The Shiny app CIRCADA-I enables interactive data analysis and supports a variety of csv data file formats (including ClockLab and LumiCycle). The app provides some initial data processing, such as detrending and binning, then applies methods for estimation of period, phase, amplitude, significance of rhythmicity, and extraction of circadian components: fitting to sine and frequency modulated Möbius models, Lomb-Scargle periodogram, autocorrelation, discrete wavelet transform, singular spectrum analysis, a discrete Fourier transform based test of significance of rhythmicity, and a Bayesian spectral analyis. Results can be downloaded as a csv spreadsheet and there is a batch option for analyzing a set of data files.
CIRCADA-I is available as a browser-based app at https://circada.shinyapps.io/circada-i/, or can be run in RStudio using the files provided here.
The two earlier Shiny apps CIRCADA-E and CIRCADA-S support exploration of circadian data, enabling visualization and analysis of circadian parameters like period and phase. Methods include the discrete wavelet transform, sine-fitting, the Lomb-Scargle periodogram, autocorrelation, and maximum entropy spectral analysis. The apps also provide educational overviews and guidance for these methods, supporting the training of those new to this type of analysis. CIRCADA-E (Circadian App for Data Analysis - Experimental Time Series) allows users to explore a large experimental dataset of mouse body temperature, locomotor activity, and PER2::LUC from multiple tissues. CIRCADA-S (Circadian App for Data Analysis - Synthetic Time Series) generates and analyzes time series with user-specified parameters, thereby demonstrating how accuracy of period and phase estimates depends on type and level of noise, sampling rate, length of recording, and method.
To run the apps, download the CIRCADA folder to your computer. In RStudio, open CIRCADA-E.R or CIRCADA-S.R and then click “Run App” (next to a green triangle). This will open the app in a new browser window or tab. Make your desired selections and then click “Go” to run the analysis. To close, either click the “Shut down app” button or click the stop sign in the RStudio Console window and then close the window or tab. All required packages will be installed as needed by running the app. Please work through the "GettingStarted" document to learn more fully about the features of the apps. You can also directly access the apps at https://circada.shinyapps.io/circada-e/ and https://circada.shinyapps.io/circada-s/, if you prefer not to work through RStudio.
Analysis results for CIRCADA-S.R are output in csv format to CIRCADA-S-output.csv. For full batch output of the experimental data set, see the files and R code available at https://osf.io/seyhp/.
For a guided tour of the apps and methods, work through the lesson at https://sophia.smith.edu/circada/ developed by Blanca Martin Burgos and Selma Tir, with assistance from Hannah Wang and Dominica Cao. The lesson includes checkpoint questions at regular intervals to support self-testing for comprehension (with immediate feedback on correctness).
