pak::pkg_install("ggtree")
pak::pkg_install("ggtreeExtra")
pak::pkg_install("ggstar")
pak::pkg_install("treeio")
pak::pkg_install("ggnewscale")Basic Information
Phylogenetic trees are used to show the evolutionary relationships between different species or genes. The ggtree package in R is a powerful tool for visualizing these trees, allowing for customization and integration with other data types.
Official tutorial: https://bioconductor.org/packages/devel/bioc/vignettes/ggtreeExtra/inst/doc/ggtreeExtra.html
Installation
Load demo files
# Load required packages
library(ggplot2)
library(ggtree)
library(ggtreeExtra)
library(ggstar)
library(treeio)
library(ggnewscale)
# The path of tree file.
trfile <- system.file("extdata", "tree.nwk", package="ggtreeExtra")
# The path of file to plot tip point.
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 imported using read.tree. If you have other tree format files, you can use corresponding functions from treeio package to read it.
tree <- read.tree(trfile)
# This dataset will to be plotted point and bar.
dat1 <- read.csv(tippoint1)
knitr::kable(head(dat1), caption = "Demo tree data 1", format = "pipe")| 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 |
# This dataset will to be plotted heatmap
dat2 <- read.csv(ring1)
knitr::kable(head(dat2), caption = "Demo tree data 2", format = "pipe")| 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 |
# This dataset will to be plotted heatmap
dat3 <- read.csv(ring2)
knitr::kable(head(dat3), caption = "Demo tree data 3", format = "pipe")| 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 |
Fan layout tree
# 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
Add annotation dataset to tree
## Next, we can use %<+% of ggtree to add annotation dataset to tree.
#p1 <- p %<+% dat1
#p1
## 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 you don't use %<+% to import annotation dataset, instead of `data` parameter of `geom_fruit`.
# You should specify the column contained tip labels to y axis of `mapping`, here is `y=ID`.
# the `position` parameter was set to `identity` to add the points on the tip nodes.
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
Add heatmap layer on the outer ring
# Next, we will add a heatmap layer on the outer ring of p2 using `geom_tile` of `ggplot2`.
# Since we want to map some variable of dataset to the `fill` aesthetics of `geom_tile`, but
# the `fill` of p2 had been mapped. So I need use `new_scale_fill` of `ggnewscale` package to initialize it.
p3 <- p2 +
new_scale_fill() +
geom_fruit(
data=dat2,
geom=geom_tile,
mapping=aes(y=ID, x=Pos, fill=Type),
offset=0.08, # The distance between external layers, default is 0.03 times of x range of tree.
pwidth=0.25 # width of the external layer, default is 0.2 times of x range of tree.
) +
scale_fill_manual(
values=c("#339933", "#dfac03"),
guide=guide_legend(keywidth=0.5, keyheight=0.5, order=3)
)
p3
Add heatmap layer for continuous values
# You can also add heatmap layer for continuous values.
p4 <- p3 +
new_scale_fill() +
geom_fruit(
data=dat3,
geom=geom_tile,
mapping=aes(y=ID, x=Type2, alpha=Alpha, fill=Type2),
pwidth=0.15,
axis.params=list(
axis="x", # add axis text of the layer.
text.angle=-45, # the text angle of x-axis.
hjust=0 # adjust the horizontal position of text of axis.
)
) +
scale_fill_manual(
values=c("#b22222", "#005500", "#0000be", "#9f1f9f"),
guide=guide_legend(keywidth=0.5, keyheight=0.5, order=4)
) +
scale_alpha_continuous(
range=c(0, 0.4), # the range of alpha
guide=guide_legend(keywidth=0.5, keyheight=0.5, order=5)
)
p4
# Then add a bar layer outside of the tree.
p5 <- p4 +
new_scale_fill() +
geom_fruit(
data=dat1,
geom=geom_col,
mapping=aes(y=ID, x=Abundance, fill=Location), #The 'Abundance' of 'dat1' will be mapped to x
pwidth=0.4,
axis.params=list(
axis="x", # add axis text of the layer.
text.angle=-45, # the text size of axis.
hjust=0 # adjust the horizontal position of text of axis.
),
grid.params=list() # add the grid line of the external bar plot.
) +
scale_fill_manual(
values=c("#F8766D", "#C49A00", "#53B400", "#00C094", "#00B6EB", "#A58AFF", "#FB61D7"),
guide=guide_legend(keywidth=0.5, keyheight=0.5, order=6)
) +
theme(#legend.position=c(0.96, 0.5), # the position of legend.
legend.background=element_rect(fill=NA), # the background of legend.
legend.title=element_text(size=7), # the title size of legend.
legend.text=element_text(size=6), # the text size of legend.
legend.spacing.y = unit(0.02, "cm") # the distance of legends (y orientation).
)
p5