A Comprehensive Model for Separating Systematic Bias and Noise in Metabolomic Timecourse Data-A Nonlinear B-Spline...

Area of Science:

• Metabolomics
• Systems Biology
• Biostatistics

Background:

• Metabolomic timecourse samples allow simultaneous detection of numerous metabolites.
• Individual metabolite analysis can conflate measurement noise with systematic bias.
• Systematic bias affects all metabolites similarly within a sample.

Purpose of the Study:

• To develop a method for identifying and correcting systematic bias in metabolomic timecourse data.
• To improve the accuracy of metabolite quantification in timecourse experiments.
• To provide a generalizable model for bias correction across research areas.

Main Methods:

• A nonlinear B-spline mixed-effects model was formulated for simultaneous fitting of all detected metabolites.
• The model was applied to real cell culture metabolomic data.
• Validation was performed using simulated timecourse data with controlled noise and bias.

Main Results:

• The proposed model successfully estimated and corrected systematic bias in metabolomic timecourse data.
• Accurate correction of 3%-10% systematic bias to within 0.5% on average was achieved.
• An R package was developed to facilitate the adoption of the bias correction model.

Conclusions:

• The nonlinear B-spline mixed-effects model offers a robust approach for correcting systematic bias in metabolomic timecourse data.
• This method enhances the reliability of metabolite quantification.
• The model's generalizability extends its utility beyond cell culture metabolomics.