Introduction

PaCeQuant is a Fiji Plug-in and it is the leading tool for pavement cell shape quantification (Moller et al, 2017). PaCeQuant can calculate 28 shape values from each pavement cell. Normally, a few hundred cells are analyzed per condition, therefore, these data sets can become difficult to manage manually. In PaCeQuant, each image generates a separate results file called "-table". In this repository, we share a custom code that compiles those result tables into one data-frame suitable for building ggplot2 graphs.

Other Solutions PaceQuant is a Fiji Plug-in. PaCeQuantAna (Poeschl et al, 2020) is a very useful Rstudio-compatible R library for analysis of the results obtained by PaCeQuant. However, the final figures are limited in their customization and specific pairwise statistical analysis is difficult to integrate.

This Solution

In this repository, we share a custom code that compiles PaCeQuant result tables into one data-frame suitable for building ggplot2 graphs. Once the data-frame is built, there is more flexibility to build graphs suited to the user’s needs.

This code : 1) will look for "results" folders and compile result tables within 2) use the name of the parent folder ("var1_var2") and assigned those variable to the compiled data 3) generate a data-frame ready to use with ggplot2. See the following example.

This is an example of a split violin graph. In y-axis is displaying one of the 28 shape values calculated from PaCeQuant, in this particular case "Lobes per cell". In the x-axis is variable1 (Genotype) and the 2 color on each violin are representing variable2 (Treament).

To use this code:

1. In RStudio, set as working directory the same filepath used in PaCeQuant.
2. Name the folders using 2 variables, e.g. “time”, “position”, “genotype”, “treatment”, “concentration”,
3. Make sure the two variables are separated with an underscore, e.g “wt_mock”, “wt_hormone”, "mutant_mock", "mutant_hormone"
4. By default these variables are “Genotype” and “Treatment”.
5. If variables different from Genotype and Treatment will be used, please introduce the name of those 2 variables in each line of the code that annotated with “### Change accordingly”

This code was built and used in the following research project:

Hierarchical global and local auxin signals coordinate cellular interdigitation in Arabidopsis. Patricio Pérez-Henríquez1,2, Hongjiang Li1,5, Xiang Zhou3,4, Xue Pan6, Wenwei Lin2, Wenxin Tang2，Shingo Nagawa2, Deshu Lin2, Tongda Xu2, Marta Michniewicz7, Michael J. Prigge8, Lucia C. Strader7, Mark Estelle8, Ken-ichiro Hayashi9, Jiří Friml5, Linlin Qi3, Zhongchi Liu3,, Jaimie Van Norman2, Zhenbiao Yang3,4,1,10*

References

1.    Möller, B., Poeschl, Y., Plötner, R., and Bürstenbinder, K. (2017). PaCeQuant: A Tool for High-Throughput Quantification of Pavement Cell Shape Characteristics. Plant Physiol. 175, 998–1017. 10.1104/pp.17.00961.
2.    Poeschl, Y., Möller, B., Müller, L., and Bürstenbinder, K. (2020). User-friendly assessment of pavement cell shape features with PaCeQuant: Novel functions and tools. Methods Cell Biol. 160, 349–363. 10.1016/bs.mcb.2020.04.010.