Awesome

ggPlantmap <img src="man/figures/hex.png" align="right" height="250"/>

Version 1.1.0

Author: Leonardo Jo (l.jo@uu.nl)

Overview

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. <img src="man/figures/ggPlantmap.example.jpg" align="center" width="1000"/>

Installation

##install devtools (if you haven't already)
install.packages("devtools")
library(devtools)

## Installing from a github respository
install_github("leonardojo/ggPlantmap")

ggPlantmap useful guides (HIGHLY RECOMMENDED!)

(NEW) Here is a practical guide on how to work with ggPlantmap to explore single-cell data

I created a step-by-step user guide to help users navigate through the package.

Here is a recorded seminar with an overview of 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.

What is a ggPlantmap?

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.

Plotting a ggPlantmap

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)

Pre-loaded ggPlantmaps

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  Description Layers Image.Reference
#>    <chr>                 <chr>   <chr>  <chr> <chr>       <chr>  <chr>          
#>  1 ggPm.At.roottip.cros… Arabid… root   cros… Cross-sect… Cells  https://www.fu…
#>  2 ggPm.At.roottip.long… 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.leafepidermi… Arabid… leaf … top … Top view o… Cells  https://www.na…
#>  5 ggPm.At.leaf.crossse… Arabid… leaves cros… Cross-sect… Cells  https://doi.or…
#>  6 ggPm.At.seed.devseri… Arabid… seed   deve… Diagram of… Cells… https://doi.or…
#>  7 ggPm.At.earlyembryog… Arabid… embryo deve… Diagram of… Cells… https://doi.or…
#>  8 ggPm.At.shootapex.lo… Arabid… shoot… long… Diagram of… Layer… https://doi.or…
#>  9 ggPm.At.inflorescenc… Arabid… inflo… cros… Cross-sect… Cells  https://academ…
#> 10 ggPm.Sl.root.crossse… Solanu… root   cros… Cross-sect… Cells  https://doi.or…
#> 11 ggPm.At.leaf.topview  Arabid… leaf   top … Top view o… Leaves http://doi.org…
#> 12 ggPm.At.rootelong.lo… Arabid… root … long… Longitudin… Cells  https://doi.or…
#> 13 ggPm.At.rootmatur.cr… Arabid… root … cros… Cross-sect… Cells  https://doi.or…
#> 14 ggPm.At.flower.diagr… Arabid… flower diag… Diagram of… Tissu… Taiz, Lincoln,…
#> 15 ggPm.At.lateralroot.… Arabid… later… deve… Diagram of… Cells… https://doi.or…
#> 16 ggPm.Ms.root.crossse… Medica… root   cros… Cross-sect… Cells  Unpublished    
#> # ℹ 2 more variables: Made.by <chr>, Contact.Info <chr>

Color mapping

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.

Overlaying external quantitative data into a 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. <img src="man/figures/guide/Slide7.JPG" align="center" width="1200"/><br />

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: <img src="man/figures/README-Slide2.PNG" align="center" width="1000"/><br /> <img src="man/figures/README-Slide3.PNG" align="center" width="1000"/><br />

Is ggPlantmap only usefull for molecular expression 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.

How can I create my own ggPlantmap?

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)

Can my ggPlantmap be included in the package?

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 (l.jo@uu.nl) 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.

Can my ggPlantmap be integrated into an ePlant EFP browser or any other graphical software?

YES!! You can convert your ggPlantmap table into an SVG file that can be used

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)

Acknowledgements

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.

How to cite

Peer-reviewed manuscript

Jo, Leonardo, and Kaisa Kajala. “ggPlantmap: an open-source R package for the creation of informative and quantitative ggplot maps derived from plant images.” Journal of Experimental Botany (2024): erae043. doi: https://doi.org/10.1093/jxb/erae043

Pre-print

(Pre-print1) Leonardo Jo, Kaisa Kajala. ggPlantmap: an R package for creation of informative and quantitative ggplot maps derived from plant images. bioRxiv 2023.11.30.569429; doi: https://doi.org/10.1101/2023.11.30.569429

(Pre-print2) Leonardo Jo, Kaisa Kajala. ggPlantmap: an R package for the graphic mapping of plant images. Authorea. September 21, 2023. DOI: 10.22541/au.169531385.58441696/v1