ts_f2 {slendr}

Calculate the f2, f3, f4, and f4-ratio statistics

Description

These functions present an R interface to the corresponding f-statistics methods in tskit.

Usage

ts_f2(
  ts,
  A,
  B,
  mode = c("site", "branch", "node"),
  span_normalise = TRUE,
  windows = NULL
)

ts_f3(
  ts,
  A,
  B,
  C,
  mode = c("site", "branch", "node"),
  span_normalise = TRUE,
  windows = NULL
)

ts_f4(
  ts,
  W,
  X,
  Y,
  Z,
  mode = c("site", "branch", "node"),
  span_normalise = TRUE,
  windows = NULL
)

ts_f4ratio(
  ts,
  X,
  A,
  B,
  C,
  O,
  mode = c("site", "branch"),
  span_normalise = TRUE
)

Arguments

ts

Tree sequence object of the class slendr_ts

mode

The mode for the calculation ("sites" or "branch")

span_normalise

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

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.

W, X, Y, Z, A, B, C, O

Character vectors of individual names (largely following the nomenclature of Patterson 2021, but see crucial differences between tskit and ADMIXTOOLS in Details)

Details

Note that the order of populations f3 statistic implemented in tskit (https://tskit.dev/tskit/docs/stable/python-api.html#tskit.TreeSequence.f3) is different from what you might expect from ADMIXTOOLS, as defined in Patterson 2012 (see https://academic.oup.com/genetics/article/192/3/1065/5935193 under heading "The three-population test and introduction of f-statistics", as well as ADMIXTOOLS documentation at https://github.com/DReichLab/AdmixTools/blob/master/README.3PopTest#L5). Specifically, the widely used notation introduced by Patterson assumes the population triplet as f3(C; A, B), with C being the "focal" sample (i.e., either the outgroup or a sample tested for admixture). In contrast, tskit implements f3(A; B, C), with the "focal sample" being A.

Although this is likely to confuse many ADMIXTOOLS users, slendr does not have much choice in this, because its ts_*() functions are designed to be broadly compatible with raw tskit methods.

Value

Data frame with statistics calculated for the given sets of individuals

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 the tree-sequence object from disk and add mutations to it
ts <- ts_load(slendr_ts, model) %>% ts_mutate(mutation_rate = 1e-8, random_seed = 42)

# calculate f2 for two individuals in a previously loaded tree sequence
ts_f2(ts, A = "AFR_1", B = "EUR_1")

# calculate f2 for two sets of individuals
ts_f2(ts, A = c("AFR_1", "AFR_2"), B = c("EUR_1", "EUR_3"))

# calculate f3 for two individuals in a previously loaded tree sequence
ts_f3(ts, A = "EUR_1", B = "AFR_1", C = "NEA_1")

# calculate f3 for two sets of individuals
ts_f3(ts, A = c("AFR_1", "AFR_2", "EUR_1", "EUR_2"),
          B = c("NEA_1", "NEA_2"),
          C = "CH_1")

# calculate f4 for single individuals
ts_f4(ts, W = "EUR_1", X = "AFR_1", Y = "NEA_1", Z = "CH_1")

# calculate f4 for sets of individuals
ts_f4(ts, W = c("EUR_1", "EUR_2"),
          X = c("AFR_1", "AFR_2"),
          Y = "NEA_1",
          Z = "CH_1")

# calculate f4-ratio for a given set of target individuals X
ts_f4ratio(ts, X = c("EUR_1", "EUR_2", "EUR_4", "EUR_5"),
               A = "NEA_1", B = "NEA_2", C = "AFR_1", O = "CH_1")