Robust Fisher's Test for Periodicity Detection in Noisy Biological Time Series

Periodicity detection in time series measurements is a usual application of signal processing in studying biological data. The reasons for detecting periodically behaving biological events are many, e.g. periodicity in gene expression time series could suggest cell cycle control over the gene expression. In this paper we present a robust version of the Fisher's test for detecting hidden periodicities in uniformly sampled time series data. The robust test performs better than the original test in case the data is not truly Gaussianly distributed. The proposed robust method is nearly as fast to evaluate as the original Fisher's test.