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

View ORCID ProfileQuentin Geissmann, View ORCID ProfileLuis Garcia Rodriguez, View ORCID ProfileEsteban J. Beckwith, View ORCID ProfileGiorgio F. Gilestro
doi: https://doi.org/10.1101/305664
Quentin Geissmann
1Department of Life Sciences, Imperial College London, London, United Kingdom
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  • For correspondence: qgeissmann@gmail.com giorgio@gilest.ro
Luis Garcia Rodriguez
2Institute for Neuro- and Behavioral Biology, Westfälische Wilhelms University, 48149 Münster, Germany
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Esteban J. Beckwith
1Department of Life Sciences, Imperial College London, London, United Kingdom
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Giorgio F. Gilestro
1Department of Life Sciences, Imperial College London, London, United Kingdom
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  • For correspondence: qgeissmann@gmail.com giorgio@gilest.ro
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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).

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Posted April 23, 2018.
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Rethomics: an R framework to analyse high-throughput behavioural data
Quentin Geissmann, Luis Garcia Rodriguez, Esteban J. Beckwith, Giorgio F. Gilestro
bioRxiv 305664; doi: https://doi.org/10.1101/305664
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Rethomics: an R framework to analyse high-throughput behavioural data
Quentin Geissmann, Luis Garcia Rodriguez, Esteban J. Beckwith, Giorgio F. Gilestro
bioRxiv 305664; doi: https://doi.org/10.1101/305664

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