TCGAbiolinks: Searching GDC database
20 March 2019
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
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.
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 (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 stratey. 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)
datatable(readr::read_csv("https://docs.google.com/spreadsheets/d/1f98kFdj9mxVDc1dv4xTZdx8iWgUiDYO-qiFJINvmTZs/export?format=csv&gid=2046985454"),
filter = 'top',
options = list(scrollX = TRUE, keys = TRUE, pageLength = 40),
rownames = FALSE)## Parsed with column specification:
## cols(
## Data.category = col_character(),
## Data.type = col_character(),
## `Workflow Type` = col_character(),
## Platform = col_character()
## )Legacy archive data options (legacy = TRUE)
datatable(readr::read_csv("https://docs.google.com/spreadsheets/d/1f98kFdj9mxVDc1dv4xTZdx8iWgUiDYO-qiFJINvmTZs/export?format=csv&gid=1817673686"),
filter = 'top',
options = list(scrollX = TRUE, keys = TRUE, pageLength = 40),
rownames = FALSE)## Parsed with column specification:
## cols(
## Data.category = col_character(),
## Data.type = col_character(),
## Platform = col_character(),
## file.type = col_character()
## )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)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 = "Sequencing Reads",
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
Array-based assays
This example 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)whole-genome bisulfite sequencing (WGBS)
query <- GDCquery(project = c("TCGA-LUAD"),
legacy = TRUE,
data.type = "Methylation percentage",
experimental.strategy = "Bisulfite-Seq")
# VCF - controlled data
query <- GDCquery(project = c("TCGA-LUAD"),
legacy = TRUE,
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 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)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.
## id
## 40 217d72e9-4d6f-409d-911c-0a70b17a0adc
## 97 973ce0ac-f613-4b99-b2ab-3e2d5548f05f
## filename
## 40 unc.edu.b469eb7c-723f-4870-b4e4-ebfaae7a118b.1536566.rsem.genes.normalized_results
## 97 unc.edu.152afe8c-f67c-4d7c-93ac-e1b7edd56c54.1544649.rsem.genes.normalized_results
## md5 size state
## 40 beda9f89f08fc6a892a72e8b704fdbd9 437283 live
## 97 84478e78d95e1155019ccb7e0e0fea2f 436272 liveATAC-seq data
For the moment, ATAC-seq data is available at the GDC publication page.
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.
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