coala-intro.Rmd

---
title: "Introduction to Coala"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{Introduction to Coala}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

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 `installation`
vignette:

```{r installation, eval=FALSE}
vignette("coala-install", package = "coala")
```

In this introduction we will stick to using `scrm`.

# Creating a model
In order to conduct simulations, we first need to specify the components of
the simulation model. The function `coal_model` creates a basic coalescent
model:

```{r create_model}
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:
```{r print_model}
model
```

Models consist of 

* _features_, which represent evolutionary forces and events present in the 
  model, 
* _parameters_, which are the model parameters, 
* _loci_, which describe the genetic regions that are simulated and 
* _summary statistics_, which describe the format of the simulated data.

In order to simulate the model we need to add a few more features and at least
one summary statistic.


# Adding Features
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:

```{r}
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 (`?feat_mutation`). 

Coala currently support the features

```{r echo=FALSE}
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.

## Multiple Populations

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,

```{r}
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 `0.5`
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 `0`
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

Adding summary statistics works in a similar fashion as adding features:

```{r}
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:

```{r echo=FALSE}
funcs[grep("^sumstat_", funcs)]
```



# Simulating the model

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. 

The
function `simulate` will call the program with the printed options, parse its
output and calculated the added summary statistics:

```{r}
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:

```{r}
names(sumstats)
```

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 `?sumstat_seg_sites`:

```{r}
sumstats$seg_sites[[1]]
```


# Adding Loci
If we want to have more loci in a model, we can add them using the `locus_` 
functions. The most basic option is to add an additional locus with a different
length:

```{r}
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:

```{r}
sumstats <- simulate(model)
sumstats$seg_sites[[1]]
sumstats$seg_sites[[2]]
```

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
```{r}
model <- model + locus_averaged(2, 750)
sumstats <- simulate(model)
length(sumstats$seg_sites)
```
adds two more loci with length of 750bp to the model.



# Adding Parameters
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
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:

```{r}
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))
```



### Parameters with Priors
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
prior distribution. 

So if we want the mutation to follow a uniform distribution between 
`0` and `10`, we can use:

```{r priors}
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
```



### Parameter Ranges
For simulations that will we used for parameter inference with the R
package `jaatha`, you need to give a range of possible values for each 
parameter. This is done using `par_range`. For instance

```{r}
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.


### Expressions
Finally, there is a very powerful type of parameters generated with `par_expr`.
Similar to parameters with priors, the value of the parameter is given as an
R expression, which is evaluated before simulation. Unlinke `par_prior`,
this expression can contain other named parameters. For example 

```{r}
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.