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Nonlinear mixed-effects modelling for single cell estimation: when, why, and how to use it.

Markus Karlsson1, David L I Janzén2,3,4,5,6, Lucia Durrieu7

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Nonlinear mixed-effects modeling (NLME) outperforms the standard two-stage (STS) approach for analyzing uninformative single-cell data. NLME provides more accurate parameter and noise estimates, crucial for understanding cell-to-cell variation.

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Area of Science:

  • Systems Biology
  • Bioanalytical Techniques
  • Pharmacometrics

Background:

  • Single-cell data analysis is increasingly important due to advancements in bioanalytical techniques.
  • The standard two-stage (STS) approach is common but can exaggerate parameter unidentifiability by analyzing cells independently.
  • Nonlinear mixed-effects modeling (NLME), used for patient-to-patient variation, offers a potential improvement for cell-to-cell variation studies.

Purpose of the Study:

  • To systematically compare the performance of NLME and STS for single-cell data analysis.
  • To evaluate their effectiveness in parameter and noise estimation, particularly with uninformative data.
  • To provide guidance on applying NLME to single-cell studies.

Main Methods:

  • Comparison of STS and NLME using linear and nonlinear models.
  • Analysis of both simulated and real experimental single-cell data.
  • Evaluation of parameter and noise estimation accuracy under varying data informativeness.

Main Results:

  • No significant difference between STS and NLME when single-cell data is informative.
  • NLME demonstrates significant superiority over STS when data is uninformative (e.g., low signal-to-noise, few data points, poor input signal).
  • Improvements with NLME stem from joint likelihood function and population parameter modeling.

Conclusions:

  • NLME provides more accurate parameter and noise estimates than traditional methods like STS and joint likelihood (JLH) for uninformative single-cell data.
  • NLME is a more robust approach for single-cell variation studies when data quality is compromised.
  • A tutorial for NLME application in single-cell analysis using Monolix software is provided.