TCGAbiolinks has provided a few functions to download and prepare data from GDC for analysis. This section starts by explaining the different downloads methods and the SummarizedExperiment object, which is the default data structure used in TCGAbiolinks, followed by some examples.
There are two methods to download GDC data using TCGAbiolinks:
client
: this method creates a MANIFEST file and downloads the data using GDC Data Transfer Tool this method is more reliable but it might be slower compared to the api method.api
: this method uses the GDC Application Programming Interface (API) to download the data. This will create a MANIFEST file and the data downloaded will be compressed into a tar.gz file. If the size and the number of the files are too big this tar.gz will be too big which might have a high probability of download failure. To solve that we created the files.per.chunk
argument which will split the files into small chunks, for example, if chunks.per.download
is equal to 10 we will download only 10 files inside each tar.gz.A SummarizedExperiment object has three main matrices that can be accessed using the SummarizedExperiment package):
colData(data)
: stores sample information. TCGAbiolinks will add indexed clinical data and subtype information from marker TCGA papers.assay(data)
: stores molecular datarowRanges(data)
: stores metadata about the features, including their genomic rangesWhen using the function GDCprepare
there is an argument called SummarizedExperiment
which defines the output type a Summarized Experiment (default option) or a data frame. To create a summarized Experiment object we annotate the data with genomic positions with last patch release version of the genome available.
Also, the latest DNA methylation metadata is available at: http://zwdzwd.github.io/InfiniumAnnotation
If you received the warning/error simpleWarning in file.create(to[okay])
try setting directory
as described by some users: https://github.com/BioinformaticsFMRP/TCGAbiolinks/issues/153#issuecomment-856385673
in GDCprepare
and GDCdownload
<simpleWarning in file.create(to[okay]): cannot create file ... reason 'No such file or directory'>
GDCdownload
Argument | Description |
---|---|
query | A query for GDCquery function |
token.file | Token file to download controlled data (only for method = “client”) |
method | Uses the API (POST method) or gdc client tool. Options “api”, “client”. API is faster, but the data might get corrupted in the download, and it might need to be executed again |
directory | Directory/Folder where the data was downloaded. Default: GDCdata |
files.per.chunk | This will make the API method only download n (files.per.chunk) files at a time. This may reduce the download problems when the data size is too large. Expected a integer number (example files.per.chunk = 6) |
GDCprepare
Argument | Description |
---|---|
query | A query for GDCquery function |
save | Save result as RData object? |
save.filename | Name of the file to be save if empty an automatic will be created |
directory | Directory/Folder where the data was downloaded. Default: GDCdata |
summarizedExperiment | Create a summarizedExperiment? Default TRUE (if possible) |
remove.files.prepared | Remove the files read? Default: FALSE This argument will be considered only if save argument is set to true |
add.gistic2.mut | If a list of genes (gene symbol) is given, columns with gistic2 results from GDAC firehose (hg19) and a column indicating if there is or not mutation in that gene (hg38) (TRUE or FALSE - use the MAF file for more information) will be added to the sample matrix in the summarized Experiment object. |
mut.pipeline | If add.gistic2.mut is not NULL this field will be taken in consideration. Four separate variant calling pipelines are implemented for GDC data harmonization. Options: muse, varscan2, somaticsniper, MuTect2. For more information: https://gdc-docs.nci.nih.gov/Data/Bioinformatics_Pipelines/DNA_Seq_Variant_Calling_Pipeline/ |
mutant_variant_classification | List of mutant_variant_classification that will be consider a sample mutant or not. Default: “Frame_Shift_Del”, “Frame_Shift_Ins”, “Missense_Mutation”, “Nonsense_Mutation”, “Splice_Site”, “In_Frame_Del”, “In_Frame_Ins”, “Translation_Start_Site”, “Nonstop_Mutation” |
In this example we will download gene expression quantification from harmonized database (data aligned against genome of reference hg38). Also, it shows the object data and metadata.
# Gene expression aligned against hg38
query <- GDCquery(
project = "TCGA-GBM",
data.category = "Transcriptome Profiling",
data.type = "Gene Expression Quantification",
workflow.type = "STAR - Counts",
barcode = c("TCGA-14-0736-02A-01R-2005-01", "TCGA-06-0211-02A-02R-2005-01")
)
GDCdownload(query = query)
data <- GDCprepare(query = query)
datatable(
as.data.frame(colData(data)),
options = list(scrollX = TRUE, keys = TRUE, pageLength = 5),
rownames = FALSE
)
datatable(
assay(data)[1:20,],
options = list(scrollX = TRUE, keys = TRUE, pageLength = 5),
rownames = TRUE
)
## GRanges object with 100 ranges and 3 metadata columns:
## seqnames ranges strand | ensembl_gene_id
## <Rle> <IRanges> <Rle> | <character>
## ENSG00000000003 chrX 100627109-100639991 - | ENSG00000000003
## ENSG00000000005 chrX 100584802-100599885 + | ENSG00000000005
## ENSG00000000419 chr20 50934867-50958555 - | ENSG00000000419
## ENSG00000000457 chr1 169849631-169894267 - | ENSG00000000457
## ENSG00000000460 chr1 169662007-169854080 + | ENSG00000000460
## ... ... ... ... . ...
## ENSG00000005421 chr7 95297676-95324707 - | ENSG00000005421
## ENSG00000005436 chr2 75652000-75710989 - | ENSG00000005436
## ENSG00000005448 chr2 74421678-74425755 + | ENSG00000005448
## ENSG00000005469 chr7 87345681-87399795 + | ENSG00000005469
## ENSG00000005471 chr7 87401697-87480435 - | ENSG00000005471
## external_gene_name original_ensembl_gene_id
## <character> <character>
## ENSG00000000003 TSPAN6 ENSG00000000003.13
## ENSG00000000005 TNMD ENSG00000000005.5
## ENSG00000000419 DPM1 ENSG00000000419.11
## ENSG00000000457 SCYL3 ENSG00000000457.12
## ENSG00000000460 C1orf112 ENSG00000000460.15
## ... ... ...
## ENSG00000005421 PON1 ENSG00000005421.7
## ENSG00000005436 GCFC2 ENSG00000005436.12
## ENSG00000005448 WDR54 ENSG00000005448.15
## ENSG00000005469 CROT ENSG00000005469.10
## ENSG00000005471 ABCB4 ENSG00000005471.14
## -------
## seqinfo: 24 sequences from an unspecified genome; no seqlengths
GDCprepare
: OutputsThis function is still under development, it is not working for all cases. See the tables below with the status. Examples of query, download, prepare can be found in this gist.
Data.category | Data.type | Workflow Type | Output |
---|---|---|---|
Transcriptome Profiling | Gene Expression Quantification | STAR - Counts | Dataframe or SummarizedExperiment |
Isoform Expression Quantification | Not needed | ||
miRNA Expression Quantification | Not needed | Dataframe | |
Copy number variation | Copy Number Segment | Dataframe | |
Masked Copy Number Segment | Dataframe | ||
Gene Level Copy Number | Dataframe | ||
DNA Methylation | Methylation Beta Value | Dataframe or SummarizedExperiment | |
Simple Nucleotide Variation | Masked Somatic Mutation | Dataframe | |
Raw Sequencing Data | |||
Biospecimen | Slide Image | ||
Biospecimen | Biospecimen Supplement | ||
Clinical |
For more examples, please check: http://rpubs.com/tiagochst/TCGAbiolinks_RNA-seq_new_projects
# mRNA pipeline: https://gdc-docs.nci.nih.gov/Data/Bioinformatics_Pipelines/Expression_mRNA_Pipeline/
query.exp.hg38 <- GDCquery(
project = "TCGA-GBM",
data.category = "Transcriptome Profiling",
data.type = "Gene Expression Quantification",
workflow.type = "STAR - Counts",
barcode = c("TCGA-14-0736-02A-01R-2005-01", "TCGA-06-0211-02A-02R-2005-01")
)
GDCdownload(query.exp.hg38)
expdat <- GDCprepare(
query = query.exp.hg38,
save = TRUE,
save.filename = "exp.rda"
)
library(TCGAbiolinks)
query.mirna <- GDCquery(
project = "TARGET-AML",
experimental.strategy = "miRNA-Seq",
data.category = "Transcriptome Profiling",
barcode = c("TARGET-20-PATDNN","TARGET-20-PAPUNR"),
data.type = "miRNA Expression Quantification"
)
GDCdownload(query.mirna)
mirna <- GDCprepare(
query = query.mirna,
save = TRUE,
save.filename = "mirna.rda"
)
query.isoform <- GDCquery(
project = "TARGET-AML",
experimental.strategy = "miRNA-Seq",
data.category = "Transcriptome Profiling",
barcode = c("TARGET-20-PATDNN","TARGET-20-PAPUNR"),
data.type = "Isoform Expression Quantification"
)
GDCdownload(query.isoform)
isoform <- GDCprepare(
query = query.isoform,
save = TRUE,
save.filename = "mirna-isoform.rda"
)
query_met.hg38 <- GDCquery(
project = "TCGA-BRCA",
data.category = "DNA Methylation",
data.type = "Methylation Beta Value",
platform = "Illumina Human Methylation 27",
barcode = c("TCGA-B6-A0IM")
)
GDCdownload(query_met.hg38)
data.hg38 <- GDCprepare(query_met.hg38)
query_met.hg38 <- GDCquery(
project= "TCGA-LGG",
data.category = "DNA Methylation",
data.type = "Methylation Beta Value",
platform = "Illumina Human Methylation 450",
barcode = c("TCGA-HT-8111-01A-11D-2399-05","TCGA-HT-A5R5-01A-11D-A28N-05")
)
GDCdownload(query_met.hg38)
data.hg38 <- GDCprepare(query_met.hg38)
query_met.hg38 <- GDCquery(
project= "HCMI-CMDC",
data.category = "DNA Methylation",
data.type = "Methylation Beta Value",
platform = "Illumina Methylation Epic",
barcode = c("HCM-BROD-0045")
)
GDCdownload(query_met.hg38)
data.hg38 <- GDCprepare(query_met.hg38)
query <- GDCquery(
project = "TCGA-BRCA",
data.category = "DNA Methylation",
data.type = "Masked Intensities",
platform = "Illumina Human Methylation 27"
)
GDCdownload(query, files.per.chunk=10)
betas <- GDCprepare(query)
query <- GDCquery(
project = "HCMI-CMDC",
data.category = "DNA Methylation",
data.type = "Masked Intensities",
platform = "Illumina Methylation Epic"
)
GDCdownload(query, files.per.chunk = 10)
betas <- GDCprepare(query)
query <- GDCquery(
project = "CPTAC-3",
data.category = "DNA Methylation",
data.type = "Masked Intensities",
platform = "Illumina Methylation Epic"
)
GDCdownload(query, files.per.chunk=10)
betas <- GDCprepare(query)
query <- GDCquery(
project = "TCGA-BRCA",
data.category = "DNA Methylation",
data.type = "Masked Intensities",
platform = "Illumina Methylation Epic"
)
GDCdownload(query, files.per.chunk = 10)
betas <- GDCprepare(query)
query <- GDCquery(
project = "TCGA-COAD",
data.category = "Clinical",
data.type = "Clinical Supplement",
data.format = "BCR XML",
barcode = "TCGA-A6-5664"
)
GDCdownload(query)
drug <- GDCprepare_clinic(query,"drug")
query <- GDCquery(
project = "TCGA-COAD",
data.category = "Clinical",
data.type = "Clinical Supplement",
data.format = "BCR OMF XML",
barcode = "TCGA-AD-6964"
)
GDCdownload(query)
query <- GDCquery(
project = "TCGA-ACC",
data.category = "Clinical",
data.type = "Clinical Supplement",
data.format = "BCR Biotab"
)
GDCdownload(query)
clinical.BCRtab.all <- GDCprepare(query)
names(clinical.BCRtab.all)
query <- GDCquery(
project = "TCGA-ACC",
data.category = "Clinical",
data.type = "Clinical Supplement",
data.format = "BCR Biotab",
file.type = "radiation"
)
GDCdownload(query)
clinical.BCRtab.radiation <- GDCprepare(query)
For more information please check: https://docs.gdc.cancer.gov/Data/Bioinformatics_Pipelines/DNA_Seq_Variant_Calling_Pipeline/
GDC Single Cell RNA-Seq information: https://docs.gdc.cancer.gov/Data/Bioinformatics_Pipelines/Expression_mRNA_Pipeline/#scrna-seq-pipeline-single-nuclei
query.sc.analysis <- GDCquery(
project = "CPTAC-3",
data.category = "Transcriptome Profiling",
access = "open",
data.type = "Single Cell Analysis",
data.format = "TSV"
)
GDCdownload(query.sc.analysis)
Single.Cell.Analysis.list <- GDCprepare(query.sc.analysis)
query.raw.counts <- GDCquery(
project = "CPTAC-3",
data.category = "Transcriptome Profiling",
access = "open",
data.type = "Gene Expression Quantification",
barcode = c("CPT0167860015","CPT0206880004"),
workflow.type = "CellRanger - 10x Raw Counts"
)
GDCdownload(query.raw.counts)
raw.counts.list <- GDCprepare(query.raw.counts)
query.filtered.counts <- GDCquery(
project = "CPTAC-3",
data.category = "Transcriptome Profiling",
access = "open",
data.type = "Gene Expression Quantification",
barcode = c("CPT0167860015","CPT0206880004"),
workflow.type = "CellRanger - 10x Filtered Counts"
)
GDCdownload(query.filtered.counts)
filtered.counts.list <- GDCprepare(query.filtered.counts)
query.sc.dea <- GDCquery(
project = "CPTAC-3",
data.category = "Transcriptome Profiling",
access = "open",
data.type = "Differential Gene Expression",
barcode = c("CPT0167860015","CPT0206880004"),
workflow.type = "Seurat - 10x Chromium"
)
GDCdownload(query.sc.dea)
sc.dea.list <- GDCprepare(query.sc.dea)