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

Simple item page
dc.contributor.authorDuda, Julia C.
dc.contributor.authorKappenberg, Franziska
dc.contributor.authorRahnenführer, Jörg
dc.date.accessioned2024-03-06T12:19:50Z
dc.date.available2024-03-06T12:19:50Z
dc.date.issued2022-02-20
dc.description.abstractWe extend the scope of application for MCP-Mod (Multiple Comparison Procedure and Modeling) to in vitro gene expression data and assess its characteristics regarding model selection for concentration gene expression curves. Precisely, we apply MCP-Mod on single genes of a high-dimensional gene expression data set, where human embryonic stem cells were exposed to eight concentration levels of the compound valproic acid (VPA). As candidate models we consider the sigmoid Emax (four-parameter log-logistic), linear, quadratic, Emax, exponential, and beta model. Through simulations we investigate the impact of omitting one or more models from the candidate model set to uncover possibly superfluous models and to evaluate the precision and recall rates of selected models. Each model is selected according to Akaike information criterion (AIC) for a considerable number of genes. For less noisy cases the popular sigmoid Emax model is frequently selected. For more noisy data, often simpler models like the linear model are selected, but mostly without relevant performance advantage compared to the second best model. Also, the commonly used standard Emax model has an unexpected low performance.en
dc.identifier.urihttp://hdl.handle.net/2003/42379
dc.identifier.urihttp://dx.doi.org/10.17877/DE290R-24215
dc.language.isoende
dc.relation.ispartofseriesBiometrical journal;64(5)
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/de
dc.subjectdose–response curvesen
dc.subjectgene expressionen
dc.subjectMCP-moden
dc.subjectmodel selectionen
dc.subjecttoxicologyen
dc.subject.ddc310
dc.titleModel selection characteristics when using MCP-Mod for dose–response gene expression dataen
dc.typeTextde
dc.type.publicationtypeResearchArticlede
dcterms.accessRightsopen access
eldorado.secondarypublicationtruede
eldorado.secondarypublication.primarycitationDuda, J.C., Kappenberg, F., & Rahnenführer, J. (2022). Model selection characteristics when using MCP-Mod for dose–response gene expression data. Biometrical Journal, 64, 883–897. https://doi.org/10.1002/bimj.202000250de
eldorado.secondarypublication.primaryidentifierhttps://doi.org/10.1002/bimj.202000250de

Files

Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
Biometrical J - 2022 - Duda - Model selection characteristics when using MCP‐Mod for dose response gene expression data.pdf
Size:
1.11 MB
Format:
Adobe Portable Document Format
Description:
DNB
Download
License bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
license.txt
Size:
4.85 KB
Format:
Item-specific license agreed upon to submission
Description:
Download

Collections

Statistische Methoden in der Genetik und Chemometrie