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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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Detalles Bibliográficos
Autores principales: Geissmann, Quentin, Garcia Rodriguez, Luis, Beckwith, Esteban J., Gilestro, Giorgio F.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6334930/
https://www.ncbi.nlm.nih.gov/pubmed/30650089
http://dx.doi.org/10.1371/journal.pone.0209331
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author Geissmann, Quentin
Garcia Rodriguez, Luis
Beckwith, Esteban J.
Gilestro, Giorgio F.
author_facet Geissmann, Quentin
Garcia Rodriguez, Luis
Beckwith, Esteban J.
Gilestro, Giorgio F.
author_sort Geissmann, Quentin
collection PubMed
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).
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spelling pubmed-63349302019-01-31 Rethomics: An R framework to analyse high-throughput behavioural data Geissmann, Quentin Garcia Rodriguez, Luis Beckwith, Esteban J. Gilestro, Giorgio F. PLoS One Research Article 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). Public Library of Science 2019-01-16 /pmc/articles/PMC6334930/ /pubmed/30650089 http://dx.doi.org/10.1371/journal.pone.0209331 Text en © 2019 Geissmann et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Geissmann, Quentin
Garcia Rodriguez, Luis
Beckwith, Esteban J.
Gilestro, Giorgio F.
Rethomics: An R framework to analyse high-throughput behavioural data
title 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_short Rethomics: An R framework to analyse high-throughput behavioural data
title_sort rethomics: an r framework to analyse high-throughput behavioural data
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6334930/
https://www.ncbi.nlm.nih.gov/pubmed/30650089
http://dx.doi.org/10.1371/journal.pone.0209331
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