Multiple biological processes are driven by oscillatory gene expression at different time scales. Oscillatory dynamics are thought to be widespread due to their superior information encoding capabilities, and single cell live imaging of gene expression has lead to a surge of dynamic, possibly oscillatory, data for different gene networks. However, the regulation of gene expression at the level of an individual cell involves reactions between finite numbers of molecules, and this can result in inherent randomness in oscillatory dynamics. Furthermore, the process of transcription has been shown to be bursty, which blurs the boundaries between aperiodic fluctuations and noisy oscillators. Thus, there is an acute need for an objective statistical method for classifying whether an experimentally derived noisy time series is periodic. Here we present a new data analysis method that combines mechanistic stochastic modelling with the powerful methods of Bayesian non-parametric regression  . Our method can distinguish oscillatory  expression from random fluctuations of non-oscillatory gene expression in single cell data , despite peak-to-peak variability in period and amplitude of single cell oscillations. We show that our method outperforms the Lomb-Scargle periodogram in successfully classifying cells as oscillatory or non-oscillatory in data simulated from a simple genetic oscillator model  . Analysis of bioluminescent live cell imaging shows a significantly greater number of oscillatory cells when luciferase is driven by a {\it Hes1 promoter (10/19), which has previously been reported to oscillate, than the constitutive MMLV promoter (0/25). The method can be applied to data from any gene network to both quantify the proportion of oscillating cells within a population and to measure the period and quality of oscillations. It is publicly available as a MATLAB package.
Multiple biological processes are driven by oscillatory gene expression at different time scales. Pulsatile dynamics are thought to be widespread , and single-cell live imaging of gene expression has lead to a surge of dynamic, possibly oscillatory, data for different gene networks. However, the regulation of gene expression at the level of an individual cell involves reactions between finite numbers of molecules, and this can result in inherent randomness in expression dynamics, which blurs the boundaries between aperiodic fluctuations and noisy oscillators. Thus, there is an acute need for an objective statistical method for classifying whether an experimentally derived noisy time series is periodic. Here we present a new data analysis method that combines mechanistic stochastic modelling with the powerful methods of non-parametric regression with Gaussian processes . Our method can distinguish oscillatory gene expression from random fluctuations of non-oscillatory expression in single-cell time series , despite peak-to-peak variability in period and amplitude of single-cell oscillations. We show that our method outperforms the Lomb-Scargle periodogram in successfully classifying cells as oscillatory or non-oscillatory in data simulated from a simple genetic oscillator model and in experimental data . Analysis of bioluminescent live cell imaging shows a significantly greater number of oscillatory cells when luciferase is driven by a {\it Hes1 promoter (10/19), which has previously been reported to oscillate, than the constitutive MoMuLV 5' LTR (MMLV) promoter (0/25). The method can be applied to data from any gene network to both quantify the proportion of oscillating cells within a population and to measure the period and quality of oscillations. It is publicly available as a MATLAB package.
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Oscillatory
Pulsatile
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due to their superior information encoding capabilities, and single cell
, and single-cell
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oscillatory dynamics. Furthermore, the process of transcription has been shown to be bursty,
expression dynamics,
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Bayesian
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with Gaussian processes
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gene
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gene expression in single cell data
expression in single-cell time series
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single cell
single-cell
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and in experimental data
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MMLV
MoMuLV 5' LTR (MMLV)