This package provides a dataset for those wishing to try out the TCGA Workflow: Analyze cancer genomics and epigenomics data using Bioconductor packages [@10.12688/f1000research.8923.2]. The data in this package are a subset of the TCGA data for LGG (Lower grade glioma) and GBM (Glioblastoma multiforme) samples.
library(TCGAWorkflowData)
data("elmerExample")
data("GBMnocnvhg19")
data("GBMIllumina_HiSeq")
data("LGGIllumina_HiSeq")
data("met20SamplesGBMLGGchr9")
data("mafMutect2LGGGBM")
data("markersMatrix")
data("histoneMarks")
data("biogrid")
data("genes_GR")The following commands were used to create the data included with this package.
Download gene information for hg19 using TCGAbiolinks, which uses biomart parckage.
library(GenomicRanges)
library(TCGAbiolinks)
##############################
## Recurrent CNV annotation ##
##############################
# Get gene information from GENCODE using biomart
genes <- TCGAbiolinks:::get.GRCh.bioMart(genome = "hg19")
genes <- genes[genes$external_gene_id != "" & genes$chromosome_name %in% c(1:22,"X","Y"),]
genes[genes$chromosome_name == "X", "chromosome_name"] <- 23
genes[genes$chromosome_name == "Y", "chromosome_name"] <- 24
genes$chromosome_name <- sapply(genes$chromosome_name,as.integer)
genes <- genes[order(genes$start_position),]
genes <- genes[order(genes$chromosome_name),]
genes <- genes[,c("external_gene_id", "chromosome_name", "start_position","end_position")]
colnames(genes) <- c("GeneSymbol","Chr","Start","End")
genes_GR <- makeGRangesFromDataFrame(genes,keep.extra.columns = TRUE)
save(genes_GR,genes,file = "genes_GR.rda", compress = "xz")Download and save a subset of GBM GISTIC results from GDAC firehose.
library(RTCGAToolbox)
# Download GISTIC results
lastAnalyseDate <- getFirehoseAnalyzeDates(1)
gistic <- getFirehoseData("GBM",gistic2_Date = lastAnalyseDate)
# get GISTIC results
gistic.allbygene <- getData(gistic,type = "GISTIC", CN = "All")
gistic.allbygene <- gistic.allbygene[1:10,]
gistic.thresholedbygene <- getData(gistic,type = "GISTIC", CN = "Thresholed")
gistic.thresholedbygene <- gistic.thresholedbygene[1:10,]
save(gistic.allbygene,gistic.thresholedbygene,file = "GBMGistic.rda", compress = "xz")The following code will download segmented CNV from SNP array (Affymetrix Genome-Wide Human SNP Array 6.0) for 20 Glioblastoma multiforme (GBM) samples.
library(TCGAbiolinks)
query.gbm.nocnv <- GDCquery(project = "TCGA-GBM",
data.category = "Copy number variation",
legacy = TRUE,
file.type = "nocnv_hg19.seg",
sample.type = c("Primary solid Tumor"))
# to reduce time we will select only 20 samples
query.gbm.nocnv$results[[1]] <- query.gbm.nocnv$results[[1]][1:20,]
GDCdownload(query.gbm.nocnv, chunks.per.download = 100)
gbm.nocnv <- GDCprepare(query.gbm.nocnv, save = TRUE, save.filename = "GBMnocnvhg19.rda")The following code will download 20 LGG (Lower grade glioma) and 20 GBM (Glioblastoma multiforme) samples that have gene expression data. The Gene expression data is the raw expression signal for expression of a gene.
query <- GDCquery(project = "TCGA-GBM",
data.category = "Gene expression",
data.type = "Gene expression quantification",
platform = "Illumina HiSeq",
file.type = "results",
sample.type = c("Primary solid Tumor"),
legacy = TRUE)
# We will use only 20 samples to make the example faster
query$results[[1]] <- query$results[[1]][1:20,]
GDCdownload(query)
gbm.exp <- GDCprepare(query, save = TRUE, summarizedExperiment = TRUE, save.filename = "GBMIllumina_HiSeq.rda")
query <- GDCquery(project = "TCGA-LGG",
data.category = "Gene expression",
data.type = "Gene expression quantification",
platform = "Illumina HiSeq",
file.type = "results",
sample.type = c("Primary solid Tumor"),
legacy = TRUE)
# We will use only 20 samples to make the example faster
query$results[[1]] <- query$results[[1]][1:20,]
GDCdownload(query)
lgg.exp <- GDCprepare(query, save = TRUE, summarizedExperiment = TRUE, save.filename = "LGGIllumina_HiSeq.rda")The following code will select 10 LGG (Lower grade glioma) and 10 GBM (Glioblastoma multiforme) samples that have both DNA methylation and gene expression data. This objects will be then prepared to the format accept by the Biocondcutor package ELMER([link])(http://bioconductor.org/packages/release/bioc/html/ELMER.html). The DNA methylation will have only probes in chromossome 9 in order to make the example of the workflow faster. For a real analysis, all chromossomes should be used. The Gene expression data is the normalized results for expression of a gene.
#----------- 8.3 Identification of Regulatory Enhancers -------
library(TCGAbiolinks)
# Samples: primary solid tumor w/ DNA methylation and gene expression
matched_met_exp <- function(project, n = NULL){
# get primary solid tumor samples: DNA methylation
message("Download DNA methylation information")
met450k <- GDCquery(project = project,
data.category = "DNA methylation",
platform = "Illumina Human Methylation 450",
legacy = TRUE,
sample.type = c("Primary solid Tumor"))
met450k.tp <- met450k$results[[1]]$cases
# get primary solid tumor samples: RNAseq
message("Download gene expression information")
exp <- GDCquery(project = project,
data.category = "Gene expression",
data.type = "Gene expression quantification",
platform = "Illumina HiSeq",
file.type = "normalized_results",
sample.type = c("Primary solid Tumor"),
legacy = TRUE)
exp.tp <- exp$results[[1]]$cases
# Get patients with samples in both platforms
patients <- unique(substr(exp.tp,1,15)[substr(exp.tp,1,12) %in% substr(met450k.tp,1,12)] )
if(!is.null(n)) patients <- patients[1:n] # get only n samples
return(patients)
}
lgg.samples <- matched_met_exp("TCGA-LGG", n = 10)
gbm.samples <- matched_met_exp("TCGA-GBM", n = 10)
samples <- c(lgg.samples,gbm.samples)
#-----------------------------------
# 1 - Methylation
# ----------------------------------
query.met <- GDCquery(project = c("TCGA-LGG","TCGA-GBM"),
data.category = "DNA methylation",
platform = "Illumina Human Methylation 450",
legacy = TRUE,
barcode = samples)
GDCdownload(query.met)
met <- GDCprepare(query.met, save = FALSE)
met <- subset(met,subset = as.character(GenomicRanges::seqnames(met)) %in% c("chr9"))
save("met.20.samples.GBM.LGG.chr9.rda")
met.elmer <- TCGAprepare_elmer(met, platform = "HumanMethylation450")
#-----------------------------------
# 2 - Expression
# ----------------------------------
query.exp <- GDCquery(project = c("TCGA-LGG","TCGA-GBM"),
data.category = "Gene expression",
data.type = "Gene expression quantification",
platform = "Illumina HiSeq",
file.type = "normalized_results",
legacy = TRUE,
barcode = samples)
GDCdownload(query.exp)
exp.lgg <- GDCprepare(query.exp, save = FALSE)
exp.elmer <- TCGAprepare_elmer(exp.lgg, platform = "IlluminaHiSeq_RNASeqV2")
save(exp.elmer, met.elmer, gbm.samples, lgg.samples, file = "elmerExample.rda", compress = "xz")The following code will downlaod Mutation annotation files from LGG and GBM samples and merge them into one single single file. The GDC Somatic Mutation Calling Workflow used is the mutect2. For more information please check GDC.
library(TCGAbiolinks)
LGGmut <- GDCquery_Maf(tumor = "LGG", pipelines = "mutect2")
GBMmut <- GDCquery_Maf(tumor = "GBM", pipelines = "mutect2")
mut <- plyr::rbind.fill(LGGmut, GBMmut)
save(mut,LGGmut,GBMmut,file ="mafMutect2LGGGBM.rda")gdac.root <- "ftp://ftp.broadinstitute.org/pub/GISTIC2.0/hg19_support/"
file <- paste0(gdac.root, "genome.info.6.0_hg19.na31_minus_frequent_nan_probes_sorted_2.1.txt")
# Retrieve probes meta file from broadinstitute website
if(!file.exists(basename(file))) downloader::download(file, basename(file))
# -=-=-=-=-=-=-=-=-=-=-=-=-=-=-=--=-==--=--==---=-=-=-=-=-=-=-=-=-=-=-=-=-=-=--=-==--=--==--
# For hg38 analysis please take a look on:
# https://gdc.cancer.gov/about-data/data-harmonization-and-generation/gdc-reference-files
# File: SNP6 GRCh38 Liftover Probeset File for Copy Number Variation Analysis
# -=-=-=-=-=-=-=-=-=-=-=-=-=-=-=--=-==--=--==---=-=-=-=-=-=-=-=-=-=-=-=-=-=-=--=-==--=--==--
markersMatrix <- readr::read_tsv(basename(file), col_names = FALSE, col_types = "ccn", progress = FALSE)
save(markersMatrix, file = "markersMatrix.rda", compress = "xz")Download biogrid information.
### read biogrid info
### Check last version in https://thebiogrid.org/download.php
file <- "http://thebiogrid.org/downloads/archives/Release%20Archive/BIOGRID-3.4.146/BIOGRID-ALL-3.4.146.tab2.zip"
if(!file.exists(gsub("zip","txt",basename(file)))){
downloader::download(file,basename(file))
unzip(basename(file),junkpaths =TRUE)
}
tmp.biogrid <- read.csv(gsub("zip","txt",basename(file)), header=TRUE, sep="\t", stringsAsFactors=FALSE)
save(tmp.biogrid, file = "biogrid.rda", compress = "xz")The code below was used to download histone marks specific for brain tissue using the AnnotationHub package that can access the Roadmap database.
R version 3.4.0 (2017-04-21)
**Platform:** x86_64-pc-linux-gnu (64-bit)
locale: LC_CTYPE=en_US.UTF-8, LC_NUMERIC=C, LC_TIME=en_US.UTF-8, LC_COLLATE=C, LC_MONETARY=en_US.UTF-8, LC_MESSAGES=en_US.UTF-8, LC_PAPER=en_US.UTF-8, LC_NAME=C, LC_ADDRESS=C, LC_TELEPHONE=C, LC_MEASUREMENT=en_US.UTF-8 and LC_IDENTIFICATION=C
attached base packages:
other attached packages:
loaded via a namespace (and not attached):