Time-course expression profiles and methods for spectrum analysis have been applied for detecting transcriptional periodicities,\r\nwhich are valuable patterns to unravel genes associated with cell cycle and circadian rhythm regulation. However, most of the\r\nproposed methods suffer fromrestrictions and large false positives to a certain extent.Additionally, in some experiments, arbitrarily\r\nirregular sampling times as well as the presence of high noise and small sample sizes make accurate detection a challenging task.\r\nA novel scheme for detecting periodicities in time-course expression data is proposed, in which a real-valued iterative adaptive\r\napproach (RIAA), originally proposed for signal processing, is applied for periodogram estimation. The inferred spectrum is then\r\nanalyzed using Fisher�s hypothesis test. With a proper ??-value threshold, periodic genes can be detected. A periodic signal, two\r\nnonperiodic signals, and four sampling strategies were considered in the simulations, including both bursts and drops. In addition,\r\ntwo yeast real datasetswere applied for validation.The simulations and real data analysis reveal that RIAAcan performcompetitively\r\nwith the existing algorithms. The advantage of RIAA is manifested when the expression data are highly irregularly sampled, and\r\nwhen the number of cycles covered by the sampling time points is very reduced.
Loading....