标题： 从生物时间序列数据中推断扰动时间 显示英文标题

标题： Inferring the perturbation time from biological time course data

摘要： 时间序列数据常用于研究扰动后生物过程的变化。统计方法已被开发出来确定这种扰动是否随时间引起变化，例如比较扰动和未扰动的时间序列数据集以发现差异。然而，现有方法并未提供一种有原则的统计方法来识别两个时间序列数据集在扰动后首次开始分化的特定时间；我们称这个时间为扰动时间。对生物过程中不同变量的扰动时间进行估计，使我们能够确定扰动后的事件顺序，从而提供有关可能因果关系的有价值见解。在本文中，我们提出了一种贝叶斯方法，根据野生型和扰动系统的时间序列数据推断扰动时间。我们使用基于高斯过程回归的非参数方法。我们推导了一个概率模型，该模型描述了在扰动时间之前来自同一轮廓的噪声污染和重复的时间序列数据，并在扰动时间之后发生分化。对于这个模型，似然函数可以精确计算，扰动时间的后验分布通过简单的直方图方法获得，而无需使用复杂的近似推理算法。我们在模拟数据上验证了该方法，并将其应用于研究拟南芥接种丁香假单胞菌番茄致病变种DC3000与无毒菌株DC3000hrpA后的转录变化。一个实现该方法的R包DEtime可在https://github.com/ManchesterBioinference/DEtime获取，同时提供了重现所有结果所需的数据和代码。 显示英文摘要

摘要： Time course data are often used to study the changes to a biological process after perturbation. Statistical methods have been developed to determine whether such a perturbation induces changes over time, e.g. comparing a perturbed and unperturbed time course dataset to uncover differences. However, existing methods do not provide a principled statistical approach to identify the specific time when the two time course datasets first begin to diverge after a perturbation; we call this the perturbation time. Estimation of the perturbation time for different variables in a biological process allows us to identify the sequence of events following a perturbation and therefore provides valuable insights into likely causal relationships. In this paper, we propose a Bayesian method to infer the perturbation time given time course data from a wild-type and perturbed system. We use a non-parametric approach based on Gaussian Process regression. We derive a probabilistic model of noise-corrupted and replicated time course data coming from the same profile before the perturbation time and diverging after the perturbation time. The likelihood function can be worked out exactly for this model and the posterior distribution of the perturbation time is obtained by a simple histogram approach, without recourse to complex approximate inference algorithms. We validate the method on simulated data and apply it to study the transcriptional change occurring in Arabidopsis following inoculation with P. syringae pv. tomato DC3000 versus the disarmed strain DC3000hrpA. An R package, DEtime, implementing the method is available at https://github.com/ManchesterBioinference/DEtime along with the data and code required to reproduce all the results.