Inferring the perturbation time from biological time course data.
Inferring the perturbation time from biological time course data.
复制标题
DOI：
10.1093/bioinformatics/btw329
复制
发表时间：
2016-10-01
期刊：
影响因子：
--
通讯作者：
Rattray M
中科院分区：
文献类型：
--
作者：
Yang J;Penfold CA;Grant MR;Rattray M
动机：时间进程数据经常被用来研究扰动后生物过程的变化。已经开发了统计方法来确定这种扰动是否会引起随时间的变化，例如比较扰动和未扰动的时间进程数据集以揭示差异。然而，现有的方法没有提供一种原则性的统计方法来识别两个时间历程数据集在扰动后第一次开始发散的具体时间；我们称之为扰动时间。对生物过程中不同变量的扰动时间的估计使我们能够识别扰动之后的事件序列，从而为可能的因果关系提供有价值的见解。结果：我们提出了一种贝叶斯方法来推断给定的时间过程数据的扰动时间从野生型和扰动系统。我们使用了基于高斯过程回归的非参数方法。我们推导出了扰动时间之前来自同一剖面，扰动时间之后发散的噪声污染和复制的时间过程数据的概率模型。该模型可以精确地计算出模型的似然函数，并通过简单的直方图方法得到扰动时间的后验分布，而不需要复杂的近似推理算法。我们在模拟数据上验证了该方法，并将其应用于研究接种紫丁香假单胞菌后拟南芥中发生的转录变化。番茄DC3000与非武装菌株DC3000hrpA。可获得性和实现：https://github.com/ManchesterBioinference/DEtime上提供了实现该方法的R包DEtime，以及复制所有结果所需的数据和代码。补充信息：补充信息：生物信息学在线。
Motivation: 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. Results: 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 Pseudomonas syringae pv. tomato DC3000 versus the disarmed strain DC3000hrpA. Availability and Implementation: 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. Contact: Jing.Yang@manchester.ac.uk or Magnus.Rattray@manchester.ac.uk Supplementary information: Supplementary data are available at Bioinformatics online.
登录
查看更多内容
影响因子：
7.3
作者：
Hua, Zhi-Ming;Yang, Xianci;Fromm, Michael E.
通讯作者：
Fromm, Michael E.
影响因子：
11.4
作者：
de Torres-Zabala, Marta;Truman, William;Grant, Murray
通讯作者：
Grant, Murray
影响因子：
3
作者：
Kim J;Ogden RT;Kim H
通讯作者：
Kim H
影响因子：
3.7
作者：
Huibers RP;Loonen AE;Gao D;Van den Ackerveken G;Visser RG;Bai Y
通讯作者：
Bai Y
影响因子：
3
作者：
Angelini C;Cutillo L;De Canditiis D;Mutarelli M;Pensky M
通讯作者：
Pensky M