Time series of gene expression often exhibit periodic behavior under the influence of multiple signal pathways, and are represented by a model that incorporates multiple harmonics and noise. Most of these data, which are observed using DNA microarrays, consist of few sampling points in time, but most periodicity detection methods require a relatively large number of sampling points. We have previously developed a detection algorithm based on the discrete Fourier transform and Akaike’s information criterion. Here we demonstrate the performance of the algorithm for small-sample time series data through a comparison with conventional and newly proposed periodicity detection methods based on a statistical analysis of the power of harmonics.We show that this method has higher sensitivity for data consisting of multiple harmonics, and is more robust against noise than other methods. Although “combinatorial explosion” occurs for large datasets, the computational time is not a problem for small-sample datasets. The MATLAB/GNU Octave script of the algorithm is available on the author’s web site: .
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机译:基因表达的时间序列通常在多个信号通路的影响下表现出周期性行为,并由包含多个谐波和噪声的模型表示。使用DNA微阵列观察到的大多数数据都只有很少的时间采样点,但是大多数周期性检测方法都需要相对大量的采样点。我们之前已经开发了一种基于离散傅里叶变换和Akaike信息准则的检测算法。在此,我们根据谐波功率的统计分析,通过与传统和新近提出的周期性检测方法进行比较,证明了小样本时间序列数据算法的性能。谐波,并且比其他方法更抗噪声。尽管大型数据集会发生“组合爆炸”,但对于小样本数据集而言,计算时间并不是问题。该算法的MATLAB / GNU Octave脚本可在作者的网站上找到。
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