Awesome
<!-- README.md is generated from README.Rmd. Please edit that file -->gganatogram
Create anatogram images for different organisms. <br/> This package uses the tissue coordinates from the figure in Expression Atlas. https://www.ebi.ac.uk/gxa/home <br/> https://github.com/ebi-gene-expression-group/anatomogram <br/>
Citation
Maag JLV. gganatogram: An R package for modular visualisation of anatograms and tissues based on ggplot2 [version 1; referees: 1 approved]. F1000Research 2018, 7:1576 (doi: 10.12688/f1000research.16409.1)
https://f1000research.com/articles/7-1576/v1
citation("gganatogram")
#>
#> Maag J (2018). "gganatogram: An R package for modular
#> visualisation of anatograms and tissues based on ggplot2."
#> _f1000research_. Version 1: Awaiting peer review, <URL:
#> https://f1000research.com/articles/7-1576/v1>.
#>
#> A BibTeX entry for LaTeX users is
#>
#> @Article{,
#> title = {gganatogram: An R package for modular visualisation of anatograms and tissues based on ggplot2},
#> author = {Jesper Maag},
#> journal = {f1000research},
#> year = {2018},
#> note = {Version 1: Awaiting peer review},
#> url = {https://f1000research.com/articles/7-1576/v1},
#> }
If you use the tissue plots from gganatogram please cite Expression Atlas as well. <br/> Petryszak et al. 2015 <br/> If you use the main cell figure, please cite The Protein Atlas. <br/> Thul PJ et al. 2017 <br/>
More plot examples can be found at https://jespermaag.github.io/blog/2018/gganatogram/
Install
Install from github using devtools.
## install from Github
devtools::install_github("jespermaag/gganatogram")
shiny
I have now included a shiny app for gganatogram. <br/> An online version can be found at shinapps.io. <br/> https://jespermaag.shinyapps.io/gganatogram/ <br/> Unfortunately, there is a limit of 25h per month of app activity, so if you know R/Rstudio, please run it locally. <br/> To run it locally, use the following command.
library(shiny)
runGitHub( "gganatogram", "jespermaag", subdir = "shiny")
Usage
This package requires ggplot2 and ggpolypath which loads when loading the package
library(gganatogram)
library(dplyr)
library(viridis)
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)
hgMale <- gganatogram(data=hgMale_key, fillOutline='#440154FF', organism='human', sex='male', fill="value") + theme_void() + scale_fill_viridis()
hgFemale <- gganatogram(data=hgFemale_key, fillOutline='#440154FF', organism='human', sex='female', fill="value") + theme_void() + scale_fill_viridis()
mmMale <- gganatogram(data=mmMale_key, fillOutline='#440154FF', organism='mouse', sex='male', fill="value") + theme_void() + scale_fill_viridis()
mmFemale <- gganatogram(data=mmFemale_key, outline = T, fillOutline='#440154FF', organism='mouse', sex='female', fill="value") +theme_void() + scale_fill_viridis()
grid.arrange(hgMale, hgFemale, mmMale, mmFemale, ncol=2)
In order to use the function gganatogram, you need to have a data frame with organ, colour, and value if you want to.
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, 2, 5, 5),
stringsAsFactors=F)
head(organPlot)
#> organ type colour value
#> 1 heart circulation red 10
#> 2 leukocyte circulation red 5
#> 3 nerve nervous system purple 1
#> 4 brain nervous system purple 8
#> 5 liver digestion orange 2
#> 6 stomach digestion orange 5
Using the function gganatogram with the filling the organs based on colour.
gganatogram(data=organPlot, fillOutline='#a6bddb', organism='human', sex='male', fill="colour")
Of course, we can use the ggplot themes and functions to adjust the plots
gganatogram(data=organPlot, fillOutline='#a6bddb', organism='human', sex='male', fill="colour") +
theme_void()
We can also plot all tissues available using hgMale_key
hgMale_key$organ
#> [1] "thyroid_gland" "bone_marrow"
#> [3] "frontal_cortex" "prefrontal_cortex"
#> [5] "pituitary_gland" "aorta"
#> [7] "gastroesophageal_junction" "left_ventricle"
#> [9] "caecum" "ileum"
#> [11] "rectum" "nose"
#> [13] "breast" "tongue"
#> [15] "left_atrium" "pulmonary_valve"
#> [17] "mitral_valve" "penis"
#> [19] "nasal_pharynx" "spinal_cord"
#> [21] "throat" "tricuspid_valve"
#> [23] "diaphragm" "liver"
#> [25] "stomach" "spleen"
#> [27] "duodenum" "gall_bladder"
#> [29] "pancreas" "colon"
#> [31] "small_intestine" "appendix"
#> [33] "smooth_muscle" "urinary_bladder"
#> [35] "bone" "cartilage"
#> [37] "esophagus" "salivary_gland"
#> [39] "parotid_gland" "submandibular_gland"
#> [41] "skin" "pleura"
#> [43] "brain" "heart"
#> [45] "adrenal_gland" "lymph_node"
#> [47] "adipose_tissue" "skeletal_muscle"
#> [49] "leukocyte" "temporal_lobe"
#> [51] "atrial_appendage" "coronary_artery"
#> [53] "hippocampus" "vas_deferens"
#> [55] "seminal_vesicle" "epididymis"
#> [57] "tonsil" "lung"
#> [59] "amygdala" "trachea"
#> [61] "bronchus" "nerve"
#> [63] "cerebellum" "cerebellar_hemisphere"
#> [65] "kidney" "renal_cortex"
#> [67] "testis" "prostate"
gganatogram(data=hgMale_key, fillOutline='#a6bddb', organism='human', sex='male', fill="colour") +theme_void()
We can also skip plotting the outline of the graph
organPlot %>%
dplyr::filter(type %in% c('circulation', 'nervous system')) %>%
gganatogram(outline=F, fillOutline='#a6bddb', organism='human', sex='male', fill="colour") +
theme_void()
We can fill the tissues based on the values given to each organ
gganatogram(data=organPlot, fillOutline='#a6bddb', organism='human', sex='male', fill="value") +
theme_void() +
scale_fill_gradient(low = "white", high = "red")
We can also use facet_wrap to compare groups. First create add two data frames together with different values and the conditions in the type column
compareGroups <- rbind(data.frame(organ = c("heart", "leukocyte", "nerve", "brain", "liver", "stomach", "colon"),
colour = c("red", "red", "purple", "purple", "orange", "orange", "orange"),
value = c(10, 5, 1, 8, 2, 5, 5),
type = rep('Normal', 7),
stringsAsFactors=F),
data.frame(organ = c("heart", "leukocyte", "nerve", "brain", "liver", "stomach", "colon"),
colour = c("red", "red", "purple", "purple", "orange", "orange", "orange"),
value = c(5, 5, 10, 8, 2, 5, 5),
type = rep('Cancer', 7),
stringsAsFactors=F))
gganatogram(data=compareGroups, fillOutline='#a6bddb', organism='human', sex='male', fill="value") +
theme_void() +
facet_wrap(~type) +
scale_fill_gradient(low = "white", high = "red")
You can also split the tissues into types while retaining the outline
gganatogram(data=hgMale_key, outline = T, fillOutline='#a6bddb', organism='human', sex='male', fill="colour") +
facet_wrap(~type, ncol=4) +
theme_void()
Added female option
All female tissues
hgFemale_key$organ
#> [1] "atrial_appendage" "ectocervix"
#> [3] "hippocampus" "pleura"
#> [5] "bronchus" "trachea"
#> [7] "lung" "tonsil"
#> [9] "submandibular_gland" "breast"
#> [11] "spinal_cord" "pancreas"
#> [13] "liver" "colon"
#> [15] "bone_marrow" "urinary_bladder"
#> [17] "stomach" "duodenum"
#> [19] "esophagus" "gall_bladder"
#> [21] "spleen" "small_intestine"
#> [23] "placenta" "endometrium"
#> [25] "vagina" "aorta"
#> [27] "pituitary_gland" "gastroesophageal_junction"
#> [29] "caecum" "appendix"
#> [31] "ileum" "left_atrium"
#> [33] "left_ventricle" "pulmonary_valve"
#> [35] "mitral_valve" "diaphragm"
#> [37] "bone" "cartilage"
#> [39] "throat" "rectum"
#> [41] "nasal_septum" "nasal_pharynx"
#> [43] "cerebellum" "cerebellar_hemisphere"
#> [45] "prefrontal_cortex" "frontal_cortex"
#> [47] "nose" "temporal_lobe"
#> [49] "cerebral_cortex" "kidney"
#> [51] "renal_cortex" "coronary_artery"
#> [53] "tricuspid_valve" "thyroid_gland"
#> [55] "skin" "parotid_gland"
#> [57] "adipose_tissue" "heart"
#> [59] "smooth_muscle" "brain"
#> [61] "adrenal_gland" "lymph_node"
#> [63] "skeletal_muscle" "ovary"
#> [65] "leukocyte" "salivary_gland"
#> [67] "fallopian_tube" "uterus"
#> [69] "uterine_cervix" "nerve"
gganatogram(data=hgFemale_key, outline = T, fillOutline='#a6bddb', organism='human', sex='female', fill="colour") +theme_void()
You can also split the tissues into types while retaining the outline
gganatogram(data=hgFemale_key, outline = T, fillOutline='#a6bddb', organism='human', sex='female', fill="colour") +
facet_wrap(~type, ncol=4) +
theme_void()
To display the female reproductive system with outline.
hgFemale_key %>%
dplyr::filter(type=='reproductive') %>%
gganatogram( outline = T, fillOutline='#a6bddb', organism='human', sex='female', fill="colour") +
theme_void() +
coord_cartesian(xlim = c(30, 75), ylim = c(-110, -80))
Added mouse
Male
mmMale_key$organ
#> [1] "aorta" "brown_adipose_tissue"
#> [3] "stomach" "duodenum"
#> [5] "pancreas" "spleen"
#> [7] "adrenal_gland" "kidney"
#> [9] "colon" "small_intestine"
#> [11] "caecum" "jejunum"
#> [13] "ileum" "esophagus"
#> [15] "gall_bladder" "lymph_node"
#> [17] "seminal_vesicle" "penis"
#> [19] "femur" "bone_marrow"
#> [21] "cartilage" "quadriceps_femoris"
#> [23] "spinal_cord" "lung"
#> [25] "diaphragm" "trachea"
#> [27] "hindlimb" "trigeminal_nerve"
#> [29] "sciatic_nerve" "intestinal_mucosa"
#> [31] "liver" "heart"
#> [33] "brain" "skeletal_muscle"
#> [35] "circulatory_system" "blood_vessel"
#> [37] "skin" "prostate_gland"
#> [39] "vas_deferens" "epididymis"
#> [41] "testis" "urinary_bladder"
#> [43] "thymus" "peripheral_nervous_system"
#> [45] "eye"
gganatogram(data=mmMale_key, outline = T, fillOutline='#a6bddb', organism='mouse', sex='male', fill="colour") +theme_void() +coord_fixed()
gganatogram(data=mmMale_key, outline = T, fillOutline='#a6bddb', organism='mouse', sex='male', fill="colour") +theme_void()+facet_wrap(~type, ncol=4)
Female
mmFemale_key$organ
#> [1] "aorta" "circulatory_system"
#> [3] "brown_adipose_tissue" "stomach"
#> [5] "duodenum" "pancreas"
#> [7] "spleen" "adrenal_gland"
#> [9] "kidney" "colon"
#> [11] "small_intestine" "caecum"
#> [13] "jejunum" "ileum"
#> [15] "esophagus" "gall_bladder"
#> [17] "vagina" "uterus"
#> [19] "urinary_bladder" "tongue"
#> [21] "Peyer's_patch" "femur"
#> [23] "bone_marrow" "cartilage"
#> [25] "quadriceps_femoris" "skeletal_muscle"
#> [27] "spinal_cord" "diaphragm"
#> [29] "hindlimb" "trigeminal_nerve"
#> [31] "eye" "intestinal_mucosa"
#> [33] "brain" "heart"
#> [35] "liver" "sciatic_nerve"
#> [37] "blood_vessel" "skin"
#> [39] "mammary_gland" "title8178"
#> [41] "reproductive_system" "lymph_node"
#> [43] "thymus" "thyroid_gland"
#> [45] "lung" "peripheral_nervous_system"
#> [47] "trachea"
gganatogram(data=mmFemale_key, outline = T, fillOutline='#a6bddb', organism='mouse', sex='female', fill="colour") +theme_void() +coord_fixed()
gganatogram(data=mmFemale_key, outline = T, fillOutline='#a6bddb', organism='mouse', sex='female', fill="colour") +theme_void()+facet_wrap(~type, ncol=4)
Cellular structures
I have now included cellular substructures, using the cell.svg from the Protein Atlas. If you use the main cell figure (hopefully more will be added), please cite Thul PJ et al. 2017 <br/>
The cellular data can be access using cell_key
length(cell_key)
#> [1] 1
cell_key
#> $cell
#> organ type colour value
#> 1 cytosol other steelblue 2.07159434
#> 4 intermediate_filaments other #984EA3 14.89497057
#> 6 actin_filaments other #FFFF33 5.87440944
#> 8 focal_adhesion_sites other #F781BF 8.12483660
#> 10 microtubule_organizing_center other #66C2A5 8.67564889
#> 12 centrosome other #8DA0CB 1.02852838
#> 13 microtubules other #E78AC3 9.48882657
#> 16 microtubule_ends other #E5C494 4.80457195
#> 18 secreted_proteins other #8DD3C7 9.20191105
#> 20 lipid_droplets other #BEBADA 3.48903574
#> 22 lysosomes other #80B1D3 3.73790434
#> 24 peroxisomes other #B3DE69 6.79465458
#> 26 endosomes other #D9D9D9 13.48636296
#> 28 endoplasmic_reticulum other #CCEBC5 11.36654344
#> 30 golgi_apparatus other #7FC97F 11.29225961
#> 32 nucleoplasm other #FDC086 2.07964782
#> 34 nuclear_membrane other #386CB0 7.98595837
#> 36 nuclear_bodies other #BF5B17 0.05868359
#> 38 nuclear_speckles other #1B9E77 0.61672243
#> 40 nucleoli other #7570B3 14.96900579
#> 42 nucleoli_fibrillar_center other #66A61E 8.72324527
#> 44 rods_and_rings other #A6761D 9.53194209
#> 46 mitochondria other #A6CEE3 1.29396698
#> 48 plasma_membrane other #B2DF8A 13.45657571
To plot the whole cell with colours or values, use the following command. If you want to specify a background colour, you either have to remove the cytosol or change the colour of cytosol to the desired colour.
gganatogram(data=cell_key[['cell']], outline = T, fillOutline='steelblue', organism="cell", fill="colour") +theme_void() + coord_fixed()
gganatogram(data=cell_key[['cell']], outline = T, fillOutline='lightgray', organism="cell", fill="value") +theme_void() + coord_fixed() + scale_fill_viridis()
To see all the subsstructures individually, you can plot the data one at a time
figureList <- list()
for (i in 1:nrow(cell_key[['cell']])) {
figureList[[i]] <- gganatogram(data=cell_key[['cell']][i,], outline = T, fillOutline='steelblue', organism="cell", fill="colour") +theme_void() +ggtitle(cell_key[['cell']][i,]$organ) + theme(plot.title = element_text(hjust=0.5, size=16)) + coord_fixed()
}
do.call(grid.arrange, c(figureList[1:4], ncol=2))
do.call(grid.arrange, c(figureList[5:8], ncol=2))
do.call(grid.arrange, c(figureList[9:12], ncol=2))
do.call(grid.arrange, c(figureList[13:16], ncol=2))
do.call(grid.arrange, c(figureList[17:20], ncol=2))
do.call(grid.arrange, c(figureList[21:24], ncol=2))
Other organisms i.e. tier 2 organisms
Expression atlas contains other organisms than human and mice, however, these are not as well anotated. <br/> All the expression atlas anatograms can be found here https://ebi-gene-expression-group.github.io/anatomogram/ <br/> Unfortunately, I won't be able to add other organs to these since I'm neither an anatomist nor artist. <br/> If anyone would like to add more organs, I would love for you to contribute. <br/> <br/> To create these plots, I have added two other objects other_key and other_list. <br/> These are lists within lists, and to plot all the organs from an organisms use other_key[["organism"]] as data, and "organism" as organism. <br/> Also, the organ names are so far a mix of UBERON and plant ids.
length(other_key)
#> [1] 24
names(other_key)
#> [1] "anolis_carolinensis"
#> [2] "arabidopsis_thaliana"
#> [3] "bos_taurus"
#> [4] "brachypodium_distachyon.flower_parts"
#> [5] "brachypodium_distachyon.whole_plant"
#> [6] "gallus_gallus"
#> [7] "hordeum_vulgare.flower_parts"
#> [8] "hordeum_vulgare.whole_plant"
#> [9] "macaca_mulatta"
#> [10] "monodelphis_domestica"
#> [11] "oryza_sativa.flower_parts"
#> [12] "oryza_sativa.whole_plant"
#> [13] "papio_anubis"
#> [14] "rattus_norvegicus"
#> [15] "solanum_lycopersicum.flower_parts"
#> [16] "solanum_lycopersicum.whole_plant"
#> [17] "sorghum_bicolor.flower_parts"
#> [18] "sorghum_bicolor.whole_plant"
#> [19] "tetraodon_nigroviridis"
#> [20] "triticum_aestivum.flower_parts"
#> [21] "triticum_aestivum.whole_plant"
#> [22] "xenopus_tropicalis"
#> [23] "zea_mays.flower_parts"
#> [24] "zea_mays.whole_plant"
To plot bos_taurus use the following command. Unfortunately, I have not managed to add the correct names yet.
other_key[["bos_taurus"]]
#> organ type colour value
#> 2 duodenum other #E41A1C 11.381132
#> 3 brain other #377EB8 2.264810
#> 4 kidney other #4DAF4A 4.131599
#> 5 lung other #984EA3 3.182946
#> 6 colon other #FF7F00 3.114481
#> 7 heart other #FFFF33 13.141334
#> 8 liver other #A65628 17.251310
#> 9 pulmonary vein other #F781BF 13.414659
#> 19 UBERON_0001013 other #999999 12.126515
#> 20 UBERON_0001013 other #66C2A5 1.898023
#> 21 UBERON_0001013 other #FC8D62 19.290389
#> 22 UBERON_0014892 other #8DA0CB 10.994221
#> 23 UBERON_0014892 other #E78AC3 16.761115
#> 24 UBERON_0014892 other #A6D854 2.468627
#> 25 UBERON_0014892 other #FFD92F 1.556285
#> 26 UBERON_0014892 other #E5C494 3.461740
#> 27 UBERON_0014892 other #B3B3B3 18.595027
gganatogram(data=other_key[["bos_taurus"]], outline = T, fillOutline='white', organism="bos_taurus", sex='female', fill="colour") +
theme_void() +
ggtitle("bos_taurus") +
theme(plot.title = element_text(hjust=0.5)) +
coord_fixed()
Here is a way to loop through all the other organisms and plot their organs.
library(gridExtra)
plotList <- list()
for (organism in names(other_key)) {
plotList[[organism]] <- gganatogram(data=other_key[[organism]], outline = T, fillOutline='white', organism=organism, sex='female', fill="colour") +
theme_void() +
ggtitle(organism) +
theme(plot.title = element_text(hjust=0.5, size=9)) +
coord_fixed()
}
do.call(grid.arrange, c(plotList[1:4], ncol=2))
do.call(grid.arrange, c(plotList[5:8], ncol=2))
do.call(grid.arrange, c(plotList[9:12], ncol=2))
do.call(grid.arrange, c(plotList[13:16], ncol=2))
do.call(grid.arrange, c(plotList[17:20], ncol=2))
do.call(grid.arrange, c(plotList[21:24], ncol=2))
