In this tutorial, we are going to download a dataset from The Cancer Genome Atlas. Please note that datasets are often huge and it will take some time.
First, let’s focus on the TCGA datasets at https://portal.gdc.cancer.gov and pick your favorite one.
For this tutorial, we choose TCGA-OV, i.e., High Grade Serous Ovarian Cancer (HGSOC) dataset.
Now, we’ll set some variables and load some useful libraries. Moreover, to better organize downloads we suggest to create a directory (“downloadTCGA”) and store everything there.
library(TCGAbiolinks)
tumor = "OV"
project = paste0("TCGA-", tumor)
genome = "hg38"
methylation_platforms <- c("Illumina Human Methylation 27",
"Illumina Human Methylation 450")
dirname = "downloadTCGA"
if (!file.exists(dirname)){
dir.create(dirname)
}We can start with the download of clinical data.
cliQuery <- GDCquery(project = project, data.category = "Clinical",
data.format = "bcr xml")
GDCdownload(cliQuery, method="api", files.per.chunk = 10,
directory = "downloadTCGA/GDCdata")
followUp <- GDCprepare_clinic(cliQuery, clinical.info = "follow_up",
directory = "downloadTCGA/GDCdata")
newTumorEvent <- GDCprepare_clinic(cliQuery, clinical.info = "new_tumor_event",
directory = "downloadTCGA/GDCdata")We have downloaded two data frames: followUp and newTumorEvent.
Now, it’s time for the expression counts.
expQuery <- GDCquery(project = project,
data.category = "Transcriptome Profiling",
data.type = "Gene Expression Quantification",
workflow.type = "STAR - Counts")
GDCdownload(expQuery, method = "api", directory = "downloadTCGA/GDCdata")
exprData <- GDCprepare(expQuery, directory = "downloadTCGA/GDCdata")We need to download methylation beta values, that are available from 2 different platforms: “Illumina Human Methylation 27” and “Illumina Human Methylation 450”. Since only 10 samples from the TCGA-OV project have been profiled with the 450k Illumina platform, we decide to download data from the 27k platform.
metQuery <- GDCquery(project = project,
data.category = "DNA Methylation",
data.type = "Methylation Beta Value",
platform = methylation_platforms[[1]])
metData <- GDCprepare(metQuery, directory = "downloadTCGA/GDCdata")Next, we download mutation data.
mutQuery <- GDCquery(
project = project,
data.category = "Simple Nucleotide Variation",
data.type = "Masked Somatic Mutation",
workflow.type = "Aliquot Ensemble Somatic Variant Merging and Masking")
GDCdownload(mutQuery, method = "api", directory = "downloadTCGA/GDCdata")
mutData <- GDCprepare(mutQuery, directory = "downloadTCGA/GDCdata")Finally, we can download the CNV data generated by the GISTIC2 pipeline, selecting only primary tumors.
gisticTable <- getGistic("OV-TP", type = "thresholded")
cnvData <- gisticTable[,-c(1:3)]
colnames(cnvData) <- substr(colnames(cnvData), 1, 12)
row.names(cnvData) <- gisticTable$`Locus ID`We save all the downloaded data in a .RData file inside
the ‘downloadTCGA’ directory, that we will load in the next tutorial for
the pre-processing.
sessionInfo()## R version 4.4.2 (2024-10-31)
## Platform: x86_64-pc-linux-gnu
## Running under: Ubuntu 22.04.5 LTS
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## Matrix products: default
## BLAS: /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.10.0
## LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.10.0
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## time zone: Europe/Rome
## tzcode source: system (glibc)
##
## attached base packages:
## [1] parallel stats4 stats graphics grDevices utils
## [7] datasets methods base
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## other attached packages:
## [1] MethylMix_2.36.0 doParallel_1.0.17
## [3] iterators_1.0.14 foreach_1.5.2
## [5] impute_1.80.0 maftools_2.22.0
## [7] TCGAbiolinks_2.34.0 devtools_2.4.5
## [9] usethis_3.0.0 kableExtra_1.4.0
## [11] MOSClip_0.99.5 graphite_1.52.0
## [13] EDASeq_2.40.0 ShortRead_1.64.0
## [15] GenomicAlignments_1.42.0 SummarizedExperiment_1.36.0
## [17] MatrixGenerics_1.18.0 matrixStats_1.4.1
## [19] Rsamtools_2.22.0 GenomicRanges_1.58.0
## [21] Biostrings_2.74.0 GenomeInfoDb_1.42.0
## [23] XVector_0.46.0 BiocParallel_1.40.0
## [25] org.Hs.eg.db_3.20.0 AnnotationDbi_1.68.0
## [27] IRanges_2.40.0 S4Vectors_0.44.0
## [29] Biobase_2.66.0 BiocGenerics_0.52.0
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## loaded via a namespace (and not attached):
## [1] fs_1.6.5 bitops_1.0-9
## [3] httr_1.4.7 RColorBrewer_1.1-3
## [5] SuperExactTest_1.1.0 Rgraphviz_2.50.0
## [7] profvis_0.4.0 tools_4.4.2
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## [39] BiocIO_1.16.0 hwriter_1.3.2.1
## [41] car_3.1-3 dplyr_1.1.4
## [43] qtl_1.70 lars_1.3
## [45] leaps_3.2 Matrix_1.7-1
## [47] interp_1.1-6 fansi_1.0.6
## [49] abind_1.4-8 R.methodsS3_1.8.2
## [51] lifecycle_1.0.4 scatterplot3d_0.3-44
## [53] multcomp_1.4-26 yaml_2.3.10
## [55] carData_3.0-5 SparseArray_1.6.0
## [57] BiocFileCache_2.14.0 grid_4.4.2
## [59] blob_1.2.4 promises_1.3.0
## [61] crayon_1.5.3 pwalign_1.2.0
## [63] miniUI_0.1.1.1 lattice_0.22-6
## [65] GenomicFeatures_1.58.0 annotate_1.84.0
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## [69] pillar_1.9.0 knitr_1.48
## [71] ComplexHeatmap_2.22.0 rjson_0.2.23
## [73] TCGAbiolinksGUI.data_1.26.0 estimability_1.5.1
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