02-treesiftr-advanced.Rmdtreesiftr is an R package for visualizing the relationship between phylogenetic trees and phylogenetic data. Phylogenetic trees are crucial to the study of comparative biology, taxonomy, and evolution. However, understanding how to read a phylogenetic tree, and how a phylogenetic tree relates to underlying phylogenetic data, remain challenging.
In today’s lab exercise, we will learn about a phylogenetic matrix, and then use the data in the matrix to visualize a phylogenetic tree.
The data matrix included with treesiftr is a matrix of binary (“0” and “1”) characters compiled to estimate a topology of living and extinct bear species [@abella12]. This matrix is fairly typical in size for a paleontological matrix, comprising 62 characters. It is, however, atypically complete, with only 18% missing data. In the following exercises, missing data will be represented by a thin black line. The “0” state will be represented in pale blue, and the “1” in brown.
treesiftr works by subsetting a phylogenetic matrix by user input. Then, a parsimony tree is constructed in Phangorn from the user-defined subset. The tree is scored under both parsimony and Lewis’ Mk model for discrete character data. The data and tree are then visualized using ggtree, based upon the ggplot2 package. This application makes use of Shiny to provide a graphical interface, but there is a second included tutorial for more experienced users of the R statistical language.
Currently, treesiftr can be installed via the devtools install_github function.
## Skipping install of 'treesiftr' from a github remote, the SHA1 (ec07434a) has not changed since last install.
## Use `force = TRUE` to force installationThe first step to making a treesiftr visualization is to select the subset of the phylogenetic matrix that we would like to visualize. This is performed via a function called generate_sliding. The below command will subset the
aln_path <- "data/bears_fasta.fa"
bears <- read_alignment(aln_path)
tree <- read.tree("data/starting_tree.tre")
sample_df <- generate_sliding(bears, start_char = 1, stop_char = 5, steps = 1)The result of this is a dataframe, shown below:
## starting_val stop_val step_val
## 1 1 2 1
## 2 2 3 1
## 3 3 4 1
## 4 4 5 1
## 5 5 6 1This dataframe dispays the start character (the first character that will be visualized) and stop character (the final character that will be visualized).
We can then build trees from each subset:
output_vector <- generate_tree_vis(sample_df = sample_df, alignment = aln_path, tree = tree, phy_mat = bears)
output_vectorPhangorn requires a starting tree to estimate a parsimony tree. We specify the tree we read in earlier for this purpose. pscore and lscore are optional arguments to view the parsimony and likelihood scores of the estimated trees. Remove these arguments before trying the exercises below.
output_vector <- generate_tree_vis(sample_df = sample_df, alignment = aln_path, tree = tree, phy_mat = bears,
p_score = TRUE)
output_vectorVisualize characters 2 and 3. What monophyletic group from tree 1 is no longer on this tree?
What is the parsimony score of the 31-34 character set? Click “Do you want to print the parsimony score?” in the infterface to check your answer.
Which character, 8, 9 or 10, represents a reveral?
What information would we need to decide if the “1” state possesed by Zaragocyon_daamsi is an autapomorphy?
Do all characters with the same parsimony score have the same likelihood score? You can add the likelihood score to the visualization like so:
output_vector <- generate_tree_vis(sample_df = sample_df, alignment = aln_path, tree = tree, phy_mat = bears, pscore = TRUE, lscore = TRUE)
output_vectorCompare characters 46-49 and 47-50. Why does set 47-50 have a better likelihood than 46-49?
What is the relationship between the likelihood score and increasing the number of characters visualized?
What is the minimum number adding a character can add to the parsimony score?
These trees are fully resolved. Based on your exploration of the data, does this degree of resolution make sense?