TCGAbiolinks has provided a few functions to search GDC database.
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:
For more information check GDC TCGA barcodes
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 | |
| 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. | |
| sample.type | A sample type to filter the files to download |
The options for the field project are below:
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.
In this example we will access the harmonized database and search for all DNA methylation data for recurrent glioblastoma multiform (GBM) and low grade gliomas (LGG) samples.
In this example we will access the harmonized database 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",
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",
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
)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
)If you want to get the manifest file from the query object you can
use the function getManifest. If you set save to
TRUE 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
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## 332 c57ce48f247f312f60f4c7ad6535361d 153861926360 released
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## 340 eea07a7861e4599efdfcd33009910baf 117366991497 released
## 341 025e4ac82543766d3ee7356951e36cd6 419024544104 releasedFor 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.
Retrieve the numner of files under each data_category + data_type + experimental_strategy + platform. Almost like https://portal.gdc.cancer.gov/exploration