Robust detection of periodic time series measured from biological systems

Background: Periodic phenomena are widespread in biology. The problem of finding periodicity in biological time series can be viewed as a multiple hypothesis testing of the spectral content of a given time series. The exact noise characteristics are unknown in many bioinformatics applications. Furthermore, the observed time series can exhibit other non-idealities, such as outliers, short length and distortion from the original wave form. Hence, the computational methods should preferably be robust against such anomalies in the data. Results: We propose a general-purpose robust testing procedure for finding periodic sequences in multiple time series data. The proposed method is based on a robust spectral estimator which is incorporated into the hypothesis testing framework using a so-called g-statistic together with correction for multiple testing. This results in a robust testing procedure which is insensitive to heavy contamination of outliers, missing-values, short time series, nonl...

• Type: Journal
• Year: 2005
• Where: BMCBI
• Authors: Miika Ahdesmäki, Harri Lähdesmäki, Ronald K. Pearson, Heikki Huttunen, Olli Yli-Harja
• Keywords: BMCBI 2005, Hypothesis Testing, Robust Testing Procedure, Time Series