Introduction to GerminaR

GerminaQuant for R allows make the calculations for germination indices incredibly easy in an interactive web applications build in R (R Core Team, 2020), based in GerminaR (Lozano Isla et al., 2020) and Shiny R package (Chang et al., 2020) . GerminaQuant app is reactive!. Outputs change instantly as users modify inputs. The principal features of the application allows to calculate the principal germination indices, statistical analysis and easy way to plot the results.

Seed germination process

The physiology and seed technology have provided valuable tools for the production of high quality seed and treatments and storage conditions (Marcos-Filho, 1998). In basic research, the seeds are studied exhaustively, and the approach of its biology is performed to fully exploit the dormancy and germination (Penfield & King, 2009). An important tool for indicate the performance of a seed lot is the precise quantification of germination through accurate analysis of the cumulative germination data (Joosen et al., 2009). Time, velocity, homogeneity, uncertainty and synchrony are measurements that inform the dynamics of the germination process. It is interesting not only for physiologists and seed technologists, but also for environmentalists, since it is possible to predict the degree of success of the species, based on the seed crop ability to redistribute germination over time, allowing the recruitment of part of the seedlings formed (Ranal & Santana, 2006).

Germination fieldbook

For a correct analysis and fast data processing is important to take into account the data organization and the proper record of the germination process. In this section we will explain how you should collect and organize your data.

Prepare you fieldbook

For using GerminaR and GerminaQuant is necessary that you have a dataset with germination values. You can use a following data as an example “prosopis”.

If you have a Google account you can made a copy of the document and edit it online or download in Excel format for your own analysis.

Data organization

The field book should have three essential parts.(1) The factor columns (red), according to the experimental design;(2) the seed number column, indicate the number of seeds sown in each experimental unit (green) and (3) the evaluation columns with the germination values (blue) (Figure @ref(fig:dtorg)). You can design your own field book with different names in the column according your experimental design.

Layout for germination evaluation process. The factor column (red) are according the experimental design. The seed number column (green) for the number of seed sown and the evaluation columns (blue) for accounting the germination.

Layout for germination evaluation process. The factor column (red) are according the experimental design. The seed number column (green) for the number of seed sown and the evaluation columns (blue) for accounting the germination.

Data collection

The evaluation of the germination process is obtained of the count of the germination in each experimental unit and It can be evaluated in any time lapse (hours, days, months, etc) in continuous interval of the same length always beginning with the time zero (ei. Ti00), until the end of the germination process or according to the researcher criteria.

Germination analysis

After the data collection, the information can be processed using GerminaR by the R console or using the GerminaQuant App. The web application can be used in any device in an interactive way. The application is compound for different tabs (Table @ref(tab:tabs)) that allow to make the analysis very easy.

Name and description of each tab of GerminaQuant to evaluate and analyze the germination process.
Tabs Description
Presentation Presentation of the application, principal characteristics and contributors
User Manual User manual explain the meaning of each index and how to collect and process your data
Fieldbook Interface to upload the field book and choose the parameter for the germination analysis, GerminaQuant allows to upload the data from google sheet or excel file
Germination Calculate automatically the germination variables and export the data file.
Boxplot Interface to explore your data and their distribution
Statistics Interface to choose the variables according the experimental design for analysis of variance and summarize the information according the principal mean comparison test
Graphics Graphic the mean comparison test for the variables selected in the statistical analysis and plot the information in customized bar or line plot.
InTime Selecting the factor from your experiment, allows plotting the germination process in time.
Tools Tool for calculate the osmotic potential for any salt or PEG for germination experiments

GerminaQuant data processing

Fieldbook

When you have your fieldbook, you can access to the app GerminaQuant for R and go “Fieldbook” tab. Figure @ref(fig:impdt).

Fieldbook interface for import your data

Fieldbook interface for import your data

You can paste a Google spread sheet URL or upload a local file in xlsx format. In “Seeds (col name)” you have to write the name of the column containing the information of the number of seed sown in each experimental unit, for “Evaluations (prefix)” you have to put the prefix of the names for the evaluated days from the germination time lapse.

Germination

If the parameter in the “Fieldbook” tab are correct, in “Germination” tab will be performed and the values of the germination indices will be shown maintaining the experimental design. GerminaQuant allows to copy or downloading the information in “csv” or “xlsx” format. Figure @ref(fig:dwl)

Dowload option for the calculated variables

Dowload option for the calculated variables

Statistical

The GerminaQuant application can perform analysis for experimental design in a Complete Randomize Design (CRD), Randomize Complete Block Design (RCBD), Latin Square Design (LSD) or factorial designs, allowing calculate the analysis of variance (AOV) and the mean differences through Student Newman Keuls (SNK), Tukey or Duncan test.

Statitical analysis with ANOVA and mean comparison test

Statitical analysis with ANOVA and mean comparison test

Graphics

Automatically after performed the statistical analysis the application will generate the graphs for the variable chosen with the mean comparison test. The app interface allows customized the graphics in a bar or line plot and export in “tiff” format for publication quality.

Customized interface for bar or line plot

Customized interface for bar or line plot

InTime

This Tab allows to visualize the germination process according one of the experimental factors. The app interface allows customized the graphic. Figure @ref(fig:gtime)

Germination in time plot

Germination in time plot

The application allows to plot two type of graphics, the first is the germination percentage in time lapse, and the second the relative germination that calculates the germination according the total number of germinated seeds.

References

Chang, W., Cheng, J., Allaire, J., Xie, Y., & McPherson, J. (2020). Shiny: Web application framework for r. https://shiny.rstudio.com

Joosen, R. V. L., Kodde, J., Willems, L. A. J., Ligterink, W., Plas, L. H. W. van der, & Hilhorst, H. W. M. (2009). Germinator: A software package for high-throughput scoring and curve fitting of arabidopsis seed germination. The Plant Journal, 62(1), 148–159. https://doi.org/10.1111/j.1365-313x.2009.04116.x

Lozano Isla, F., Benites Alfaro, O., & Pompelli, M. F. (2020). GerminaR: Indices and graphics for assess seed germination process. https://CRAN.R-project.org/package=GerminaR

Marcos-Filho, J. (1998). New approaches to seed vigor testing. Scientia Agricola, 55, 27–33. https://doi.org/10.1590/S0103-90161998000500005

Penfield, S., & King, J. (2009). Towards a systems biology approach to understanding seed dormancy and germination. Proceedings of the Royal Society B: Biological Sciences, 276(1673), 3561–3569. https://doi.org/10.1098/rspb.2009.0592

Ranal, M. A., & Santana, D. G. de. (2006). How and why to measure the germination process? Revista Brasileira de Botânica, 29(1), 1–11. https://doi.org/10.1590/s0100-84042006000100002

R Core Team. (2020). R: A language and environment for statistical computing. R Foundation for Statistical Computing. https://www.R-project.org/