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Please note that demografr is still under active development. As a result, the interface (names of functions, names of function arguments, but even the functionality in general) does change on rather short notice and quite unpredictably. Before there is a first release of the R package on CRAN (and an associated preprint with it), you shouldn’t be using demografr for your work. If you’d like to be notified of updates and releases, click on on “Watch” on top of the main GitHub page. Thanks for understanding!
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The goal of demografr is to simplify and streamline the development of simulation-based inference pipelines in population genetics, such as Approximate Bayesian Computation (ABC) or parameter grid inferences, and make them more reproducible. demografr also aims to make the inferences orders of magnitude faster and more efficient by leveraging the tree sequences as an internal data structure and computation engine.
Unlike traditional ABC and other simulation-based approaches, which generally involve custom-built pipelines and scripts for population genetic simulation and computation of summary statistics, demografr makes it possible to perform simulation, data analysis, and the inference itself entirely in R within a single reproducible analysis script. By eliminating the need to write custom simulation code and scripting for integration of various population genetic tools for computing summary statistics, it lowers the barrier to entry for new users and facilitates reproducibility for all users regardless of their level of experience by eliminating many common sources of bugs.
demografr streamlines every step of a typical ABC pipeline by leveraging the slendr framework as a building block for simulation and data analysis, making it possible to write complete ABC workflows in R. Specifically:
You can install the development version of demografr from GitHub with:
devtools::install_github("bodkan/demografr")
Note that this requires an R package devtools, which you can obtain simply by running install.packages("devtools")
.
Because demografr is tightly linked to the slendr simulation package (in fact, new developments in slendr ale currently driven by requirements of demografr), you will also need the development version of slendr itself:
devtools::install_github("bodkan/slendr")
demografr is very much in an experimental stage at this point. Although ABC fitting of “standard” demographic models (i.e. estimating N_{e}, split times and gene-flow parameters for non-spatial models) already works very nicely, our long-term ambitions for the project are much higher and extend, for instance, towards inferences of spatial models. As such, please be aware that the interface might change on a rather short notice to accomodate features for estimating parameters of much more complex custom models.
If you want to follow updates on demografr, you can do this also on my Mastodon and by checking out the changelog from time to time.
You can open an RStudio session and test examples from the vignettes directly in your web browser by clicking this button (no installation is needed!):
In case the RStudio instance appears to be starting very slowly, please be patient (Binder is a freely available service with limited computational resources provided by the community). If Binder crashes, try reloading the web page, which will restart the cloud session.
Once you get a browser-based RStudio session, you can navigate to the vignettes/
directory and test the examples on your own!
Tying the results of demografr ABC inference (internally facilitated by the abc package) to various diagnostics features of abc and other tools.
Imagine that we sequenced genomes of individuals from populations “popA”, “popB”, “popC”, and “popD”.
Let’s also assume that we know that the three populations are phylogenetically related in the following way with an indicated gene-flow event at a certain time in the past, but we don’t know anything else (i.e., we have no idea about their N_{e} or split times):
After sequencing the genomes of individuals from these populations, we computed the nucleotide diversity in these populations as well as their pairwise genetic divergence, and observed the following values which we saved in two standard R data frames:
observed_diversity <- read.table(system.file("examples/observed_diversity.tsv", package = "demografr"), header = TRUE)
observed_diversity
#> set diversity
#> 1 popA 8.037847e-05
#> 2 popB 3.242467e-05
#> 3 popC 1.021123e-04
#> 4 popD 8.968777e-05
observed_divergence <- read.table(system.file("examples/observed_divergence.tsv", package = "demografr"), header = TRUE)
observed_divergence
#> x y divergence
#> 1 popA popB 0.0002387594
#> 2 popA popC 0.0002391843
#> 3 popA popD 0.0002389617
#> 4 popB popC 0.0001089125
#> 5 popB popD 0.0001155571
#> 6 popC popD 0.0001105323
observed_f4 <- read.table(system.file("examples/observed_f4.tsv", package = "demografr"), header = TRUE)
observed_f4
#> W X Y Z f4
#> 1 popA popB popC popD -3.433205e-06
#> 2 popA popC popB popD -7.125812e-07
#> 3 popA popD popB popC 2.720624e-06
This is how we would use demografr to estimate the N_{e} and split times for all populations (and the rate of the indicated gene-flow event) with Approximate Bayesian Computation in a single R script:
library(demografr)
library(slendr)
# set up the internal tskit/msprime environment
init_env()
# set up parallelization across all CPUs on the current machine
library(future)
plan(multisession, workers = availableCores())
#--------------------------------------------------------------------------------
# bind data frames with empirical summary statistics into a named list
observed <- list(
diversity = observed_diversity,
divergence = observed_divergence,
f4 = observed_f4
)
#--------------------------------------------------------------------------------
# define a model generating function using the slendr interface
# (each of the function parameters correspond to a parameter we want to infer)
model <- function(Ne_A, Ne_B, Ne_C, Ne_D, T_AB, T_BC, T_CD, gf_BC) {
# define populations
popA <- population("popA", time = 1, N = Ne_A)
popB <- population("popB", time = T_AB, N = Ne_B, parent = popA)
popC <- population("popC", time = T_BC, N = Ne_C, parent = popB)
popD <- population("popD", time = T_CD, N = Ne_D, parent = popC)
# define gene-flow events
gf <- gene_flow(from = popB, to = popC, start = 9000, end = 9301, rate = gf_BC)
# compile a slendr model
model <- compile_model(
populations = list(popA, popB, popC, popD), gene_flow = gf,
generation_time = 1, simulation_length = 10000,
direction = "forward", serialize = FALSE
)
# set up sampling schedule (2 diploid individuals from each population at
# the end of the simulation) -- this step is optional
samples <- schedule_sampling(
model, times = 10000,
list(popA, 2), list(popB, 2), list(popC, 2), list(popD, 2),
strict = TRUE
)
# a return statement is mandatory!
# if a sampling schedule is not generated, use return(model)
return(list(model, samples))
}
#--------------------------------------------------------------------------------
# setup priors for model parameters
priors <- list(
Ne_A ~ runif(1, 10000),
Ne_B ~ runif(1, 10000),
Ne_C ~ runif(1, 10000),
Ne_D ~ runif(1, 10000),
T_AB ~ runif(1, 10000),
T_BC ~ runif(1, 10000),
T_CD ~ runif(1, 10000),
gf_BC ~ runif(0, 1)
)
#--------------------------------------------------------------------------------
# define summary functions to be computed on simulated data (must be of the
# same format as the summary statistics computed on empirical data)
compute_diversity <- function(ts) {
samples <- ts_names(ts, split = "pop")
ts_diversity(ts, sample_sets = samples)
}
compute_divergence <- function(ts) {
samples <- ts_names(ts, split = "pop")
ts_divergence(ts, sample_sets = samples)
}
compute_f4 <- function(ts) {
samples <- ts_names(ts, split = "pop")
A <- samples["popA"]; B <- samples["popB"]
C <- samples["popC"]; D <- samples["popD"]
rbind(
ts_f4(ts, A, B, C, D),
ts_f4(ts, A, C, B, D),
ts_f4(ts, A, D, B, C)
)
}
# the summary functions must be also bound to an R list named in the same
# way as the empirical summary statistics
functions <- list(
diversity = compute_diversity,
divergence = compute_divergence,
f4 = compute_f4
)
#--------------------------------------------------------------------------------
# validate the individual ABC components for correctness and consistency
validate_abc(model, priors, functions, observed)
#--------------------------------------------------------------------------------
# run ABC simulations
data <- simulate_abc(
model, priors, functions, observed, iterations = 10000,
sequence_length = 50e6, recombination_rate = 1e-8, mutation_rate = 1e-8
)
#--------------------------------------------------------------------------------
# infer posterior distributions of parameters using the abc R package
abc <- run_abc(data, engine = "abc", tol = 0.01, method = "neuralnet")
After we run this R script, we end up with an object called abc
here. This object contains the complete information about the results of our inference. In particular, it carries the posterior samples for our parameters of interest (N_{e} of populations and their split times).
For instance, we can get a table of all posterior values with the function extract_summary()
:
extract_summary(abc)
#> Ne_A Ne_B Ne_C Ne_D T_AB
#> Min.: 804.0797 408.1848 -4305.207 502.5387 1607.479
#> Weighted 2.5 % Perc.: 902.1518 457.8242 6383.142 2259.9200 1665.661
#> Weighted Median: 2015.7613 1000.0325 8405.996 3714.8076 2010.293
#> Weighted Mean: 1997.8872 958.8280 8516.165 3852.1225 2016.526
#> Weighted Mode: 2195.5323 1067.1600 8619.141 3564.2851 1995.493
#> Weighted 97.5 % Perc.: 3434.1764 1563.1426 11309.784 6598.2645 2262.405
#> Max.: 3646.4941 1919.2377 11815.139 6641.5602 2450.616
#> T_BC T_CD gf_BC
#> Min.: 4843.964 7228.372 -0.21801869
#> Weighted 2.5 % Perc.: 4908.179 7286.378 -0.04095791
#> Weighted Median: 5936.615 7874.670 0.11279850
#> Weighted Mean: 5909.206 7907.132 0.11161160
#> Weighted Mode: 5832.770 7724.623 0.07205975
#> Weighted 97.5 % Perc.: 6651.473 8734.573 0.26094451
#> Max.: 6793.562 9936.191 1.41335586
We can also specify a subset of model parameters to select, or provide a regular expression for this subsetting:
extract_summary(abc, param = "Ne")
#> Ne_A Ne_B Ne_C Ne_D
#> Min.: 804.0797 408.1848 -4305.207 502.5387
#> Weighted 2.5 % Perc.: 902.1518 457.8242 6383.142 2259.9200
#> Weighted Median: 2015.7613 1000.0325 8405.996 3714.8076
#> Weighted Mean: 1997.8872 958.8280 8516.165 3852.1225
#> Weighted Mode: 2195.5323 1067.1600 8619.141 3564.2851
#> Weighted 97.5 % Perc.: 3434.1764 1563.1426 11309.784 6598.2645
#> Max.: 3646.4941 1919.2377 11815.139 6641.5602
We can also visualize the posterior distributions. Rather than plotting many different distributions at once, let’s first check out the posterior distributions of inferred N_{e} values:
plot_posterior(abc, param = "Ne")
Similarly, we can take a look at the inferred posteriors of the split times:
plot_posterior(abc, param = "T")
And, finally, the rate of gene flow:
plot_posterior(abc, param = "gf") + ggplot2::coord_cartesian(xlim = c(0, 1))
Finally, we have the diagnostic functionality of the abc R package at our disposal:
plot(abc, param = "Ne_C")
demografr also provides a couple of functions designed to make troubleshooting a little easier.
For instance, assuming we have priors
set up as above, we can visualize the prior distribution(s) like this:
plot_prior(priors, "Ne")
plot_prior(priors, c("^T", "^gf"), facet = TRUE)