ggPlantmap is an open-source R package with the goal of facilitating the generation of informative ggplot maps from plant images to explore quantitative cell-type specific data. When combined with external quantitative data, ggPlantmap can be used for the visualization and displaying of spatial profiles in distinct parts/cells of the plant.
Included in the package there is a set of pre-loaded maps created from previously published plant images that can be directly inserted into a ggplot coding workflow. ggPlantmap enables users to plot heatmap signatures of gene expression or any spatial quantitative data onto plant images providing a customizable and extensible platform for visualizing, and analyzing spatial quantitative patterns within specific plant regions This package uses the flexibility of the well-known ggplot2 R package to allow users to tailor maps to their specific research questions.
## ggPlantmap installation requires devtools.
library(devtools)
## If an error message appear, you might need to install it
install.packages("devtools")
library(devtools)
## Now you can try to install ggPlantmap
install_github("leonardojo/ggPlantmap")ggPlantmap requires the following packages: ggplot2, dplyr, XML the package will try to install them with the most updated version from CRAN, you can ignore them if you want. A warning message can appear, but you can still try to run it.
library(ggPlantmap)The list of preloaded objects can be found in the ggPm.summary file
head(ggPm.summary)
#> # A tibble: 6 × 9
#> ggPlantmap.name Species Tissue Type Description Layers Image.Reference
#> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
#> 1 ggPm.At.roottip.cross… Arabid… root cros… Cross-sect… Cells https://www.fu…
#> 2 ggPm.At.roottip.longi… Arabid… root long… Longitudin… Cells https://doi.or…
#> 3 ggPm.At.3weekrosette.… Arabid… roset… top … Top view o… Leaves https://doi.or…
#> 4 ggPm.At.leafepidermis… Arabid… leaf … top … Top view o… Cells https://www.na…
#> 5 ggPm.At.leaf.crosssec… Arabid… leaves cros… Cross-sect… Cells https://doi.or…
#> 6 ggPm.At.seed.devseries Arabid… seed deve… Diagram of… Cells… https://doi.or…
#> # ℹ 2 more variables: Made.by <chr>, Contact.Info <chr>In this summary you can find the name of the ggPlantmap objects loaded in the package as well as other information about the map, such as species, tissue, description and the reference paper where the map was based on. In addition, information about the author of the map can also be found.
These are the pre-loaded ggPlantmap objects:
ggPm.summary$ggPlantmap.name
#> [1] "ggPm.At.roottip.crosssection"
#> [2] "ggPm.At.roottip.longitudinal"
#> [3] "ggPm.At.3weekrosette.topview"
#> [4] "ggPm.At.leafepidermis.topview"
#> [5] "ggPm.At.leaf.crosssection"
#> [6] "ggPm.At.seed.devseries"
#> [7] "ggPm.At.earlyembryogenesis.devseries"
#> [8] "ggPm.At.shootapex.longitudinal"
#> [9] "ggPm.At.inflorescencestem.crosssection"
#> [10] "ggPm.Sl.root.crosssection"
#> [11] "ggPm.At.leaf.topview"
#> [12] "ggPm.At.rootelong.longitudinal"
#> [13] "ggPm.At.rootmatur.crosssection"
#> [14] "ggPm.At.flower.diagram"
#> [15] "ggPm.At.lateralroot.devseries"
#> [16] "ggPm.Ms.root.crosssection"To load a pre-loaded ggPlantmap, just write their name on your console. These objects can be assigned as objects and they can be considered as regular tibbles.
ggPm.At.lateralroot.devseries
#> # A tibble: 3,415 × 7
#> ROI.name Stage Cell ROI.id point x y
#> <chr> <chr> <chr> <int> <int> <dbl> <dbl>
#> 1 Stage1.Pericycle Stage1 Pericycle 1 1 109. -407.
#> 2 Stage1.Pericycle Stage1 Pericycle 1 2 108. -191.
#> 3 Stage1.Pericycle Stage1 Pericycle 1 3 126. -194.
#> 4 Stage1.Pericycle Stage1 Pericycle 1 4 136. -407.
#> 5 Stage1.Pericycle Stage1 Pericycle 2 1 108. -478.
#> 6 Stage1.Pericycle Stage1 Pericycle 2 2 109. -407.
#> 7 Stage1.Pericycle Stage1 Pericycle 2 3 136. -408.
#> 8 Stage1.Pericycle Stage1 Pericycle 2 4 141. -479.
#> 9 Stage1.Pericycle Stage1 Pericycle 3 1 108. -479.
#> 10 Stage1.Pericycle Stage1 Pericycle 3 2 141. -479.
#> # ℹ 3,405 more rows
p <- ggPm.At.shootapex.longitudinal
p
#> # A tibble: 1,218 × 7
#> ROI.name Zone Layer ROI.id point x y
#> <chr> <chr> <chr> <int> <int> <dbl> <dbl>
#> 1 CZ.L1 CZ L1 1 1 757. -254.
#> 2 CZ.L1 CZ L1 1 2 757. -196.
#> 3 CZ.L1 CZ L1 1 3 796. -175.
#> 4 CZ.L1 CZ L1 1 4 839. -179.
#> 5 CZ.L1 CZ L1 1 5 841. -248.
#> 6 CZ.L1 CZ L1 1 6 812. -269.
#> 7 CZ.L1 CZ L1 1 7 774. -268.
#> 8 CZ.L1 CZ L1 2 1 841. -179.
#> 9 CZ.L1 CZ L1 2 2 886. -162.
#> 10 CZ.L1 CZ L1 2 3 932. -173.
#> # ℹ 1,208 more rows
For more information, please open the R documentation file for each specific object or function
?ggPm.At.lateralroot.devseries
?ggPm.At.seed.devseries
?ggPm.Ms.root.crosssection
## Same for all the functions
?XML.to.ggPlantmap()
?ggPlantmap.heatmap()
?ggPlantmap.plot()
?ggPlantmap.merge()Run the ggPlantmap.plot() function to create a plot:
ggPlantmap.plot(data=ggPm.At.lateralroot.devseries) ## or ggPlantmap.plot(ggPm.At.lateralroot.devseries)
library(ggplot2)
ggplot(ggPm.At.lateralroot.devseries,aes(x,y)) +
geom_polygon(aes(group=ROI.id,fill=ROI.name),colour="black") +
coord_fixed()
You can change the color mapping based on different layers of each ROI of the ggPlantmap Each ggPlantmap is separated by specific layers, you can access them by checking the column name. For example, the shoot apex ggPlantmap is divided into Zone and Layers
head(ggPm.At.shootapex.longitudinal)
#> # A tibble: 6 × 7
#> ROI.name Zone Layer ROI.id point x y
#> <chr> <chr> <chr> <int> <int> <dbl> <dbl>
#> 1 CZ.L1 CZ L1 1 1 757. -254.
#> 2 CZ.L1 CZ L1 1 2 757. -196.
#> 3 CZ.L1 CZ L1 1 3 796. -175.
#> 4 CZ.L1 CZ L1 1 4 839. -179.
#> 5 CZ.L1 CZ L1 1 5 841. -248.
#> 6 CZ.L1 CZ L1 1 6 812. -269.To decide which layer should be used to colormap, add the name of the column in the layer parameter of the function.
ggPlantmap.plot(ggPm.At.shootapex.longitudinal,layer=Zone)
ggPlantmap.plot(ggPm.At.shootapex.longitudinal,layer=Layer)
ggPlantmap.plot(ggPm.At.shootapex.longitudinal,layer=ROI.id,show.legend = FALSE)
## Note that in the last example I included show.legend=FALSE, that's because if you map based on individual ROIs,
## there will be too many of them, so you can exclude the legend in the plot by adding the optionBecause each polygon on ggPlantmap is characterized by specific levels (examples: Region,Stage,Part), you can color map them individually. Using ggPlantmap, you can color map based on unique layers of the ggPlantmap. Each map will have their own classification. If you would like to adjust or create your own classification, you can save the ggPlantmap as a table and modify it on to mach the degree of separation you want to show.
library(ggplot2)
library(cowplot)
head(ggPm.At.seed.devseries)
#> # A tibble: 6 × 8
#> ROI.name Stage Part Region ROI.id point x y
#> <chr> <chr> <chr> <chr> <int> <int> <dbl> <dbl>
#> 1 Preglobular.seedcoat.Distal Seed … Preg… seed… Dista… 1 1 277. -693.
#> 2 Preglobular.seedcoat.Distal Seed … Preg… seed… Dista… 1 2 280. -689.
#> 3 Preglobular.seedcoat.Distal Seed … Preg… seed… Dista… 1 3 280. -685.
#> 4 Preglobular.seedcoat.Distal Seed … Preg… seed… Dista… 1 4 285. -681.
#> 5 Preglobular.seedcoat.Distal Seed … Preg… seed… Dista… 1 5 286. -675.
#> 6 Preglobular.seedcoat.Distal Seed … Preg… seed… Dista… 1 6 286. -669.
## Stage: Seed development stage
## Part: Distinct parts of a seed (Seed coat, Endosperm and Embryo)
## Region: Specific regions of each part of the Arabidopsis seed
## Reference: Belmonte, Mark F., et al. "Comprehensive developmental profiles of gene activity in regions and subregions of the Arabidopsis seed." Proceedings of the National Academy of Sciences 110.5 (2013): E435-E444.
a <- ggPlantmap.plot(ggPm.At.seed.devseries,Region,linewidth = 0.5) +
scale_fill_brewer(palette="Set3") +
ggtitle("Regions of Arabidopsis seed development") +
theme(legend.key.height= unit(0.25, 'cm'),
legend.key.width= unit(0.25, 'cm'))
b <- ggPlantmap.plot(ggPm.At.seed.devseries,Stage,linewidth = 0.5) +
scale_fill_brewer(palette="Set1") +
ggtitle("Stages of Arabidopsis seed development") +
theme(legend.key.height= unit(0.25, 'cm'),
legend.key.width= unit(0.25, 'cm'))
c <- ggPlantmap.plot(ggPm.At.seed.devseries,Part,linewidth = 0.5) +
scale_fill_brewer(palette="Set1") +
ggtitle("Parts of Arabidopsis seed development") +
theme(legend.key.height= unit(0.25, 'cm'),
legend.key.width= unit(0.25, 'cm'))
plot_grid(a,b,c,ncol=1,labels=c("a","b","c"),align = "v")
You can also change the width of the tracing line
ggPlantmap.plot(ggPm.At.shootapex.longitudinal,layer=Zone,linewidth = 1)
ggPlantmap.plot(ggPm.At.shootapex.longitudinal,layer=Zone,linewidth = 5)
ggPlantmap is a ggplot based package, so you can change the aesthetics using ggplot coding logic.
library(ggplot2)
library(viridis)
#> Loading required package: viridisLite
## Changing the colors
ggPlantmap.plot(ggPm.At.inflorescencestem.crosssection) +
scale_fill_brewer(palette="Paired")
ggPlantmap.plot(ggPm.At.leaf.topview,show.legend = F) +
scale_fill_viridis(option="H",discrete=T) +
theme_bw()
With ggPlantmap you can overlay quantitative data into your ggPlantmap
to visualize it as sort of a heatmap. To do so, you will need another
table with contains quantitative data attributed to your ROIs.
### IMPORTANT!!!!
Note that the name of the ROI levels on your map should exactly match the ones on your quantitative data!!!
Included in the package is a sample quantitative data: The expression of LEC1 during the development of Arabidopsis seed.
ggPm.At.seed.expression.sample
#> # A tibble: 30 × 2
#> ROI.name AT5G47670.expression
#> <chr> <int>
#> 1 Globular.endosperm.Chalazal Endosperm 16
#> 2 Globular.seedcoat.Chalazal Seed Coat 8
#> 3 Globular.embryo.Embryo Proper 10
#> 4 Globular.endosperm.Micropylar Endosperm 48
#> 5 Globular.endosperm.Peripheral Endosperm 10
#> 6 Globular.seedcoat.Distal Seed Coat 10
#> 7 Heart.endosperm.Chalazal Endosperm 16
#> 8 Heart.seedcoat.Chalazal Seed Coat 11
#> 9 Heart.embryo.Embryo Proper 23
#> 10 Heart.endosperm.Micropylar Endosperm 39
#> # ℹ 20 more rowsLet’s combine this dataset with the ggPlantmap for Arabidopsis seeds
ggPm.At.seed.devseries
#> # A tibble: 5,369 × 8
#> ROI.name Stage Part Region ROI.id point x y
#> <chr> <chr> <chr> <chr> <int> <int> <dbl> <dbl>
#> 1 Preglobular.seedcoat.Distal Seed… Preg… seed… Dista… 1 1 277. -693.
#> 2 Preglobular.seedcoat.Distal Seed… Preg… seed… Dista… 1 2 280. -689.
#> 3 Preglobular.seedcoat.Distal Seed… Preg… seed… Dista… 1 3 280. -685.
#> 4 Preglobular.seedcoat.Distal Seed… Preg… seed… Dista… 1 4 285. -681.
#> 5 Preglobular.seedcoat.Distal Seed… Preg… seed… Dista… 1 5 286. -675.
#> 6 Preglobular.seedcoat.Distal Seed… Preg… seed… Dista… 1 6 286. -669.
#> 7 Preglobular.seedcoat.Distal Seed… Preg… seed… Dista… 1 7 285. -663.
#> 8 Preglobular.seedcoat.Distal Seed… Preg… seed… Dista… 1 8 284. -659.
#> 9 Preglobular.seedcoat.Distal Seed… Preg… seed… Dista… 1 9 284. -656.
#> 10 Preglobular.seedcoat.Distal Seed… Preg… seed… Dista… 1 10 286. -653.
#> # ℹ 5,359 more rowsTo do so, let’s use the ggPlantmap.merge() function You need to specify as a character the name of the column in which the common names between tables are found In this example: ROI.name
##ggPlantmap comes first, expression data comes second.
ggPlantmap.merge(ggPm.At.seed.devseries,ggPm.At.seed.expression.sample,"ROI.name")
#> # A tibble: 5,369 × 9
#> ROI.name Stage Part Region ROI.id point x y AT5G47670.expression
#> <chr> <chr> <chr> <chr> <int> <int> <dbl> <dbl> <int>
#> 1 Preglobular… Preg… seed… Dista… 1 1 277. -693. 8
#> 2 Preglobular… Preg… seed… Dista… 1 2 280. -689. 8
#> 3 Preglobular… Preg… seed… Dista… 1 3 280. -685. 8
#> 4 Preglobular… Preg… seed… Dista… 1 4 285. -681. 8
#> 5 Preglobular… Preg… seed… Dista… 1 5 286. -675. 8
#> 6 Preglobular… Preg… seed… Dista… 1 6 286. -669. 8
#> 7 Preglobular… Preg… seed… Dista… 1 7 285. -663. 8
#> 8 Preglobular… Preg… seed… Dista… 1 8 284. -659. 8
#> 9 Preglobular… Preg… seed… Dista… 1 9 284. -656. 8
#> 10 Preglobular… Preg… seed… Dista… 1 10 286. -653. 8
#> # ℹ 5,359 more rows
quant.data <- ggPlantmap.merge(ggPm.At.seed.devseries,ggPm.At.seed.expression.sample,"ROI.name")What if the column names for my ROI levels are not the same between the data? In this case, you can specify which column is to used to merge the two tables.
ggPlantmap.merge(ggPm.Sl.root.crosssection,ggPm.tomatoatlas.expression.sample,"ROI.name","Cell.layer")
#> # A tibble: 4,314 × 6
#> ROI.name ROI.id point x y SCR.expression
#> <chr> <int> <int> <dbl> <dbl> <dbl>
#> 1 Exodermis 1 1 615. -370. 0
#> 2 Exodermis 1 2 601. -349. 0
#> 3 Exodermis 1 3 598. -327. 0
#> 4 Exodermis 1 4 617. -312. 0
#> 5 Exodermis 1 5 636. -307. 0
#> 6 Exodermis 1 6 651. -310. 0
#> 7 Exodermis 1 7 671. -327. 0
#> 8 Exodermis 1 8 684. -343. 0
#> 9 Exodermis 1 9 676. -354. 0
#> 10 Exodermis 1 10 668. -365. 0
#> # ℹ 4,304 more rowsYou can now produce a heatmap from your ggPlantmap by running the function ggPlantmap.heatmap() In addition to the merged object, you also need to include the column where the quant values are found. In this example: AT5G47670.expression
ggPlantmap.heatmap(quant.data,AT5G47670.expression)
## you can change the gradient
ggPlantmap.heatmap(quant.data,AT5G47670.expression) +
scale_fill_gradient(low="white",high="darkred")
Hopefully by now you should be a little familiar with the package. But I encourage you to create your own ggPlantmap. To do so, we will require a specific open-source imaging software called Icy (https://icy.bioimageanalysis.org/). Source image: https://www.mdpi.com/ijms/ijms-16-13989/article_deploy/html/images/ijms-16-13989-g007.png
Now that you have your .xml file, let’s run the function
XML.to.ggPlantmap() function to create your map
new.ggPlantmap <- XML.to.ggPlantmap("data/roi.xml")
ggPlantmap.plot(new.ggPlantmap,ROI.name)
A more detailed tutorial can be found here on how to generate xml images from plant images.
You can convert your ggPlantmap table into an SVG file that can be used to create a ePlant EFP Browser or to be opened as a interactive object in a graphic software. For example: Illustrator or Powerpoint
ggPlantmap.to.SVG(ggPm.At.3weekrosette.topview,
group.name = "ROI.name",
author = "ggPlantmap",
svg.name="ggPlantmap.svg")
## This will create an image file (.svg) that can be opened in an graphic software (Illustrator, Power-Point)Note that the polygons will be grouped based on a column that you input in the group.name argument. The default is “ROI.name”
Hopefully everything ran smoothly. Thank you very much for your interest in ggPlantmap! If you want your map to be included in the package. Please send me an email with a tab delimited table version of your ggPlantmap with specific description. I will make sure to include it in future versions. Looking forward to hear from you any suggestions on how to improve ggPlantmap!