Calopy — an advanced framework for the integration and analysis of indirect calorimetry data

Here we introduce Calopy, an innovative software suite for the intuitive and comprehensive analysis of indirect calorimetry data. Calopy is an open-source, web-based Shiny for Python application that is accessible online or locally; it is platform-independent and available via any web browser at https://www.calopy.app.

Indirect calorimetry is a widely used technique for measuring energy metabolism in both humans and animals¹. At its core, indirect calorimetry tracks oxygen consumption \((\dot{V}{\mathrm{O}}_{2})\) and carbon dioxide production \((\dot{V}{\mathrm{CO}}_{2})\) to analyse energy metabolism through parameters such as energy expenditure and resting metabolic rate (RMR)² or respiratory exchange ratio (RER), a key indicator of substrate utilization³. In comprehensive indirect calorimetry systems for animals, additional relevant metabolic parameters such as locomotor activity, food and water intake, and body temperature can also be measured at high temporal resolution, along with environmental factors such as cage temperature, lighting and humidity. Continuous or categorical phenotypic variables, such as body weight, genotype and treatments, are also recorded. Noteworthy indirect calorimetry data may further be affected through factors that entail mutual dependencies such as circadian rhythms, food intake, environmental conditions and data noise, which adds further complexity⁴. Together, these data form a complex set of time-resolved, continuous and categorical variables, which require sophisticated statistics and methods for their analysis.