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Compute the allele frequency spectrum (AFS)

Source: R/tree-sequences.R
ts_afs.Rd

This function computes the AFS with respect to the given set of individuals or nodes.

Usage

ts_afs(
  ts,
  sample_sets = NULL,
  mode = c("site", "branch", "node"),
  windows = NULL,
  span_normalise = FALSE,
  polarised = TRUE
)

Arguments

ts

Tree sequence object of the class slendr_ts

sample_sets

A list (optionally a named list) of character vectors with individual names (one vector per set). If NULL, allele frequency spectrum for all individuals in the tree sequence will be computed.

mode

The mode for the calculation ("sites" or "branch")

windows

Coordinates of breakpoints between windows. The first coordinate (0) and the last coordinate (equal to ts$sequence_length) are added automatically)

span_normalise

Argument passed to tskit's allele_frequency_spectrum method

polarised

When TRUE (the default) the allele frequency spectrum will not be folded (i.e. the counts will assume knowledge of which allele is ancestral, and which is derived, which is known in a simulation)

Value

Allele frequency spectrum values for the given sample set. Note that the contents of the first and last elements of the AFS might surprise you. Read the links in the description for more detail on how tskit handles things.

Details

For more information on the format of the result and dimensions, in particular the interpretation of the first and the last element of the AFS (when complete = TRUE), please see the tskit manual at https://tskit.dev/tskit/docs/stable/python-api.html and the example section dedicated to AFS at https://tskit.dev/tutorials/analysing_tree_sequences.html#allele-frequency-spectra.

Examples

check_dependencies(python = TRUE, quit = TRUE) # dependencies must be present

init_env()
#> The interface to all required Python modules has been activated.

# load an example model with an already simulated tree sequence
slendr_ts <- system.file("extdata/models/introgression.trees", package = "slendr")
model <- read_model(path = system.file("extdata/models/introgression", package = "slendr"))

# load the tree-sequence object from disk
ts <- ts_load(slendr_ts, model) %>% ts_mutate(mutation_rate = 1e-8, random_seed = 42)

samples <- ts_samples(ts) %>% .[.$pop %in% c("AFR", "EUR"), ]

# compute AFS for the given set of individuals
ts_afs(ts, sample_sets = list(samples$name))
#>  [1] 1123    4    2    0    0    1    0    0    1    0   20    0    0    0    0
#> [16]    0    0    0    0    0 1033