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Model selection characteristics when using MCP-Mod for dose-response gene expression data.

Julia C Duda1, Franziska Kappenberg1, Jörg Rahnenführer1

  • 1Department of Statistics, TU Dortmund University, Dortmund, Germany.

Biometrical Journal. Biometrische Zeitschrift
|February 21, 2022
PubMed
Summary
This summary is machine-generated.

We applied Multiple Comparison Procedure and Modeling (MCP-Mod) to in vitro gene expression data, finding that model selection depends on data noise levels. Simpler models are often chosen for noisy data, but may not offer significant advantages.

Keywords:
MCP-moddose-response curvesgene expressionmodel selectiontoxicology

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Area of Science:

  • Pharmacogenomics
  • Computational Biology
  • Toxicogenomics

Background:

  • Gene expression analysis is crucial for understanding drug effects.
  • Accurate modeling of concentration-response relationships is essential for interpreting gene expression data.
  • Multiple Comparison Procedure and Modeling (MCP-Mod) is a statistical framework for analyzing dose-response relationships.

Purpose of the Study:

  • To evaluate the applicability and performance of MCP-Mod for in vitro gene expression data.
  • To assess model selection characteristics for concentration-gene expression curves using various candidate models.
  • To investigate the impact of model set composition on selection accuracy and performance.

Main Methods:

  • Applied MCP-Mod to high-dimensional gene expression data from human embryonic stem cells exposed to valproic acid (VPA).
  • Considered candidate models including sigmoid, linear, quadratic, exponential, and beta.
  • Utilized Akaike information criterion (AIC) for model selection and conducted simulations to assess precision and recall.

Main Results:

  • The sigmoid model was frequently selected for less noisy gene expression data.
  • Simpler models, like the linear model, were often selected for noisier data but without substantial performance gains.
  • The standard log-logistic model showed unexpectedly low performance in this context.

Conclusions:

  • MCP-Mod is a viable tool for analyzing in vitro gene expression data and modeling concentration-response relationships.
  • Model selection performance is influenced by data noise and the diversity of candidate models.
  • Careful consideration of candidate models is necessary for robust analysis of gene expression data.