Simulation lecture for PopGen 2022

You can find the slides here.

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

This README summarizes steps needed to set up your machine for the lecture and exercises. After you're done installing everything, make sure to run a small testing simulation (code below) to know that everything works as needed.

Installation instructions

Cloud-based RStudio session (updated on 2022-08-24)

The course has a fancy cloud-based RStudio session which you can run in your browser. If you can't run the installation instructions below on your own machine use the browser-based RStudio. Otherwise I strongly recommend using your own laptop because it might be much faster.

On-site participants: http://ricco.popgen.dk:3838
Remote participants: http://cloud.popgen.dk:8787

Setting up your local machine

Prerequisites

Working R installation(at least R 3.6, preferably 4.x)
RStudio highly recommended
macOS or Linux (but Windows should work too! see the bottom of this page)

slendr simulation package

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

First, run this in your R console:

install.packages("slendr")

Assuming the above runs successfully, you will next need to setup a dedicated Python environment with tools we'll be using for simulation and analysis. To do this, slendr provides a helper function setup_env() that takes care of everything on your behalf.

First, load slendr itself.

library(slendr)

This will very likely write a message that you are:

missing SLiM -- this is OK, feel free to ignore this
missing a Python environment.

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()

Other R package dependencies

I will use some tidyverse packages for analysis and plotting.

I recommend you install the following packages:

install.packages(c("tidyverse", "MASS"))

Note that tidyverse is a huge collection of packages so the installation might take a bit of time.

Testing the setup

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

library(slendr)

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 = 10e6, recombination_rate = 1e-8)

ts

If this runs without error and you get a small summary table from the ts object, you're all set!

IF THE ABOVE DOESN'T WORK

Reach out to the organizes (ideally Fernando) and ask them to get help from me (Martin P.). Or find me in person in the course (I'll be around Wednesday to Friday).

The slendr software I will be teaching is currently fully supported on macOS and Linux. However, the parts we'll be covering in the lecture and exercises should also work on Windows. The only thing that will not work on Windows for the moment are spatial slendr simulations using SLiM but we won't be using those in the course, so this is not a problem.

isinaltinkaya / popgen2022