Significance-testing of periodogram for short time series

Andrzej Kudlicki, Maga Rowicka, Zbyszek Otwinowski

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations

Abstract

Periodicity detection in timecourse gene expression data is usually based on periodogram method and a pre-defined significance threshold. Existing periodogram significance testing formulae are often inaccurate for short time series. We demonstrate that the magnitude of the error resulting from using the theoretical approximations is not negligible in such case and discuss how it depends on different factors. Our conclusions are illustrated by examples of short time series typically produced by microarray studies. In these examples, we show a substantial discrepancy between different theoretical formulae and results of numerical simulations in the number of periodic genes found. We demonstrate that the accuracy of simulations is much higher than that of any of the theoretical approximations and conclude that estimation of significance should be based on comparisons with simulated datasets rather than theoretical approximations whenever feasible.

Original languageEnglish (US)
Title of host publicationProceedings of the 2008 International Conference on Bioinformatics and Computational Biology, BIOCOMP 2008
Pages424-430
Number of pages7
StatePublished - 2008
Event2008 International Conference on Bioinformatics and Computational Biology, BIOCOMP 2008 - Las Vegas, NV, United States
Duration: Jul 14 2008Jul 17 2008

Publication series

NameProceedings of the 2008 International Conference on Bioinformatics and Computational Biology, BIOCOMP 2008

Other

Other2008 International Conference on Bioinformatics and Computational Biology, BIOCOMP 2008
Country/TerritoryUnited States
CityLas Vegas, NV
Period7/14/087/17/08

Keywords

  • Microarray
  • Periodogram
  • Statistical significance
  • Time series

ASJC Scopus subject areas

  • Computer Science Applications
  • Software
  • Biomedical Engineering
  • Health Informatics

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