SleepInvestigatoR: a flexible R function for analyzing scored sleep in rodents

Analyzing scored sleep is a fundamental prerequisite to understanding how sleep changes between health and disease. Classically, this is accomplished by manually calculating various measures (e.g. percent of non-rapid eye movement sleep) from a collection of scored sleep files. This process can be tedious and error-prone, especially when studies include large animal numbers or involve long recording sessions. To address this issue, we present SleepInvestigatoR, a versatile tool that can quickly organize and analyze multiple scored sleep files into a single output. The function is written in the open-source statistical language R and has a total of 25 parameters that can be set to match a wide variety of experimental needs. SleepInvestigatoR delivers a total of 23 unique measures of sleep, including all measures commonly reported in the rodent literature. A simple plotting function is also provided to quickly graph and visualize the scored data. All code is designed to be implemented with little formal coding knowledge, and step-by-step instructions are provided on the corresponding GitHub page. Overall, SleepInvestigatoR provides the sleep researcher a critical tool to increase efficiency, interpretation, and reproducibility in analyzing scored rodent sleep.

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Publication Details

Journal
SLEEP Advances
Published
2025-04-01
DOI
https://doi.org/10.1093/sleepadvances/zpaf032
Primary Topic
Sleep and Wakefulness Research
Type
article
Field-Weighted Citation Impact
0.00

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article

SleepInvestigatoR: a flexible R function for analyzing scored sleep in rodents

Benjamin R. Williams, James T Mckenna, Ryan W. Logan, Mackenzie C. Gamble
SLEEP Advances
Sleep and Wakefulness Research
article

SleepInvestigatoR: a flexible R function for analyzing scored sleep in rodents

Benjamin R. Williams, James T Mckenna, Ryan W. Logan, Mackenzie C. Gamble
article en

Abstract

Analyzing scored sleep is a fundamental prerequisite to understanding how sleep changes between health and disease. Classically, this is accomplished by manually calculating various measures (e.g. percent of non-rapid eye movement sleep) from a collection of scored sleep files. This process can be tedious and error-prone, especially when studies include large animal numbers or involve long recording sessions. To address this issue, we present SleepInvestigatoR, a versatile tool that can quickly organize and analyze multiple scored sleep files into a single output. The function is written in the open-source statistical language R and has a total of 25 parameters that can be set to match a wide variety of experimental needs. SleepInvestigatoR delivers a total of 23 unique measures of sleep, including all measures commonly reported in the rodent literature. A simple plotting function is also provided to quickly graph and visualize the scored data. All code is designed to be implemented with little formal coding knowledge, and step-by-step instructions are provided on the corresponding GitHub page. Overall, SleepInvestigatoR provides the sleep researcher a critical tool to increase efficiency, interpretation, and reproducibility in analyzing scored rodent sleep.

SLEEP AdvancesVol. 6(2)
Boston University (US), Harvard University (US), University of Massachusetts Chan Medical School (US), VA Boston Healthcare System (US)
National Institutes of Health
Openalex Percentile: Top 89%
Sleep and Wakefulness Research
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