-415K_G4124A	IlluminaGA_miRNASeq
	HumanHap550	IlluminaHiSeq_miRNASeq
	ABI	H-miRNA_8x15K
	HG-CGH-244A	SOLiD_DNASeq
	IlluminaDNAMethylation_OMA003_CPI	IlluminaGA_DNASeq_automated
	IlluminaDNAMethylation_OMA002_CPI	HG-U133_Plus_2
	HuEx- 1_0-st-v2	Mixed_DNASeq
	H-miRNA_8x15Kv2	IlluminaGA_DNASeq_curated
	MDA_RPPA_Core	IlluminaHiSeq_TotalRNASeqV2
	HT_HG-U133A	IlluminaHiSeq_DNASeq_automated
	diagnostic_images	microsat_i
	IlluminaHiSeq_RNASeq	SOLiD_DNASeq_curated
	IlluminaHiSeq_DNASeqC	Mixed_DNASeq_curated
	IlluminaGA_RNASeq	IlluminaGA_DNASeq_Cont_automated
	IlluminaGA_DNASeq	IlluminaHiSeq_WGBS
	pathology_reports	IlluminaHiSeq_DNASeq_Cont_automated
	Genome_Wide_SNP_6	bio
	tissue_images	Mixed_DNASeq_automated
	HumanMethylation27	Mixed_DNASeq_Cont_curated
	IlluminaHiSeq_RNASeqV2	Mixed_DNASeq_Cont
file.type	To be used in the legacy database for some platforms, to define which file types to be used.
barcode	A list of barcodes to filter the files to download
experimental.strategy	Filter to experimental strategy. Harmonized: WXS, RNA-Seq, miRNA-Seq, Genotyping Array. Legacy: WXS, RNA-Seq, miRNA-Seq, Genotyping Array, DNA-Seq, Methylation array, Protein expression array, WXS,CGH array, VALIDATION, Gene expression array,WGS, MSI-Mono-Dinucleotide Assay, miRNA expression array, Mixed strategies, AMPLICON, Exon array, Total RNA-Seq, Capillary sequencing, Bisulfite-Seq
sample.type	A sample type to filter the files to download

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, note that these tables are still incomplete.

Harmonized data options (legacy = FALSE)

Legacy archive data options (legacy = TRUE)

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 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 = "STAR - Counts"
)

# 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 = "STAR - Counts",
    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 example 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 = "TCGA-ACC", 
    data.category = "Sequencing Reads",
    data.type = "Aligned Reads", 
    data.format = "bam",
    workflow.type = "STAR 2-Pass Transcriptome"
)
# Only first 10 to make render faster
datatable(
    getResults(query, rows = 1:10,cols = c("file_name","cases")), 
    filter = 'top',
    options = list(scrollX = TRUE, keys = TRUE, pageLength = 5), 
    rownames = FALSE
)

query <- GDCquery(
    project = "TCGA-ACC", 
    data.category = "Sequencing Reads",
    data.type = "Aligned Reads", 
    data.format = "bam",
    workflow.type = "STAR 2-Pass Genome"
)
# Only first 10 to make render faster
datatable(
    getResults(query, rows = 1:10,cols = c("file_name","cases")), 
    filter = 'top',
    options = list(scrollX = TRUE, keys = TRUE, pageLength = 5), 
    rownames = FALSE
)

query <- GDCquery(
    project = "TCGA-ACC", 
    data.category = "Sequencing Reads",
    data.type = "Aligned Reads", 
    data.format = "bam",
    workflow.type = "STAR 2-Pass Chimeric"
)
# Only first 10 to make render faster
datatable(
    getResults(query, rows = 1:10,cols = c("file_name","cases")), 
    filter = 'top',
    options = list(scrollX = TRUE, keys = TRUE, pageLength = 5), 
    rownames = FALSE
)

query <- GDCquery(
    project = "TCGA-ACC", 
    data.category = "Sequencing Reads",
    data.type = "Aligned Reads", 
    data.format = "bam",
    workflow.type = "BWA-aln"
)
# Only first 10 to make render faster
datatable(
    getResults(query, rows = 1:10,cols = c("file_name","cases")), 
    filter = 'top',
    options = list(scrollX = TRUE, keys = TRUE, pageLength = 5), 
    rownames = FALSE
)

query <- GDCquery(
    project = "TCGA-ACC", 
    data.category = "Sequencing Reads",
    data.type = "Aligned Reads", 
    data.format = "bam",
    workflow.type = "BWA with Mark Duplicates and BQSR"
)
# Only first 10 to make render faster
datatable(
    getResults(query, rows = 1:10,cols = c("file_name","cases")), 
    filter = 'top',
    options = list(scrollX = TRUE, keys = TRUE, pageLength = 5), 
    rownames = FALSE
)

Legacy archive examples

DNA methylation

Array-based assays

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

query <- GDCquery(
    project = c("TCGA-GBM"),
    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
)

whole-genome bisulfite sequencing (WGBS)

query <- GDCquery(
    project = c("TCGA-LUAD"),
    legacy = TRUE,
    data.category = "DNA methylation",
    data.type = "Methylation percentage",
    experimental.strategy = "Bisulfite-Seq"
)

# VCF - controlled data
query <- GDCquery(
    project = c("TCGA-LUAD"),
    legacy = TRUE,
    data.category = "DNA methylation",
    data.type = "Bisulfite sequence alignment",
    experimental.strategy = "Bisulfite-Seq"
)


# WGBS BAM files - controlled data
query <- GDCquery(
    project = c("TCGA-LUAD"),
    legacy = TRUE,
    data.type = "Aligned reads",
    data.category = "Raw sequencing data",
    experimental.strategy = "Bisulfite-Seq"
)

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 about file.types check GDC TCGA file types

# 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
)

Get Manifest file

If you want to get the manifest file from the query object you can use the function getManifest. If you set save to TRUEm a txt file that can be used with GDC-client Data transfer tool (DTT) or with its GUI version ddt-ui will be created.

getManifest(query.exp.hg19,save = FALSE) 

ATAC-seq data

For the moment, ATAC-seq data is available at the GDC publication page. Also, for more details, you can check an ATAC-seq workshop at http://rpubs.com/tiagochst/atac_seq_workshop

The list of file available is below:

datatable(
    getResults(TCGAbiolinks:::GDCquery_ATAC_seq())[,c("file_name","file_size")], 
    filter = 'top',
    options = list(scrollX = TRUE, keys = TRUE, pageLength = 5), 
    rownames = FALSE
)

You can use the function GDCquery_ATAC_seq filter the manifest table and use GDCdownload to save the data locally.

query <- TCGAbiolinks:::GDCquery_ATAC_seq(file.type = "rds") 
GDCdownload(query,method = "client")

query <- TCGAbiolinks:::GDCquery_ATAC_seq(file.type = "bigWigs") 
GDCdownload(query,method = "client")

Summary of available files per patient

Retrieve the numner of files under each data_category + data_type + experimental_strategy + platform. Almost like https://portal.gdc.cancer.gov/exploration

tab <-  getSampleFilesSummary(project = "TCGA-ACC")
datatable(
    head(tab),
    filter = 'top',
    options = list(scrollX = TRUE, keys = TRUE, pageLength = 5), 
    rownames = FALSE
)