mut_matrix_stranded {MutationalPatterns} | R Documentation

## Make mutation count matrix of 96 trinucleotides with strand information

### 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")

### Arguments

`vcf_list` | List of collapsed vcf objects
`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"

### 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:
    vcfs <- 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:
    # source("https://bioconductor.org/biocLite.R")
    # biocLite("TxDb.Hsapiens.UCSC.hg19.knownGene")
    # library("TxDb.Hsapiens.UCSC.hg19.knownGene")

    ## For this example, we preloaded the data for you:
    genes_hg19 <- readRDS(system.file("states/genes_hg19.rds",
                            package="MutationalPatterns"))

    mut_mat_s = mut_matrix_stranded(vcfs, ref_genome, genes_hg19,
                                    mode = "transcription")

    ## 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(vcfs, ref_genome, repli_strand_granges,
                                    mode = "replication")

[Package MutationalPatterns version 1.4.3 Index]