Analysis of telemetric time series data for periodic components using DQ-FIT

DQ-FIT and CV-SORT have been developed to facilitate the automatic analysis of data sampled by radiotelemetry, but they can also be used with other data sampled in chronobiological settings. After import of data, DQ-FIT performs conventional linear, as well as rhythm analysis according to user-defined specifications. Linear analysis includes calculation of mean values, load values (percentage of values above a defined limit), highest and lowest readings, and areas under the (parameter-time) curve (AUC). All of these parameters are calculated for the total sampling interval and for user-defined day and night periods. Rhythm analysis is performed by fitting of partial Fourier series with up to six harmonics. The contribution of each harmonic to the overall variation of data is tested statistically; only those components are included in the best-fit function that contribute significantly. Parameters calculated in DQ-FIT's rhythm analysis include mesor, amplitudes, and acrophases of all rhythmic components; significance and percentage rhythm of the combined best fit; maximum and minimum of the fitted curve and times of their occurrence. In addition, DQ-FIT uses the first derivative of the fitted curve (i.e., its slope) to determine the time and extent of maximal increases and decreases within the total sampling interval or user-defined intervals of interest, such as the times of lights on or off. CV-SORT can be used to create tables or graphs from groups of data sets analyzed by DQ-FIT. Graphs are created in CV-SORT by calculation of group mean profiles from individual best-fit curves rather than their curve parameters. This approach allows the user to combine data sets that differ in the number and/or period length of harmonics included. In conclusion, DQ-FIT and CV-SORT can be helpful in the analysis of time-dependent data sampled by telemetry or other monitoring systems. The software can be obtained on request by every interested researcher.