Run a slendr model in msprime — msprime • slendr

This function will execute a built-in msprime script and run a compiled slendr demographic model.

Usage

msprime(
  model,
  sequence_length,
  recombination_rate,
  samples = NULL,
  random_seed = NULL,
  verbose = FALSE,
  debug = FALSE,
  run = TRUE,
  path = NULL
)

Arguments

model: Model object created by the compile function
sequence_length: Total length of the simulated sequence (in base-pairs)
recombination_rate: Recombination rate of the simulated sequence (in recombinations per basepair per generation)
samples: A data frame of times at which a given number of individuals should be remembered in the tree-sequence (see schedule_sampling for a function that can generate the sampling schedule in the correct format). If missing, only individuals present at the end of the simulation will be recorded in the final tree-sequence file.
random_seed: Random seed (if NULL, a seed will be generated between 0 and the maximum integer number available)
verbose: Write the log information from the SLiM run to the console (default FALSE)?
debug: Write msprime's debug log to the console (default FALSE)?
run: Should the msprime engine be run? If FALSE, the command line msprime command will be printed (and returned invisibly as a character vector) but not executed.
path: Path to the directory where simulation result files will be saved. If NULL, this directory will be automatically created as a temporary directory. If TRUE, this path will be also returned by the function. If a string is given, it is assumed to be a path to a directory where simulation results will be saved. In this case, the function will return this path invisibly. Note that if a tree-sequence file should be simulated (along with other files, potentially), that tree-sequence file (named 'msprime.trees' by default) will have to be explicitly loaded using ts_read().

Value

A tree-sequence object loaded via Python-R reticulate interface function ts_read

(internally represented by the Python object tskit.trees.TreeSequence). If the path argument was set, it will return the path as a single-element character vector.

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
model <- read_model(path = system.file("extdata/models/introgression", package = "slendr"))

# afr and eur objects would normally be created before slendr model compilation,
# but here we take them out of the model object already compiled for this
# example (in a standard slendr simulation pipeline, this wouldn't be necessary)
afr <- model$populations[["AFR"]]
eur <- model$populations[["EUR"]]
chimp <- model$populations[["CH"]]

# schedule the sampling of a couple of ancient and present-day individuals
# given model at 20 ky, 10 ky, 5ky ago and at present-day (time 0)
modern_samples <- schedule_sampling(model, times = 0, list(afr, 10), list(eur, 100), list(chimp, 1))
ancient_samples <- schedule_sampling(model, times = c(40000, 30000, 20000, 10000), list(eur, 1))

# sampling schedules are just data frames and can be merged easily
samples <- rbind(modern_samples, ancient_samples)

# run a simulation using the msprime back end from a compiled slendr model object
ts <- msprime(model, sequence_length = 1e5, recombination_rate = 0, samples = samples)

# simulated tree-sequence object can be saved to a file using ts_write()...
ts_file <- normalizePath(tempfile(fileext = ".trees"), winslash = "/", mustWork = FALSE)
ts_write(ts, ts_file)
# ... and, at a later point, loaded by ts_read()
ts <- ts_read(ts_file, model)

summary(ts)
#> ╔═══════════════════════════╗
#> ║TreeSequence               ║
#> ╠═══════════════╤═══════════╣
#> ║Trees          │          1║
#> ╟───────────────┼───────────╢
#> ║Sequence Length│     100000║
#> ╟───────────────┼───────────╢
#> ║Time Units     │generations║
#> ╟───────────────┼───────────╢
#> ║Sample Nodes   │        230║
#> ╟───────────────┼───────────╢
#> ║Total Size     │   39.4 KiB║
#> ╚═══════════════╧═══════════╝
#> ╔═══════════╤════╤═════════╤════════════╗
#> ║Table      │Rows│Size     │Has Metadata║
#> ╠═══════════╪════╪═════════╪════════════╣
#> ║Edges      │ 471│ 14.7 KiB│          No║
#> ╟───────────┼────┼─────────┼────────────╢
#> ║Individuals│ 115│  3.2 KiB│          No║
#> ╟───────────┼────┼─────────┼────────────╢
#> ║Migrations │   0│  8 Bytes│          No║
#> ╟───────────┼────┼─────────┼────────────╢
#> ║Mutations  │   0│ 16 Bytes│          No║
#> ╟───────────┼────┼─────────┼────────────╢
#> ║Nodes      │ 472│ 12.9 KiB│          No║
#> ╟───────────┼────┼─────────┼────────────╢
#> ║Populations│   4│338 Bytes│         Yes║
#> ╟───────────┼────┼─────────┼────────────╢
#> ║Provenances│   1│  3.0 KiB│          No║
#> ╟───────────┼────┼─────────┼────────────╢
#> ║Sites      │   0│ 16 Bytes│          No║
#> ╚═══════════╧════╧═════════╧════════════╝
#>