Gaussian process regression bootstrapping: exploring the effects of uncertainty in time course data

Area of Science:

• Computational Biology
• Systems Biology
• Statistical Modeling

Background:

• High-throughput biological data are inherently noisy, yet the impact of this uncertainty on conclusions is often underestimated.
• Quantifying data uncertainty is essential for assigning confidence levels to scientific findings.
• Bootstrap resampling offers a method to assess data uncertainty.

Purpose of the Study:

• To present a parametric bootstrapping approach for time-course data using Gaussian process regression (GPR).
• To develop a method that accounts for the time dependence of data when assessing uncertainty.
• To provide a versatile approach applicable to various biological data analysis problems.

Main Methods:

• Utilized Gaussian process regression (GPR) to build a probabilistic model for time-course data.
• Employed parametric bootstrapping by drawing replicates from the GPR model.
• Applied the GPR bootstrapping method to two existing biological datasets.

Main Results:

• Investigated the impact of data uncertainty on parameter estimation in an ordinary differential equations (ODE) model of cell signaling.
• Found parameter estimates to be relatively robust to uncertainty but identified a distinct second set of estimates.
• Demonstrated that network topology inferred from time-course gene expression data can be sensitive to data uncertainty, though some network edges may be robust.

Conclusions:

• The GPR bootstrapping method effectively quantifies the impact of data uncertainty on biological model analysis.
• The approach reveals sensitivities and robust features in models and networks derived from time-course data.
• This method enhances the reliability of conclusions drawn from noisy biological datasets.