slendr (version 1.1.0)

ts_simplify: Simplify the tree sequence down to a given set of individuals

Description

This function is a convenience wrapper around the simplify method implemented in tskit, designed to work on tree sequence data simulated by SLiM using the slendr R package.

Usage

ts_simplify(
  ts,
  simplify_to = NULL,
  keep_input_roots = FALSE,
  keep_unary = FALSE,
  keep_unary_in_individuals = FALSE,
  filter_nodes = TRUE
)

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.

Arguments

ts

Tree sequence object of the class slendr_ts

simplify_to

A character vector of individual names. If NULL, all explicitly remembered individuals (i.e. those specified via the schedule_sampling function will be left in the tree sequence after the simplification.

keep_input_roots

Should the history ancestral to the MRCA of all samples be retained in the tree sequence? Default is FALSE.

keep_unary

Should unary nodes be preserved through simplification? Default is FALSE.

keep_unary_in_individuals

Should unary nodes be preserved through simplification if they are associated with an individual recorded in the table of individuals? Default is FALSE. Cannot be set to TRUE if keep_unary is also TRUE

filter_nodes

Should nodes be reindexed after simplification? Default is TRUE. See tskit's documentation for the Python method simplify()

Details

The simplification process is used to remove redundant information from the tree sequence and retains only information necessary to describe the genealogical history of a set of samples.

For more information on how simplification works in pyslim and tskit, see the official documentation at https://tskit.dev/tskit/docs/stable/python-api.html#tskit.TreeSequence.simplify and https://tskit.dev/pyslim/docs/latest/tutorial.html#simplification.

A very clear description of the difference between remembering and retaining and how to use these techniques to implement historical individuals (i.e. ancient DNA samples) is in the pyslim documentation at https://tskit.dev/pyslim/docs/latest/tutorial.html#historical-individuals.

See Also

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

Examples

check_dependencies(python = TRUE, quit = TRUE) # dependencies must be present

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"))

ts <- ts_read(slendr_ts, model)
ts

# simplify tree sequence to sampled individuals
ts_simplified <- ts_simplify(ts)

# simplify 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"))

ts_small