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",
      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"
    )

[Package MutationalPatterns version 3.0.1 Index]