Compute the allele frequency spectrum (AFS)Source:
This function computes the AFS with respect to the given set of individuals or nodes.
ts_afs( ts, sample_sets = NULL, mode = c("site", "branch", "node"), windows = NULL, span_normalise = FALSE, polarised = TRUE )
Tree sequence object of the class
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.
The mode for the calculation ("sites" or "branch")
Coordinates of breakpoints between windows. The first coordinate (0) and the last coordinate (equal to
ts$sequence_length) are added automatically)
Argument passed to tskit's
When TRUE (the default) the allele frequency spectrum will not be folded (i.e. the counts will assume knowledge of which allele is ancestral, and which is derived, which is known in a simulation)
Allele frequency spectrum values for the given sample set. Note that the contents of the first and last elements of the AFS might surprise you. Read the links in the description for more detail on how tskit handles things.
For more information on the format of the result and dimensions, in
particular the interpretation of the first and the last element of the AFS
complete = TRUE), please see the tskit manual at
https://tskit.dev/tskit/docs/stable/python-api.html and the example
section dedicated to AFS at
check_dependencies(python = TRUE, quit = TRUE) # dependencies must be present init_env() #> The interface to all required Python modules has been activated. # load an example model with an already simulated tree sequence slendr_ts <- system.file("extdata/models/introgression.trees", package = "slendr") model <- read_model(path = system.file("extdata/models/introgression", package = "slendr")) # load the tree-sequence object from disk ts <- ts_load(slendr_ts, model) %>% ts_mutate(mutation_rate = 1e-8, random_seed = 42) samples <- ts_samples(ts) %>% .[.$pop %in% c("AFR", "EUR"), ] # compute AFS for the given set of individuals ts_afs(ts, sample_sets = list(samples$name)) #>  1123 4 2 0 0 1 0 0 1 0 20 0 0 0 0 #>  0 0 0 0 0 1033