feat_ignore_singletons: Feature: Ignore Singletons

In coala: A Framework for Coalescent Simulation

Feature: Ignore Singletons

Description

Mutations that are observed in just one haplotype ('singletons') are often regarded as likely candidates for sequencing errors. Sometimes, it can be advantageous to exclude them from an analysis. This feature removes all singletons from the simulated data before the summary statistics are calculated.

Usage

feat_ignore_singletons(locus_group = "all")

Arguments

locus_group

The loci for which this features is used. Can either be "all" (default), in which case the feature is used for simulating all loci, or a numeric vector. In the latter case, the feature is only used for the loci added in locus_ commands with the corresponding index starting from 1 in order in which the commands where added to the model. For example, if a model has locus_single(10) + locus_averaged(10, 11) + locus_single(12) and this argument is c(2, 3), than the feature is used for all but the first locus (that is locus 2 - 12).

Details

This function assumes that a singleton is a mutation for which the derived allele is observed exactly once in all sequences, regardless of the population structure.

Value

The feature, which can be added to a model created with coal_model using +.

See Also

For creating a model: coal_model

Other features: feat_growth(), feat_migration(), feat_mutation(), feat_outgroup(), feat_pop_merge(), feat_recombination(), feat_sample(), feat_selection(), feat_size_change(), feat_unphased()

Examples

model <- coal_model(2, 1) +
  feat_mutation(10) +
  feat_ignore_singletons() +
  sumstat_sfs("n_mut", transformation = sum)
# In this model, all mutations are singletons. Therefore,
# the number of mutations is 0 when removing singletons:
simulate(model)$n_mut

coala documentation built on Jan. 5, 2023, 5:11 p.m.