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  • Title: A Computational Error and Restricted Use of Time-series Analyses Underlie the Failure to Replicate period-Dependent Song Rhythms in Drosophila.
    Author: Kyriacou CP, Dowse HB, Zhang L, Green EW.
    Journal: J Biol Rhythms; 2020 Jun; 35(3):235-245. PubMed ID: 32096437.
    Abstract:
    From 1980 to 1991, Kyriacou, Hall, and collaborators (K&H) reported that the Drosophila melanogaster courtship song has a 1-min cycle in the length of mean interpulse intervals (IPIs) that is modulated by circadian rhythm period mutations. In 2014, Stern failed to replicate these results using a fully automated method for detecting song pulses. Manual annotation of Stern's song records exposed a ~50% error rate in detection of IPIs, but the corrected data revealed period-dependent IPI cycles using a variety of statistical methods. In 2017, Stern et al. dismissed the sine/cosine method originally used by K&H to detect significant cycles, claiming that randomized songs showed as many significant values as real data using cosinor analysis. We first identify a simple mathematical error in Stern et al.'s cosinor implementation that invalidates their critique of the method. Stern et al. also concluded that although the manually corrected wild-type and perL mutant songs show similar periods to those observed by K&H, each song is usually not significantly rhythmic by the Lomb-Scargle (L-S) periodogram, so any genotypic effect simply reflects "noise." Here, we observe that L-S is extremely conservative compared with 3 other time-series analyses in assessing the significance of rhythmicity, both for conventional locomotor activity data collected in equally spaced time bins and for unequally spaced song records. Using randomization of locomotor and song data to generate confidence limits for L-S instead of the theoretically derived values, we find that L-S is now consistent with the other methods in determining significant rhythmicity in locomotor and song records and that it confirms period-dependent song cycles. We conclude that Stern and colleagues' failure to identify song cycles stems from the limitations of automated methods in accurately reflecting song parameters, combined with the use of an overly stringent method to discriminate rhythmicity in courtship songs.
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