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

With the increased availability of large datasets in the scientific field, it is important to develop user-friendly tools to allow researchers to quickly analyze these large datasets with accuracy and ease. One type of the common large dataset comes from the use of the luciferase gene as a reporter, which has allowed an easy, continuous, and noninvasive examination of gene expression in live cells and organisms. Automation in luminescence recording has transformed the measurement of luciferase luminescence and led to an expansion of data collection, in particular, in the circadian clock field^1,2. Using 96-well microplates and an automatic plate reader with a stacker, thousands of samples expressing the luciferase gene can be individually assayed in time series, sometimes at one-hour intervals for days, in one experiment. Such high-throughput experiments have resulted in the production of large datasets that traditional gene expression experiments using hand sample collection, followed by RNA processing, could not possibly achieve. Analyzing such large datasets in a timely manner is important but can be challenging.

Although there exists a plethora of tools to analyze data for rhythmicity, many of the tools analyze animal behavior-based assays rather than luminescence reporter expression^3,4,5,6,7 (Supplemental Table S1). Some tools require researchers to have prior computer programming skills, such as Python skills or access to MATLAB. Other tools require the purchase of software, which can be costly. Some free workable solutions are available online. One such tool is BioDare2⁸, which offers a variety of different methods to analyze rhythmicity data. BioDare2 is a user-friendly online tool and requires minimal computational expertise. Users need to upload data input online and download data output from the online interface for further processing.

Here, we present user-friendly R scripts with multiple capabilities for analyzing large-scale datasets with ease. We use the free, open-source software RStudio⁹, an interface for R and Python, to run the scripts. RStudio can be used on various computer systems, including Windows, Mac, and Linux. In this report, detailed stepwise instructions are provided to guide users on how to use the R scripts, specifically in protocol sections 1 and 2. This method requires minimal input from the user. A beginner who does not have prior R knowledge and who has no programming experience shall be able to use the method to analyze large datasets from luciferase assays or other types of datasets with time series data. All input and output data are stored on a local computer, and thus, an analysis can be done anywhere without the restriction of internet access, once all the relevant R packages are downloaded for the first time. The output data are sorted into well-organized folders with results ready to be processed for publications. Statistical analyses are also included as part of the output to provide a quick assessment of the differences among the samples. Thus, the R method could provide a facile and powerful solution for researchers in analyzing large datasets.

1. Luciferase-based circadian clock analysis

1. Experimental setup for the luciferase assay
   NOTE: The luciferase assay is a common method used to gauge circadian clock activity in real time. Luminescence, emitted from the luciferase reporter carried by live transgenic organisms and/or cells, can be recorded automatically, leading to the production of large-scale time series datasets. Our laboratory primarily uses Arabidopsis thaliana seedlings expressing the luciferase gene as a reporter. Figure 1 and Figure 2 show a flow chart of the typical experimental setup for the luciferase assay with seedlings in a 96-well format in our laboratory, modified based on a previous description².
   1. Sterilize seeds with bleach vapor generated by adding 3 mL of 36% (w/w) HCl to 100 mL house bleach in a beaker for 3 h. Place the beaker in a bell jar with a sealed lid, placed in a chemical fume hood. After the sterilization, plate the seeds on 1/2MS plates containing 0.5% sucrose and 0.8% agar (pH 5.7), using a sterile glass Pasteur pipette, in a laminar flow cabinet. Collect the chemical waste in a jar with a lid and dispose of it by working with institutional environmental health service department.
   2. Put plates in 4 °C for 2 days before moving them to a tissue culture chamber with 12 h light and 12 h dark light cycle (LD).
   3. Let seedlings grow for 4 d in LD.
   4. Transfer seedlings to a 96-well plate; each well containing 180 µL of assay media (1/2MS, 0.5% sucrose, 0.4% agar, 0.25 mM luciferin). Place the plates for 1 day in LD followed by 1 day in 24 h of constant light (LL).
   5. Add treatments or mock solution to the wells and record luminescence at 1 h intervals for 5-7 days in LL. Set the emission filter lens at 3,600 for gain and the measurement interval time at 1 s.
      NOTE: The starting time for recording can be at any time. However, because the R script only takes integers (whole numbers), the recording intervals must be a whole number. For example, a 1 h interval is permitted, but a 15 min interval is not permitted for the R script.
   6. At the end of the recording, take a photo of the plate to record seedling growth.
   7. Save raw data as a CSV file for R analysis, using the save as function with the data analysis software for the plate reader.
2. Stepwise explanation of R analysis of luciferase-based circadian data
   ​NOTE: This R analysis uses the R script Supplemental File 1 (LUC_2025.R), developed for circadian data analysis, and the input file Supplemental File 2 (NO7.csv). The R script along with the input file is publicly available via the online repository platform Github (https://github.com/elvenfire29/Circadian-Luciferase-Analysis).
   1. Download RStudio software that is compatible with the computer system^9,10.
   2. Download the RStudio algorithmic packages required by Supplemental File 1 (LUC_2025.R): MetaCycle¹¹: ARSER algorithm¹² of the MetaCycle package, ggplot2¹³, dplyr¹⁴, magrittr¹⁵, stringr¹⁶, filesstrings¹⁷, circular¹⁸, AICcmodavg¹⁹, and broom²⁰.
      NOTE: See also the top of the R script for the list of packages.
   3. Change User Input I (according to a specific dataset on a local computer, including the working directory, the name of the input file, labels for the treatments, and the relative start time of the assay.
      NOTE: Steps 1.2.1 and 1.2.2 only need to be completed once; after which the RStudio interface console is ready to take user input, with modifications for each new analysis. There are two parts in the User Input section (Supplemental Figure S1).
      1. Select the working directory. Indicate where the input file resides. The same folder will be the location for the output files.
      2. Select the input file, a CSV file correctly formatted for the R script (Figure 2B). Specifically, the top row has the time series, and the first column contains individual sample positions on a 96-well plate.
         NOTE: An example of such an input CSV file can be found in Supplemental File 2 (NO7.csv).
      3. Name the samples and/or treatments. As this analysis is used for data obtained from 96-well settings with a time course, design the experiments as 8 replicates per treatment for a total of up to 12 treatments per plate, or as 12 replicates per treatment for a total of up to 8 treatments per plate. Be sure to enter the correct number of samples, either 8 or 12, because the sample number counts; if the sample count is not 8 or 12, the code will not run. However, if there are rows of empty wells, just list them as such in the space for treatment names, for example, naming as empty 1, empty 2...
         NOTE: For sample names, one should avoid using backslashes or similar symbols as they may cause file name issues. Additionally, one should also avoid using "NA" as a label when selecting to use ANOVA in User Input II. Any treatments with the exact same name will be combined into one large treatment group.
      4. Indicate the relative start time for the assay. For a given day of 24 h, subjective dawn is when the chamber light is turned on during a normal light/dark cycle. For example, if light is on at 7 am, consider this time the circadian time 0. If a luciferase assay begins at 9 am, the assay time is at circadian time 2; input 2 as the relative start time.
         NOTE: The relative start time must be a whole number and cannot be negative. This will affect the phase reading of the samples.
   4. Change User Input II options per specific needs.
      NOTE: The instructions below provide a more detailed explanation of the User Input sections and a step-by-step guide for making the changes.
      1. Include graphs: luminescence curves, plots for the period, phase, and amplitude per genotype and treatment.
      2. Use the ANOVA test with the Tukey HSD to compare treatments on their period, phase, and amplitude. Choose a control for the ANOVA test output; specify the control for the ANOVA test or leave the option empty to use each treatment as a control in the pairwise comparison. Alternatively, choose whether to number the ANOVA output files for quicker reference and organization.
      3. Use a t-test to compare treatments on their period, phase, and amplitude. Choose whether the t-test should be a pairwise comparison; if a pairwise t-test is selected, select whether the data are paired. Choose a control for the t-test; specify the control for the t-test or leave the option empty to use each treatment as a control. Choose whether to number the t-test output files for quicker reference and organization.
         NOTE: This should only be used to compare two treatments at once, such as two lines of the same genotype with either SA or Mock added. The p-values shown are not adjusted to account for multiple comparisons with the same line.
      4. Choose whether to round the time points. If the time points are off by only a minute or two, round to the nearest hour and use this analysis.
         NOTE: This code calculates the period, phase, and amplitude using the ARS method¹². This method requires a consistent time series in hours to run.
      5. Depending on how the plate reader records wells, change the way the input is read. Choose either the standard data listing of wells by A1, A2, A3... or the one for wells listed by A1, B1, C1...
   5. Run the analysis by simply clicking the Source button on the top right corner of the console.
      NOTE: The analysis takes less than 1 min to complete. The output of the R analysis will automatically appear in the same file folder as the input dataset.
   6. View output: The output folder has several documents and sub-folders to provide a comprehensive analysis, including statistical information of the averaged period, phase, and amplitude for replicates of each genotype and/or treatment.
      ​NOTE: For the input file Supplemental File 2 (NO7.csv), a list of the output documents generated by the Supplemental File 1 (LUC_2025.R) script in protocol section 1 is described in Table 1 and can be conveniently viewed with the tree structure in Supplemental Figure S2.

2. Luminol-based ROS Assay

1. Experimental setup for ROS assay
   1. Cut 4 mm-diameter leaf discs from the fourth to seventh leaves of 25-day-old plants with a biopsy puncher.
   2. Float the leaf discs with the hairy side up atop 100 µL of sterile water in a 96-well plate.
   3. Cover the plate with clean tin foil and place it in an LD growth chamber overnight.
   4. Replace water with 100 µL of luminol solution (300 µM in 10 mM MOPS buffer with pH 7.4, and 1 µM flg22 or another elicitor). Use mock solution in place of flg22 as a control.
   5. Immediately start recording luminescence readings every minute for 40-60 min.
2. Stepwise explanation of R analysis of ROS data
   NOTE: This R analysis uses Rstudio, the R script, Supplemental File 3 (ROS_2025.R), and the input files Supplemental File 4 (ROS_flg22.csv) or Supplemental File 5 (ROS_elf26.csv). The R script along with the input file Supplemental File 4 (ROS_flg22.csv) is publicly available via the online repository platform Github (https://github.com/elvenfire29/ROS-Luminol-Analysis)
   1. Download RStudio packages: gplot2¹³, dplyr¹⁴, magrittr¹⁵, stringr¹⁶, and filesstrings¹⁷.
      NOTE: Steps 2.2.1 only need to be completed once. To help users to tailor each analysis to a specific set of data on the computer, a section called "User Input" was created to allow users to indicate specific parameters for their experiments (Supplemental Figure S3).
   2. Change User Input I according to a specific dataset on a local computer, including the working directory, the name of the input file, labels for the treatments, and the relative start time of the assay.
      1. Select the working directory. Indicate where the input file resides. The same folder will be the location for the output files.
      2. Select the input file, a CSV file correctly formatted for the R script (Figure 2B). Specifically, the top row has the time series, and the first column contains individual sample positions on a 96-well plate.
         NOTE: An example of such an input CSV file can be found in the supplemental material, Supplemental File 4 (ROS_flg22.csv) or Supplemental File 5 (ROS_elf26.csv).
      3. Name the samples and/or treatments. As this analysis is used for data obtained from 96-well settings with a time course, design the experiments as 8 replicates per treatment for a total of up to 12 treatments per plate, or as 12 replicates per treatment for a total of up to 8 treatments per plate. If there are rows of empty wells, just list them as such in the space for treatment names, for example, naming as empty 1, empty 2...
         NOTE: It is important to enter the correct number of samples, either 8 or 12, because the sample number counts. If the sample count is not 8 or 12, the code will not run. For sample names, avoid using backslashes or similar symbols as they may cause file name issues. Also avoid using "NA" as a label when selecting to use ANOVA in User Input II. Unlike the Supplemental File 1 (LUC_2025.R) script, treatments with the same name do not combine into a single group in this Supplemental File 3 (ROS_2025.R) script.
   3. Change User Input II options per user's specific needs.
      NOTE: Example User Input can be seen in Supplemental Figure S3.
      1. Use the ANOVA test with the Tukey HSD to compare treatment luminescence sums.
      2. Use a two-sided t-test to compare the data from only two treatments at once.
         NOTE: The p-values shown are not adjusted to account for multiple comparisons with the same line.
      3. Graph the fluorescence curves and a bar plot comparing the total sum of fluorescence. Add standard deviation or standard error of mean to the graphs.
      4. Depending on how the plate reader records wells, change the way the input is read. Either use the standard data listing of wells by A1, A2, A3... or the one for wells listed by A1, B1, C1...
3. Run analysis by simply clicking the Source button on the top right corner of the console.
   NOTE: The analysis takes less than 1 min to complete. The output of the R analysis will automatically appear in the same file folder as the input dataset.
4. View output: The output folder has several documents and sub-folders to provide a comprehensive analysis.
   NOTE: For the input file Supplemental File 4 (ROS_flg22.csv), a tree structure of the output documents generated by the Supplemental File 3 (ROS_2025.R) script described in protocol section 2 is shown in Supplemental Figure S4.

Case study 1. The luminescence assay for circadian clock activity with Arabidopsis seedlings

We previously showed that the GLYCINE-RICH RNA-BINDING PROTEIN 7 (GRP7) gene was controlled by the master clock protein CIRCADIAN CLOCK-ASSOCIATED 1 (CCA1) and circadian expression of GRP7 is important for its role in plant defense, using transgenic Col-0 plants expressing the luciferase reporter under the control of the wildtype GRP7 promoter (pGRP7wt:LUC)21. We analyzed the circadian clock activities of these transgenic plants along with the control plant CCA1:LUC/Col-0, using the R script named LUC_2025.R (Supplemental File 1 in protocol section 1).

The input file named NO7.csv (Supplemental File 2) has luminescence readings for seven independent pGRP7wt:LUC lines and the CCA1:LUC/Col-0 control ( Supplemental File 2 (NO7.csv)). After running the script, the output subfolder called NO7 output will be generated under the same folder as the input file NO7.csv (Supplemental File 2 (NO7.csv)). The files of the NO7 output folder are described in Table 1 and can be conveniently viewed with the tree structure in Supplemental Figure S2. Values in the NO7 output folder were further processed to make Figure 3 and Figure 4. Figure 3 shows that the CCA1:LUC reporter displayed an amplitude of 3,000 RLU, a period of 23.5 h, and a phase of 3.5 h. These clock parameters are largely consistent with previous reports22,23. A different expression pattern was observed for the pGRP7wt:Luc lines. While all the pGRP7wt:LUC lines appeared to be similar in period and phase, there were differences in the amplitude values of these lines, likely due to positional effect of the transgenes in chromosomes. These observations were further confirmed when the period, amplitude, and phase parameters were computed via the R script (Figure 4). To validate this analysis, the same dataset was reanalyzed using BioDare2, a free online platform for circadian data analysis8. The results from the R analysis were comparable to those obtained from BioDare2 FFT-NLLS (NLLS) algorithm8,24 (Figure 4).

Case study 2. The luminescence assay for circadian clock activity with mammalian cells
The R script LUC_2025.R (Supplemental File 1 was further used to analyze the circadian clock activity displayed by mammalian cells25. The U2 OS cell line expressing a circadian clock reporter is a commonly used model cell line to gauge mammalian circadian clock activities26,27. We re-analyzed time series data generated with U2 OS cells expressing the Per2d:Luc reporter cultured in a 96-well plate. The cells were treated with siRNA molecules targeting specific genes. Figure 5 shows that the negative control cells, which were not treated with siRNA, showed a period of 23.3 h, a phase of 2.8 h, and an amplitude of 184.8 RLU. As expected, the siRNA targeting the CRY2 gene significantly dampened the amplitude and affected the period and phase of the reporter. PSMD4 and PSMD7 genes encode proteins that are a part of 26S proteasome lid component for protein degradation. Consistent with the previous report25, the R analysis shows that knocking down of PSMD4 or PSMD7 by their respective siRNA does not affect the clock parameters. Thus, this R script is readily applicable to different experimental systems for circadian clock studies.

Case study 3. The ROS assay for a defense response
In addition to large datasets from luminescent circadian clock assays, the R script can be adapted to analyze other data types. Here we present one such application for quantifying reactive oxygen species (ROS). Plants are known to have evolved various strategies to fight against pathogen invasion. One of the strategies is to recognize non-self-molecules from a pathogen and subsequently activate innate immune responses. One such early immune response is a ROS burst, occurring within minutes when a host encounters a non-self-molecule. A typical ROS assay was conducted with a 96-well plate, containing 12 leaf discs per genotype per treatment (protocol section 2). Here, two common elicitor molecules, flg22, a 22-amino acid peptide derived from the conserved region of bacterial flagellin proteins28, and elf26, a 26-amino acid peptide from the elongation factor Tu protein29, were used to induce ROS burst. The script, Supplemental File 3 (ROS_2025.R), was developed for ROS data analysis. Two CVS files from ROS assays, Supplemental File 4 (ROS_flg22.csv) and Supplemental File 5 (ROS_elf26.csv), that were converted to the format of the R analysis, can be downloaded from the Supplementary Material section. After R analysis, the output folders shall be generated in the same folder as each input file in one's own computer, containing the ROS burst curves and the total ROS values during the assay time, along with statistical analyses (Supplemental Figure S4). The data were further processed to make Figure 6. The results shown here are similar to those published, which were processed manually30.

Figure 1: Flow chart of the luciferase assay for R analysis. Seedlings expressing a luciferase reporter driven by a clock promoter were sterilized and grown on 1/2 MS media in LD for 4 days. Seedlings were transferred to 96-well plates containing 180 µL of 1/2 MS medium containing D-luciferin. Each well contained one seedling. After 1 day in LD followed by 1 day in LL, luminescence was recorded with a plate reader. The seedlings on a plate were typically recorded for luminescence in LL at 1 h intervals for 5-7 days. After the recording, plates were photographed to assess seedling growth and the raw data were saved as a CSV file for R analysis. Abbreviations: LD = 12 h light/12 h dark; LL = constant light. Please click here to view a larger version of this figure.

Figure 2: Flow chart of luminescence data acquisition and R analysis. ( A) A five-step procedure is outlined for circadian clock analysis using the R script. Step 1. Set up experiments either as 8 or 12 seedlings per genotype and/or per treatment; Step 2. Record luminescence in LL at 1 h intervals for 5-7 days; Step 3. Obtain and format data in a CSV file; Step 4. Analyze data using R; and Step 5. View output data. The starting time for recording can be at any time. However, because the R script only takes integers (whole numbers), the recording intervals must be a whole number. (B) Screenshot of an input CSV file correctly formatted for the R script. The original input file, NO7.csv, can be found in Supplemental File 2. Please click here to view a larger version of this figure.

Figure 3: Circadian expression of pGRP7wt:LUC in transgenic plants. Luminescence traces of pGRP7wt:LUC are shown. The bars under the x-axis indicate subjective day (open bars) and night (gray bars). The luminescence trace of each genotype is an average of 12 replicates. Error bars were not shown due to the large number of curves. Abbreviation: RLU = Relative luminescence units. Please click here to view a larger version of this figure.

Figure 4: A comparison of output data from the R script and BioDare2. The same set of data shown in Figure 3 was analyzed by the R script and by BioDare2 for circadian clock parameters, amplitude, period, and phase. Data represent mean ± SEM (n=12). Different letters indicate significant difference among the samples (P < 0.05; One-way ANOVA with post hoc Tukey's HSD test). Please click here to view a larger version of this figure.

Figure 5: Analysis of circadian clock activity with mammalian cells. Time series data generated with U2 OS cells expressing the Per2dLuc reporter cultured on a 96-well plate in DD were described previously 25. siRNA molecules targeting CRY2, PSMD4, or PSMD7 were used to treat the cells. ( A) Luminescence traces. ( B) Amplitude, Period, and Phase of Per2d:Luc. Data represent mean ± SEM (n = 3). Different letters in panel ( B) indicate significant difference between the negative control and an siRNA-treated sample (P<0.05; One-way ANOVA with post hoc Tukey's HSD test). Abbreviation: RLU = relative luminescence unit. Please click here to view a larger version of this figure.

Figure 6: ROS burst analysis by R. Seedlings were recorded for relative luminescence unit immediately after being treated with 1 µM flg22 (left) or 1 µM elf26 (right). ( A) Luminescence traces averaged from 12 seedlings per genotype (n = 12) in a time course post elicitation. The average values per genotype per treatment are part of the R output. ( B) Averaged total luminescence counts for each genotype with either flg22 or elf26 treatment. Data represent mean ± SEM (n = 12). Different letters indicate significant difference among the samples (P < 0.05; One-way ANOVA with post hoc Tukey's HSD test). Abbreviation: RLU = relative luminescence unit. Please click here to view a larger version of this figure.

Table 1: A list of the output documents from R analysis. This is a list of the output documents generated by the LUC_2025.R script (Supplemental File 1) and the input file NO7.csv (Supplemental File 2).

Supplemental Figure S1: Screenshots for Input I and Input II in protocol section 1. User Input I must be changed to tailor the analysis to a specific dataset on a local computer. Changes to User Input II are optional, depending on the experimental setting. It is important to note that the Supplemental File 1 (LUC_2025.R) script expects all wells to be present in the file and not just selected or used wells. Please click here to download this figure.

Supplemental Figure S2: Tree structure for the output documents. This output was generated by using the LUC_2025.R script (Supplemental File 1) and the input file NO7.csv (Supplemental File 2). The LUC_2025.R script generates an output folder based on the input file name. For more details about the output files, see Table 1. Boxes represent file folders. Please click here to download this figure.

Supplemental Figure S3: Screenshots for User Input I and User Input II in protocol section 2. The Supplemental File 3 (ROS_2025.R) script uses the same general input format as the Supplemental File 1 (LUC_2025.R) script. User Input I must be changed to tailor the analysis to a specific dataset on a local computer. Changes to User Input II are optional, depending on the experimental setting. It is important to note that the Supplemental File 3 (ROS_2025.R) script expects all wells to be present in the file and not just selected or used wells. Please click here to download this figure.

Supplemental Figure S4: Tree structure for the output documents. This output was generated by using the ROS_2025.R script (Supplemental File 3) and the input file ROS_flg22.csv (Supplemental File 4). The ROS_2025.R script generates an output folder based on the input file name. Within that folder is a file for Total ROS Counts and a file for graphs. There are also subfolders for PRISM and graphing data, the ANOVA test, and the t-tests. Boxes represent file folders. Please click here to download this figure.

Supplemental Table S1: A list of available bioinformatics tools for circadian data analysis. Please click here to download this file.

Supplemental File 1: LUC_2025.R. This is the R script used for analysing circadian clock data. Please click here to download this file.

Supplemental File 2: NO7.csv. This is the input file containing an example of circadian clock data. Please click here to download this file.

Supplemental File 3: ROS_2025.R. This is the R script used for analysing ROS data. Please click here to download this file.

Supplemental File 4: ROS_fig22.csv. This is the input file containing an example of ROS data. ROS was induced by 1 µM flg22 treatment. Please click here to download this file.

Supplemental File 5: ROS_elf26.csv. This is the input file containing an example of ROS data. ROS was induced by 1 µM elf26 treatment. Please click here to download this file.

We present here using R scripts in RStudio to provide a user-friendly method for analyzing large-scale data obtained from 96-well format time series experiments. This method has allowed us to quickly and easily analyze luminescence recording data obtained from thousands of samples in time series experiments to gauge the circadian clock activity from plant seedlings to mammalian cell cultures, in addition to data from ROS assays.

BioDare2 is a free repository that has been quite commonly used for circadian data analysis. While BioDare2 is a well-made online tool, researchers would need to upload their data online and then download the results after each analysis. It also requires lengthy data labeling and manipulation to get the data into a usable format. The R method described here provides several functions that could improve user-friendliness. First, this method requires minimal input from a user. It only requires a 4-step, simple input to indicate the working directory, the input file, labels for the treatments, and the relative start time of a clock assay. Additional optional input can be added per the user's specific needs. Detailed instructions are provided here to better guide users on how to make the changes. Experiments on different plates can be run in back-to-back analyses without having to stop to type in repeat information. This script can be easily used to analyze experiments in 96-well format, either with 8 or 12 replicates per treatment, without the necessity to look up the labels of the plate wells. Second, the input data are from a local computer, and the output results are also saved in the same folder of the local computer. Therefore, the R analysis can be done anywhere and anytime and is not limited by access to the internet. Third, the output results are organized to allow easy interpretation and can be conveniently processed to generate figures for publications. Fourth, anyone with RStudio downloaded on their computer would be able to do the analysis without the necessity of prior computer programming skills.

The most critical step to learn this method to analyze circadian data would be to first use the example data provided here to set up the RStudio system in one's own computer (see protocol section 1). If one can run with the provided examples, then they can move on to their own data and only need to make some common modifications described in the Input I section step 1.2.3 in protocol section 1. It is important to know that for circadian experiments, while a recording can begin at any time, the recording intervals must be a whole number because the R script only takes integers (whole numbers). The R method can be easily adapted to analyze other type of data than just the circadian clock data; we already included such an example, the ROS assay data (see protocol section 2). This R analysis is highly recommended to be used with data from experiments using 96-well plates. However, even if an experiment is not conducted with a 96-well plate, as long as the data are arranged according to the same data format, this R method is applicable to analyze such data.

The output generated by this R method contains limited statistical analyses intended for rapid exploratory analysis. Users should assess whether their data meet the assumptions of parametric tests before drawing conclusions. For instance, this method does not provide a rhythmicity test to exclude non-rhythmic datasets. Readers should ensure that only rhythmic datasets are used in protocol section 1 for circadian analysis.

In conclusion, the method described here involving the R scripts in RStudio provides a user-friendly and convenient tool for researchers working with time series data in 96-well format. This method has been successfully used to analyze large datasets from circadian clock and ROS burst assays and can be easily adapted to analyze similar large datasets from time series experiments, preferably conducted with 96-well plates.