Creating Detailed Metadata for an R Shiny Analysis of Rodent Behavior Sequence Data Detected Along One Light-Dark Cycle

Automated mouse phenotyping through the high-throughput analysis of home cage behavior has brought hope of a more effective and efficient method for testing rodent models of diseases. Advanced video analysis software is able to derive behavioral sequence data sets from multiple-day recordings. Howev...

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Autores principales:	Julien Colomb, York Winter
Formato:	article
Lenguaje:	EN
Publicado:	Frontiers Media S.A. 2021
Materias:	home cage scan mus musculus rodent automatic machine learning multidimensional analysis Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571
Acceso en línea:	https://doaj.org/article/5c38a0f151fc496f82969b02773e4207
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spelling	oai:doaj.org-article:5c38a0f151fc496f82969b02773e42072021-12-01T07:16:17ZCreating Detailed Metadata for an R Shiny Analysis of Rodent Behavior Sequence Data Detected Along One Light-Dark Cycle1662-453X10.3389/fnins.2021.742652https://doaj.org/article/5c38a0f151fc496f82969b02773e42072021-11-01T00:00:00Zhttps://www.frontiersin.org/articles/10.3389/fnins.2021.742652/fullhttps://doaj.org/toc/1662-453XAutomated mouse phenotyping through the high-throughput analysis of home cage behavior has brought hope of a more effective and efficient method for testing rodent models of diseases. Advanced video analysis software is able to derive behavioral sequence data sets from multiple-day recordings. However, no dedicated mechanisms exist for sharing or analyzing these types of data. In this article, we present a free, open-source software actionable through a web browser (an R Shiny application), which performs an analysis of home cage behavioral sequence data, which is designed to spot differences in circadian activity while preventing p-hacking. The software aligns time-series data to the light/dark cycle, and then uses different time windows to produce up to 162 behavior variables per animal. A principal component analysis strategy detected differences between groups. The behavior activity is represented graphically for further explorative analysis. A machine-learning approach was implemented, but it proved ineffective at separating the experimental groups. The software requires spreadsheets that provide information about the experiment (i.e., metadata), thus promoting a data management strategy that leads to FAIR data production. This encourages the publication of some metadata even when the data are kept private. We tested our software by comparing the behavior of female mice in videos recorded twice at 3 and 7 months in a home cage monitoring system. This study demonstrated that combining data management with data analysis leads to a more efficient and effective research process.Julien ColombYork WinterYork WinterFrontiers Media S.A.articlehome cage scanmus musculusrodentautomaticmachine learningmultidimensional analysisNeurosciences. Biological psychiatry. NeuropsychiatryRC321-571ENFrontiers in Neuroscience, Vol 15 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	home cage scan mus musculus rodent automatic machine learning multidimensional analysis Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571
spellingShingle	home cage scan mus musculus rodent automatic machine learning multidimensional analysis Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571 Julien Colomb York Winter York Winter Creating Detailed Metadata for an R Shiny Analysis of Rodent Behavior Sequence Data Detected Along One Light-Dark Cycle
description	Automated mouse phenotyping through the high-throughput analysis of home cage behavior has brought hope of a more effective and efficient method for testing rodent models of diseases. Advanced video analysis software is able to derive behavioral sequence data sets from multiple-day recordings. However, no dedicated mechanisms exist for sharing or analyzing these types of data. In this article, we present a free, open-source software actionable through a web browser (an R Shiny application), which performs an analysis of home cage behavioral sequence data, which is designed to spot differences in circadian activity while preventing p-hacking. The software aligns time-series data to the light/dark cycle, and then uses different time windows to produce up to 162 behavior variables per animal. A principal component analysis strategy detected differences between groups. The behavior activity is represented graphically for further explorative analysis. A machine-learning approach was implemented, but it proved ineffective at separating the experimental groups. The software requires spreadsheets that provide information about the experiment (i.e., metadata), thus promoting a data management strategy that leads to FAIR data production. This encourages the publication of some metadata even when the data are kept private. We tested our software by comparing the behavior of female mice in videos recorded twice at 3 and 7 months in a home cage monitoring system. This study demonstrated that combining data management with data analysis leads to a more efficient and effective research process.
format	article
author	Julien Colomb York Winter York Winter
author_facet	Julien Colomb York Winter York Winter
author_sort	Julien Colomb
title	Creating Detailed Metadata for an R Shiny Analysis of Rodent Behavior Sequence Data Detected Along One Light-Dark Cycle
title_short	Creating Detailed Metadata for an R Shiny Analysis of Rodent Behavior Sequence Data Detected Along One Light-Dark Cycle
title_full	Creating Detailed Metadata for an R Shiny Analysis of Rodent Behavior Sequence Data Detected Along One Light-Dark Cycle
title_fullStr	Creating Detailed Metadata for an R Shiny Analysis of Rodent Behavior Sequence Data Detected Along One Light-Dark Cycle
title_full_unstemmed	Creating Detailed Metadata for an R Shiny Analysis of Rodent Behavior Sequence Data Detected Along One Light-Dark Cycle
title_sort	creating detailed metadata for an r shiny analysis of rodent behavior sequence data detected along one light-dark cycle
publisher	Frontiers Media S.A.
publishDate	2021
url	https://doaj.org/article/5c38a0f151fc496f82969b02773e4207
work_keys_str_mv	AT juliencolomb creatingdetailedmetadataforanrshinyanalysisofrodentbehaviorsequencedatadetectedalongonelightdarkcycle AT yorkwinter creatingdetailedmetadataforanrshinyanalysisofrodentbehaviorsequencedatadetectedalongonelightdarkcycle AT yorkwinter creatingdetailedmetadataforanrshinyanalysisofrodentbehaviorsequencedatadetectedalongonelightdarkcycle
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Creating Detailed Metadata for an R Shiny Analysis of Rodent Behavior Sequence Data Detected Along One Light-Dark Cycle

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