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Open Access Highly Accessed Technical Note

WAVOS: a MATLAB toolkit for wavelet analysis and visualization of oscillatory systems

Richard Harang1*, Guillaume Bonnet2 and Linda R Petzold1

Author affiliations

1 Department of Computer Science, University of California, Santa Barbara, CA, USA

2 Department of Statistics and Applied Probability, University of California, Santa Barbara, CA, and Google Inc, Mountain View, CA, USA

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Citation and License

BMC Research Notes 2012, 5:163  doi:10.1186/1756-0500-5-163

Published: 26 March 2012

Abstract

Background

Wavelets have proven to be a powerful technique for the analysis of periodic data, such as those that arise in the analysis of circadian oscillators. While many implementations of both continuous and discrete wavelet transforms are available, we are aware of no software that has been designed with the nontechnical end-user in mind. By developing a toolkit that makes these analyses accessible to end users without significant programming experience, we hope to promote the more widespread use of wavelet analysis.

Findings

We have developed the WAVOS toolkit for wavelet analysis and visualization of oscillatory systems. WAVOS features both the continuous (Morlet) and discrete (Daubechies) wavelet transforms, with a simple, user-friendly graphical user interface within MATLAB. The interface allows for data to be imported from a number of standard file formats, visualized, processed and analyzed, and exported without use of the command line. Our work has been motivated by the challenges of circadian data, thus default settings appropriate to the analysis of such data have been pre-selected in order to minimize the need for fine-tuning. The toolkit is flexible enough to deal with a wide range of oscillatory signals, however, and may be used in more general contexts.

Conclusions

We have presented WAVOS: a comprehensive wavelet-based MATLAB toolkit that allows for easy visualization, exploration, and analysis of oscillatory data. WAVOS includes both the Morlet continuous wavelet transform and the Daubechies discrete wavelet transform. We have illustrated the use of WAVOS, and demonstrated its utility for the analysis of circadian data on both bioluminesence and wheel-running data. WAVOS is freely available at http://sourceforge.net/projects/wavos/files/ webcite