Shiny-Calorie: a context-aware application for indirect calorimetry data analysis and visualization using R

Area of Science:

• Metabolic phenotyping
• Bioinformatics
• Animal research

Background:

• Indirect calorimetry is crucial for metabolic phenotyping in pre-clinical research.
• Existing platforms lack flexible, user-friendly software for data integration and analysis.
• A unified approach is needed for diverse metabolic phenotyping data.

Purpose of the Study:

• To introduce Shiny-Calorie, an open-source application for indirect calorimetry data.
• To enable transparent data/metadata integration, statistical analysis, and visualization.
• To streamline analysis workflows and improve reproducibility in metabolic studies.

Main Methods:

• Developed Shiny-Calorie, an interactive application using GNU R.
• Implemented support for standard data formats from major commercial platforms (TSE, Sable Systems, COSMED, CLAMS/Columbus).
• Integrated advanced statistical methods for hypothesis testing and phenotype interpretation.

Main Results:

• Shiny-Calorie supports diverse data formats and enables standardized data export.
• The application facilitates intuitive exploration of complex, multi-modal longitudinal datasets.
• It incorporates robust statistical methods for analyzing energy metabolism phenotypes.

Conclusions:

• Shiny-Calorie provides a flexible, extensible, and user-friendly solution for indirect calorimetry data analysis.
• The application enhances transparency and reproducibility in metabolic phenotyping.
• It facilitates biologically meaningful interpretation of energy metabolism and expenditure.