使用高斯过程识别单细胞活体成像时间序列中的随机振荡
Identifying stochastic oscillations in single-cell live imaging time series using Gaussian processes
摘要：
多种生物过程由不同时间尺度的振荡基因表达驱动。脉动动力学被认为是广泛的，并且基因表达的单细胞活体成像已经导致了不同基因网络的动态（可能是振荡的）数据的激增。然而，在单个细胞水平上对基因表达的调节涉及有限数量的分子之间的反应，这可能导致表达动力学中固有的随机性，这模糊了非周期性波动和噪声振荡器之间的界限。因此，迫切需要一种客观的统计方法来分类实验得出的噪声时间序列是否是周期性的。在这里，我们提出了一种新的数据分析方法，结合了机械随机建模与强大的方法与高斯过程的非参数回归。我们的方法可以区分振荡基因表达的随机波动的非振荡表达在单细胞时间序列，尽管峰到峰的变化周期和振幅的单细胞振荡。我们表明，我们的方法优于Lomb-Scargle周期图成功地分类细胞振荡或非振荡的数据模拟从一个简单的遗传振荡器模型和实验数据。生物发光活细胞成像的分析显示，当荧光素酶由{ \it Hes 1}启动子驱动时（10/19），与组成型MoMuLV 5' LTR（MMLV）启动子（0/25）相比，振荡细胞的数量显著更大，先前已报道该启动子振荡。该方法可以应用于来自任何基因网络的数据，以量化群体中振荡细胞的比例，并测量振荡的周期和质量。它作为MATLAB包公开提供。
英文摘要：
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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