Rethomics: an R framework to analyse high-throughput behavioural data
File(s)
Published version
Author(s)
Geissmann, Quentin
Garcia Rrodriguez, Luis
Beckwith, Esteban
Gilestro, Giorgio
Type
Journal Article
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).
Date Issued
2019-01-16
Date Acceptance
2018-12-12
Citation
PLoS ONE, 2019, 14 (1)
ISSN
1932-6203
Publisher
Public Library of Science (PLoS)
Journal / Book Title
PLoS ONE
Volume
14
Issue
1
Copyright Statement
© 2019 Geissmann et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Sponsor
Biotechnology and Biological Sciences Research Council (BBSRC)
Biotechnology and Biological Sciences Research Cou
The Gas Safety Trust
European Molecular Biology Organization
European Research Council
Grant Number
BB/M003930/1
4020012831
WSSA_P64107
ALTF 57-2014
project 705930 - SEX_FIGHT_SLEEP
Subjects
Science & Technology
Multidisciplinary Sciences
Science & Technology - Other Topics
BIG DATA
DROSOPHILA
PERIODOGRAM
ETHOMICS
MD Multidisciplinary
General Science & Technology
Publication Status
Published
Article Number
e0209331