TCGAbiolinks has provided a few functions to download and prepare data from GDC for analysis. This section starts by explaning the different downloads methods and the SummarizedExperiment object, which is the default data structure used in TCGAbiolinks, followed by some examples.


Downloading and preparing data for analysis

Data download: Methods differences

There are two methods to download GDC data using TCGAbiolinks:

  • client: this method creates a MANIFEST file and download the data using GDC Data Transfer Tool this method is more reliable but it might be slower compared to the api method.
  • api: this methods used 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.
Data prepared: SummarizedExperiment object

A SummarizedExperiment object has three main matrices that can be accessed using the SummarizedExperiment package):

  • Sample matrix information is accessed via colData(data): stores sample information. TCGAbiolinks will add indexed clinical data and subtype information from marker TCGA papers.
  • Assay matrix information is accessed via assay(data): stores molecular data
  • Feature matrix information (gene information) is accessed via rowRanges(data): stores metadata about the features, including their genomic ranges
Summarized Experiment: annotation information

When using the funciton 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. For legacy data (data aligned to hg19) TCGAbiolinks is using GRCh37.p13 and for harmonized data (data aligned to hg38) now it is using GRCh38.p7 (May 2017).

Unfortunately, some of the updates changes/remove gene symbols, change coordinates, etc. Which might introduce some loss of data. For example, if the gene was removed we cannot map it anymore and that information will be lost in the SummarizedExperiment.

If you set SummarizedExperiment to FALSE, you will get the data unmofied just as they are in the files and ad your own annotation.

Also, there are no updated for DNA methylation data. But the last metadata available can be found here: http://zwdzwd.github.io/InfiniumAnnotation

Related discussions: issue 91, issue 50

Arguments

GDCdownload

Argument Description
query A query for GDCquery function
token.file Token file to download controled 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”

Search and download data from legacy database using GDC api method

In this example we will download gene expression data from legacy database (data aligned against genome of reference hg19) using GDC api method and we will show object data and metadata.

## GRanges object with 100 ranges and 3 metadata columns:
##                seqnames              ranges strand |      gene_id
##                   <Rle>           <IRanges>  <Rle> |  <character>
##           A1BG    chr19   58856544-58864865      - |         A1BG
##            A2M    chr12     9220260-9268825      - |          A2M
##           NAT1     chr8   18027986-18081198      + |         NAT1
##           NAT2     chr8   18248755-18258728      + |         NAT2
##   RP11-986E7.7    chr14   95058395-95090983      + | RP11-986E7.7
##            ...      ...                 ...    ... .          ...
##         ADORA1     chr1 203059782-203136533      + |       ADORA1
##        ADORA2A    chr22   24813847-24838328      + |      ADORA2A
##        ADORA2B    chr17   15848231-15879060      + |      ADORA2B
##         ADORA3     chr1 112025970-112106584      - |       ADORA3
##          ADPRH     chr3 119298115-119308792      + |        ADPRH
##                entrezgene ensembl_gene_id
##                 <numeric>     <character>
##           A1BG          1 ENSG00000121410
##            A2M          2 ENSG00000175899
##           NAT1          9 ENSG00000171428
##           NAT2         10 ENSG00000156006
##   RP11-986E7.7         12 ENSG00000273259
##            ...        ...             ...
##         ADORA1        134 ENSG00000163485
##        ADORA2A        135 ENSG00000128271
##        ADORA2B        136 ENSG00000170425
##         ADORA3        140 ENSG00000121933
##          ADPRH        141 ENSG00000144843
##   -------
##   seqinfo: 24 sequences from an unspecified genome; no seqlengths

Search and download data for two samples from database

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.

## 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: Outputs

This 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.

Harmonized data

Data.category Data.type Workflow Type Status
Transcriptome Profiling Gene Expression Quantification HTSeq - Counts Data frame or SE (losing 5% of information when mapping to genomic regions)
HTSeq - FPKM-UQ Returning only a (losing 5% of information when mapping to genomic regions)
HTSeq - FPKM Returning only a (losing 5% of information when mapping to genomic regions)
Isoform Expression Quantification Not needed
miRNA Expression Quantification Not needed Returning only a dataframe for the moment
Copy number variation Copy Number Segment Returning only a dataframe for the moment
Masked Copy Number Segment Returning only a dataframe for the moment
Gene Level Copy Number Scores Returning only a dataframe for the moment
Simple Nucleotide Variation
Raw Sequencing Data
Biospecimen Slide Image
Biospecimen Biospecimen Supplement
Clinical

Legacy data

Data.category Data.type Platform file.type Status
Transcriptome Profiling
Copy number variation - Affymetrix SNP Array 6.0 nocnv_hg18.seg Working
- Affymetrix SNP Array 6.0 hg18.seg Working
- Affymetrix SNP Array 6.0 nocnv_hg19.seg Working
- Affymetrix SNP Array 6.0 hg19.seg Working
- Illumina HiSeq Several Working
Simple Nucleotide Variation Simple somatic mutation
Raw Sequencing Data
Biospecimen
Clinical
Protein expression MDA RPPA Core - Working
Gene expression Gene expression quantification Illumina HiSeq normalized_results Working
Illumina HiSeq results Working
HT_HG-U133A - Working
AgilentG4502A_07_2 - Data frame only
AgilentG4502A_07_1 - Data frame only
HuEx-1_0-st-v2 FIRMA.txt Not Preparing
gene.txt Not Preparing
Isoform expression quantification
miRNA gene quantification
Exon junction quantification
Exon quantification
miRNA isoform quantification
DNA methylation Illumina Human Methylation 450 Not used Working
Illumina Human Methylation 27 Not used Working
Illumina DNA Methylation OMA003 CPI Not used Working
Illumina DNA Methylation OMA002 CPI Not used Working
Illumina Hi Seq Not working
Raw Microarray Data
Structural Rearrangement
Other

Examples

Legacy archive

Harmonized database