title: LSPR: an integrated periodicity detection algorithm for unevenly sampled temporal microarray data.
abstract: 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.
  
  
  BACKGROUND
  LSPR algorithm is implemented as MATLAB software and is available at http://bioinformatics.cau.edu.cn/LSPR.
authors.affiliation: Division of Bioinformatics, State Key Laboratory of Plant Physiology and Biochemistry, College of Biological Sciences, China Agricultural University, Beijing, China.
authors.forename: Rendong
authors.initials: R
authors.lastname: Yang
authors.forename: Chen
authors.initials: C
authors.lastname: Zhang
authors.forename: Zhen
authors.initials: Z
authors.lastname: Su
mesh_terms: D000465:Algorithms; D017360:Arabidopsis / Q000235:genetics; D002940:Circadian Rhythm / Q000235:genetics; D020869:Gene Expression Profiling / Q000379:methods*; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D010507:Periodicity; D012984:Software