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

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 an...

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Main Authors: Quentin Geissmann, Luis Garcia Rodriguez, Esteban J Beckwith, Giorgio F Gilestro
Format: Article
Language:English
Published: Public Library of Science (PLoS) 2019-01-01
Series:PLoS ONE
Online Access:https://doi.org/10.1371/journal.pone.0209331
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spelling doaj-7375aa6bf7954e3885a8b29e618b836e2021-03-03T20:57:56ZengPublic Library of Science (PLoS)PLoS ONE1932-62032019-01-01141e020933110.1371/journal.pone.0209331Rethomics: An R framework to analyse high-throughput behavioural data.Quentin GeissmannLuis Garcia RodriguezEsteban J BeckwithGiorgio F GilestroThe 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).https://doi.org/10.1371/journal.pone.0209331
collection DOAJ
language English
format Article
sources DOAJ
author Quentin Geissmann
Luis Garcia Rodriguez
Esteban J Beckwith
Giorgio F Gilestro
spellingShingle Quentin Geissmann
Luis Garcia Rodriguez
Esteban J Beckwith
Giorgio F Gilestro
Rethomics: An R framework to analyse high-throughput behavioural data.
PLoS ONE
author_facet Quentin Geissmann
Luis Garcia Rodriguez
Esteban J Beckwith
Giorgio F Gilestro
author_sort Quentin Geissmann
title Rethomics: An R framework to analyse high-throughput behavioural data.
title_short Rethomics: An R framework to analyse high-throughput behavioural data.
title_full Rethomics: An R framework to analyse high-throughput behavioural data.
title_fullStr Rethomics: An R framework to analyse high-throughput behavioural data.
title_full_unstemmed Rethomics: An R framework to analyse high-throughput behavioural data.
title_sort rethomics: an r framework to analyse high-throughput behavioural data.
publisher Public Library of Science (PLoS)
series PLoS ONE
issn 1932-6203
publishDate 2019-01-01
description 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).
url https://doi.org/10.1371/journal.pone.0209331
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