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Robust regression for periodicity detection in non-uniformly sampled time-course gene expression data
Miika Ahdesmäki1, Harri Lähdesmäki, Andrew Gracey
1Institute of Signal Processing, Tampere University of Technology, Tampere, Finland. miika.ahdesmaki@tut.fi
BMC Bioinformatics
|July 4, 2007
Summary
Robust regression effectively detects periodicity in biological time series data, even with uneven sampling. This approach overcomes limitations of methods assuming fixed intervals, improving accuracy for real-world biological measurements.
Area of Science:
- Bioinformatics
- Computational Biology
- Data Analysis
Background:
- Biological time series data, such as gene microarrays, are often collected at irregular intervals.
- Detecting periodic patterns in such data is crucial for understanding biological processes.
- Existing methods struggle with uneven sampling and unknown noise types.
Purpose of the Study:
- To develop a general framework for robust periodicity detection.
- To evaluate and compare different periodicity detection approaches using simulations.
- To apply these methods to real biological measurement data.
Main Methods:
- Utilizing robust regression techniques to handle uneven sampling.
- Developing a regression-based formulation for periodicity detection.
- Employing M-estimation for a balance of robustness and computational efficiency.
Main Results:
- Methods assuming even sampling become ineffective with increasing data unevenness.
- Robust regression methods demonstrate superior performance in simulations with unevenly sampled data.
- M-estimation offers a practical balance of robustness and computational efficiency.
Conclusions:
- Robust methods are essential for analyzing biological time series due to frequent uneven sampling.
- The regression-based approach is adaptable to non-uniform sampling.
- Robust regression effectively identifies and excludes outlier data points, enhancing reliability.
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