Abstract
A brief overview is provided of cosinor-based techniques for the analysis of time series in chronobiology. Conceived as a regression problem, the method is applicable to non-equidistant data, a major advantage. Another dividend is the feasibility of deriving confidence intervals for parameters of rhythmic components of known periods, readily drawn from the least squares procedure, stressing the importance of prior (external) information. Originally developed for the analysis of short and sparse data series, the extended cosinor has been further developed for the analysis of long time series, focusing both on rhythm detection and parameter estimation. Attention is given to the assumptions underlying the use of the cosinor and ways to determine whether they are satisfied. In particular, ways of dealing with non-stationary data are presented. Examples illustrate the use of the different cosinor-based methods, extending their application from the study of circadian rhythms to the mapping of broad time structures (chronomes).
Highlights
Non-random variations are found as a function of time at the cellular level, in tissue culture, as well as in multi-cellular organisms at different levels of physiologic organization [1]
Summary statistics Before proceeding with any data analysis, it is recommended to first plot the data as a function of time. Such a chronogram can be informative in several ways
A chronogram of residuals can provide valuable information regarding the adequacy of the model, and the need for data transformation
Summary
Non-random variations are found as a function of time at the cellular level, in tissue culture, as well as in multi-cellular organisms at different levels of physiologic organization [1]. After the existence of ubiquitous circadian rhythms was demonstrated, it was possible to apply the single cosinor method in many experiments aimed at determining the times of highest efficacy and lowest toxicity in response to a variety of drugs and other stimuli by fitting a 24-hour cosine curve to 6 values, 4 hours apart, each value representing the number of experimental animals that survived a given intervention applied at one of the 6 timepoints when overall about 50% of the animals had died These results led to the fields of chronopharmacy and chronotherapy [39,40,41,42]. Estimates for M, β, and γ are obtained by solving the normal equations, obtained by expressing that RSS is minimal when its first-order derivatives with respect to each parameter are zero
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
