Rethomics: An R framework to analyse high-throughput behavioural data

Quentin Geissmann; Luis Garcia Rodriguez; Esteban J. Beckwith; Giorgio F. Gilestro

doi:10.1371/journal.pone.0209331

Rethomics: An R framework to analyse high-throughput behavioural data

Quentin Geissmann^*, Luis Garcia Rodriguez, Esteban J. Beckwith, Giorgio F. Gilestro

^*Corresponding author for this work

Research output: Contribution to journal/Conference contribution in journal/Contribution to newspaper › Journal article › Research › peer-review

71 Citations (Scopus)

Abstract

The recent development of automatised methods to score various behaviours on a large number of animals provides biologists with an unprecedented set of tools to decipher these complex phenotypes. Analysing such data comes with several challenges that are largely shared across acquisition platform and paradigms. Here, we present rethomics, a set of R packages that unifies the analysis of behavioural datasets in an efficient and flexible manner. rethomics offers a computational solution to storing, manipulating and visualising large amounts of behavioural data. We propose it as a tool to bridge the gap between behavioural biology and data sciences, thus connecting computational and behavioural scientists. rethomics comes with a extensive documentation as well as a set of both practical and theoretical tutorials (available at https://rethomics.github.io).

Original language	English
Article number	e0209331
Journal	PLOS ONE
Volume	14
Issue	1
ISSN	1932-6203
DOIs	https://doi.org/10.1371/journal.pone.0209331
Publication status	Published - Jan 2019
Externally published	Yes

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Rethomics: An R framework to analyse high-throughput behavioural data

Abstract

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