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Judgment periodicity of biological time series data is important and done widely to find the circadian expression of genes, monthly change of hormones, etc. To keep complete reproducibility of judgment is a problem because popular judgment methods such as curve fitting, Fourier analysis, etc. need judgment criteria determined by analysts considering experimental conditions and results (level, S/N, distribution, etc.) based on their experience. Judgment results are often affected by analysts' subjects. Reproducible criterion determination is therefore strongly needed. We propose introducing the information criterion to replace analysts' criteria. A judgment algorithm by combining Bayesian information criterion (BIC) and discrete Fourier transform (DFT) has been developed and has proved its ability through application to mice microarray data and finding of circadian genes. Our method, named "Piccolo", shows higher sensitivity than the simple DFT (without BIC) method with reproducibility, and can be fully automated.

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 data consisting of multiple harmonics, and is more robust against noise than other methods. Although "combinatorial explosion" occurs for large datasets, the computational time is not a problem for small-sample datasets. The MATLAB/GNU Octave script of the algorithm is available on the author's web site: http://www.cbrc.jp/%7Etominaga/piccolo/. [ABSTRACT FROM AUTHOR]