Write a Mutational Matrix to input into the sigprofiler web application
Source:R/write_sigprofiler_input.R
write_mutational_matrix.RdCreates a .txt file from mutation data that can be used for mutational signatures analysis using the SigProfiler web application. Can handle group analyses (ex dose, tissue, etc). Currently only supports SBS matrices i.e. snvs.
Usage
write_mutational_matrix(
mutation_data,
group = "dose",
subtype_resolution = "base_96",
mf_type = "min",
output_path = NULL
)Arguments
- mutation_data
The object containing the mutation data. The output of import_mut_data() or import_vcf_data().
- group
The column in the mutation data used to aggregate groups (e.g., sample, tissue, dose).
- subtype_resolution
The resolution of the mutation subtypes. Options are "base_6" or "base_96". Default is "base_96".
- mf_type
The mutation counting method to use. Options are "min" or "max". Default is "min".
- output_path
The path to save the output file. If not provided, the file will be saved in the current working directory. Default is NULL.
Value
a .txt file that can be uploaded to the SigProfiler Assignment web application (https://cancer.sanger.ac.uk/signatures/assignment/) as a "Mutational Matrix".
Details
Mutations will be be filtered for SNVs. Mutations flagged in
filter_mut will be excluded from the output. Mutations will be summed
across the groups specified in the group argument.
Examples
# Example data consists of 24 mouse bone marrow
# samples exposed to three doses of BaP alongside vehicle controls.
# Libraries were sequenced with Duplex Sequencing using
# the TwinStrand Mouse Mutagenesis Panel which consists of 20 2.4kb
# targets = 48kb of sequence. Example data can be retrieved from
# MutSeqRData, an ExperimentHub data package:
## library(ExperimentHub)
## eh <- ExperimentHub()
## query(eh, "MutSeqRData")
# The data is a subset of variants from the target chr1
# from samples of the high dose group (50mg).
example_data <- readRDS(system.file("extdata", "Example_files",
"variants_subset_d50_chr1.rds",
package = "MutSeqR")
)
write_mutational_matrix(
mutation_data = example_data,
group = "sample",
subtype_resolution = "base_96",
mf_type = "min",
output_path = tempdir()
)
#> Warning: No depth data provided. Mutation frequencies will not be calculated. Mutation subtype proportions will not be normalized to the total_depth.
list.files(tempdir())
#> [1] "bslib-e392982257a0a7e1a4ae522df2f1f23b"
#> [2] "downlit"
#> [3] "file255744d7c73a"
#> [4] "file255749206024"
#> [5] "file255763f87a68"
#> [6] "file2557e240741"
#> [7] "libloc_177_2cc935f9605fe05a.rds"
#> [8] "libloc_182_8ba9a5ce909cb448.rds"
#> [9] "libloc_182_d3198563f59e5c50.rds"
#> [10] "libloc_191_2359b704beb3739.rds"
#> [11] "mutation_calling_file.txt"
#> [12] "repos_https%3A%2F%2Fbioconductor.org%2Fpackages%2F3.23%2Fdata%2Fannotation%2Fsrc%2Fcontrib.rds"
#> [13] "sample_base_96_mutational_matrix.txt"
#> [14] "temp_libpath2557288a0290"
#> [15] "test_list.xlsx"
#> [16] "test_single.xlsx"
# The file is saved in the temporary directory
# To view the file, use the following code:
## output_file <- file.path(tempdir(), "sample_base_96_mutational_matrix.txt")
## file.show(output_file)