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

Anne Marie Busch; Irina Kovlyagina; Beat Lutz; Hristo Todorov; Susanne Gerber

doi:10.1093/bioadv/vbac082

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

Bioinform Adv. 2022 Nov 7;2(1):vbac082. doi: 10.1093/bioadv/vbac082. eCollection 2022.

Authors

Anne Marie Busch¹, Irina Kovlyagina², Beat Lutz^{2

3}, Hristo Todorov¹, Susanne Gerber¹

Affiliations

¹ Institute of Human Genetics, University Medical Center of the Johannes Gutenberg University Mainz, Mainz 55128, Germany.
² Institute of Physiological Chemistry, University Medical Center of the Johannes Gutenberg University Mainz, Mainz 55128, Germany.
³ Leibniz Institute for Resilience Research (LIR), Mainz 55122, Germany.

Abstract

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.