330 lines
12 KiB
R
330 lines
12 KiB
R
# BIN-PHYLO-Tree_analysis.R
|
|
#
|
|
# Purpose: A Bioinformatics Course:
|
|
# R code accompanying the BIN-PHYLO-Tree_analysis unit.
|
|
#
|
|
# Version: 1.1
|
|
#
|
|
# Date: 2017 10 - 2019 01
|
|
# Author: Boris Steipe (boris.steipe@utoronto.ca)
|
|
#
|
|
# Versions:
|
|
# 1.1 Change from require() to requireNamespace(),
|
|
# use <package>::<function>() idiom throughout,
|
|
# use Biocmanager:: not biocLite()
|
|
# 1.0.2 Typo in variable name, style changes
|
|
# 1.0.1 Wrong section heading
|
|
# 1.0 First 2017 version
|
|
# 0.1 First code copied from 2016 material.
|
|
#
|
|
#
|
|
# TODO:
|
|
#
|
|
#
|
|
# == DO NOT SIMPLY source() THIS FILE! =======================================
|
|
#
|
|
# If there are portions you don't understand, use R's help system, Google for an
|
|
# answer, or ask your instructor. Don't continue if you don't understand what's
|
|
# going on. That's not how it works ...
|
|
#
|
|
# ==============================================================================
|
|
|
|
|
|
#TOC> ==========================================================================
|
|
#TOC>
|
|
#TOC> Section Title Line
|
|
#TOC> --------------------------------------------------
|
|
#TOC> 1 Preparation and Tree Plot 46
|
|
#TOC> 2 Tree Analysis 86
|
|
#TOC> 2.1 Rooting Trees 145
|
|
#TOC> 2.2 Rotating Clades 190
|
|
#TOC> 2.3 Computing tree distances 241
|
|
#TOC>
|
|
#TOC> ==========================================================================
|
|
|
|
|
|
# = 1 Preparation and Tree Plot ===========================================
|
|
|
|
|
|
if (! requireNamespace("ape", quietly = TRUE)) {
|
|
install.packages("ape")
|
|
}
|
|
# Package information:
|
|
# library(help = ape) # basic information
|
|
# browseVignettes("ape") # available vignettes
|
|
# data(package = "ape") # available datasets
|
|
|
|
|
|
# Read the species tree that you have created at the phyloT Website:
|
|
fungiTree <- ape::read.tree("fungiTree.txt")
|
|
|
|
plot(fungiTree)
|
|
|
|
# The tree produced by phyloT contains full length species names, but it would
|
|
# be more convenient if it had bicodes instead.
|
|
str(fungiTree)
|
|
|
|
# The species names are in a vector $tip.label of this list. We can use bicode()
|
|
# to shorten them - but note that they have underscores as word separators. Thus
|
|
# we will use gsub("-", " ", ...) to replace the underscores with spaces.
|
|
|
|
for (i in seq_along(fungiTree$tip.label)) {
|
|
fungiTree$tip.label[i] <- biCode(gsub("_", " ", fungiTree$tip.label[i]))
|
|
}
|
|
|
|
# Plot the tree
|
|
plot(fungiTree, cex = 1.0, root.edge = TRUE, no.margin = TRUE)
|
|
ape::nodelabels(text = fungiTree$node.label,
|
|
cex = 0.6,
|
|
adj = 0.2,
|
|
bg = "#D4F2DA")
|
|
# Note that you can use the arrow buttons in the menu above the plot to scroll
|
|
# back to plots you have created earlier - so you can reference back to the
|
|
# species tree.
|
|
|
|
|
|
# = 2 Tree Analysis =======================================================
|
|
|
|
|
|
# 1.1 Visualizing your tree
|
|
# The trees that are produced by Rphylip are stored as an object of class
|
|
# "phylo". This is a class for phylogenetic trees that is widely used in the
|
|
# community, practically all R phylogenetics packages will options to read and
|
|
# manipulate such trees. Outside of R, a popular interchange format is the
|
|
# Newick_format that you have seen above. It's easy to output your calculated
|
|
# trees in Newick format and visualize them elsewhere.
|
|
|
|
# The "phylo" class object is one of R's "S3" objects and methods to plot and
|
|
# print it have been defined with the Rphylip package, and in ape. You can
|
|
# simply call plot(<your-tree>) and R knows what to do with <your-tree> and how
|
|
# to plot it. The underlying function is plot.phylo(), and documentation for its
|
|
# many options can by found by typing:
|
|
|
|
?plot.phylo
|
|
|
|
# We load the APSES sequence tree that you produced in the
|
|
# BIN-PHYLO-Tree_building unit:
|
|
load(file = "APSEStreeRproml.RData")
|
|
|
|
plot(apsTree) # default type is "phylogram"
|
|
plot(apsTree, type = "unrooted")
|
|
plot(apsTree, type = "fan", no.margin = TRUE)
|
|
|
|
# rescale to show all of the labels:
|
|
# record the current plot parameters by assigning them to a variable ...
|
|
(tmp <- plot(apsTree, type="fan", no.margin = TRUE, plot=FALSE))
|
|
# ... and adjust the plot limits for a new plot:
|
|
plot(apsTree,
|
|
type = "fan",
|
|
x.lim = tmp$x.lim * 1.8,
|
|
y.lim = tmp$y.lim * 1.8,
|
|
cex = 0.8,
|
|
no.margin = TRUE)
|
|
|
|
# Inspect the tree object
|
|
str(apsTree)
|
|
apsTree$tip.label
|
|
apsTree$edge
|
|
apsTree$edge.length
|
|
|
|
# show the node / edge and tip labels on a plot
|
|
plot(apsTree)
|
|
ape::nodelabels()
|
|
ape::edgelabels()
|
|
ape::tiplabels()
|
|
|
|
# show the number of nodes, edges and tips
|
|
ape::Nnode(apsTree)
|
|
ape::Nedge(apsTree)
|
|
ape::Ntip(apsTree)
|
|
|
|
|
|
# Finally, write the tree to console in Newick format
|
|
ape::write.tree(apsTree)
|
|
|
|
# == 2.1 Rooting Trees =====================================================
|
|
|
|
# In order to analyse the tree, it is helpful to root it first and reorder its
|
|
# clades. Contrary to documentation, Rproml() returns an unrooted tree.
|
|
|
|
ape::is.rooted(apsTree)
|
|
|
|
# You can root the tree with the command root() from the "ape" package.
|
|
|
|
plot(apsTree)
|
|
|
|
# add labels for internal nodes and tips
|
|
ape::nodelabels(cex = 0.5, frame = "circle")
|
|
ape::tiplabels(cex = 0.5, frame = "rect")
|
|
|
|
# The outgroup of the tree is tip "11" in my sample tree, it may be a different
|
|
# number in yours. Substitute the correct node number below for "outgroup".
|
|
apsTree <- ape::root(apsTree, outgroup = 11, resolve.root = TRUE)
|
|
plot(apsTree)
|
|
ape::is.rooted(apsTree)
|
|
|
|
# This tree _looks_ unchanged, beacuse when the root trifurcation was resolved,
|
|
# an edge of length zero was added to connect the MRCA (Most Recent Common
|
|
# Ancestor) of the ingroup.
|
|
|
|
# The edge lengths are stored in the phylo object:
|
|
apsTree$edge.length
|
|
|
|
# ... and you can assign a small arbitrary value to the edge
|
|
# to show how it connects to the tree without having an
|
|
# overlap.
|
|
apsTree$edge.length[1] <- 0.1
|
|
plot(apsTree, cex = 0.7)
|
|
ape::nodelabels(text = "MRCA", node = 12, cex = 0.5, adj = 0.1, bg = "#ff8866")
|
|
|
|
|
|
# This procedure does however not assign an actual length to a root edge, and
|
|
# therefore no root edge is visible on the plot. Why? , you might ask. I ask
|
|
# myself that too. We'll just add a length by hand.
|
|
|
|
apsTree$root.edge <- mean(apsTree$edge.length) * 1.5
|
|
plot(apsTree, cex = 0.7, root.edge = TRUE)
|
|
ape::nodelabels(text = "MRCA", node = 12, cex = 0.5, adj = 0.8, bg = "#ff8866")
|
|
|
|
|
|
# == 2.2 Rotating Clades ===================================================
|
|
|
|
# To interpret the tree, it is useful to rotate the clades so that they appear
|
|
# in the order expected from the cladogram of species.
|
|
|
|
# We can either rotate around individual internal nodes ...
|
|
layout(matrix(1:2, 1, 2))
|
|
plot(apsTree, no.margin = TRUE, root.edge = TRUE)
|
|
ape::nodelabels(node = 13, cex = 0.7, bg = "#ff8866")
|
|
plot(ape::rotate(apsTree, node = 13), no.margin = TRUE, root.edge = TRUE)
|
|
ape::nodelabels(node = 13, cex = 0.7, bg = "#88ff66")
|
|
# Note that the species at the bottom of the clade descending from node
|
|
# 17 is now plotted at the top.
|
|
layout(matrix(1), widths = 1.0, heights = 1.0)
|
|
|
|
# ... or we can plot the tree so it corresponds as well as possible to a
|
|
# predefined tip ordering. Here we use the ordering that phyloT has returned
|
|
# for the species tree.
|
|
|
|
# (Nb. we need to reverse the ordering for the plot. This is why we use the
|
|
# expression [nOrg:1] below instead of using the vector directly.)
|
|
|
|
nOrg <- length(apsTree$tip.label)
|
|
|
|
layout(matrix(1:2, 1, 2))
|
|
plot(fungiTree,
|
|
no.margin = TRUE, root.edge = TRUE)
|
|
ape::nodelabels(text = fungiTree$node.label,
|
|
cex = 0.5,
|
|
adj = 0.2,
|
|
bg = "#D4F2DA")
|
|
|
|
plot(ape::rotateConstr(apsTree, apsTree$tip.label[nOrg:1]),
|
|
no.margin = TRUE,
|
|
root.edge = TRUE)
|
|
ape::add.scale.bar(length = 0.5)
|
|
layout(matrix(1), widths = 1.0, heights = 1.0)
|
|
|
|
# Task: Study the two trees and consider their similarities and differences.
|
|
# What do you expect? What do you find? Note that this is not a "mixed"
|
|
# gene tree yet, since it contains only a single gene for the species
|
|
# we considered. All of the branch points in this tree are speciation
|
|
# events. Thus the gene tree should have the same topology as the
|
|
# species tree. Does it? Are the differences important? How many
|
|
# branches would you need to remove and reinsert elsewhere to get the
|
|
# same topology as the species tree?
|
|
|
|
# In order to quantiofy how different these tow trees are, we need to compute
|
|
# tree distances.
|
|
|
|
|
|
# == 2.3 Computing tree distances ==========================================
|
|
|
|
|
|
# Many superb phylogeny tools are contributed by the phangorn package.
|
|
|
|
if (! requireNamespace("phangorn", quietly = TRUE)) {
|
|
install.packages("phangorn")
|
|
}
|
|
# Package information:
|
|
# library(help = phangorn) # basic information
|
|
# browseVignettes("phangorn") # available vignettes
|
|
# data(package = "phangorn") # available datasets
|
|
|
|
# To compare two trees, they must have the same tip labels. We delete "MBP1_" or
|
|
# "KILA_" from the existing tip labels in a copy of our APSES domain tree.
|
|
apsTree2 <- apsTree
|
|
apsTree2$tip.label <- gsub("(MBP1_)|(KILA_)", "", apsTree2$tip.label)
|
|
|
|
# phangorn provides several functions to compute tree-differences (and there
|
|
# is a _whole_ lot of theory on how to compare trees). treedist() returns the
|
|
# "symmetric difference"
|
|
phangorn::treedist(fungiTree, apsTree2, check.labels = TRUE)
|
|
|
|
# Numbers. What do they mean? How much more similar is our apsTree to the
|
|
# (presumably) ground truth of fungiTree than a random tree would be?
|
|
# The ape package provides the function rtree()
|
|
# to compute random trees.
|
|
|
|
ape::rtree(n = length(apsTree2$tip.label), # number of tips
|
|
rooted = TRUE, # we rooted the tree above,
|
|
# and fungiTree is rooted anyway
|
|
tip.label = apsTree2$tip.label, # use the apsTree2 labels
|
|
br = NULL) # don't generate branch lengths since
|
|
# fungiTree has none, so we can't
|
|
# compare them anyway.
|
|
|
|
# Let's compute some random trees this way, calculate the distances to
|
|
# fungiTree, and then compare the values we get for apsTree2. The random
|
|
# trees are provided by ape::rtree().
|
|
|
|
N <- 10000 # takes about 15 seconds
|
|
myTreeDistances <- matrix(numeric(N * 2), ncol = 2)
|
|
colnames(myTreeDistances) <- c("symm", "path")
|
|
|
|
set.seed(112358)
|
|
for (i in 1:N) {
|
|
xTree <- ape::rtree(n = length(apsTree2$tip.label),
|
|
rooted = TRUE,
|
|
tip.label = apsTree2$tip.label,
|
|
br = NULL)
|
|
myTreeDistances[i, ] <- phangorn::treedist(fungiTree, xTree)
|
|
}
|
|
set.seed(NULL) # reset the random number generator
|
|
|
|
table(myTreeDistances[, "symm"])
|
|
|
|
(symmObs <- phangorn::treedist(fungiTree, apsTree2)[1])
|
|
|
|
# Random events less-or-equal to observation, divided by total number of
|
|
# events gives us the empirical p-value.
|
|
cat(sprintf("\nEmpirical p-value for symmetric diff. of observed tree is %1.4f\n",
|
|
(sum(myTreeDistances[ , "symm"] <= symmObs) + 1) / (N + 1)))
|
|
|
|
hist(myTreeDistances[, "path"],
|
|
col = "aliceblue",
|
|
main = "Distances of random Trees to fungiTree")
|
|
(pathObs <- phangorn::treedist(fungiTree, apsTree2)[2])
|
|
abline(v = pathObs, col = "chartreuse")
|
|
|
|
# Random events less-or-equal to observation, divided by total number of
|
|
# events gives us the empirical p-value.
|
|
cat(sprintf("\nEmpirical p-value for path diff. of observed tree is %1.4f\n",
|
|
(sum(myTreeDistances[ , "path"] <= symmObs) + 1) / (N + 1)))
|
|
|
|
# Indeed, our apsTree is _very_ much more similar to the species tree than
|
|
# we would expect by random chance.
|
|
|
|
# What do we gain from that analysis? Analyzing the tree we get from a single
|
|
# gene of orthologous sequences is a positive control in our computational
|
|
# experiment. If these genes are indeed orthologues, a correct tree-building
|
|
# program ought to give us a tree that exactly matches the species tree.
|
|
# Evaluating how far off we are from the known correct result gives us a way to
|
|
# validate our workflow and our algorithm. If we can't get that right, we can't
|
|
# expect to get "real" data right either. Employing such positive controls in
|
|
# every computational experiment is essential for research. Not doing so is
|
|
# Cargo Cult Bioinformatics.
|
|
|
|
|
|
# [END]
|