Powerful and interpretable behavioural features for quantitative phenotyping of C. elegans

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Title: Powerful and interpretable behavioural features for quantitative phenotyping of C. elegans
Authors: Javer, A
Ripoll-Sanchez, L
Brown, AE
Item Type: Journal Article
Abstract: Behaviour is a sensitive and integrative readout of nervous system function and therefore an attractive measure for assessing the effects of mutation or drug treatment on animals. Video data provide a rich but high-dimensional representation of behaviour, and so the first step of analysis is often some form of tracking and feature extraction to reduce dimensionality while maintaining relevant information. Modern machine-learning methods are powerful but notoriously difficult to interpret, while handcrafted features are interpretable but do not always perform as well. Here, we report a new set of handcrafted features to compactly quantify Caenorhabditis elegans behaviour. The features are designed to be interpretable but to capture as much of the phenotypic differences between worms as possible. We show that the full feature set is more powerful than a previously defined feature set in classifying mutant strains. We then use a combination of automated and manual feature selection to define a core set of interpretable features that still provides sufficient power to detect behavioural differences between mutant strains and the wild-type. Finally, we apply the new features to detect time-resolved behavioural differences in a series of optogenetic experiments targeting different neural subsets.
Issue Date: 19-Oct-2018
Date of Acceptance: 6-Aug-2018
ISSN: 0962-8436
Publisher: Royal Society, The
Journal / Book Title: Philosophical Transactions B: Biological Sciences
Volume: 373
Issue: 1758
Copyright Statement: © 2018 The Authors. Published by the Royal Society under the terms of the Creative Commons Attribution License, which permits unrestricted use, provided the original author and source are credited.
Sponsor/Funder: European Research Council
Funder's Grant Number: ERC-STG-2016-714853
Keywords: Science & Technology
Life Sciences & Biomedicine
Life Sciences & Biomedicine - Other Topics
computational ethology
C. elegans
worm tracking
06 Biological Sciences
11 Medical And Health Sciences
Evolutionary Biology
Publication Status: Published
Article Number: 20170375
Online Publication Date: 2018-09-10
Appears in Collections:Clinical Sciences
Molecular Sciences
Faculty of Medicine

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