Periodicity detection method for small-sample time series datasets.

ABSTRACT: Time series of gene expression often exhibit periodic behavior under the influence of multiple signal pathways, and are represented by a model that incorporates multiple harmonics and noise. Most of these data, which are observed using DNA microarrays, consist of few sampling points in time, but most periodicity detection methods require a relatively large number of sampling points. We have previously developed a detection algorithm based on the discrete Fourier transform and Akaike's information criterion. Here we demonstrate the performance of the algorithm for small-sample time series data through a comparison with conventional and newly proposed periodicity detection methods based on a statistical analysis of the power of harmonics.We show that this method has higher sensitivity for da

SUBMITTER: Tominaga D 

PROVIDER: S-EPMC2998870 | biostudies-literature | 2010 Nov

REPOSITORIES: biostudies-literature