Calculate pairwise divergence between sets of individualsSource:
Calculate pairwise divergence between sets of individuals
ts_divergence( ts, sample_sets, mode = c("site", "branch", "node"), windows = NULL, span_normalise = TRUE )
Tree sequence object of the class
A list (optionally a named list) of character vectors with individual names (one vector per set)
The mode for the calculation ("sites" or "branch")
Coordinates of breakpoints between windows. The first coordinate (0) and the last coordinate (equal to
ts$sequence_length) do not have to be specified as they are added automatically.
Divide the result by the span of the window? Default TRUE, see the tskit documentation for more detail.
For each pairwise calculation, either a single divergence value or a vector of divergence values (one for each window)
check_dependencies(python = TRUE) # make sure dependencies are present init_env() #> The interface to all required Python modules has been activated. # load an example model with an already simulated tree sequence slendr_ts <- system.file("extdata/models/introgression.trees", package = "slendr") model <- read_model(path = system.file("extdata/models/introgression", package = "slendr")) # load the tree-sequence object from disk ts <- ts_load(slendr_ts, model, mutate = TRUE, mutation_rate = 1e-8, random_seed = 42) # collect sampled individuals from all populations in a list sample_sets <- ts_samples(ts) %>% split(., .$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 0.0000442 #> 2 AFR NEA 0.000383 #> 3 EUR NEA 0.000387 #> 4 CH NEA 0.00412 #> 5 AFR CH 0.00415 #> 6 CH EUR 0.00416