Methodology article

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

Miika Ahdesmäki¹ , Harri Lähdesmäki^2,1 , Andrew Gracey³ , llya Shmulevich² and Olli Yli-Harja¹

¹Institute of Signal Processing, Tampere University of Technology, P.O.Box 553, 33101 Tampere, Finland

²Institute for Systems Biology, WA 98103, USA

³Marine Environmental Biology, University of Southern California, CA 90089, USA

author email corresponding author email

BMC Bioinformatics 2007, 8:233doi:10.1186/1471-2105-8-233

Published:	2 July 2007

Abstract

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 M-estimation provides a good compromise between robustness and computational efficiency.

Conclusion

Since uneven sampling occurs often in biological measurements, the robust methods developed in this paper are expected to have many uses. The regression based formulation of the periodicity detection problem easily adapts to non-uniform sampling. Using robust regression helps to reject inconsistently behaving data points.

Availability

The implementations are currently available for Matlab and will be made available for the users of R as well. More information can be found in the web-supplement [1].