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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2019-01-01
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Online Access: | https://doi.org/10.1371/journal.pone.0209331 |
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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 |
work_keys_str_mv |
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