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beeRapp: an R shiny app for automated high-throughput explorative analysis of multivariate behavioral data

SUMMARY: Animal behavioral studies typically generate high-dimensional datasets consisting of multiple correlated outcome measures across distinct or related behavioral domains. Here, we introduce the BEhavioral Explorative analysis R shiny APP (beeRapp) that facilitates explorative and inferential...

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Detalles Bibliográficos
Autores principales: Busch, Anne Marie, Kovlyagina, Irina, Lutz, Beat, Todorov, Hristo, Gerber, Susanne
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9710645/
https://www.ncbi.nlm.nih.gov/pubmed/36699414
http://dx.doi.org/10.1093/bioadv/vbac082
Descripción
Sumario:SUMMARY: Animal behavioral studies typically generate high-dimensional datasets consisting of multiple correlated outcome measures across distinct or related behavioral domains. Here, we introduce the BEhavioral Explorative analysis R shiny APP (beeRapp) that facilitates explorative and inferential analysis of behavioral data in a high-throughput fashion. By employing an intuitive and user-friendly graphical user interface, beeRapp empowers behavioral scientists without programming and data science expertise to perform clustering, dimensionality reduction, correlational and inferential statistics and produce up to thousands of high-quality output plots visualizing results in a standardized and automated way. AVAILABILITY AND IMPLEMENTATION: The code and data underlying this article are available at https://github.com/anmabu/beeRapp.