TreeAndLeaf: A graph layout strategy for binary trees with focus on the leaves
20 February 2026
Abstract
Dendrograms are classical diagrams for visualizing binary trees. Although effective for representing hierarchical relationships, they provide limited space for displaying information associated with leaf elements, particularly in large trees. TreeAndLeaf implements a hybrid layout strategy that emphasizes leaf-level representation in tree diagrams. By integrating force-directed graph and tree layout algorithms, TreeAndLeaf enables the projection of multiple layers of information onto a graph–tree visualization.Package
TreeAndLeaf 1.23.2
1 Overview
A dendrogram diagram displays binary trees focused on representing hierarchical relations between the tree elements. A dendrogram contains nodes, branches (edges), a root, and leaves (Figure 1A). The root is where the branches and nodes come from, indicating the direction to the leaves, i.e., the terminal nodes.
Most of the space of a dendrogram layout is used to arrange branches and inner nodes, with limited space to the leaves. For large dendrograms, the leaf labels are often squeezed to fit into small slots. Therefore, a dendrogram may not provide the best layout when the information to be displayed should highlight the leaves.
The TreeAndLeaf package aims to improve the visualization of the dendrogram leaves by combining tree and force-directed layout algorithms, shifting the focus of analysis to the leaves (Figure 1B). The package's workflow is summarized in Figure 1C.
Figure 1. TreeAndLeaf workflow summary. (A,B) The dendrogram in A is used to construct the graph in B. (C) The main input for the TreeAndLeaf package consists of a dendrogram, and then the package transforms the dendrogram into a graph representation. The finest graph layout is achieved by a two-steps process, starting with an unrooted tree diagram, which is subsequently relaxed by a force-directed algorithm applied to the terminal nodes of the tree. The final tree-and-leaf layout varies depending on the initial state, which can be obtained by other tree layout algorithms (see section 3 for examples using ggtree layouts to setup the initial state).
2 Quick Start
This section provides a basic example using the R built-in USArrests dataset.
The USArrests is a dataframe available in the user's workspace. To know more
about this dataframe, please query ?USArrests in the R console. We will build
a dendrogram from the USArrests dataset, then transform the dendrogram into
a tree-and-leaf diagram, and the result will be visualized in the RedeR
application.
2.1 Package and data requirements
#-- Libraries required in this section:
#-- TreeAndLeaf(>=1.4.2), RedeR(>=1.40.4), Bioconductor >= 3.13 (R >= 4.0)
# BiocManager::install(c("TreeAndLeaf","RedeR"))
# install.packages(c("igraph","RColorBrewer"))
#-- Load packages
library(TreeAndLeaf)
library(RedeR)
library(igraph)
library(RColorBrewer)2.2 Building a dendrogram example
In order to build a dendrogram from the USArrests dataset, we need a distance
matrix. We will use the default "euclidean distance" method from the dist()
function, and then the "average" method from hclust() function to create the
dendrogram.
hc <- hclust(dist(USArrests), "ave")
plot(hc, main="Dendrogram for the 'USArrests' dataset",
xlab="", sub="")
2.3 Converting the hclust object into a tree-and-leaf object
The treeAndLeaf function will transform the hclust into an igraph object,
including some basic graph attributes to display in the RedeR application.
2.4 Setting graph attributes
The att.mapv() function can be used to add external annotations to an igraph
object, for example, mapping new variables to the graph vertices. We will add
all USArrests variables to the tal object. To map one object to another
it is essential to use the same mapping IDs, set by the refcol parameter,
which points to a column in the input annotation dataset. In this example,
refcol = 0 indicates that the USArrests rownames will be used as
mapping IDs. To check the IDs in the igraph vertices, please type
V(tal)$name in the R console.
#--- Map attributes to the tree-and-leaf
#Note: 'refcol = 0' indicates that 'dat' rownames will be used as mapping IDs
tal <- att.mapv(g = tal, dat = USArrests, refcol = 0)Now we use the att.setv() wrapper function to set attributes in the
tree-and-leaf diagram. For all attributes available to display in the
RedeR application, see the command-line attributes section
in vignette("RedeR").
#--- Set graph attributes using the 'att.setv' wrapper function
pal <- brewer.pal(9, "Reds")
tal <- att.setv(g = tal, from = "Murder", to = "nodeColor",
cols = pal, nquant = 5)
tal <- att.setv(g = tal, from = "UrbanPop", to = "nodeSize",
xlim = c(10, 50, 5), nquant = 5)
#--- Set graph attributes using 'att.addv' and 'att.adde' functions
tal <- att.addv(tal, "nodeLabelSize", value = 15, index = V(tal)$isLeaf)
tal <- att.adde(tal, "edgeWidth", value = 3)2.5 Plotting a tree-and-leaf diagram
The next steps will call the RedeR application, and then display the tree-and-leaf diagram in an interactive R/Java interface. The initial layout will show an unrooted tree diagram, which will be subsequently relaxed by a force-directed algorithm applied to the terminal nodes of the tree.
#--- Send the tree-and-leaf to the interactive R/Java interface
addGraphToRedeR(g = tal, zoom=75)
#--- Call 'relax' to fine-tune the leaf nodes
relaxRedeR(p1=25, p2=200, p3=5, p5=5)At this point, the user can interact with the layout process to achieve the
best or desired layout; we suggest fine-tuning the force-directed algorithm
parameters, either through the R/Java interface or the command line relaxation
function. Note that the unroot tree diagram represents the initial state; then
a relaxing process should start until the finest graph layout is achieved.
The final layout varies depending on the initial state, which can also be
adjusted by providing more or less room for the spatial configuration
(e.g. via gzoom parameter).
3 Setting the initial tree-and-leaf state with ggtree layouts
The tree diagram represents the initial state of a tree-and-leaf, which is then
relaxed by a force-directed algorithm applied to the terminal nodes. Therefore,
the final tree-and-leaf layout varies depending on the initial state. The
treeAndLeaf package also accepts ggtree layouts to setup the initial state.
For example, next we show a tree diagram generated by the ggtree package,
and then we apply the tree-and-leaf transformation.
3.1 Package and data requirements
#-- Libraries required in this section:
# BiocManager::install(c("TreeAndLeaf","RedeR","ggtree))
# install.packages(c("igraph","ape", "dendextend", "dplyr",
# "ggplot2", "RColorBrewer"))
#-- Load packages
library(TreeAndLeaf)
library(RedeR)
library(igraph)
library(ape)
library(ggtree)
library(dendextend)
library(dplyr)
library(ggplot2)
library(RColorBrewer)3.2 Building and plotting a phylo tree with ggtree layouts
#--- Generate a random phylo tree
phylo_tree <- rcoal(300)
#--- Set groups and node sizes
group <- size <- dendextend::cutree(phylo_tree, 10)
group[] <- LETTERS[1:10][group]
size[] <- sample(size)
group.df <- data.frame(label=names(group), group=group, size=size)
phylo_tree <- dplyr::full_join(phylo_tree, group.df, by='label')
#--- Generate a ggtree with 'daylight' layout
pal <- brewer.pal(10, "Set3")
ggt <- ggtree(phylo_tree, layout = 'daylight', branch.length='none')
#--- Plot the ggtree
ggt + geom_tippoint(aes(color=group, size=size)) +
scale_color_manual(values=pal) + scale_y_reverse()3.3 Applying tree-and-leaf transformation to ggtree layouts
#-- Convert the 'ggtree' object into a 'tree-and-leaf' object
tal <- treeAndLeaf(ggt)
#--- Map attributes to the tree-and-leaf
#Note: 'refcol = 1' indicates that 'dat' col 1 will be used as mapping IDs
tal <- att.mapv(g = tal, dat = group.df, refcol = 1)
#--- Set graph attributes using the 'att.setv' wrapper function
tal <- att.setv(g = tal, from = "group", to = "nodeColor",
cols = pal)
tal <- att.setv(g = tal, from = "size", to = "nodeSize",
xlim = c(10, 50, 5))
#--- Set graph attributes using 'att.addv' and 'att.adde' functions
tal <- att.addv(tal, "nodeLabelSize", value = 1)
tal <- att.addv(tal, "nodeLineWidth", value = 0)
tal <- att.addv(tal, "nodeColor", value = "black", index=!V(tal)$isLeaf)
tal <- att.adde(tal, "edgeWidth", value = 3)
tal <- att.adde(tal, "edgeColor", value = "black")#--- Send the tree-and-leaf to the interactive R/Java interface
addGraphToRedeR(g = tal, zoom=50)
#--- Select inner nodes, preventing them from relaxing
selectNodes(V(tal)$name[!V(tal)$isLeaf], anchor=TRUE)
#--- Call 'relax' to fine-tune the leaf nodes
relaxRedeR(p1=25, p2=100, p3=5, p5=1, p8=5)
#--- Add legends
addLegendToRedeR(tal, type = "nodecolor", title = "Group", stretch = 0.2)
addLegendToRedeR(tal, type = "nodesize", title = "Size")
4 Case Study 1: visualizing a large dendrogram
4.1 Context
This section follows the same steps described in the Quick Start, but
using a larger dendrogram derived from the R built-in quakes dataset.
The quakes is a dataframe available in the user's workspace. To know more
about this dataframe, please query ?quakes in the R console.
We will build a dendrogram from the quakes dataset, then transform the
dendrogram into a tree-and-leaf diagram, and the result will be visualized
in the RedeR application.
4.2 Package and data requirements
#-- Libraries required in this section:
# BiocManager::install(c("TreeAndLeaf","RedeR"))
# install.packages(c("igraph", "RColorBrewer"))
#-- Load packages
library(TreeAndLeaf)
library(RedeR)
library(igraph)
library(RColorBrewer)#-- Check data
dim(quakes)
#> [1] 1000 5
head(quakes)
#> lat long depth mag stations
#> 1 -20.42 181.62 562 4.8 41
#> 2 -20.62 181.03 650 4.2 15
#> 3 -26.00 184.10 42 5.4 43
#> 4 -17.97 181.66 626 4.1 19
#> 5 -20.42 181.96 649 4.0 11
#> 6 -19.68 184.31 195 4.0 12#-- Building a large dendrogram
hc <- hclust(dist(quakes), "ave")
plot(hc, main="Dendrogram for the 'quakes' dataset",
xlab="", sub="")
4.3 Building and plotting a large tree-and-leaf diagram
#--- Map attributes to the tree-and-leaf
#Note: 'refcol = 0' indicates that 'dat' rownames will be used as mapping IDs
tal <- att.mapv(tal, quakes, refcol = 0)
#--- Set graph attributes using the 'att.setv' wrapper function
pal <- brewer.pal(9, "Greens")
tal <- att.setv(g = tal, from = "mag", to = "nodeColor",
cols = pal, nquant = 10)
tal <- att.setv(g = tal, from = "depth", to = "nodeSize",
xlim = c(40, 120, 20), nquant = 5)
#--- Set graph attributes using 'att.addv' and 'att.adde' functions
tal <- att.addv(tal, "nodeLabelSize", value = 1)
tal <- att.adde(tal, "edgeWidth", value = 10)The next steps will call the RedeR application, and then display the tree-and-leaf diagram in an interactive R/Java interface. The initial layout will show an unrooted tree diagram, which will be subsequently relaxed by a force-directed algorithm applied to the terminal nodes of the tree.
#--- Send the tree-and-leaf to the interactive R/Java interface
addGraphToRedeR(g = tal, zoom=10)
#--- Call 'relax' to fine-tune the leaf nodes
relaxRedeR(p1=25, p2=200, p3=10, p4=100, p5=10)#--- Add legends
addLegendToRedeR(tal, type = "nodecolor", title = "Richter Magnitude")
addLegendToRedeR(tal, type = "nodesize", title = "Depth (km)")
5 Case Study 2: visualizing a non-binary tree
5.1 Context
The TreeAndLeaf package is designed to layout binary trees, but it can also layout other graph configurations. To exemplify this case, we will use a larger phylogenetic tree available from the geneplast package, and for which some inner nodes have more than two children, or non-binary nodes.
5.2 Package and data requirements
5.3 Building and plotting a tree-and-leaf for a non-binary tree
#--- Map attributes to the tree-and-leaf using "%>%" operator
tal <- tal %>%
att.mapv(dat = spdata, refcol = 1) %>%
att.setv(from = "genome_size_Mb", to = "nodeSize",
xlim = c(10, 50, 1), nquant = 5) %>%
att.setv(from = "proteins", to = "nodeColor", nquant = 5,
cols = brewer.pal(9, "Blues"), na.col = "black") %>%
att.setv(from = "sp_name", to = "nodeLabel") %>%
att.adde(to = "edgeWidth", value = 20) %>%
att.addv(to = "nodeLabelSize", value = 1) %>%
att.addv(to = "nodeLabelSize", value = 20,
filter = list("name" = sample(phylo_tree$tip.label, 30))) %>%
att.addv(to = "nodeLabelSize", value = 20,
filter = list("name" = "9606"))# Call RedeR
startRedeR()
resetRedeR()
#--- Send the tree-and-leaf to the interactive R/Java interface
addGraphToRedeR(g = tal, zoom=50)
#--- Call 'relax' to fine-tune the leaf nodes
relaxRedeR(p1=25, p2=200, p3=10, p4=100, p5=10)#--- Add legends
addLegendToRedeR(tal, type = "nodecolor", title = "Proteome Size (n)", stretch = 0.5)
addLegendToRedeR(tal, type = "nodesize", title = "Genome size (Mb)")
6 Citation
If you use TreeAndLeaf, please cite:
- Cardoso MA, Rizzardi LEA, Kume LW, Groeneveld C, Trefflich S, Morais DAA, Dalmolin RJS, Ponder BAJ, Meyer KB, Castro MAA. "TreeAndLeaf: an R/Bioconductor package for graphs and trees with focus on the leaves." Bioinformatics, 38(5):1463-1464, 2022. Doi:10.1093/bioinformatics/btab819.
7 Session information
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