Author: Leonardo Jo ([email protected])
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.
##install devtools (if you haven't already)
install.packages("devtools")
library(devtools)
## Installing from a github respository
install_github("leonardojo/ggPlantmap")I created a step-by-step user guide to help users navigate through the package.
I also created a step-by-step guide with tips on how to create your own ggPlantmap.
Finally, here are some instructions on how your newly created ggPlantmap can be included in the package.
Each unique ggPlantmap is a table (tibble) object with points coordinates (x,y) of specific points of polygons (ROIs) extracted from plant images.
library(ggPlantmap)
head(ggPm.At.roottip.longitudinal)
#> # A tibble: 6 × 7
#> ROI.name Level1 Level2 ROI.id point x y
#> <chr> <chr> <chr> <int> <int> <dbl> <dbl>
#> 1 Meristem.QC Meristem QC 1 1 121. -323.
#> 2 Meristem.QC Meristem QC 1 2 127. -315.
#> 3 Meristem.QC Meristem QC 1 3 134. -315.
#> 4 Meristem.QC Meristem QC 1 4 149. -318.
#> 5 Meristem.QC Meristem QC 1 5 149. -329.
#> 6 Meristem.QC Meristem QC 1 6 134. -327.
ggPlantmaps can be easily plotted using the ggPlantmap.plot() function.
library(ggPlantmap)
ggPlantmap.plot(ggPm.At.earlyembryogenesis.devseries,Cell)
ggPlantmap.plot(ggPm.At.roottip.longitudinal,Level1)
The package contain a series of pre-loaded ggPlantmaps created from previously published plant images. I hope to update the package with the contribution of the plant research community.
library(ggPlantmap)
ggPm.summary
#> # A tibble: 16 × 9
#> ggPlantmap.name Species Tissue Type Descr…¹ Layers Image…² Made.by Conta…³
#> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
#> 1 ggPm.At.roottip.… Arabid… root cros… Cross-… Cells https:… Leonar… jo.leo…
#> 2 ggPm.At.roottip.… Arabid… root long… Longit… Cells https:… Leonar… jo.leo…
#> 3 ggPm.At.3weekros… Arabid… roset… top … Top vi… Leaves https:… Leonar… jo.leo…
#> 4 ggPm.At.leafepid… Arabid… leaf … top … Top vi… Cells https:… Leonar… jo.leo…
#> 5 ggPm.At.leaf.cro… Arabid… leaves cros… Cross-… Cells https:… Leonar… jo.leo…
#> 6 ggPm.At.seed.dev… Arabid… seed deve… Diagra… Cells… https:… Leonar… jo.leo…
#> 7 ggPm.At.earlyemb… Arabid… embryo deve… Diagra… Cells… https:… Leonar… jo.leo…
#> 8 ggPm.At.shootape… Arabid… shoot… long… Diagra… Layer… https:… Leonar… jo.leo…
#> 9 ggPm.At.inflores… Arabid… inflo… cros… Cross-… Cells https:… Leonar… jo.leo…
#> 10 ggPm.Sl.root.cro… Solanu… root cros… Cross-… Cells https:… Leonar… jo.leo…
#> 11 ggPm.At.leaf.top… Arabid… leaf top … Top vi… Leaves http:/… Leonar… jo.leo…
#> 12 ggPm.At.rootelon… Arabid… root … long… Longit… Cells https:… Leonar… jo.leo…
#> 13 ggPm.At.rootmatu… Arabid… root … cros… Cross-… Cells https:… Leonar… jo.leo…
#> 14 ggPm.At.flower.d… Arabid… flower diag… Diagra… Tissu… Taiz, … Leonar… jo.leo…
#> 15 ggPm.At.lateralr… Arabid… later… deve… Diagra… Cells… https:… Leonar… jo.leo…
#> 16 ggPm.Ms.root.cro… Medica… root cros… Cross-… Cells Unpubl… Leonar… jo.leo…
#> # … with abbreviated variable names ¹Description, ²Image.Reference,
#> # ³Contact.Info
These maps can be easily loaded into a ggplot coding environment and their color mapping changed based on the distinct layer classification of each ggPlantmap.
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 that contains quantitative data attributed to your ROIs.
This approach can be very helpful for R Shiny app developers to create web interactive tools to visualize quantitative data in plant cell or structures.
Some examples of heatmaps generated from available published data:
Not at all. ggPlantmap can also be used to produce many other type of plots. Essentially anything that you can trace, you can create! Be creative! I hope to build a community where people explore the usage of ggPlantmap for the communication of Plant science.
The principle of creating a ggPlantmap is fairly simple. We generate a list of ROIs (region of interests) in the Icy open-source software (https://icy.bioimageanalysis.org/) from any plant image. These ROIs are saved as XML files and later be converted into ggPlantmaps with the XML.to.ggPlantmap() function. We created step-by-step guide with tips on how to generate xml images from plant images.
new.ggPlantmap <- XML.to.ggPlantmap("data/ggPm.sample.xml")
ggPlantmap.plot(new.ggPlantmap,ROI.name)
YES!!! Any Plant map can be included in the package. Here are some instructions on how your newly created ggPlantmap can be included in the package.
If you create one, please email me ([email protected]) your ggPlantmap as tab-delimited table and I’ll make sure to include in the package. You will be credited and your information will be displayed in the summary file. I really hope this becomes an organic package with the contribution of the plant research community.
I would like to acknowledge Kaisa Kajala, Lisa Oskam, Monica Garcia Gomez, Pierre Gautrat and Kyra van der Velde for testing ggPlantmap. I also would like to acknowledge Andres Romanowski for providing some data for the initial tests of ggPlantmap.
(Soon)