knitr::opts_chunk$set(echo = TRUE) knitr::opts_knit$set(progress = FALSE)
library(TCGAbiolinks) library(SummarizedExperiment) library(dplyr) library(DT)
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.
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)
data <- gbm.exp.harmonized
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 ) rowRanges(data)
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 | | | |
query <- GDCquery( project = "TCGA-ACC", data.category = "Copy Number Variation", data.type = "Copy Number Segment", barcode = c( "TCGA-OR-A5KU-01A-11D-A29H-01", "TCGA-OR-A5JK-01A-11D-A29H-01") ) GDCdownload(query) data <- GDCprepare(query)
query <- GDCquery( project = "TCGA-ACC", data.category = "Copy Number Variation", data.type = "Gene Level Copy Number", access = "open" ) GDCdownload(query) data <- GDCprepare(query)
query <- GDCquery( project = "TCGA-ACC", data.category = "Copy Number Variation", data.type = "Allele-specific Copy Number Segment", access = "open" ) GDCdownload(query) data <- GDCprepare(query)
query <- GDCquery( project = "TCGA-ACC", data.category = "Copy Number Variation", data.type = "Masked Copy Number Segment", access = "open" ) GDCdownload(query) data <- GDCprepare(query)
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)
```{R, eval = FALSE} 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)
### Proteome Profiling #### Protein Expression Quantification ```r query.rppa <- GDCquery( project = "TCGA-ESCA", data.category = "Proteome Profiling", data.type = "Protein Expression Quantification" ) GDCdownload(query.rppa) rppa <- GDCprepare(query.rppa)
```{R, eval = FALSE}
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)
### Simple Nucleotide Variation #### Masked Somatic Mutation For more information please check: https://docs.gdc.cancer.gov/Data/Bioinformatics_Pipelines/DNA_Seq_Variant_Calling_Pipeline/ ```r query <- GDCquery( project = "TCGA-HNSC", data.category = "Simple Nucleotide Variation", data.type = "Masked Somatic Mutation", access = "open" ) GDCdownload(query) maf <- GDCprepare(query)
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.hdF5 <- GDCquery( project = "CPTAC-3", data.category = "Transcriptome Profiling", access = "open", data.type = "Single Cell Analysis", barcode = c("CPT0167860015","CPT0206880004"), data.format = "HDF5" ) GDCdownload(query.hdF5) df.HDF5 <- GDCprepare(query.hdF5)
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)
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