TCGAbiolinks: Searching, downloading and visualizing mutation files

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 downoad MAF aligned against hg19

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")

# 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 exmaple 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)

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

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

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)