Setup
1. Introduction
Phylogenetic trees was often used with associated data in various biological studies. ggtree, a flexible R package to visualize phylogenetic tree, had been developed by GuangChuang Yu (Yu et al. 2017). It provided geom_facet
function to align associated graphs to the tree (Yu et al. 2018; Yu 2020). However, This function did not support the tree created using circular
, fan
or radial
layout. To solve the problem, We developed ggtreeExtra
, which can align associated graphs to circular
, fan
or radial
and other rectangular
layout tree. ggtreeExtra
provides function, geom_fruit
to align graphs to the tree. But the associated graphs will align in different position. So we also developed geom_fruit_list
to add multiple layers in the same position. Furthermore, axis
of external layers can be added using the axis.params=list(axis="x",...)
in geom_fruit
. The grid lines
of external layers can be added using the grid.params=list()
in geom_fruit
. These functions are based on ggplot2 using grammar of graphics (Wickham 2016). More vignettes can be found on the chapter10
of online book.
2. Install
You can use the following to install it
# for devel
if(!requireNamespace("remotes", quietly=TRUE)){
install.packages("remotes")
}
remotes::install_github("YuLab-SMU/ggtreeExtra")
# for release
if (!requireNamespace("BiocManager", quietly=TRUE))
install.packages("BiocManager")
## BiocManager::install("BiocUpgrade") ## you may need this
BiocManager::install("ggtreeExtra")
3. Usage
To show the package profiling, I will use a tree file downloaded from plotTree. The associated datasets were simulated.
3.1 add single layer
# The path of tree file.
trfile <- system.file("extdata", "tree.nwk", package="ggtreeExtra")
# The path of file to plot tippoint.
tippoint1 <- system.file("extdata", "tree_tippoint_bar.csv", package="ggtreeExtra")
# The path of first layer outside of tree.
ring1 <- system.file("extdata", "first_ring_discrete.csv", package="ggtreeExtra")
# The path of second layer outside of tree.
ring2 <- system.file("extdata", "second_ring_continuous.csv", package="ggtreeExtra")
# The tree file was import using read.tree. If you have other format of tree, you can use corresponding function of treeio to read it.
tree <- read.tree(trfile)
# This dataset will to be plotted point and bar.
dat1 <- read.csv(tippoint1)
knitr::kable(head(dat1))
ID | Location | Length | Group | Abundance |
---|---|---|---|---|
DE0655_HCMC_2001 | HK | 0.1786629 | Yes | 12.331055 |
MS0111_HCMC_1996 | HK | 0.2105236 | Yes | 9.652052 |
MS0063_HCMC_1995 | HK | 1.4337983 | Yes | 11.584822 |
DE0490_HCMC_2000 | HK | 0.3823731 | Yes | 7.893231 |
DE0885_HCMC_2001 | HK | 0.8478901 | Yes | 12.117232 |
DE0891_HCMC_2001 | HK | 1.5038646 | Yes | 10.819734 |
ID | Pos | Type |
---|---|---|
DE0846_HCMC_2001 | 8 | type2 |
MS0034_HCMC_1995 | 8 | type2 |
EG1017_HCMC_2009 | 6 | type2 |
KH18_HCMC_2009 | 5 | type2 |
10365_HCMC_2010 | 7 | type2 |
EG1021_HCMC_2009 | 1 | type1 |
ID | Type2 | Alpha |
---|---|---|
MS0004_HCMC_1995 | p3 | 0.2256195 |
DE1150_HCMC_2002 | p2 | 0.2222086 |
MS0048_HCMC_1995 | p2 | 0.1881510 |
HUE57_HCMC_2010 | p3 | 0.1939088 |
DE1486_HCMC_2002 | p2 | 0.2018493 |
DE1165_HCMC_2002 | p3 | 0.1812997 |
# The format of the datasets is the long shape for ggplot2. If you have short shape dataset,
# you can use melt of reshape2 or pivot_longer of tidyr to convert it.
# We use ggtree to create fan layout tree.
p <- ggtree(tree, layout="fan", open.angle=10, size=0.5)
#> Scale for 'y' is already present. Adding another scale for 'y', which will
#> replace the existing scale.
p
# We use geom_star to add point layer outside of tree.
p2 <- p1 +
geom_star(
mapping=aes(fill=Location, size=Length, starshape=Group),
starstroke=0.2
) +
scale_size_continuous(
range=c(1, 3),
guide=guide_legend(
keywidth=0.5,
keyheight=0.5,
override.aes=list(starshape=15),
order=2)
) +
scale_fill_manual(
values=c("#F8766D", "#C49A00", "#53B400", "#00C094", "#00B6EB", "#A58AFF", "#FB61D7"),
guide="none" # don't show the legend.
) +
scale_starshape_manual(
values=c(1, 15),
guide=guide_legend(
keywidth=0.5, # adjust width of legend
keyheight=0.5, # adjust height of legend
order=1 # adjust the order of legend for multiple legends.
),
na.translate=FALSE # to remove the NA legend.
)
p2
# Or if we don't use %<+% to add annotation dataset, instead of data parameter of geom_fruit.
# You should specify the y column (tip label), here is y=ID.
p2 <- p +
geom_fruit(
data=dat1,
geom=geom_star,
mapping=aes(y=ID, fill=Location, size=Length, starshape=Group),
position="identity",
starstroke=0.2
) +
scale_size_continuous(
range=c(1, 3), # the range of size.
guide=guide_legend(
keywidth=0.5,
keyheight=0.5,
override.aes=list(starshape=15),
order=2
)
) +
scale_fill_manual(
values=c("#F8766D", "#C49A00", "#53B400", "#00C094", "#00B6EB", "#A58AFF", "#FB61D7"),
guide="none"
) +
scale_starshape_manual(
values=c(1, 15),
guide=guide_legend(
keywidth=0.5,
keyheight=0.5,
order=1
)
)
p2