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Optimization of experimental designs for biological rhythm discovery

Fig 6

Irregular designs improve simulated periodogram analysis at frequencies up to the Nyquist rate.

(A)-(B) Oscillations were detected using periodogram analysis with measurements (N = 40 samples) from either an equispaced or irregular design optimized for the frequencies . Each signal in the dataset was assigned an oscillatory (amplitude A = 2, noise strength ) or non-oscillatory (amplitude A = 0, noise strength ) state with equal probability. The acrophase of the oscillatory signals was assigned uniformly at random (). (A) Performance of the irregular and equispaced designs at detecting oscillations across frequencies (x-axis) included in the optimization, summarized by the AUC score (y-axis) of a receiver operator characteristic curve with p-values generated from a Lomb-Scargle periodogram. The AUC score for each frequency was computed by testing for oscillations in a dataset of oscillatory and white-noise signals (n = 10⁴ signals per dataset). The dashed line indicates the Nyquist rate of the equispaced design. (B) The same analysis as (A) but at frequencies above the Nyquist rate of the equispaced design. Periodogram analysis was performed using the lomb library [37] and AUC scores were computed using the pROC library [38]. Irregular designs were generated using the same differential evolution parameters as in Fig 4.

doi: https://doi.org/10.1371/journal.pcbi.1013662.g006