ts_load {slendr}

Load a tree sequence file produced by a given model

Description

This function loads a tree sequence file simulated from a given slendr model. Optionally, the tree sequence can be recapitated and simplified.

Usage

ts_load(file, model = NULL)

Arguments

file

A path to the tree-sequence file (either originating from a slendr model or a standard non-slendr tree sequence).

model

Optional slendr_model object which produced the tree-sequence file. Used for adding various annotation data and metadata to the standard tskit tree-sequence object.

Details

The loading, recapitation and simplification is performed using the Python module pyslim which serves as a link between tree sequences generated by SLiM and the tskit module for manipulation of tree sequence data. All of these steps have been modelled after the official pyslim tutorial and documentation available at: https://tskit.dev/pyslim/docs/latest/tutorial.html.

The recapitation and simplification steps can also be performed individually using the functions ts_recapitate and ts_simplify.

Value

Tree-sequence object of the class slendr_ts, which serves as an interface point for the Python module tskit using slendr functions with the ts_ prefix.

See Also

ts_nodes for extracting useful information about individuals, nodes, coalescent times and geospatial locations of nodes on a map

Examples

init_env()

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

# load tree sequence generated by a given model
ts <- ts_load(slendr_ts, model)

# even tree sequences generated by non-slendr models can be
msprime_ts <- system.file("extdata/models/msprime.trees", package = "slendr")
ts <- ts_load(msprime_ts)

# load tree sequence and immediately simplify it only to sampled individuals
# (note that the example tree sequence is already simplified so this operation
# does not do anything in this case)
ts <- ts_load(slendr_ts, model = model) %>% ts_simplify(keep_input_roots = TRUE)

# load tree sequence and simplify it to a subset of sampled individuals
ts_small <- ts_simplify(ts, simplify_to = c("CH_1", "NEA_1", "NEA_2",
                                            "AFR_1", "AFR_2", "EUR_1", "EUR_2"))

# load tree sequence, recapitate it and simplify it
ts <- ts_load(slendr_ts, model) %>%
  ts_recapitate(recombination_rate = 1e-8, Ne = 10000, random_seed = 42) %>%
  ts_simplify()

# load tree sequence, recapitate it, simplify it and overlay neutral mutations
ts <- ts_load(slendr_ts, model) %>%
  ts_recapitate(recombination_rate = 1e-8, Ne = 10000, random_seed = 42) %>%
  ts_simplify() %>%
  ts_mutate(mutation_rate = 1e-8)

ts