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This function calculates expected relative rates of trinucleotide-context-specific SNV mutations within tumors by attributing SNVs to mutational processes represented in mutation signature sets (such as "COSMIC v3.2"). Signature extraction can be done with MutationalPatterns (default) or deconstructSigs. Tumors with targeted sequencing data are assigned the average trinucleotide mutation rates calculated across all exome/genome data, which means that you need at least some exome or genome data to run.

Usage

trinuc_mutation_rates(
  cesa,
  signature_set = NULL,
  signature_exclusions = character(),
  samples = character(),
  cores = 1,
  signature_extractor = "MutationalPatterns",
  mp_strict_args = list(),
  bootstrap_mutations = FALSE,
  assume_identical_mutational_processes = FALSE,
  sample_group = NULL,
  sig_averaging_threshold = 50,
  signatures_to_remove = NULL
)

Arguments

cesa

CESAnalysis object

signature_set

Name of built-in signature set (see list_ces_signature_sets()), or a custom signature set (see details)

signature_exclusions

Specify any signatures to exclude from analysis; use suggest_cosmic_signature_exclusions() for advice on COSMIC signatures

samples

Which samples to include in the current run. Defaults to all samples. Can be a vector of Unique_Patient_Identifiers, or a data.table containing rows from the CESAnalysis sample table.

cores

How many cores to use for processing tumors in parallel.

signature_extractor

One of "MutationalPatterns" (default) or "deconstructSigs".

mp_strict_args

Named list of arguments to pass to MutationalPatterns' fit_to_signatures_strict function. Note that mut_matrix and signatures arguments are generated automatically, and that if you'd rather not use the strict method, you can emulate fit_to_signatures() by setting max_delta = 0.

bootstrap_mutations

T/F (default FALSE). Instead of using actual SNV counts for the samples, do a single bootstrap sampling of each sample (in other words, run MutationalPatterns::fit_to_signatures_bootstrapped() with n_boot=1). This can be useful if you intend to run this function multiple times to get a distribution of signature attributions and trinuc rates (and downstream cancer effect sizes). This option may be replaced with more thorough support for bootstrapping in the future.

assume_identical_mutational_processes

use well-mutated tumors (those with number of eligible mutations meeting sig_averaging_threshold) to calculate group average signature weights, and assign these (and implied trinucleotide mutation rates) to all tumors

sig_averaging_threshold

Mutation prevalence threshold (default 50) that determines which tumors inform the calculation of group-average signature weights. When assume_identical_mutational_processes == FALSE (the default), these group averages are blended into the signature weights of sub-threshold tumors.

signatures_to_remove

Deprecated; use the renamed argument signature_exclusions.

Value

CESAnalysis with sample-specific signature weights and inferred trinucleotide-context-specific relative mutation rates. The snv_counts matrix gives the counts of SNVs in each trinucleotide context for all samples in the CESAnalysis. (While recurrent mutations are excluded from signature analysis in trinuc_mutation_rates(), they are present in snv_counts for completeness.) The snv_counts matrix, produced by `trinuc_snv_counts()`, can be fed directly into MutationalPatterns if you wish to run your own extended signature analysis.

The raw_attributions table contains signature attributions as produced by MutationalPatterns or deconstructSigs. The biological_weights table has several differences:

  • Weights for signatures associated with artifactual (as opposed to biological) processes are set to zero.

  • The remaining weights are normalized to sum to 1.

  • Tumors with few mutations (defined by `sig_averaging_threshold`, default = 50) have their weights redefined using a blend of their original weights and weights derived from running signature extraction en masse on tumors with above-threshold mutation counts. These samples are identifiable by filtering the table on `group_avg_blended == TRUE`, and we recommend excluding them from most downstream signature analysis. (These weights are useful as a best guess of mutational processes, but they shouldn't be reported in any way that implies their independence from the group-average weights.)

    Biological weights can be interpreted as follows: Out of all the mutations caused by biological processes represented in the signatures, the proportion of mutations attributed to given signature is its weight.

    Either signature attributions table can be converted into the matrix format used by MutationalPatterns with `convert_signature_weights_for_mp()`.

Details

To reduce the influence of selection on the estimation of relative trinucleotide mutation rates, only non-recurrent SNVs (those that do not appear in more than one sample in the current run) are used.

A custom signature set should be given as a named three-item list, where "signatures" is a pure data.frame with signature definitions, "name" is a 1-length character naming the set, and "metadata" is a data.table with a "Signature" column that matches rownames in the signature definitions. The following columns allow special functionality:

  • Etiology: Known or hypothesized mutational processes underlying the signature. Used for human-readable tables and plots, so best to enter something like "Unknown" rather than leaving any entries empty or NA

  • Likely_Artifact (logical T/F): Marks signatures that are believed to derive from sample processing, sequencing, calling error, or other non-biological sources. cancereffectsizeR adjusts for artifact signatures when inferring relative trinucleotide mutation rates.

  • Exome_Min: Minimum number of mutations a WES sample must have for the presence of the signature to be plausible. This information is used to prevent hypermutation signatures from being found in tumors with few mutations. Can be left NA or 0 for non-hypermutation signatures. If this column is present, Genome_Min must be present and always greater than or equal to Exome_Min.

  • Genome_Min: Minimum number of mutations a WGS sample must have for the presence of the signature to be plausible. This information is used to prevent hypermutation signatures from being found in tumors with few mutations. Can be left NA or 0 for non-hypermutation signatures. If this column is present, Exome_Min must be present and always less than or equal to Genome_Min.

If you don't have any metadata available for your signature set, an empty data table can also be supplied. For a template signature set object, run sig_set = get_ces_signature_set("ces.refset.hg19", "COSMIC_v3.2").