# TCGAbiolinks: Searching, downloading and visualizing mutation files

#### 16 June 2022

# 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`, `GDCdownload` and `GDCpreprare` to 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 Aggregate GDC MAFs. For more information please access https://github.com/NCI-GDC/gdc-maf-tool and GDC docs.

    query <- GDCquery(
        project = "TCGA-CHOL",
        data.category = "Simple Nucleotide Variation",
        access = "open",
        legacy = FALSE,
        data.type = "Masked Somatic Mutation",
        workflow.type = "Aliquot Ensemble Somatic Variant Merging and Masking"
    )
    GDCdownload(query)
    maf <- GDCprepare(query)

    # 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
    getResults(query.maf.hg19) %>%
        dplyr::select(-contains("sample_type")) %>%
        dplyr::select(-contains("cases")) %>%
        DT::datatable(
            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)

## 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)
    query <- GDCquery(
        project = "TCGA-CHOL",
        data.category = "Simple Nucleotide Variation",
        access = "open",
        legacy = FALSE,
        data.type = "Masked Somatic Mutation",
        workflow.type = "Aliquot Ensemble Somatic Variant Merging and Masking"
    )
    GDCdownload(query)
    maf <- GDCprepare(query)

    maf <- maf %>% maftools::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)