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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...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2022
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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 |
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. |
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