Viewing a phylogenetic tree with ggtree

The ggtree package extends ggplot2 package to support viewing phylogenetic tree. It implements geom_tree layer for displaying phylogenetic tree, as shown below:

ggplot(tree, aes(x, y)) + geom_tree() + theme_tree()

The function, ggtree, was implemented as a short cut to visualize a tree, and it works exactly the same as shown above.

ggtree takes all the advantages of ggplot2. For example, we can change the color, size and type of the lines as we do with ggplot2.

ggtree(tree, color="firebrick", size=1, linetype="dotted")

By default, the tree is viewed in ladderize form, user can set the parameter ladderize = FALSE to disable it.

ggtree(tree, ladderize=FALSE)

The branch.length is used to scale the edge, user can set the parameter branch.length = "none" to only view the tree topology (cladogram) or other numerical variable to scale the tree (e.g. dN/dS, see also in Tree Annotation vignette).

ggtree(tree, branch.length="none")

Layout

Currently, ggtree supports several layouts, including:

• rectangular (by default)
• slanted
• circular
• fan

for Phylogram (by default) and Cladogram if user explicitly setting branch.length='none'. ggtree also supports unrooted layout.

ggtree(tree) + ggtitle("(Phylogram) rectangular layout")

ggtree(tree, layout="slanted") + ggtitle("(Phylogram) slanted layout")

ggtree(tree, layout="circular") + ggtitle("(Phylogram) circular layout")

ggtree(tree, layout="fan", open.angle=180) + ggtitle("(Phylogram) fan layout")

ggtree(tree, branch.length='none') + ggtitle("(Cladogram) rectangular layout")

ggtree(tree, layout="slanted", branch.length='none') + ggtitle("(Cladogram) slanted layout")

ggtree(tree, layout="circular", branch.length="none") + ggtitle("(Cladogram) circular layout")

ggtree(tree, layout="fan", open.angle=180, branch.length="none") + ggtitle("(Cladogram) circular layout")

ggtree(tree, layout="unrooted") + ggtitle("unrooted layout")

tree2d <- read.beast(system.file("extdata", "twoD.tree", package="treeio"))
ggtree(tree2d, mrsd = "2014-05-01") + theme_tree2()

ggtree(tree2d, mrsd = "2014-05-01",
       yscale="NGS", yscale_mapping=c(N2=2, N3=3, N4=4, N5=5, N6=6, N7=7)) +
           theme_classic() + theme(axis.line.x=element_line(), axis.line.y=element_line()) +
               theme(panel.grid.major.x=element_line(color="grey20", linetype="dotted", size=.3),
                     panel.grid.major.y=element_blank()) +
                         scale_y_continuous(labels=paste0("N", 2:7))

Displaying tree scale (evolution distance)

To show tree scale, user can use geom_treescale() layer.

ggtree(tree) + geom_treescale()

geom_treescale() supports the following parameters:

• x and y for tree scale position
• width for the length of the tree scale
• fontsize for the size of the text
• linesize for the size of the line
• offset for relative position of the line and the text
• color for color of the tree scale

ggtree(tree)+geom_treescale(x=0, y=12, width=6, color='red')

ggtree(tree)+geom_treescale(fontsize=8, linesize=2, offset=-1)

We can also use theme_tree2() to display the tree scale by adding x axis.

ggtree(tree) + theme_tree2()

Tree scale is not restricted to evolution distance, ggtree can re-scale the tree with other numerical variable. More details can be found in the Tree Annotation vignette.

Displaying nodes/tips

Showing all the internal nodes and tips in the tree can be done by adding a layer of points using geom_nodepoint, geom_tippoint or geom_point.

ggtree(tree)+geom_point(aes(shape=isTip, color=isTip), size=3)

p <- ggtree(tree) + geom_nodepoint(color="#b5e521", alpha=1/4, size=10)
p + geom_tippoint(color="#FDAC4F", shape=8, size=3)

p + geom_tiplab(size=3, color="purple")

ggtree(tree, layout="circular") + geom_tiplab(aes(angle=angle), color='blue')

ggtree(tree, layout="circular") + geom_tiplab2(color='blue')

p + geom_tiplab(aes(x=branch), size=3, color="purple", vjust=-0.3)

update tree view with a new tree

In previous example, we have a p object that stored the tree viewing of 13 tips and internal nodes highlighted with specific colored big dots. If users want to apply this pattern (we can imaging a more complex one) to a new tree, you don’t need to build the tree step by step. ggtree provides an operator, %<%, for applying the visualization pattern to a new tree.

For example, the pattern in the p object will be applied to a new tree with 50 tips as shown below:

p %<% rtree(50)

Another example can be found in the Tree Data Import vignette.

theme

theme_tree() defined a totally blank canvas, while theme_tree2() adds phylogenetic distance (via x-axis). These two themes all accept a parameter of bgcolor that defined the background color.

multiplot(
    ggtree(rtree(30), color="red") + theme_tree("steelblue"),
    ggtree(rtree(20), color="white") + theme_tree("black"),
    ncol=2)

Visualize a list of trees

ggtree supports multiPhylo object and a list of trees can be viewed simultaneously.

trees <- lapply(c(10, 20, 40), rtree)
class(trees) <- "multiPhylo"
ggtree(trees) + facet_wrap(~.id, scale="free") + geom_tiplab()

One hundred bootstrap trees can also be view simultaneously.

btrees <- read.tree(system.file("extdata/RAxML", "RAxML_bootstrap.H3", package="treeio"))
ggtree(btrees) + facet_wrap(~.id, ncol=10)

Another way to view the bootstrap trees is to merge them together to form a density tree. We can add a layer of the best tree on the top of the density tree.

p <- ggtree(btrees, layout="rectangular",   color="lightblue", alpha=.3)

best_tree <- read.tree(system.file("extdata/RAxML", "RAxML_bipartitionsBranchLabels.H3", package="treeio"))
df <- fortify(best_tree, branch.length = 'none')
p+geom_tree(data=df, color='firebrick')