tskitr.md

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Tskit and R

To interface with tskit in R, we can use the reticulate R package, which lets you call Python functions within an R session. In this short tutorial, we'll go through a couple of examples to show you how to get started. If you haven't done so already, you'll need to install reticulate in your R session via install.packages("reticulate").

We'll begin by simulating a small tree sequence using msprime.

msprime <- reticulate::import("msprime")

ts <- msprime$sim_ancestry(10, sequence_length = 100, random_seed=42)
ts

reticulate allows us to access a Python object's attributes or call its methods via the $ operator. For example, we can access (and assign to a variable) the number of samples in the tree sequence:

n <- ts$num_samples
n

We can also use tskit's powerful Statistics framework to efficiently compute many different summary statistics from a tree sequence. To illustrate this, we'll first add some mutations to our tree sequence with msprime's sim_mutations function, and then compute the genetic diversity for each of the tree sequence's sample nodes:

ts_mut = msprime$sim_mutations(ts, rate = 1e-2, random_seed = 42)
ts_mut$num_mutations
ts_mut$diversity()

As a final example, we can also use the tree sequence genotype_matrix() method to return the genotypes of the the tree sequence as a matrix object in R.

G = ts_mut$genotype_matrix()
G

We can then use R functions directly on the genotype matrix:

allele_frequency = rowMeans(G)
allele_frequency

It's as simple as that! Be sure to check out the reticulate documentation, in particular on Calling Python from R, which includes important information on how R data types are converted to their equivalent Python types.