A User-friendly and Powerful R Analysis of Large-scale Datasets

We aim to understand the molecular basis underlying how plants defend against their pathogens, and ultimately use this knowledge to improve plant health and crop yield. Large datasets can be relatively easily generated in a biological field, however, analyzing such larger datasets in a timely manner can be challenging. To begin, add the seeds to a microcentrifuge tube with three holes at the top and place the tube inside a bell jar.

Then generate bleach vapor by adding three milliliters of 36%hydrochloric acid to 100 milliliters of household bleach in a beaker inside a fume hood. Place the beaker in the bell jar and leave it in a chemical fume hood for three hours. After sterilization, using a sterile glass Pasteur pipette, plate the seeds onto Murashige and Skoog medium plates containing 0.5%sucrose and 0.8%auger inside a laminar flow cabinet.

Place the plated and sealed Murashige and Skoog plates in a four-degree Celsius environment for two days before moving them into a tissue culture chamber set to a 12-hour light and 12-hour dark cycle. Then, transfer the seedlings to a 96-well plate, ensuring each well contains 180 microliters of assay media. Cover the plate with clear film and poke two holes per well for aeration.

Place the plates for one day under 12-hour light and 12-hour dark cycles, followed by one day under 24 hours of constant light. Add treatments or mock solution to each well. Then, record luminescence readings under constant light conditions with the emission filter lens gain set to 3, 600 and the measurement interval time set to one second and start the data acquisition.

At the end of the luminescence recording, take a photograph of the 96-well plate to document the seedling growth. Observe the data curves from the luminescence assay to confirm treatment consistency and save the raw luminescence data as a CSV file for downstream R analysis using the save as function in the plate reader's data analysis software. After installing RStudio software and the required algorithmic packages, select the working directory where the input file is stored, which will also serve as the output folder for results.

Then, select the correctly formatted CSV input file. Ensure the top row contains time points and the first column lists the individual sample positions on the 96-well plate. Name the samples and treatments according to the 96-well plate layout, ensuring that the design includes either eight replicates per treatment with up to 12 treatments, or 12 replicates per treatment with up to eight treatments.

If any rows or columns are empty, assign names such as Empty1, Empty2, and so on to the treatment label list. Then, indicate the relative start time for the luciferase assay based on the time of light onset in the chamber. Modify the User Input II section to suit specific analysis needs.

Include graph generation for luminescence curves and for period, phase, and amplitude comparisons across genotypes and treatments. Use ANOVA test with Tukey's honest significant difference test to compare treatments based on period, phase, and amplitude. Choose a control treatment for the analysis output or leave the control field empty to compare all treatments pairwise.

Optionally, number the analysis output files for easier reference and organization. Now, use a t-test to compare treatments based on their period, phase, and amplitude. Choose whether the t-test should be conducted as a pairwise comparison, and if so, specify whether the data are paired.

Choose whether to round the time points in the dataset. If time values differ only by a few minutes, round them to the nearest hour before proceeding with the analysis. Now, set the input format for well identification based on how the plate reader exports data.

Choose between the standard format where wells are listed by A1, A2, A3, and so on, or the alternative format where wells are listed by A1, B1, C1, et cetera. Then, run the luciferase data analysis by clicking the Source button located in the top right corner of the RStudio console. View the analysis output in the designated output folder, which contains documents and sub-folders summarizing the averaged period, phase, and amplitude statistics for each genotype and treatment.

Using a biopsy puncture, cut four-millimeter diameter leaf discs from the fourth to seventh leaves of 25-day-old plants. Float the leaf discs with the hairy side facing up in 100 microliters of sterile water inside a 96-well plate. Cover the 96-well plate with clean tinfoil and place it inside a light and dark growth chamber overnight.

Then, remove the plate and replace sterile water with 100 microliters of luminol solution. Immediately begin recording luminescence every minute for 40 to 60 minutes. After downloading the required RStudio packages, select the working directory.

Select the input file, ensuring it is a correctly formatted CSV file. The top row should include time series data, and the first column should contain the sample positions on a 96-well plate. Name the samples and treatments according to the plate layout as described earlier.

Label empty wells explicitly as Empty1, Empty2, and so on. Use ANOVA test with Tukey's honest significant difference to compare total luminescence sums between treatments. Use a two-sided t-test when comparing data from only two treatments at a time.

Generate graphical outputs including fluorescence curves and a bar plot showing the total luminescence sum across treatments. Add either standard deviation or standard error of the mean to the plotted bars. Adjust the input reading format based on how the plate reader outputs well identifiers.

Choose between standard listing by A1, A2, A3, or vertical listing by A1, B1, C1.To run the analysis, click the Source button located in the top right corner of the RStudio console. View the output in the generated folder, which contains multiple documents and sub-folders summarizing the full analysis. The luciferase assay was performed using one transgenic line expressing the CCA1 luciferase reporter and seven independently transformed transgenic lines expressing the GRP7 luciferase reporter.

The luminescence traces of these plants were measured over 168 hours. Using the R method, computed clock parameters for the CCA1 luciferase reporter were obtained with an amplitude of 3, 000 relative luminescence units per second per seedling, a period of 23.5 hours, and a phase of 3.5 hours. All pGRP7 luciferase lines displayed similar period and phase values, but varied in amplitude.

The period of pGRP7 luciferase is 24.2 hours, while the phase is 12 hours. To further validate the R analysis, the same dataset was reanalyzed using BioDare2, a free online platform for circadian data analysis eight, and similar results were obtained. The circadian data generated with U2 OS cells expressing the Per2dLuc reporter were reanalyzed using the R method.

The control group displayed an amplitude of 184.8 relative luminescence units, a period of 23.3 hours, and a phase of 2.8 hours. Knockdown of CRY2, but not PSMD4 and PSMD7, significantly affected circadian parameters, amplitude, phase, and period. These results were consistent with the published results.

Our research has revealed that the circadian clock is important for planned defense against pathogens. We have routinely used the protocol described here to analyze large-scale time series datasets from both clock and defense assays. Our protocol using R scripts in RStudio provides a user-friendly and convenient tool for researchers working with large-scale time series data.

Our protocol, it's easy to use, has multiple statistical options, and allows a beginner who does not have prior R knowledge or programming experience to use it.