Agyepong Kwadwo S, Hsu Fang-Han, Dougherty Edward R, Serpedin Erchin
Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX 77843-3128, USA.
Adv Bioinformatics. 2013;2013:171530. doi: 10.1155/2013/171530. Epub 2013 Feb 28.
Time-course expression profiles and methods for spectrum analysis have been applied for detecting transcriptional periodicities, which are valuable patterns to unravel genes associated with cell cycle and circadian rhythm regulation. However, most of the proposed methods suffer from restrictions and large false positives to a certain extent. Additionally, in some experiments, arbitrarily irregular sampling times as well as the presence of high noise and small sample sizes make accurate detection a challenging task. A novel scheme for detecting periodicities in time-course expression data is proposed, in which a real-valued iterative adaptive approach (RIAA), originally proposed for signal processing, is applied for periodogram estimation. The inferred spectrum is then analyzed using Fisher's hypothesis test. With a proper p-value threshold, periodic genes can be detected. A periodic signal, two nonperiodic signals, and four sampling strategies were considered in the simulations, including both bursts and drops. In addition, two yeast real datasets were applied for validation. The simulations and real data analysis reveal that RIAA can perform competitively with the existing algorithms. The advantage of RIAA is manifested when the expression data are highly irregularly sampled, and when the number of cycles covered by the sampling time points is very reduced.
时间进程表达谱和频谱分析方法已被用于检测转录周期性，这是揭示与细胞周期和昼夜节律调节相关基因的有价值模式。然而，大多数提出的方法在一定程度上受到限制且存在大量假阳性。此外，在一些实验中，任意不规则的采样时间以及高噪声和小样本量的存在使得准确检测成为一项具有挑战性的任务。本文提出了一种用于检测时间进程表达数据中周期性的新方案，其中最初为信号处理提出的实值迭代自适应方法（RIAA）被应用于周期图估计。然后使用Fisher假设检验对推断出的频谱进行分析。通过适当的p值阈值，可以检测出周期性基因。在模拟中考虑了一个周期性信号、两个非周期性信号和四种采样策略，包括突发和下降情况。此外，还应用了两个酵母真实数据集进行验证。模拟和实际数据分析表明，RIAA与现有算法相比具有竞争力。当表达数据采样高度不规则且采样时间点覆盖的周期数非常少时，RIAA的优势就会显现出来。
