TCGAbiolinks: Searching, downloading and visualizing mutation files

In TCGAbiolinks: TCGAbiolinks: An R/Bioconductor package for integrative analysis with GDC data

knitr::opts_chunk$set(echo = TRUE)
knitr::opts_knit$set(progress = FALSE)

library(TCGAbiolinks)
library(SummarizedExperiment)
library(dplyr)
library(DT)

Search and Download

TCGAbiolinks has provided a few functions to download mutation data from GDC. There are two options to download the data:

1. Use GDCquery_Maf which will download MAF aligned against hg38
2. Use GDCquery, GDCdownload and GDCpreprare to download MAF aligned against hg19
3. Use getMC3MAF(), to download MC3 MAF from https://gdc.cancer.gov/about-data/publications/mc3-2017

Mutation data (hg38)

This example will download MAF (mutation annotation files) for variant calling pipeline muse. Pipelines options are: muse, varscan2, somaticsniper, mutect. For more information please access GDC docs.

maf <- GDCquery_Maf("CHOL", pipelines = "muse")

maf <- chol_maf@data

# Only first 50 to make render faster
datatable(maf[1:20,],
          filter = 'top',
          options = list(scrollX = TRUE, keys = TRUE, pageLength = 5), 
          rownames = FALSE)

Mutation data (hg19)

This example will download MAF (mutation annotation files) aligned against hg19 (Old TCGA maf files)

query.maf.hg19 <- GDCquery(project = "TCGA-CHOL", 
                           data.category = "Simple nucleotide variation", 
                           data.type = "Simple somatic mutation",
                           access = "open", 
                           legacy = TRUE)

# Check maf availables
datatable(dplyr::select(getResults(query.maf.hg19),-contains("cases")),
          filter = 'top',
          options = list(scrollX = TRUE, keys = TRUE, pageLength = 10), 
          rownames = FALSE)

query.maf.hg19 <- GDCquery(project = "TCGA-CHOL", 
                           data.category = "Simple nucleotide variation", 
                           data.type = "Simple somatic mutation",
                           access = "open", 
                           file.type = "bcgsc.ca_CHOL.IlluminaHiSeq_DNASeq.1.somatic.maf",
                           legacy = TRUE)
GDCdownload(query.maf.hg19)
maf <- GDCprepare(query.maf.hg19)

data <- bcgsc.ca_CHOL.IlluminaHiSeq_DNASeq.1.somatic.maf

# Only first 50 to make render faster
datatable(maf[1:20,],
          filter = 'top',
          options = list(scrollX = TRUE, keys = TRUE, pageLength = 5), 
          rownames = FALSE)

Mutation data MC3 file

This will download the MC3 MAF file from https://gdc.cancer.gov/about-data/publications/mc3-2017, and add project each sample belongs.

maf <- getMC3MAF()

Visualize the data

To visualize the data you can use the Bioconductor package maftools. For more information, please check its vignette.

library(maftools)
library(dplyr)
maf <- GDCquery_Maf("CHOL", pipelines = "muse") %>% read.maf

library(maftools)
library(dplyr)
maf <- chol_maf

datatable(getSampleSummary(maf),
          filter = 'top',
          options = list(scrollX = TRUE, keys = TRUE, pageLength = 5), 
          rownames = FALSE)
plotmafSummary(maf = maf, rmOutlier = TRUE, addStat = 'median', dashboard = TRUE)

oncoplot(maf = maf, top = 10, removeNonMutated = TRUE)
titv = titv(maf = maf, plot = FALSE, useSyn = TRUE)
#plot titv summary
plotTiTv(res = titv)

TCGAbiolinks documentation built on Nov. 8, 2020, 5:37 p.m.