genomic_distribution: Find overlaps between mutations and a genomic region.

In MutationalPatterns: Comprehensive genome-wide analysis of mutational processes

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	GRangesList or GRanges object.
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'),
#              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"
))

# 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"
))

regions <- GRangesList(promoter_g, flanking_g, CTCF_g)

names(regions) <- c("Promoter", "Promoter flanking", "CTCF")

# 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)

MutationalPatterns documentation built on Nov. 14, 2020, 2:03 a.m.