Coalescent simulation refers to the idea of simulating the evolution of
biological sequences like DNA by tracing their ancestry back in time. The
coala package is an interface for calling a number of commonly used
coalescent simulators from R. It should be able to use the simulator
scrm out of the box. Other simulators need to be installed separately
and must be activated explicitly. This is described in the
vignette("coala-install", package = "coala")
In this introduction we will stick to using
In order to conduct simulations, we first need to specify the components of
the simulation model. The function
coal_model creates a basic coalescent
library(coala) model <- coal_model(sample_size = 3, loci_number = 1)
This creates a basic model with one population of constant size. One genetic locus for three haplotypes is sampled from the population.
Printing the model gives a short summary of this content:
Models consist of
In order to simulate the model we need to add a few more features and at least one summary statistic.
For simulating sequences, we need to add mutations to the model. To do so,
we create a corresponding feature using
feat_mutation and add it to the
existing model using the plus operator:
model <- model + feat_mutation(rate = 1, model = "IFS") model
Now, mutations occur with rate 1 and according to an infinite-sites mutation
model (IFS). Details of rate and mutation model are given in the help
page of the feature (
Coala currently support the features
funcs <- ls("package:coala") funcs[grep("^feat_", funcs)]
However, not all combination of all features might be possible. Please refer to the features help pages for detailed information.
If we build a model consisting of multiple populations, we need to state the sample sizes as a vector of sample sizes for the different populations. The lines,
model <- coal_model(sample_size = c(5, 2, 0), loci_number = 1) + feat_migration(rate = 0.5, symmetric = TRUE) + feat_pop_merge(0.5, 3, 2) + feat_pop_merge(0.8, 2, 1)
create a model of three populations, with a symmetric migration rate of
between them. When viewed backwards in time, population 3 merges into population 2
0.5 coalescent time units in the past and population 2 into population 1
0.3 time units further into the past. Looking forwards in time, this
represents two speciation events with migration going on afterwards. At time
five haploids are sampled from population 1 and two from population 2. Please
note that sample sizes for all populations must be given, even if no haploid
is sampled from a population, as it is the case for population 3 here.
Adding summary statistics works in a similar fashion as adding features:
model <- coal_model(3, 1) + feat_mutation(rate = 1) + sumstat_seg_sites() model
This adds the segregating sites summary statistic to the model, which is a
basic summary statistic in population genetics. Again, refer to
?sumstat_seg_sites for details.
Available summary statistics are:
Now we can simulate the model. The printed output of a model contains information which program will be used for the simulation and which arguments will be used. As coala is in an early stage, please make sure to always check both.
simulate will call the program with the printed options, parse its
output and calculated the added summary statistics:
sumstats <- simulate(model, seed = 123)
The returned object
sumstats is a list, in which each entry corresponds to one
summary statistic. As there is only one summary statistic in our model,
the list has only one entry:
The structure in
sumstats$seg_sites is given by the segregating sites
statistic. It is again a list, where each entry represents one locus. For each
locus, it contains a matrix as specified in
If we want to have more loci in a model, we can add them using the
functions. The most basic option is to add an additional locus with a different
model <- model + locus_single(500) model
Now the model consists of two loci, the first with length 1000, the second with 500. Simulation now produces a segregating sites list with two entries corresponding to the loci:
sumstats <- simulate(model) sumstats$seg_sites[] sumstats$seg_sites[]
Another possibility is to add multiple loci with the same length using
locus_averaged, which gives better performance than adding the loci
one by one. For example
model <- model + locus_averaged(2, 750) sumstats <- simulate(model) length(sumstats$seg_sites)
adds two more loci with length of 750bp to the model.
So far, we have used a model without parameters that can vary between simulations. In particular for fitting a model to data via ABC or Jaatha, it is useful to add parameters to a previous model instead of creating a new model for each simulation.
Named parameters values can be specified in the simulation command. If we want, for example, to launch simulations for a model with different values of the mutation rate, we can use a named parameter:
model <- coal_model(5, 1) + feat_mutation(rate = par_named("theta")) + sumstat_seg_sites() sumstats1 <- simulate(model, pars = c(theta = 2.5)) sumstats2 <- simulate(model, pars = c(theta = 4.3))
A parameter distributed according to a prior can be specified using the
par_prior function. The function's first argument is a name for the parameter,
the second an expression that, when evaluated, produces a sample from the
So if we want the mutation to follow a uniform distribution between
10, we can use:
model <- coal_model(5, 1) + feat_mutation(rate = par_prior("theta", runif(1, 0, 10))) + sumstat_seg_sites() sumstats <- simulate(model) sumstats$pars sumstats2 <- simulate(model) sumstats2$pars
For simulations that will we used for parameter inference with the R
jaatha, you need to give a range of possible values for each
parameter. This is done using
par_range. For instance
model <- coal_model(5, 1) + feat_mutation(rate = par_range("theta", 0.1, 5)) + sumstat_seg_sites()
sets a possible range from 0.1 to 5 for the mutation rate. The actual
rate is given in the
simulate function, just as with named parameters.
Finally, there is a very powerful type of parameters generated with
Similar to parameters with priors, the value of the parameter is given as an
R expression, which is evaluated before simulation. Unlinke
this expression can contain other named parameters. For example
model <- coal_model(4, 2) + feat_mutation(rate = par_named("theta")) + feat_recombination(rate = par_expr(theta * 2))
creates a model with a a recombination rate that always is twice as high as the mutation rate.
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