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tcga_data_download.R
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tcga_data_download.R
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# script to download data from TCGA using TCGAbiolinks
# setwd("~/Desktop/demo/TCGAbiolinks")
library(TCGAbiolinks)
library(tidyverse)
library(maftools)
library(pheatmap)
library(SummarizedExperiment)
# get a list of projects
gdcprojects <- getGDCprojects()
getProjectSummary('TCGA-BRCA')
# building a query
query_TCGA <- GDCquery(project = 'TCGA-BRCA',
data.category = 'Transcriptome Profiling')
output_query_TCGA <- getResults(query_TCGA)
# build a query to retrieve gene expression data ------------
query_TCGA <- GDCquery(project = 'TCGA-BRCA',
data.category = 'Transcriptome Profiling',
experimental.strategy = 'RNA-Seq',
workflow.type = 'STAR - Counts',
access = 'open',
barcode = c('TCGA-LL-A73Y-01A-11R-A33J-07', 'TCGA-E2-A1IU-01A-11R-A14D-07','TCGA-AO-A03U-01B-21R-A10J-07'))
getResults(query_TCGA)
# download data - GDCdownload
GDCdownload(query_TCGA)
# prepare data
tcga_brca_data <- GDCprepare(query_TCGA, summarizedExperiment = TRUE)
brca_matrix <- assay(tcga_brca_data, 'fpkm_unstrand')
# build a query to retrieve DNA methylation data --------------
query_methly <- GDCquery(project = 'TCGA-GBM',
data.category = 'DNA Methylation',
platform = 'Illumina Human Methylation 27',
access = 'open',
data.type = 'Methylation Beta Value',
barcode = c('TCGA-19-0962-01B-01D-0521-05', 'TCGA-06-0137-01A-01D-0218-05'))
output_query_methyl <- getResults(query_methly)
GDCdownload(query_methly)
# plot probes showing differences in beta values between samples
dna.meth <- GDCprepare(query_methly, summarizedExperiment = TRUE)
assay(dna.meth)
idx <- dna.meth %>%
assay %>%
rowVars() %>%
order(decreasing = TRUE) %>%
head(10)
# plot
pheatmap(assay(dna.meth)[idx,])
# download and visualize mutation data from TCGA ----------------------
query_mutation <- GDCquery(project = 'TCGA-BRCA',
data.category = 'Simple Nucleotide Variation',
access = 'open',
barcode = c('TCGA-LL-A73Y-01A-11D-A33E-09,TCGA-LL-A73Y-10B-01D-A33H-09',
'TCGA-E9-A1NH-01A-11D-A14G-09,TCGA-E9-A1NH-11A-33D-A14G-09'))
output_query_mutation <- getResults(query_mutation)
GDCdownload(query_mutation)
maf <- GDCprepare(query_mutation, summarizedExperiment = TRUE)
# maftools utils to read and create dashboard
maftools.input <- read.maf(maf)
plotmafSummary(maf = maftools.input,
addStat = 'median',
rmOutlier = TRUE,
dashboard = TRUE)
# oncoprint
oncoplot(maf = maftools.input,
top = 10,
removeNonMutated = TRUE)