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.

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 ggplot2 ⁵ 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 ggplot2 ⁵ , this program allows for modular anatograms to be generated.

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).

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.

library (gganatogram) gganatogram(hgMale _ key, fillOutline= "#a6bddb" , organism= "human" , sex= "male" , fill= "colour" ) + theme _ void() + facet _ wrap( ~ type)

Because I elected to use ggplot2 ⁵ 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).

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)

In summary, I have designed and implemented an R package to easily visualise anatograms based on ggplot2 ⁵ and the anatograms from Expression Atlas ² , 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.

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