gganatogram: An R package for modular visualisation of anatograms and tissues based on ggplot2

Introduction

Efficiently displaying tissue information in multicellular organisms can be a laborious and time consuming process. Often researchers want to showcase differences in values, such as gene expression or pharmacokinetics between tissues in one organism, or between similar tissues in different groups.

Whereas bar charts and heatmaps provide an informative view of the differences between groups, it can be difficult to immediately observe the biological significance (Figure 1a–b). As compared to an anatogram, where it is easy to quickly spot the differences between tissues or groups, and immediately provide biological context to these observations (Figure 1c). This also has the added benefit that the audience, whether reading a paper or attending a lecture, will have to spend less time and effort to grasp the results.

Figure 1. Comparison between barplot (top left), heatmap (top right), and anatogram (bottom) to display tissue values between groups.

The values in the graphs are the same.

Several online tools to display gene expression in different tissues already exist^1–4. Although these tools provide important information regarding gene expression in various tissues and organisms, other disciplines besides genetics are unable to utilise these applications due to the focus on genes. Moreover, these tools often only include a predefined set of experiments that can be visualised, leading to difficulties in presenting your own data. Other caveats with these tools are that it can be laborious to recreate the plot or automatically create plots from results.

Here I present gganatogram, an open source R package based on ggplot2⁵ utilising the publicly available mouse and human anatograms from the Expression Atlas^1,2. With this package it is easy for any R user to quickly visualise anatograms with specified colours, groups, and values. Using the familiar grammar from ggplot2⁵, this program allows for modular anatograms to be generated.

Methods

source("https://neuroconductor.org/neurocLite.R")
neuro_install("gganatogram", release = "stable",
release_repo = latest_neuroc_release(release = "stable"))

devtools::install_github("jespermaag/gganatogram")

Operation

gganatogram requires an installation of R≥3.0.0, ggplot2⁵ v.3.0.0 and ggpolypath⁷ v.0.1.0. The program should be able to run on any computer with the system requirements for R. Plots can be generated using a basic data.frame containing organ name, colour, type, or value, with the specified column names below. Organs are plotted one at a time based on the order of the data.frame. The tissue of each consecutive row will be layered on top of the previous. The gganatogram package provides four such data.frames containing all tissues available to plot, one for each human and mouse, and divided by sex.

hgMale_key, hgFemale_key, mmMale_key, mmFemale_key

These data frames have already specified colour, type, and an assigned random number to facilitate the start of plotting.

head(hgFemale_key)
            organ  colour      type     value
1        pancreas  orange digestion 10.373146
2           liver  orange digestion 19.723172
3           colon  orange digestion 14.853335
4     bone_marrow #41ab5d     other 19.681587
5 urinary_bladder  orange digestion 14.914273
6         stomach  orange digestion  2.667599

The main function is called gganatogram(). By default, and without any arguments, it plots the outline of a male human with standard ggplot2 parameters. By adding just a few options, it is possible to quickly change to female, fill specified organs by selected colour, or fill the organs based on a value (Figure 2).

Figure 2.

(A) Default plot generated by calling gganatogram(), (B) adding female, plotting specified organs by (C) colour, (D) value.

library(gganatogram)
library(gridExtra)
organPlot <- data.frame(organ = c("heart", "leukocyte", "nerve", "brain",
"liver", "stomach", "colon"),
type = c("circulation", "circulation", "nervous␣system", "nervous␣system",
"digestion", "digestion", "digestion"),
colour = c("red", "red", "purple", "purple", "orange", "orange", "orange"),
value = c(10, 5, 1, 8, 10, 5, 10),
stringsAsFactors=F)

A <- gganatogram()
B <- gganatogram(fillOutline="#a6bddb", sex="female") + theme_void()
C <- gganatogram(data=organPlot, fillOutline="#a6bddb", organism="human",
      sex="female", fill="colour")+ theme_void()
D <- gganatogram(data=organPlot, fillOutline="#a6bddb", organism="human",
      sex="female", fill="value")+ theme_void()
grid.arrange(A, B, C, D, ncol=4)

Use cases

This section provides additional plotting examples.

To plot all tissues per organism, use the provided key files that exist per organism and sex. This displays all tissues in the order of each data frame. To change the order in which organs are layered on top of each other, reorder the data frame to have those tissues at the bottom (Figure 3).

Figure 3. Displaying all tissues available for human and mouse, male and female.

The colours are specified in the provided key data frames.

library(gganatogram)
library(gridExtra)
hgMale <- gganatogram(data=hgMale_key, fillOutline="#a6bddb", organism="human",
           sex="male", fill="colour") + theme_void()

hgFemale <- gganatogram(data=hgFemale_key, fillOutline="#a6bddb",
             organism="human", sex="female", fill="colour") + theme_void()

mmMale <- gganatogram(data=mmMale_key, fillOutline="#a6bddb", organism="mouse",
           sex="male", fill="colour") + theme_void()

mmFemale <- gganatogram(data=mmFemale_key, outline = T, fillOutline="#a6bddb",
             organism="mouse", sex="female", fill="colour") + theme_void()

grid.arrange(hgMale, hgFemale, mmMale, mmFemale, ncol=4)

To compare anatograms, e.g. draw one specific anatogram side by side and compare values, a long table has to be created with the type column changed to the variables to compare. The following code recreates (Figure 1c).

normal <- data.frame(organ = c("heart", "leukocyte", "nerve", "brain", "liver", 
"stomach", "colon"),
value = c(10, 5, 1, 2, 2, 5, 5),
type = rep("Normal", 7),
stringsAsFactors=F)

cancer <- data.frame(organ = c("heart", "leukocyte", "nerve", "brain", "liver", 
"stomach", "colon"),
value = c(5, 5, 10, 12, 15, 5, 10), type = rep("Cancer", 7),
stringsAsFactors=F)

compareGroups <- rbind(normal, cancer)

gganatogram(data=compareGroups, fillOutline="white", organism="human",
sex="male", fill="value") +
              theme_void() +
              facet_wrap(~type) +
              scale_fill_gradient(low = "white", high = "steelblue")

Organs can also be separated by faceting, as per standard ggplot2 using facet_wrap (Figure 4). This can help to display organs that are nested on top of each other.

Figure 4. Faceting tissues based on type and displaying the corresponding colour.

library(gganatogram)

gganatogram(hgMale_key, fillOutline="#a6bddb", organism="human",
sex="male", fill="colour") +
theme_void() +
facet_wrap(~type)

Because I elected to use ggplot2⁵ for the package, the user can add additional layers from standard plots. This can be useful to show highlight features, such as metastasis, location of tissue biopsies, or gene expression of specific biopsies (Figure 5).

Figure 5. Geom points added to a gganatogram to show the location of tissue biopsies (top left) along with a barplot of biopsy expression for an example gene (bottom).

Another option is to fill both tissues and points by value (top right). Red colour around plot added for emphasis.

library(gganatogram)
library(dplyr)
library(gridExtra)

biopsies <- data.frame(biopsy = c("liver", "heart", "prostate", "stomach", "brain"),
         x = c(50, 55, 53, 60, 57),
         y = c(60, 48, 95, 68, 10),
         value = c(10, 15, 5, 2, 15))
p <- hgMale_key %>%
        dplyr::filter(organ %in% c("liver", "heart", "prostate", "stomach", 
        "brain")) %>%
        gganatogram(fillOutline="lightgray", organism="human", sex="male",
        fill="colour") + theme_void() +
        ggtitle("Position of biopsies")
        
p <- p + geom_point(data = biopsies, pch=21, size=2, aes(x =x, y = -y,
fill = biopsy, colour= biopsy))

p2 <- ggplot(biopsies, aes(x = biopsy, y = value, fill = biopsy)) +
         geom_bar(stat= "identity", col="black") +
         theme_minimal() +
         theme(legend.position= "none")+
         theme(axis.text.x = element_text(angle = 60, hjust = 1))+
         ggtitle("Gene1␣expression")
        
p3 <- hgMale_key%>%
         dplyr::filter(organ %in% c("liver", "heart", "prostate", "stomach", 
         "brain"))%>%
         gganatogram(fillOutline="lightgray", organism="human", sex="male", 
         fill="value") + theme_void() +
         ggtitle("Value␣of␣biopsies") +
         geom_point(data = biopsies, pch=21, size=3, aes(x =x, y = -y,
         fill = value), colour="red")
        
lay <- rbind(c(1,2), c(1,2), c(3, NULL))
grid.arrange(p, p3, p2, layout_matrix = lay)

Summary

In summary, I have designed and implemented an R package to easily visualise anatograms based on ggplot2⁵ and the anatograms from Expression Atlas², which when combined create a powerful tool to plot and display tissue information.

The one line command to generate these plots should allow for users with even limited R knowledge to create informative anatograms for publications or presentations.

Data availability

SVG files are available from GitHub: https://github.com/ebi-gene-expression-group/anatomogram/tree/master/src/svg

Software availability