/mpieva-slendr-2023

Gentle introduction to population genetic simulations at MPI EVA (2023)

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slendr simulation workshop at MPI EVA (2023)

You can find the slides here.

Here is a render of the slides as a single HTML page (easier for reference).

This README describes how to set up your machine for the practical workshop. After you're done installing everything, make sure to run a small testing simulation (here) to make sure that everything works as needed.

Installation instructions

Prerequisites

  • Working R installation(at least R 3.6, preferably 4.x)
  • RStudio highly recommended
  • macOS / Linux / Windows (the latter won't support SLiM-based simulations, but that's OK!)

slendr simulation package

Getting slendr to work is critical. The whole workshop is dedicated to this package.

You can install slendr with this:

install.packages("slendr")

After installation, load slendr itself.

library(slendr)

This will very likely write a message that:

  1. you are missing SLiM -- this is OK, feel free to ignore this

  2. you are missing a Python environment -- we will fix this in the next step.

Next, run the following command. This will ask for permission to install an isolated Python mini-environment just for slendr -- this won't affect your own Python setup at all, so don't be afraid to confirm this!

setup_env()

Finally, make sure you get a positive confirmation from the following check:

check_env()

On future occasions, you will have to call init_env() after running library(slendr)!

Testing the setup

Copy the following script to your R session after you successfully installed your R dependencies as described above.

First run this:

library(slendr)
init_env()

Followed by this:

o <- population("outgroup", time = 1, N = 100)
b <- population("b", parent = o, time = 500, N = 100)
c <- population("c", parent = b, time = 1000, N = 100)
x1 <- population("x1", parent = c, time = 2000, N = 100)
x2 <- population("x2", parent = c, time = 2000, N = 100)
a <- population("a", parent = b, time = 1500, N = 100)

gf <- gene_flow(from = b, to = x1, start = 2100, end = 2150, rate = 0.1)

model <- compile_model(
  populations = list(a, b, x1, x2, c, o), gene_flow = gf,
  generation_time = 1, simulation_length = 2200
)

ts <- msprime(model, sequence_length = 1e6, recombination_rate = 1e-8)

ts_samples(ts)

If this runs without error and you get a small summary table with simulated samples, you're all set!

Other R package dependencies

I will use some tidyverse packages for analysis and plotting.

I recommend you install the following packages:

install.packages(c("dplyr", "ggplot2", "cowplot", "magrittr"))

Python+RStudio issue

RStudio sometimes interferes with Python setup needed for simulation. To fix this, go to Tools -> Global Options in RStudio and set the following options:

This work is licensed under a Creative Commons Attribution 4.0 International License.

CC BY 4.0