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Automated Analysis of Dynamic Ca2+ Signals in Image Sequences
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Analysis of noisy transient signals based on Gaussian process regression
Iuliia Baglaeva1, Bogdan Iaparov1, Ivan Zahradník1
1Department of Cellular Cardiology, Institute of Experimental Endocrinology, Biomedical Research Center, Slovak Academy of Sciences, Bratislava, Slovakia.
Biophysical Journal
|January 7, 2023
Summary
This study introduces a new model-independent method using Gaussian process regression to analyze noisy transient signals from dynamic systems like cells. The TransientAnalyzer software accurately estimates signal parameters, even with low signal-to-noise ratios.
Area of Science:
- Biophysics
- Computational Biology
- Signal Processing
Background:
- Dynamic systems (cells, tissues) generate transient signals crucial for information transfer.
- Low signal-to-noise ratio (SNR) and signal distortion from filtering limit transient characterization.
- Existing functional approximations may fail under changing recording conditions.
Purpose of the Study:
- To develop a model-independent method for approximating general noisy transient signals.
- To implement this method in user-friendly software (TransientAnalyzer).
- To assess the accuracy and robustness of the method for signal parameter estimation.
Main Methods:
- Gaussian process regression for model-independent signal approximation.
- Transient detection, Gaussian process fitting, and surrogate spline function construction.
- Software implementation in TransientAnalyzer for automated analysis.
Main Results:
- Accurate estimation of signal parameters with errors <7.5% at SNR=5 for cellular calcium transients.
- Low coefficient of variation (<17%) for estimates, indicating reliability.
- Superior performance compared to traditional function fitting for diverse experimental signals.
Conclusions:
- Gaussian process regression offers a robust, model-independent approach for analyzing noisy biological transients.
- TransientAnalyzer provides an effective tool for accurate signal parameter estimation in various experimental contexts.
- The method enhances the characterization of transient signals from dynamic systems, overcoming limitations of traditional techniques.
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