213 lines
6.8 KiB
R
213 lines
6.8 KiB
R
# BIN-PHYLO-Tree_analysis.R
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#
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# Purpose: A Bioinformatics Course:
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# R code accompanying the BIN-PHYLO-Tree_analysis unit.
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#
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# Version: 1.0
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#
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# Date: 2017 10. 31
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# Author: Boris Steipe (boris.steipe@utoronto.ca)
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#
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# Versions:
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# 1.0 First 2017 version
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# 0.1 First code copied from 2016 material.
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#
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#
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# TODO:
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#
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#
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# == DO NOT SIMPLY source() THIS FILE! =======================================
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#
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# If there are portions you don't understand, use R's help system, Google for an
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# answer, or ask your instructor. Don't continue if you don't understand what's
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# going on. That's not how it works ...
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#
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# ==============================================================================
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# = 1 ___Section___
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if (!require(Rphylip, quietly=TRUE)) {
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install.packages("Rphylip")
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library(Rphylip)
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}
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# Package information:
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# library(help = Rphylip) # basic information
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# browseVignettes("Rphylip") # available vignettes
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# data(package = "Rphylip") # available datasets
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# Read the species tree that you have created at the phyloT Website:
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fungiTree <- read.tree("fungiTree.txt")
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plot(fungiTree)
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# The tree produced by phyloT contains full length species names, but it would
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# be more convenient if it had bicodes instead.
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str(fungiTree)
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# The species names are in a vector $tip.label of this list. We can use bicode()
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# to shorten them - but note that they have underscores as word separators. Thus
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# we will use gsub("-", " ", ...) to replace the underscores with spaces.
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for (i in seq_along(fungiTree$tip.label)) {
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fungiTree$tip.label[i] <- biCode(gsub("_", " ", fungiTree$tip.label[i]))
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}
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# Plot the tree
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plot(fungiTree, cex=1.0, root.edge=TRUE, no.margin=TRUE)
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nodelabels(text=orgTree$node.label, cex=0.6, adj=0.2, bg="#D4F2DA")
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# Note that you can use the arrow buttons in the menu above the plot to scroll
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# back to plots you have created earlier - so you can reference back to the
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# species tree.
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# = 1 Tree Analysis
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# 1.1 Visualizing your tree
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# The trees that are produced by Rphylip are stored as an object of class
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# "phylo". This is a class for phylogenetic trees that is widely used in the
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# community, practically all R phylogenetics packages will options to read and
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# manipulate such trees. Outside of R, a popular interchange format is the
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# Newick_format that you have seen above. It's easy to output your calculated
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# trees in Newick format and visualize them elsewhere.
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# The "phylo" class object is one of R's "S3" objects and methods to plot and
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# print it have been defined with the Rphylip package, and the package ape that
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# Rphylip has loaded. You can simply call plot(<your-tree>) and R knows what to
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# do with <your-tree> and how to plot it. The underlying function is
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# plot.phylo(), and documentation for its many options can by found by typing:
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?plot.phylo
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# We load the APSES sequence tree that you produced in the
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# BIN-PHYLO-Tree_building unit:
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load(file = "APSEStreeRproml.RData")
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plot(apsTree) # default type is "phylogram"
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plot(apsTree, type="unrooted")
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plot(apsTree, type="fan", no.margin = TRUE)
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# rescale to show all of the labels:
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# record the current plot parameters by assigning them to a variable ...
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(tmp <- plot(apsTree, type="fan", no.margin = TRUE, plot=FALSE))
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# ... and adjust the plot limits for a new plot:
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plot(apsTree,
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type="fan",
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x.lim = tmp$x.lim * 1.8,
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y.lim = tmp$y.lim * 1.8,
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cex = 0.8,
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no.margin = TRUE)
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# Inspect the tree object
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str(apsTree)
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apsTree$tip.label
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apsTree$edge
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apsTree$edge.length
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# show the node / edge and tip labels on a plot
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plot(apsTree)
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nodelabels()
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edgelabels()
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tiplabels()
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# show the number of nodes, edges and tips
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Nnode(apsTree)
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Nedge(apsTree)
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Ntip(apsTree)
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# Finally, write the tree to console in Newick format
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write.tree(apsTree)
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# = 1.1 Rooting Trees
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# In order to analyse the tree, it is helpful to root it first and reorder its
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# clades. Contrary to documentation, Rproml() returns an unrooted tree.
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is.rooted(apsTree)
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# You can root the tree with the command root() from the "ape" package. ape is
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# automatically installed and loaded with Rphylip.
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plot(apsTree)
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# add labels for internal nodes and tips
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nodelabels(cex=0.5, frame="circle")
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tiplabels(cex=0.5, frame="rect")
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# The outgroup of the tree is tip "11" in my sample tree, it may be a different
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# number in yours. Substitute the correct node number below for "outgroup".
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apsTree <- root(apsTree, outgroup = 11, resolve.root = TRUE)
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plot(apsTree)
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is.rooted(apsTree)
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# This tree _looks_ unchanged, beacuse when the root trifurcation was resolved,
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# an edge of length zero was added to connect the MRCA (Most Recent Common
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# Ancestor) of the ingroup.
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# The edge lengths are stored in the phylo object:
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apsTree$edge.length
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# ... and you can assign a small arbitrary value to the edge
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# to show how it connects to the tree without having an
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# overlap.
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apsTree$edge.length[1] <- 0.1
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plot(apsTree, cex=0.7)
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nodelabels(text="MRCA", node=12, cex=0.5, adj=0.1, bg="#ff8866")
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# This procedure does however not assign an actual length to a root edge, and
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# therefore no root edge is visible on the plot. Why? , you might ask. I ask
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# myself that too. We'll just add a length by hand.
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apsTree$root.edge <- mean(apsTree$edge.length) * 1.5
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plot(apsTree, cex=0.7, root.edge=TRUE)
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nodelabels(text="MRCA", node=12, cex=0.5, adj=0.8, bg="#ff8866")
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# = 1.1 Rotating Clades
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# To interpret the tree, it is useful to rotate the clades so that they appear
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# in the order expected from the cladogram of species.
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# We can either rotate around individual internal nodes ...
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layout(matrix(1:2, 1, 2))
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plot(apsTree, no.margin=TRUE, root.edge=TRUE)
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nodelabels(node=17, cex=0.7, bg="#ff8866")
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plot(rotate(apsTree, node=17), no.margin=TRUE, root.edge=TRUE)
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nodelabels(node=17, cex=0.7, bg="#88ff66")
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# Note that the species at the bottom of the clade descending from node
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# 17 is now plotted at the top.
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layout(matrix(1), widths=1.0, heights=1.0)
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# ... or we can plot the tree so it corresponds as well as possible to a
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# predefined tip ordering. Here we use the ordering that phyloT has returned
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# for the species tree.
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# (Nb. we need to reverse the ordering for the plot. This is why we use the
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# expression [nOrg:1] below instead of using the vector directly.)
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nOrg <- length(apsTree$tip.label)
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layout(matrix(1:2, 1, 2))
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plot(fungiTree,
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no.margin=TRUE, root.edge=TRUE)
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nodelabels(text=fungiTree$node.label, cex=0.5, adj=0.2, bg="#D4F2DA")
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plot(rotateConstr(apsTree, apsTree$tip.label[nOrg:1]),
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no.margin=TRUE, root.edge=TRUE)
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add.scale.bar(length=0.5)
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layout(matrix(1), widths=1.0, heights=1.0)
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# Study the two trees and consider their similarities and differences. What do
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# you expect? What do you find? Note that this is not a "mixed" gene tree yet,
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# since it contains only a single gene for the species we considered. All of the
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# branch points in this tree are speciation events.
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# [END]
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