Abstract
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.
Highlights
Any attempt to identify the behavioral phenotype of an animal can be a highly tedious undertaking
We present an integrated solution for the analysis and management of home cage video monitoring data
In order to facilitate the analysis of data from different sources, we proposed a format for organizing the data and the metadata, such that the R Shiny apps can access the different files automatically
Summary
Any attempt to identify the behavioral phenotype of an animal can be a highly tedious undertaking. Various technical solutions can provide a detailed analysis of single-housed mouse behavior sequences by analyzing a video (Figure 1). These include proprietary systems such as the HomeCageScan (HCS) software (Cleversys, Steele et al, 2007), and the phenorack system (Viewpoint S.A., France Bains et al, 2018), as well as an open source solution (Jhuang et al, 2010), and manual video annotation (see Jhuang et al, 2010 for an example). In addition to the raw behavior sequence data, the HCS software, as one example, may create summaries of the time spent performing each recognized behavior, as well as of the distance traveled (horizontally) for time intervals from minutes to several days (Figure 1)
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