Comparison of observation-based and model-based identification of alert concentrations from concentration–expression data

Author:

Kappenberg Franziska1ORCID,Grinberg Marianna1,Jiang Xiaoqi2,Kopp-Schneider Annette2,Hengstler Jan G3,Rahnenführer Jörg1

Affiliation:

1. Department of Statistics, TU Dortmund University, Dortmund 44221, Germany

2. Division of Biostatistics, German Cancer Research Center (DKFZ), Heidelberg 69120, Germany

3. Department of Toxikologie/Systemtoxikologie, Leibniz Research Centre for Working Environment and Human Factors (IfADo), TU Dortmund University, Dortmund 44139, Germany

Abstract

Abstract Motivation An important goal of concentration–response studies in toxicology is to determine an ‘alert’ concentration where a critical level of the response variable is exceeded. In a classical observation-based approach, only measured concentrations are considered as potential alert concentrations. Alternatively, a parametric curve is fitted to the data that describes the relationship between concentration and response. For a prespecified effect level, both an absolute estimate of the alert concentration and an estimate of the lowest concentration where the effect level is exceeded significantly are of interest. Results In a simulation study for gene expression data, we compared the observation-based and the model-based approach for both absolute and significant exceedance of the prespecified effect level. Results show that, compared to the observation-based approach, the model-based approach overestimates the true alert concentration less often and more frequently leads to a valid estimate, especially for genes with large variance. Availability and implementation The code used for the simulation studies is available via the GitHub repository: https://github.com/FKappenberg/Paper-IdentificationAlertConcentrations. Supplementary information Supplementary data are available at Bioinformatics online.

Funder

Bundesministerium für Bildung und Forschung

Publisher

Oxford University Press (OUP)

Subject

Computational Mathematics,Computational Theory and Mathematics,Computer Science Applications,Molecular Biology,Biochemistry,Statistics and Probability

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