Shiny-Calorie: a context-aware application for indirect calorimetry data analysis and visualization using R.
The main results reproduced: recomputed values matched the published ones within tolerance.
Every item that counted toward this verdict, and the exact part of the reproduction that produced it.
- ✓Same input data as the authors
- ✓No relevant deviation in data/preprocessing
- ✓No authors-side cause for any deviation
- ✓Reported values are derivable from the shared data
- ✓Any deviation was negligible
- ✓The central claim held under reproduction
- ✓Overall, the reproduction was clean
- 🟡Reported values were only indirectly comparable
A 0–100 reproducibility-quality score from the per-question grades, shown as a z-score: standard deviations above (+) or below (−) the mean of comparable assessments.
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Provenance — full disclosure
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- Reproduced
- 2026-07-29
- Rubric version
- v1.0
- Assessed by
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🤖 AI curator · claude (ai-curator headless) · v1.0 · run #1 2026-07-31no human curator yet
- Last updated
- 2026-07-31
Provisional, curator- or AI-assessed, and independently checkable. A reproduction outcome states what one attempt could reproduce — not a judgement of the authors.
Deep full-text extraction
Model: opusThe paper addresses whether a flexible, extensible, and user-friendly software suite can enable standardized integration of data and metadata from diverse indirect calorimetry (IC) metabolic phenotyping platforms followed by unified statistical analysis and visualization — a capability the authors state is currently absent.
- ★ Shiny-Calorie is an open-source interactive application for transparent data and metadata integration, statistical analysis, and visualization of indirect calorimetry datasets. resource
- ★ Shiny-Calorie supports the majority of standard data formats across commercial metabolic phenotyping platforms, including TSE, Sable Systems, COSMED, and CLAMS/Columbus instruments, plus CalR and CaloPy exports. resource
- ★ The application performs automatic harmonization of metadata labels across datasets, consolidating variable names and resolving inconsistencies to support joint multi-cohort analysis. method
- ★ Shiny-Calorie reconstructs energy expenditure from raw IC data using standard equations, including the Heldmaier equation, and derives secondary quantities such as resting metabolic rate (RMR) and activity-dependent energy expenditure (AEE). method
- ★ The platform incorporates statistical methods for hypothesis testing (multi-way ANOVA, ANCOVA, generalized linear models) with covariate correction, assumption tests, and post-hoc multiple-testing correction. method
- Existing IC software solutions focus on individual experiments, poorly support cross-study analyses, and offer limited extensibility or reproducibility, requiring manual metadata curation. finding
- Shiny-Calorie is distributed in three deployment modes — hosted web application, Docker/OCI container, and standalone desktop installer — implemented in GNU R with Shiny under the BSD-3-Clause license. resource
- Data curation features include automated consistency checks (negative gas-exchange values, high-frequency point filtering, complete-day enforcement, temperature validation) and interactive or z-score-based outlier removal. method
| Assay | System | Perturbation | Readout | Platform |
|---|---|---|---|---|
| Indirect calorimetry (software data import/processing) | Datasets from TSE Systems LabMaster/PhenoMaster instruments | none (metadata-based stratification, e.g. genotype, treatment, diet, supported) | O2 and CO2 gas exchange over time, RER, energy expenditure (kJ/h), RMR, AEE | TSE Systems LabMaster, PhenoMaster |
| Indirect calorimetry (software data import/processing) | Datasets from Sable Systems platforms | none | O2 and CO2 gas exchange, derived energy expenditure metrics | Sable Systems |
| Indirect calorimetry (software data import/processing) | Datasets from Columbus Instruments CLAMS Oxymax | none | O2 and CO2 gas exchange, derived energy expenditure metrics | Columbus Instruments CLAMS Oxymax |
| Indirect calorimetry (software data import/processing) | Human/clinical datasets from COSMED platforms | none | O2 and CO2 gas exchange, derived energy expenditure metrics | COSMED (Generic and QNRG) |
| Indirect calorimetry (software data import/processing) | Datasets from PhenoSys Calobox | none | O2 and CO2 gas exchange, derived energy expenditure metrics | PhenoSys Calobox |
| Import of processed data from third-party IC analysis software | CalR and CaloPy data exports | none | Consolidated IC variables for joint/cross-study analysis | CalR; CaloPy |
| Physical activity / locomotion analysis | IC datasets containing physical activity channels (animal subjects) | none | Locomotion density maps, behavioral summaries, locomotor activity traces | — |
| Statistical analysis and time-series/wavelet analysis module | Imported single-study and multi-cohort IC datasets (bundled example datasets) | metadata-defined groupings (genotype, treatment, diet) | ANOVA/ANCOVA/GLM test statistics, covariate-corrected group comparisons, Shapiro–Wilk and Levene test outcomes, hourly RMR/EE time-trace profiles | GNU R / Shiny framework |
- – Shiny-Calorie enables import, harmonization, analysis, visualization, and export of IC data across multiple commercial platforms in a single reactive web application.
- ▼ Metadata harmonization reduces the risk of errors from inconsistent labeling and enables reproducible downstream and cross-cohort analysis.
- – Energy expenditure is computed as EE [kJ/h] = (4.44 + 1.43 * RER) * VO2 [ml/h] * 3.6/1000 with RER = VCO2/VO2 (Heldmaier equation); alternative equations are also supported.
- – Test assumption results are summarized in binary check-mark format and significance in conventional asterisk notation, facilitating interpretation for users without statistical expertise.
- – Processed data can be exported as vector/bitmap figures, interactive HTML plots, CSV tables, and Excel or CalR-compatible consolidated multi-cohort datasets.
- ▼ Automating data harmonization and integrating statistical and visualization methods into one platform substantially reduces overhead of metadata curation and manual processing.
- – Derived metrics allow assessment of substrate utilization (fat versus carbohydrate oxidation) from the gas exchange signals.
- – No new experimental data were generated for this work; example datasets are bundled with the application and deposited in the GitHub repository.
- other EE [kJ/h] = (4.44 + 1.43 * RER) * VO2 [ml/h] * 3.6/1000 (Heldmaier (1975) energy expenditure equation implemented in Shiny-Calorie)
- other RER = VCO2 [ml/h] / VO2 [ml/h] (Respiratory exchange ratio definition used in the EE calculation)
- count 3 (Number of distribution modes: web application, Docker image, standalone desktop installer)
- other 450149205 - TRR 333/1 (BATenergy) (German Research Foundation grant supporting the work)
Statistical methods review
Model: sonnetA neutral, descriptive read of the statistical approach — what was done, and (for shared learning, not as criticism) what could also have been done.
This is a software application-note describing Shiny-Calorie, an R/Shiny tool for indirect calorimetry data integration, visualization, and statistical analysis; it does not itself report an original experiment with study-specific test results. Instead, it describes the statistical methods the software makes available to its users: multi-way ANOVA, ANCOVA, and generalized linear models for group comparisons with optional covariate correction (e.g. body weight), parametric-assumption diagnostics (Shapiro-Wilk, Levene's test), post-hoc comparisons with multiple-testing correction, and significance reporting via asterisk notation.
| Test | Applied to | n | Assumptions |
|---|---|---|---|
| multi-way ANOVA | general hypothesis testing across comparison groups (software feature, Fig. 1B) | — | stated |
| ANCOVA | group comparisons with covariate correction, e.g. body weight (software feature, Fig. 1B) | — | stated |
| generalized linear models | hypothesis testing for variable numbers of comparison groups (software feature, Section 2.5) | — | not stated |
| post-hoc comparisons (unspecified method) with multiple-testing correction | follow-up to ANOVA/ANCOVA/GLM group comparisons (software feature, Section 2.5) | — | na |
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Statistical significance in the software output is displayed using conventional asterisk notation.↳ Could also: reporting exact p-values alongside or instead of asterisks — exact p-values convey the precise strength of evidence and allow readers to apply their own significance thresholds, which asterisk categories do not
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The software offers Shapiro-Wilk and Levene's test to check parametric assumptions before ANOVA/ANCOVA/GLM.↳ Could also: non-parametric alternatives such as Mann-Whitney U, Kruskal-Wallis, or rank-based ANOVA — when normality or homogeneity-of-variance assumptions are not met, non-parametric tests provide an approach that does not rely on those distributional assumptions
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Post-hoc comparisons include correction for multiple testing, described generically without naming a specific method.↳ Could also: explicitly named procedures such as Tukey HSD, Bonferroni, or Benjamini-Hochberg FDR — naming the specific correction method makes the control of family-wise error rate or false discovery rate transparent and reproducible for readers
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ANCOVA is offered for covariate correction (e.g. body weight) in cross-sectional group comparisons.↳ Could also: linear mixed-effects models with subject as a random effect — indirect calorimetry data are often longitudinal/repeated-measures within the same animals, and mixed models can account for within-subject correlation while still adjusting for covariates
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Assumption-test results are summarized for users in a binary check-mark format.↳ Could also: also displaying the underlying test statistics and p-values — showing the full diagnostic output lets more statistically experienced users evaluate borderline cases themselves rather than relying solely on a pass/fail indicator
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Windowed time-trace analysis is used to detect time-specific differences between groups (e.g. hourly RMR/EE profiles).↳ Could also: functional data analysis or mixed models with an autocorrelation structure (e.g. generalized additive mixed models) — these approaches can model the continuous temporal trajectory and its within-subject correlation directly, which may complement window-based comparisons of longitudinal metabolic data
What was reproduced
The exact results taken into scope, with each reported value next to the value our attempt produced.
Assessments & scoring basis
Each contributor’s verdict, the per-question basis, and the auditable, itemised worksheet behind it.
An automated assessment. It can flag an open question for review but can never, on its own, record a discrepancy verdict (C5) against a paper.
Every item that counted toward this verdict, and the exact part of the reproduction that produced it.
This is a software/application note where the artifact under test is the authors' own repo (ICB-DCM/Shiny-Calorie) and the Data Availability Statement declares no new data. Both in-scope computational claims reproduce exactly: extract_rmr(df,5,5,15) on the shipped 4521-row input yields all 1810 output rows with max abs diff = 0 for HP and Time against the authors' own output_rmr_for_testing.csv, and all 16 metadata assertions (11 covariate columns; n=16 UCP1KO, n=23 DAKO; light-on/off 6/18) pass. The two caveats are structural, not substantive: the paper states no numeric endpoint that could be independently checked (hence q2 yellow — the comparison anchor is a repo-internal fixture, so exactness is self-consistency rather than external confirmation), and test_integration.R is a dead test file whose test blocks the authors themselves commented out pending a UI refactor, leaving the visualization claims unverified. Neither is a discrepancy on the numbers, and the HPC tidyverse/shinytest2 install failure is a disclosed environment limitation that never touched the compared functions — overall a clean, fair green.
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