genomic_distribution {MutationalPatterns} | R Documentation

## Find overlaps between mutations and a genomic region.

### Description

Function finds the number of mutations that reside in genomic region and takes surveyed area of genome into account.

### Usage

    genomic_distribution(vcf_list, surveyed_list, region_list)

### Arguments

`vcf_list` | A GRangesList or a list with VCF GRanges objects.
`surveyed_list` | A GRangesList or a list with GRanges of regions of the genome that have been surveyed (e.g. determined using GATK CallableLoci).
`region_list` | A GRangesList or a list with GRanges objects containing locations of genomic regions.

### Value

A data.frame containing the number observed and number of expected mutations in each genomic region.

### See Also

`read_vcfs_as_granges`

### Examples

    ## See the 'read_vcfs_as_granges()' example for how we obtained the
    ## following data:
    vcfs <- readRDS(system.file("states/read_vcfs_as_granges_output.rds",
                    package="MutationalPatterns"))

    ## Use biomaRt to obtain data.
    ## We can query the BioMart database, but this may take a long time,
    ## so we take some shortcuts by loading the results from our
    ## examples. The corresponding code for downloading the data can be
    ## found above the command we run.

    # mart="ensemble"
    # library(biomaRt)

    # regulatory <- useEnsembl(biomart="regulation",
    #                          dataset="hsapiens_regulatory_feature",
    #                          GRCh = 37)
    regulatory <- readRDS(system.file("states/regulatory_data.rds",
                                        package="MutationalPatterns"))

    ## Download the regulatory CTCF binding sites and convert them to
    ## a GRanges object.
    # CTCF <- getBM(attributes = c('chromosome_name',
    #                             'chromosome_start',
    #                             'chromosome_end',
    #                             'feature_type_name',
    #                             'cell_type_name'),
    #              filters = "regulatory_feature_type_name",
    #              values = "CTCF Binding Site",
    #              mart = regulatory)
    #
    # CTCF_g <- reduce(GRanges(CTCF$chromosome_name,
    #                 IRanges(CTCF$chromosome_start,
    #                 CTCF$chromosome_end)))

    CTCF_g <- readRDS(system.file("states/CTCF_g_data.rds",
                        package="MutationalPatterns"))

    ## Download the promoter regions and conver them to a GRanges object.
    # promoter = getBM(attributes = c('chromosome_name', 'chromosome_start',
    #                                 'chromosome_end', 'feature_type_name'),
    #                  filters = "regulatory_feature_type_name",
    #                  values = "Promoter",
    #                  mart = regulatory)
    # promoter_g = reduce(GRanges(promoter$chromosome_name,
    #                     IRanges(promoter$chromosome_start,
    #                             promoter$chromosome_end)))

    promoter_g <- readRDS(system.file("states/promoter_g_data.rds",
                            package="MutationalPatterns"))

    # open = getBM(attributes = c('chromosome_name', 'chromosome_start',
    #                             'chromosome_end', 'feature_type_name'),
    #               filters = "regulatory_feature_type_name",
    #               values = "Open chromatin",
    #               mart = regulatory)
    # open_g = reduce(GRanges(open$chromosome_name,
    #                 IRanges(open$chromosome_start,
    #                         open$chromosome_end)))

    open_g <- readRDS(system.file("states/open_g_data.rds",
                        package="MutationalPatterns"))

    # flanking = getBM(attributes = c('chromosome_name',
    #                                 'chromosome_start',
    #                                 'chromosome_end',
    #                                 'feature_type_name'),
    #                  filters = "regulatory_feature_type_name",
    #                  values = "Promoter Flanking Region",
    #                  mart = regulatory)
    # flanking_g = reduce(GRanges(
    #                        flanking$chromosome_name,
    #                        IRanges(flanking$chromosome_start,
    #                        flanking$chromosome_end)))

    flanking_g <- readRDS(system.file("states/promoter_flanking_g_data.rds",
                                        package="MutationalPatterns"))

    # TF_binding = getBM(attributes = c('chromosome_name', 'chromosome_start',
    #                                   'chromosome_end', 'feature_type_name'),
    #                      filters = "regulatory_feature_type_name",
    #                      values = "TF binding site",
    #                      mart = regulatory)
    # TF_binding_g = reduce(GRanges(TF_binding$chromosome_name,
    #                               IRanges(TF_binding$chromosome_start,
    #                               TF_binding$chromosome_end)))

    TF_binding_g <- readRDS(system.file("states/TF_binding_g_data.rds",
                                        package="MutationalPatterns"))

    regions <- GRangesList(promoter_g, flanking_g, CTCF_g, open_g, TF_binding_g)

    names(regions) <- c("Promoter", "Promoter flanking", "CTCF",
                        "Open chromatin", "TF binding")

    # Use a naming standard consistently.
    seqlevelsStyle(regions) <- "UCSC"

    ## Get the filename with surveyed/callable regions
    surveyed_file <- system.file("extdata/callableloci-sample.bed",
                                package="MutationalPatterns")

    ## Import the file using rtracklayer and use the UCSC naming standard
    library(rtracklayer)
    surveyed <- import(surveyed_file)
    seqlevelsStyle(surveyed) <- "UCSC"

    ## For this example we use the same surveyed file for each sample.
    surveyed_list <- rep(list(surveyed), 9)

    ## Calculate the number of observed and expected number of mutations in
    ## each genomic regions for each sample.
    distr <- genomic_distribution(vcfs, surveyed_list, regions)

[Package MutationalPatterns version 1.99.3 Index]