Optimal experimental design to estimate statistically significant periods of oscillations in time course data.
We investigated commonly used methods (Autocorrelation, Enright, and Discrete Fourier Transform) to estimate the periodicity of oscillatory data and determine which method most accurately estimated periods while being least vulnerable to the presence of noise. Both simulated and experimental data were used in the analysis performed. We determined the significance of calculated periods by applying these methods to several random permutations of the data and then calculating the probability of obtaining the period's peak in the corresponding periodograms. Our analysis suggests that the Enright method is the most accurate for estimating the period of oscillatory data. We further show that to accurately estimate the period of oscillatory data, it is necessary that at least five cycles of data are sampled, using at least four data points per cycle. These results suggest that the Enright method should be more widely applied in order to improve the analysis of oscillatory data.
Department of Molecular and Integrative Physiology, University of Michigan Medical School, Ann Arbor, Michigan, United States of America.
Márcio
M
Mourão
Department of Pharmacology, University of Michigan Medical School, Ann Arbor, Michigan, United States of America; Brehm Center for Diabetes Research, University of Michigan Medical School, Ann Arbor, Michigan, United States of America.
Leslie
L
Satin
Department of Molecular and Integrative Physiology, University of Michigan Medical School, Ann Arbor, Michigan, United States of America; Department for Computational Medicine & Bioinformatics, University of Michigan Medical School, Ann Arbor, Michigan, United States of America; Brehm Center for Diabetes Research, University of Michigan Medical School, Ann Arbor, Michigan, United States of America.
Santiago
S
Schnell
D003198:Computer Simulation; D005583:Fourier Analysis; D010507:Periodicity; D012107:Research Design; D012815:Signal Processing, Computer-Assisted