Strengths and limitations of period estimation methods for circadian data.
Strengths and limitations of period estimation methods for circadian data.
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DOI：
10.1371/journal.pone.0096462
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发表时间：
2014
期刊：
影响因子：
3.7
通讯作者：
Millar AJ
中科院分区：
文献类型：
--
作者：
Zielinski T;Moore AM;Troup E;Halliday KJ;Millar AJ
昼夜节律数据分析的关键步骤是准确估计潜在的周期。有许多不同的技术和算法用于确定周期，所有这些都具有不同的假设和不同的复杂程度。选择哪种算法，哪种实现以及使用哪种准确性度量可能会带来许多陷阱，特别是对于非专家来说。我们开发了BioDare系统，这是一种在线服务，可以分享数据（包括向公众传播）、处理数据和进行分析。昼夜节律实验是BioDare的主要焦点，因此执行周期分析是该系统的主要功能。BioDare中已纳入六种方法：Enright和Lomb-Scargle周期图，FFT-NLLS，mFourfit，梅萨和Spectrum Resistance。在这里，我们将回顾这六种技术，解释每种算法背后的原理，并评估它们的性能。为了量化方法的准确性，我们针对人工数学测试信号和模型生成的mRNA数据来检查算法。我们在Java中重新实现了每个方法，可以对与每个算法相关的计算复杂度和计算时间进行有意义的比较。最后，我们提供了哪些算法最适合哪些数据类型的指导方针，并对实验设计提出了建议，以提取最佳数据进行分析。
A key step in the analysis of circadian data is to make an accurate estimate of the underlying period. There are many different techniques and algorithms for determining period, all with different assumptions and with differing levels of complexity. Choosing which algorithm, which implementation and which measures of accuracy to use can offer many pitfalls, especially for the non-expert. We have developed the BioDare system, an online service allowing data-sharing (including public dissemination), data-processing and analysis. Circadian experiments are the main focus of BioDare hence performing period analysis is a major feature of the system. Six methods have been incorporated into BioDare: Enright and Lomb-Scargle periodograms, FFT-NLLS, mFourfit, MESA and Spectrum Resampling. Here we review those six techniques, explain the principles behind each algorithm and evaluate their performance. In order to quantify the methods' accuracy, we examine the algorithms against artificial mathematical test signals and model-generated mRNA data. Our re-implementation of each method in Java allows meaningful comparisons of the computational complexity and computing time associated with each algorithm. Finally, we provide guidelines on which algorithms are most appropriate for which data types, and recommendations on experimental design to extract optimal data for analysis.
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影响因子：
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作者：
通讯作者：
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影响因子：
2
作者：
Cohen, Andrew L.;Leise, Tanya L.;Welsh, David K.
通讯作者：
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影响因子：
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作者：
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通讯作者：
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影响因子：
11.6
作者：
Gould, PD;Locke, JCW;Hall, A
通讯作者：
Hall, A
DOI：
10.1073/pnas.88.20.9107
发表时间：
1991-10-01
影响因子：
11.1
作者：
GOLDBETER, A
通讯作者：
GOLDBETER, A