Robust regression for periodicity detection in non-uniformly sampled time-course gene expression data

Background: In practice many biological time series measurements, including gene microarrays, are conducted at time points that seem to be interesting in the biologist's opinion and not necessarily at fixed time intervals. In many circumstances we are interested in finding targets that are expressed periodically. To tackle the problems of uneven sampling and unknown type of noise in periodicity detection, we propose to use robust regression. Methods: The aim of this paper is to develop a general framework for robust periodicity detection and review and rank different approaches by means of simulations. We also show the results for some real measurement data. Results: The simulation results clearly show that when the sampling of time series gets more and more uneven, the methods that assume even sampling become unusable. We find that Mestimation provides a good compromise between robustness and computational efficiency. Conclusion: Since uneven sampling occurs often in biological ...

• Type: Journal
• Year: 2007
• Where: BMCBI
• Authors: Miika Ahdesmäki, Harri Lähdesmäki, Andrew Gracey, Ilya Shmulevich, Olli Yli-Harja
• Keywords: BMCBI 2007, Periodicity Detection, Time Series, Uneven Sampling