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Related Experiment Video

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A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

Published on: March 1, 2022

Judgment algorithm for periodicity of time series data based on Bayesian information criterion.

Daisuke Tominaga¹, Katsuhisa Horimoto

¹Computational Biology Research Center, National Institute of Advanced Industrial Science and Technology, 2-42, Aomi, Koto, Tokyo 135-0064, Japan. tominaga@cbrc.jp

Journal of Bioinformatics and Computational Biology

|September 4, 2008

Summary

This study introduces Piccolo, an automated algorithm combining Bayesian Information Criterion (BIC) and Discrete Fourier Transform (DFT) to reproducibly identify periodicity in biological time series data, improving gene expression analysis.

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Area of Science:

Chronobiology
Bioinformatics
Systems Biology

Background:

Accurate periodicity judgment in biological time series is crucial for understanding circadian gene expression and hormonal changes.
Current methods like curve fitting and Fourier analysis lack reproducibility due to subjective analyst criteria.
Analyst subjectivity in determining criteria can significantly impact the reliability of biological time series analysis.

Purpose of the Study:

To develop a reproducible and automated method for determining periodicity in biological time series data.
To replace subjective analyst criteria with an objective information criterion for enhanced reproducibility.
To improve the sensitivity and automation of periodicity detection in gene expression data.

Main Methods:

Introduction of an information criterion, specifically the Bayesian Information Criterion (BIC), to replace subjective analyst judgment.
Development of a novel algorithm, named "Piccolo", combining BIC with Discrete Fourier Transform (DFT).
Application of the Piccolo algorithm to analyze mice microarray data for identifying circadian genes.

Main Results:

The Piccolo algorithm demonstrated successful application in finding circadian genes within mice microarray data.
Piccolo exhibited higher sensitivity compared to the standard Discrete Fourier Transform (DFT) method alone.
The developed method ensures reproducibility and allows for full automation of the periodicity judgment process.

Conclusions:

The Piccolo algorithm offers a reproducible and automated solution for periodicity analysis in biological time series.
Integrating BIC with DFT provides a more sensitive and objective approach than traditional methods.
This advancement has significant implications for chronobiology and the analysis of gene expression patterns.