Calculate pairwise divergence between sets of individuals

ts_divergence(
  ts,
  sample_sets,
  mode = c("site", "branch", "node"),
  windows = NULL,
  span_normalise = 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)

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) do not have to be specified as they are added automatically.

span_normalise

Divide the result by the span of the window? Default TRUE, see the tskit documentation for more detail.

Value

For each pairwise calculation, either a single divergence value or a vector of divergence values (one for each window)

Examples

check_dependencies(python = TRUE) # make sure dependencies are present

# 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, mutate = TRUE, mutation_rate = 1e-8, random_seed = 42)

# collect sampled individuals from all populations in a list
sample_sets <- ts_samples(ts) %>%
  split(., .$pop) %>%
  lapply(function(pop) pop$name)

# compute the divergence between individuals from each sample set (list of
# individual names generated in the previous step)
ts_divergence(ts, sample_sets) %>% .[order(.$divergence), ]
#> # A tibble: 6 × 3
#>   x     y     divergence
#>   <chr> <chr>      <dbl>
#> 1 AFR   EUR    0.0000436
#> 2 AFR   NEA    0.000412 
#> 3 EUR   NEA    0.000420 
#> 4 CH    NEA    0.00393  
#> 5 AFR   CH     0.00397  
#> 6 CH    EUR    0.00397