Permutation test for periodicity in short time series data.
Permutation test for periodicity in short time series data.
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DOI：
10.1186/1471-2105-7-s2-s10
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发表时间：
2006-09-06
影响因子：
3
通讯作者：
Gimble JM
中科院分区：
文献类型：
--
作者：
Ptitsyn AA;Zvonic S;Gimble JM
周期性过程，如昼夜节律，是调节和协调调控关键代谢途径的基因转录的重要因素。从理论上讲，即使是在不同组织之间的昼夜节律基因表达模式的编排小的波动可能会导致在生物体水平上的功能紊乱，并可能导致广泛的病理性疾病。在时间序列数据中识别昼夜表达模式是重要的，但同样具有挑战性。微阵列技术允许在每个时间点估计数千个基因的相对表达。然而，这种估计往往缺乏精度和微阵列实验是昂贵的，限制了时间序列表达谱中的数据点的数量。在这些实验中产生的数据带有高度的随机变化，模糊了周期性模式和有限数量的重复，通常覆盖不超过两个完整的振荡周期。为了解决这个问题，我们已经开发了一个简单的，但有效的，计算技术，在相对较短的时间序列，典型的生物钟表达的微阵列研究的周期性模式的识别。该测试基于时间点的随机排列，以估计周期图的非随机性。置换时间或Pt检验能够检测由高度随机波动或不同不相关频率的振荡主导的表达谱中给定时间段内的振荡。我们已经进行了全面的研究，昼夜节律的表达在PBRC产生的一个大的数据集，代表三个不同的外周小鼠组织。我们还重新分析了一些类似的时间序列数据集，这些数据集在过去几年中由其他研究小组独立制作和发布。置换时间测试（Pt-测试）被证明是有效的检测周期性在短时间序列的高密度微阵列实验的典型。该软件是一组C++程序，作者可以在开源的基础上提供。
Periodic processes, such as the circadian rhythm, are important factors modulating and coordinating transcription of genes governing key metabolic pathways. Theoretically, even small fluctuations in the orchestration of circadian gene expression patterns among different tissues may result in functional asynchrony at the organism level and may contribute to a wide range of pathologic disorders. Identification of circadian expression pattern in time series data is important, but equally challenging. Microarray technology allows estimation of relative expression of thousands of genes at each time point. However, this estimation often lacks precision and microarray experiments are prohibitively expensive, limiting the number of data points in a time series expression profile. The data produced in these experiments carries a high degree of stochastic variation, obscuring the periodic pattern and a limited number of replicates, typically covering not more than two complete periods of oscillation. To address this issue, we have developed a simple, but effective, computational technique for the identification of a periodic pattern in relatively short time series, typical for microarray studies of circadian expression. This test is based on a random permutation of time points in order to estimate non-randomness of a periodogram. The Permutated time, or Pt-test, is able to detect oscillations within a given period in expression profiles dominated by a high degree of stochastic fluctuations or oscillations of different irrelevant frequencies. We have conducted a comprehensive study of circadian expression on a large data set produced at PBRC, representing three different peripheral murine tissues. We have also re-analyzed a number of similar time series data sets produced and published independently by other research groups over the past few years. The Permutated time test (Pt-test) is demonstrated to be effective for detection of periodicity in short time series typical for high-density microarray experiments. The software is a set of C++ programs available from the authors on the open source basis.