Statistical Analysis of Zebrafish Locomotor Behaviour by Generalized Linear Mixed Models

Abstract Upon a drastic change in environmental illumination, zebrafish larvae display a rapid locomotor response. This response can be simultaneously tracked from larvae arranged in multi-well plates. The resulting data have provided new insights into neuro-behaviour. The features of these data, ho...

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Autores principales:	Yiwen Liu, Ping Ma, Paige A. Cassidy, Robert Carmer, Gaonan Zhang, Prahatha Venkatraman, Skye A. Brown, Chi Pui Pang, Wenxuan Zhong, Mingzhi Zhang, Yuk Fai Leung
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2017
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/4d0d2869bfb1435792434ad31c0ac410
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spelling	oai:doaj.org-article:4d0d2869bfb1435792434ad31c0ac4102021-12-02T16:07:49ZStatistical Analysis of Zebrafish Locomotor Behaviour by Generalized Linear Mixed Models10.1038/s41598-017-02822-w2045-2322https://doaj.org/article/4d0d2869bfb1435792434ad31c0ac4102017-06-01T00:00:00Zhttps://doi.org/10.1038/s41598-017-02822-whttps://doaj.org/toc/2045-2322Abstract Upon a drastic change in environmental illumination, zebrafish larvae display a rapid locomotor response. This response can be simultaneously tracked from larvae arranged in multi-well plates. The resulting data have provided new insights into neuro-behaviour. The features of these data, however, present a challenge to traditional statistical tests. For example, many larvae display little or no movement. Thus, the larval responses have many zero values and are imbalanced. These responses are also measured repeatedly from the same well, which results in correlated observations. These analytical issues were addressed in this study by the generalized linear mixed model (GLMM). This approach deals with binary responses and characterizes the correlation of observations in the same group. It was used to analyze a previously reported dataset. Before applying the GLMM, the activity values were transformed to binary responses (movement vs. no movement) to reduce data imbalance. Moreover, the GLMM estimated the variations among the effects of different well locations, which would eliminate the location effects when two biological groups or conditions were compared. By addressing the data-imbalance and location-correlation issues, the GLMM effectively quantified true biological effects on zebrafish locomotor response.Yiwen LiuPing MaPaige A. CassidyRobert CarmerGaonan ZhangPrahatha VenkatramanSkye A. BrownChi Pui PangWenxuan ZhongMingzhi ZhangYuk Fai LeungNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 7, Iss 1, Pp 1-9 (2017)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Yiwen Liu Ping Ma Paige A. Cassidy Robert Carmer Gaonan Zhang Prahatha Venkatraman Skye A. Brown Chi Pui Pang Wenxuan Zhong Mingzhi Zhang Yuk Fai Leung Statistical Analysis of Zebrafish Locomotor Behaviour by Generalized Linear Mixed Models
description	Abstract Upon a drastic change in environmental illumination, zebrafish larvae display a rapid locomotor response. This response can be simultaneously tracked from larvae arranged in multi-well plates. The resulting data have provided new insights into neuro-behaviour. The features of these data, however, present a challenge to traditional statistical tests. For example, many larvae display little or no movement. Thus, the larval responses have many zero values and are imbalanced. These responses are also measured repeatedly from the same well, which results in correlated observations. These analytical issues were addressed in this study by the generalized linear mixed model (GLMM). This approach deals with binary responses and characterizes the correlation of observations in the same group. It was used to analyze a previously reported dataset. Before applying the GLMM, the activity values were transformed to binary responses (movement vs. no movement) to reduce data imbalance. Moreover, the GLMM estimated the variations among the effects of different well locations, which would eliminate the location effects when two biological groups or conditions were compared. By addressing the data-imbalance and location-correlation issues, the GLMM effectively quantified true biological effects on zebrafish locomotor response.
format	article
author	Yiwen Liu Ping Ma Paige A. Cassidy Robert Carmer Gaonan Zhang Prahatha Venkatraman Skye A. Brown Chi Pui Pang Wenxuan Zhong Mingzhi Zhang Yuk Fai Leung
author_facet	Yiwen Liu Ping Ma Paige A. Cassidy Robert Carmer Gaonan Zhang Prahatha Venkatraman Skye A. Brown Chi Pui Pang Wenxuan Zhong Mingzhi Zhang Yuk Fai Leung
author_sort	Yiwen Liu
title	Statistical Analysis of Zebrafish Locomotor Behaviour by Generalized Linear Mixed Models
title_short	Statistical Analysis of Zebrafish Locomotor Behaviour by Generalized Linear Mixed Models
title_full	Statistical Analysis of Zebrafish Locomotor Behaviour by Generalized Linear Mixed Models
title_fullStr	Statistical Analysis of Zebrafish Locomotor Behaviour by Generalized Linear Mixed Models
title_full_unstemmed	Statistical Analysis of Zebrafish Locomotor Behaviour by Generalized Linear Mixed Models
title_sort	statistical analysis of zebrafish locomotor behaviour by generalized linear mixed models
publisher	Nature Portfolio
publishDate	2017
url	https://doaj.org/article/4d0d2869bfb1435792434ad31c0ac410
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Statistical Analysis of Zebrafish Locomotor Behaviour by Generalized Linear Mixed Models

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