How to estimate the fixation index, FST, to test for population differentiation in R
I was looking for a tutorial to estimate FST for my microbial (aka haploid) dataset but sadly there was no specific instruction, although other researchers are definitely looking for it (see here). Sadly, one respondent said that “there is no way to estimate Fst for microbial data”. However, ultimately I found a “way” to estimate Fst, and here is a quick R tutorial.
The fixation index, otherwise known as FST, is population differentiation due to genetic structure and is one of the fundamental concepts in population genetics. Essentially, FST is the proportion of the total genetic variance contained in a subpopulation (the S-subscript) compared to the total genetic variance (the T-subscript). Its value can range from 0 to 1. Higher FST indicates a considerable degree of differentiation among populations. The figure below illustrates what it means for two populations to have very different genetic structures.
Tanglegrams are co-phylogeny which is a very powerful visualization tool to examine co-evolution. Here is a tutorial on how to make them in R.
Tanglegram is a representation of co-phylogeny where two phylogenetic trees are linked. This method is super useful to visualize common traits shared by both trees. For example, it can be used to visualize host-pathogen (or host-symbiotic) evolution and visualize if there is any phylogenetic concordance between the two phylogenetic trees.
I was in need to visualize co-phylogeny of phylogenetic tree reconstructed from chromosomal and symbiotic genes. Surprisingly, I didn’t find any strait-forward solution in R that can be used for drawing tanglegram. Particularly I wanted to leverage the beautiful ggtree library. After trying out several methods, I found the following approach works well for me so far.
In this post, I’m going to use two toy trees with the following Newick format. Note that they have the same isolate, but different tree-topology (since supposedly different gene-set were used to reconstruct them).