CalR: A Web-Based Analysis Tool for Indirect Calorimetry Experiments
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Abstract
We report a web-based tool for analysis of experiments using indirect calorimetry to measure physiological energy balance. CalR simplifies the process to import raw data files, generate plots, and determine the most appropriate statistical tests for interpretation. Analysis using the generalized linear model (which includes ANOVA and ANCOVA) allows for flexibility in interpreting diverse experimental designs, including those of obesity and thermogenesis. Users also may produce standardized output files for an experiment that can be shared and subsequently re-evaluated using CalR. This framework will provide the transparency necessary to enhance consistency, rigor, and reproducibility. The CalR analysis software will greatly increase the speed and efficiency with which metabolic experiments can be organized, analyzed per accepted norms, and reproduced and will likely become a standard tool for the field. CalR is accessible at https://CalRapp.org/.
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Energy expenditure data from indirect calorimetry were analyzed using two methods: NIDDK MMPC regression and CalR analysis of covariance.
“The energy expenditure data generated by the indirect calorimetry experiment were analyzed using two methods: 1. the NIDDK Mouse Metabolic Phenotyping Centers using their energy expenditure analysis page (http://www.mmpc.org/shared/regression.aspx) and 2. the R programming language with CalR, a custom package for analysis of indirect calorimetry using analysis of covariance with a graphical user”
From IDOL regulates systemic energy balance through control of neuronal VLDLR expression (2019) · cites this paper as [31]
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- Authors
- Mina, A. I., LeClair, R. A., LeClair, K. B., Cohen, D. E., Lantier, L., & Banks, A. S.
- Affiliations
- Brigham and Women's Hospital (US)
- Weill Cornell Medicine (US)
- Vanderbilt University (US)
- Corresponding author
- Banks, Alexander S.
- Venue
- Cell Metabolism
- Publisher
- Elsevier BV
- Published
- 2018-10
- Volume
- 28
- Issue
- 4
- Pages
- 656-666.e1
- ISSN
- 1550-4131 (ISSN-L)
- PMC
- PMC6170709
- OpenAlex
- W2950484331
- Cited by
- 458
- References
- 2
- Citations by year
- 2026: 77 · 2025: 89 · 2024: 88 · 2023: 69 · 2022: 58 · 2021: 44 · 2020: 19 · 2019: 7 · 2018: 6 · 2012: 1
- FWCI
- 13.03
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Cell Metabolism (publishedVersion)
- SSRN Electronic Journal (publishedVersion)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- NIDDK (DK076169, DK115255)
- Harvard Digestive Disease Center (DK034854)
- NIH (OD020100, DK048873-14S2, DK048873, DK056626, DK103046, DK107717)
- Topics
- Physical Sciences › Chemistry › Physical and Theoretical Chemistry › thermodynamics and calorimetric analyses
- Health Sciences › Medicine › Physiology › Adipose Tissue and Metabolism
- Social Sciences › Decision Sciences › Information Systems and Management › Scientific Computing and Data Management
- Keywords
- calreticulin, indirect calorimetry, web-based analysis tool, metabolic rate, obesity, thermogenesis, generalized linear models, analysis of variance, covariance analysis, reproducibility, data analysis software
- MeSH terms
- Cloud Computing, Data Visualization, Analysis of Variance, Animals, Calorimetry, Indirect, Energy Metabolism, Humans, Obesity, Pulmonary Gas Exchange, Reproducibility of Results, Weight Loss, Linear Models, Thermogenesis, Mice, Workflow, Web Browser
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