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

High 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, interact...

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Autores principales: Duda, Julia C., Drenda, Carolin, Kästel, Hue, Rahnenführer, Jörg, Kappenberg, Franziska
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10682470/
https://www.ncbi.nlm.nih.gov/pubmed/38012163
http://dx.doi.org/10.1038/s41598-023-47057-0
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author Duda, Julia C.
Drenda, Carolin
Kästel, Hue
Rahnenführer, Jörg
Kappenberg, Franziska
author_facet Duda, Julia C.
Drenda, Carolin
Kästel, Hue
Rahnenführer, Jörg
Kappenberg, Franziska
author_sort Duda, Julia C.
collection PubMed
description High 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.
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spelling pubmed-106824702023-11-30 Benefit of using interaction effects for the analysis of high-dimensional time-response or dose-response data for two-group comparisons Duda, Julia C. Drenda, Carolin Kästel, Hue Rahnenführer, Jörg Kappenberg, Franziska Sci Rep Article High 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. Nature Publishing Group UK 2023-11-27 /pmc/articles/PMC10682470/ /pubmed/38012163 http://dx.doi.org/10.1038/s41598-023-47057-0 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Duda, Julia C.
Drenda, Carolin
Kästel, Hue
Rahnenführer, Jörg
Kappenberg, Franziska
Benefit of using interaction effects for the analysis of high-dimensional time-response or dose-response data for two-group comparisons
title Benefit of using interaction effects for the analysis of high-dimensional time-response or dose-response data for two-group comparisons
title_full Benefit of using interaction effects for the analysis of high-dimensional time-response or dose-response data for two-group comparisons
title_fullStr Benefit of using interaction effects for the analysis of high-dimensional time-response or dose-response data for two-group comparisons
title_full_unstemmed Benefit of using interaction effects for the analysis of high-dimensional time-response or dose-response data for two-group comparisons
title_short Benefit of using interaction effects for the analysis of high-dimensional time-response or dose-response data for two-group comparisons
title_sort benefit of using interaction effects for the analysis of high-dimensional time-response or dose-response data for two-group comparisons
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10682470/
https://www.ncbi.nlm.nih.gov/pubmed/38012163
http://dx.doi.org/10.1038/s41598-023-47057-0
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