ggtree for phylogenetic trees

用于系统发育树的 ggtree

Plot
Author

Jie Hua

Published

Jun 24, 2025

Modified

Jun 24, 2025

基本信息 / Basic Information

系统发育树用于展示不同物种或基因之间的进化关系。R 中的 ggtree 包是可视化这类树的强大工具，支持自定义，也可以与其他类型的数据整合。

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.

官方教程：https://bioconductor.org/packages/devel/bioc/vignettes/ggtreeExtra/inst/doc/ggtreeExtra.html

Official tutorial: https://bioconductor.org/packages/devel/bioc/vignettes/ggtreeExtra/inst/doc/ggtreeExtra.html

https://yulab-smu.top/treedata-book/

安装 / Installation

Code 
pak::pkg_install("ggtree")
pak::pkg_install("ggtreeExtra")
pak::pkg_install("ggstar")
pak::pkg_install("treeio")
pak::pkg_install("ggnewscale")

载入示例文件 / Load demo files

Code 
# 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")
Demo tree data 1
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
Code 
# This dataset will to be plotted heatmap
dat2 <- read.csv(ring1)
knitr::kable(head(dat2), caption = "Demo tree data 2", format = "pipe")
Demo tree data 2
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
Code 
# This dataset will to be plotted heatmap
dat3 <- read.csv(ring2)
knitr::kable(head(dat3), caption = "Demo tree data 3", format = "pipe")
Demo tree data 3
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

扇形布局树 / Fan layout tree

Code 
# 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 + theme_blog_overlay

Fan layout tree

Fan layout tree

为树添加注释数据 / Add annotation dataset to tree

Code 
## 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 + theme_blog_overlay

Fan layout tree with annotation

Fan layout tree with annotation

在外圈添加热图层 / Add heatmap layer on the outer ring

Code 
# 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 + theme_blog_overlay

Fan layout tree with heatmap

Fan layout tree with heatmap

为连续值添加热图层 / Add heatmap layer for continuous values

Code 
# 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 + theme_blog_overlay

Fan layout tree with heatmap for continuous values

Fan layout tree with heatmap for continuous values
Code 
# 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 + theme_blog_overlay

Fan layout tree with heatmap for continuous values

Fan layout tree with heatmap for continuous values

在同一位置添加多个图层 / Add multiple layers on the same position

Code 
# To reproduce.
set.seed(1024)
# generate a tree contained 100 tip labels.
tr <- rtree(100)
# generate three datasets, which are the same except the third column name.
dt <- data.frame(id=tr$tip.label, value=abs(rnorm(100)), group=c(rep("A",50),rep("B",50)))
df <- dt
dtf <- dt
colnames(df)[[3]] <- "group2"
colnames(dtf)[[3]] <- "group3"
# plot tree 
p <- ggtree(tr, layout="fan", open.angle=0)
#> Scale for y is already present.
#> Adding another scale for y, which will replace the existing scale.
p + theme_blog_overlay

Fan layout tree with heatmap for continuous values

Fan layout tree with heatmap for continuous values
Code 
# the first ring.
p1 <- p + 
      geom_fruit(
          data = dt,
          geom = geom_col,
          mapping = aes(y=id, x=value, fill=group),
      ) + 
      new_scale_fill()
p1 + theme_blog_overlay

Fan layout tree with heatmap for continuous values

Fan layout tree with heatmap for continuous values
Code 
# the second ring
# geom_fruit_list is a list, which first element must be layer of geom_fruit.
p2 <- p1 + 
      geom_fruit_list(
          geom_fruit(
              data = df,
              geom = geom_col,
              mapping = aes(y=id, x=value, fill=group2),
          ),
          scale_fill_manual(values=c("blue", "red")), # To map group2
          new_scale_fill(), # To initialize fill scale.
          geom_fruit(
              data = dt,
              geom = geom_star,
              mapping = aes(y=id, x=value, fill=group),
              size = 2.5,
              color = NA,
              starstroke = 0
          )
      ) + 
      new_scale_fill()
p2 + theme_blog_overlay

Fan layout tree with heatmap for continuous values

Fan layout tree with heatmap for continuous values
Code 
# The third ring
p3 <- p2 + 
      geom_fruit(
          data = dtf,
          geom = geom_col,
          mapping = aes(y=id, x=value, fill=group3),
          stat = "identity"
      ) +
      scale_fill_manual(values=c("#00AED7", "#009E73"))
#> Warning in geom_col(data = structure(list(label = c("t1", "t10", "t100", :
#> Ignoring unknown parameters: `stat`
p3 + theme_blog_overlay

Fan layout tree with heatmap for continuous values

Fan layout tree with heatmap for continuous values