Spectral Analysis on Time-Course Expression Data : Detecting Periodic Genes Using a Real-Valued Iterative Adaptive Approach
Joint Authors
Agyepong, Kwadwo S.
Serpedin, Erchin
Hsu, Fang-Han
Dougherty, Edward R.
Source
Issue
Vol. 2013, Issue 2013 (31 Dec. 2013), pp.1-10, 10 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2013-02-28
Country of Publication
Egypt
No. of Pages
10
Main Subjects
Natural & Life Sciences (Multidisciplinary)
Biology
Abstract EN
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.
American Psychological Association (APA)
Agyepong, Kwadwo S.& Hsu, Fang-Han& Dougherty, Edward R.& Serpedin, Erchin. 2013. Spectral Analysis on Time-Course Expression Data : Detecting Periodic Genes Using a Real-Valued Iterative Adaptive Approach. Advances in Bioinformatics،Vol. 2013, no. 2013, pp.1-10.
https://search.emarefa.net/detail/BIM-451580
Modern Language Association (MLA)
Agyepong, Kwadwo S.…[et al.]. Spectral Analysis on Time-Course Expression Data : Detecting Periodic Genes Using a Real-Valued Iterative Adaptive Approach. Advances in Bioinformatics No. 2013 (2013), pp.1-10.
https://search.emarefa.net/detail/BIM-451580
American Medical Association (AMA)
Agyepong, Kwadwo S.& Hsu, Fang-Han& Dougherty, Edward R.& Serpedin, Erchin. Spectral Analysis on Time-Course Expression Data : Detecting Periodic Genes Using a Real-Valued Iterative Adaptive Approach. Advances in Bioinformatics. 2013. Vol. 2013, no. 2013, pp.1-10.
https://search.emarefa.net/detail/BIM-451580
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-451580