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Bioinformatics. 2011 Apr 1;27(7):1023-5. doi: 10.1093/bioinformatics/btr041. Epub 2011 Feb 3.

LSPR: an integrated periodicity detection algorithm for unevenly sampled temporal microarray data.

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Division of Bioinformatics, State Key Laboratory of Plant Physiology and Biochemistry, College of Biological Sciences, China Agricultural University, Beijing, China.


We propose a three-step periodicity detection algorithm named LSPR. Our method first preprocesses the raw time-series by removing the linear trend and filtering noise. In the second step, LSPR employs a Lomb-Scargle periodogram to estimate the periodicity in the time-series. Finally, harmonic regression is applied to model the cyclic components. Inferred periodic transcripts are selected by a false discovery rate procedure. We have applied LSPR to unevenly sampled synthetic data and two Arabidopsis diurnal expression datasets, and compared its performance with the existing well-established algorithms. Results show that LSPR is capable of identifying periodic transcripts more accurately than existing algorithms.


LSPR algorithm is implemented as MATLAB software and is available at

[Indexed for MEDLINE]

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