Benefit of using interaction effects for the analysis of high-dimensional time-response or dose-response data for two-group comparisons

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dc.contributor.authorDuda, Julia C.
dc.contributor.authorDrenda, Carolin
dc.contributor.authorKästel, Hue
dc.contributor.authorRahnenführer, Jörg
dc.contributor.authorKappenberg, Franziska
dc.date.accessioned2024-04-05T08:42:32Z
dc.date.available2024-04-05T08:42:32Z
dc.date.issued2023-11-27
dc.description.abstractHigh throughput RNA sequencing experiments are widely conducted and analyzed to identify differentially expressed genes (DEGs). The statistical models calculated for this task are often not clear to practitioners, and analyses may not be optimally tailored to the research hypothesis. Often, interaction effects (IEs) are the mathematical equivalent of the biological research question but are not considered for different reasons. We fill this gap by explaining and presenting the potential benefit of IEs in the search for DEGs using RNA-Seq data of mice that receive different diets for different time periods. Using an IE model leads to a smaller, but likely more biologically informative set of DEGs compared to a common approach that avoids the calculation of IEs.en
dc.identifier.urihttp://hdl.handle.net/2003/42423
dc.identifier.urihttp://dx.doi.org/10.17877/DE290R-24259
dc.language.isoende
dc.relation.ispartofseriesScientific reports;13
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/de
dc.subjectBiotechnologyen
dc.subjectFunctional genomicsen
dc.subjectGenomicsen
dc.subject.ddc310
dc.titleBenefit of using interaction effects for the analysis of high-dimensional time-response or dose-response data for two-group comparisonsen
dc.typeTextde
dc.type.publicationtypeArticlede
dcterms.accessRightsopen access
eldorado.secondarypublicationtruede
eldorado.secondarypublication.primarycitationDuda, J.C., Drenda, C., Kästel, H. et al. Benefit of using interaction effects for the analysis of high-dimensional time-response or dose-response data for two-group comparisons. Sci Rep 13, 20804 (2023). https://doi.org/10.1038/s41598-023-47057-0de
eldorado.secondarypublication.primaryidentifierhttps://doi.org/10.1038/s41598-023-47057-0de

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Statistische Methoden in der Genetik und Chemometrie