mut_matrix_stranded: Make mutation count matrix of 96 trinucleotides with strand...

In MutationalPatterns: Comprehensive genome-wide analysis of mutational processes

Description

Make a mutation count matrix with 192 features: 96 trinucleotides and 2 strands, these can be transcription or replication strand

Usage

mut_matrix_stranded(
  vcf_list,
  ref_genome,
  ranges,
  mode = "transcription",
  extension = 1
)

Arguments

vcf_list	GRangesList or GRanges object.
ref_genome	BSGenome reference genome object
ranges	GRanges object with the genomic ranges of: 1. (transcription mode) the gene bodies with strand (+/-) information, or 2. (replication mode) the replication strand with 'strand_info' metadata
mode	"transcription" or "replication", default = "transcription"
extension	The number of bases, that's extracted upstream and downstream of the base substitutions. (Default: 1).

Value

192 mutation count matrix (96 X 2 strands)

See Also

read_vcfs_as_granges, mut_matrix, mut_strand

Examples

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

## Load the corresponding reference genome.
ref_genome <- "BSgenome.Hsapiens.UCSC.hg19"
library(ref_genome, character.only = TRUE)

## Transcription strand analysis:
## You can obtain the known genes from the UCSC hg19 dataset using
## Bioconductor:
# BiocManager::install("TxDb.Hsapiens.UCSC.hg19.knownGene")
library("TxDb.Hsapiens.UCSC.hg19.knownGene")
genes_hg19 <- genes(TxDb.Hsapiens.UCSC.hg19.knownGene)

mut_mat_s <- mut_matrix_stranded(grl, ref_genome, genes_hg19,
  mode = "transcription"
)

## You can also use a longer context
mut_mat_s <- mut_matrix_stranded(grl, ref_genome, genes_hg19,
  mode = "transcription", extension = 2
)

## Replication strand analysis:
## Read example bed file with replication direction annotation
repli_file <- system.file("extdata/ReplicationDirectionRegions.bed",
  package = "MutationalPatterns"
)
repli_strand <- read.table(repli_file, header = TRUE)
repli_strand_granges <- GRanges(
  seqnames = repli_strand$Chr,
  ranges = IRanges(
    start = repli_strand$Start + 1,
    end = repli_strand$Stop
  ),
  strand_info = repli_strand$Class
)
## UCSC seqlevelsstyle
seqlevelsStyle(repli_strand_granges) <- "UCSC"
# The levels determine the order in which the features
# will be countend and plotted in the downstream analyses
# You can specify your preferred order of the levels:
repli_strand_granges$strand_info <- factor(
  repli_strand_granges$strand_info,
  levels = c("left", "right")
)

mut_mat_s_rep <- mut_matrix_stranded(grl, ref_genome, repli_strand_granges,
  mode = "replication"
)

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