repeated-measures-anova-design
License: restricted — no clear open-source license detected for the underlying tool; verify licensing before commercial use or redistribution.
Summary
Repeated measures ANOVA ranks metabolites in longitudinal metabolomics datasets by statistical significance, enabling prioritized feature selection for downstream analysis. This skill applies ANOVA with subject-level random effects to imputed long-format data to identify metabolites with strongest evidence of group differences across time.
When to use
Apply this skill when you have an imputed, long-format metabolomics dataset with repeated measurements (multiple time points) per subject, a categorical grouping variable (e.g., treatment, phenotype), and you need to rank metabolites by statistical significance to prioritize features for validation or mechanistic studies.
When NOT to use
- Input data is in wide format (metabolites as columns) without prior conversion to long format.
- Dataset contains missing values that have not been imputed prior to model fitting.
- No repeated measurements per subject exist, or subjects are measured only once.
- Grouping variable is continuous rather than categorical; use linear regression or mixed models instead.
Inputs
- Long-format imputed metabolomics dataset (columns: subject_id, time_point, grouping_variable, metabolite_name, numeric_value)
- Subject identifier column
- Categorical grouping/treatment variable
Outputs
- Ranked metabolite feature table (CSV: metabolite_name, F-statistic, p-value, rank)
- ANOVA model objects for each metabolite
How to apply
Load the imputed long-format metabolomics data containing columns for subject ID, time point, grouping variable, metabolite names, and numeric values. For each metabolite, fit a repeated measures ANOVA model with the grouping variable as a fixed effect and subject ID as a random effect (blocking factor). Extract the F-statistic and p-value from each model's ANOVA table. Rank all metabolites in ascending order by p-value (or descending by F-statistic) to identify those with the strongest evidence of group differences. The ranking prioritizes metabolites most likely to reflect true biological effects rather than random variation. Export the results as a CSV table with columns for metabolite name, F-statistic, p-value, and rank for downstream interpretation.
Related tools
- MeTEor (R Shiny application implementing repeated measures ANOVA, mixed ANOVA, and Friedman test for longitudinal metabolomics feature ranking and visualization) — https://github.com/scibiome/meteor
- R (Statistical computing environment for model fitting and ANOVA table extraction)
- tidyverse (Data wrangling and long-format manipulation prior to model fitting)
Examples
library(tidyverse); library(MeTEor); data(example_data); long_data <- pivot_longer(example_data, cols=-c(id, time, group), names_to='metabolite', values_to='value'); anova_results <- long_data %>% group_by(metabolite) %>% do(broom::tidy(aov(value ~ group + Error(id/group), data=.))) %>% arrange(p.value)
Evaluation signals
- ANOVA output includes valid F-statistics and p-values for all metabolites; no missing or infinite values.
- Ranked table is sorted in ascending order by p-value with no ties or duplicate ranks unless metabolites have identical p-values.
- P-value distribution shows expected right-skew with most metabolites near p=1 and a subset with p<0.05 for statistical significance.
- Model convergence: each fitted repeated measures ANOVA model converges without singular fit warnings or zero variance components.
- Subject ID is correctly specified as a random effect blocking factor; F-statistics reflect within-subject variation and between-group effects, not subject-level confounding.
Limitations
- Repeated measures ANOVA assumes sphericity (homogeneity of variance across time differences); violations may inflate Type I error. Consider Mauchly's test or Greenhouse–Geisser correction.
- Unbalanced designs (missing measurements for some subjects at some time points) complicate analysis; balance across subjects and time is preferred.
- P-value ranking alone does not correct for multiple testing across metabolites; family-wise error rate control (e.g., Bonferroni, FDR) should be applied downstream.
- The method assumes metabolite values are normally distributed or can be approximated as such after imputation; highly skewed distributions may require transformation.
- Small sample sizes (few subjects) reduce statistical power; repeated measures ANOVA is most robust with ≥10–15 subjects per group.
Evidence
- [other] Repeated measures ANOVA with grouping and random effects specification: "For each metabolite, fit a repeated measures ANOVA model with the grouping variable as the fixed effect and id as the random effect (subject)."
- [other] Long-format input data structure: "Load the imputed long-format metabolomics dataset (with id, time, categorical grouping variable, metabolite names, and numeric values)."
- [other] Ranking and export of results: "Rank all metabolites by p-value (ascending) or F-statistic (descending) to prioritize those with strongest evidence of difference across groups. Export the ranked feature table as a CSV file with"
- [readme] MeTEor implements repeated measures ANOVA for metabolomics: "Statistical models: Linear Mixed Models, Repeated Measures ANOVA, Mixed ANOVA, Friedman test"
- [other] Data format requirement for analysis: "The data format is being transformed from wide to long format to make it compatible with MeTEor."
1---2name: repeated-measures-anova-design3description: Use when you have an imputed, long-format metabolomics dataset with repeated measurements (multiple time points) per subject, a categorical grouping variable (e.4license: CC-BY-4.05---67# repeated-measures-anova-design89> **License: restricted** — no clear open-source license detected for the underlying tool; verify licensing before commercial use or redistribution. <!-- asb-license-banner -->10## Summary1112Repeated measures ANOVA ranks metabolites in longitudinal metabolomics datasets by statistical significance, enabling prioritized feature selection for downstream analysis. This skill applies ANOVA with subject-level random effects to imputed long-format data to identify metabolites with strongest evidence of group differences across time.1314## When to use1516Apply this skill when you have an imputed, long-format metabolomics dataset with repeated measurements (multiple time points) per subject, a categorical grouping variable (e.g., treatment, phenotype), and you need to rank metabolites by statistical significance to prioritize features for validation or mechanistic studies.1718## When NOT to use1920- Input data is in wide format (metabolites as columns) without prior conversion to long format.21- Dataset contains missing values that have not been imputed prior to model fitting.22- No repeated measurements per subject exist, or subjects are measured only once.23- Grouping variable is continuous rather than categorical; use linear regression or mixed models instead.2425## Inputs2627- Long-format imputed metabolomics dataset (columns: subject_id, time_point, grouping_variable, metabolite_name, numeric_value)28- Subject identifier column29- Categorical grouping/treatment variable3031## Outputs3233- Ranked metabolite feature table (CSV: metabolite_name, F-statistic, p-value, rank)34- ANOVA model objects for each metabolite3536## How to apply3738Load the imputed long-format metabolomics data containing columns for subject ID, time point, grouping variable, metabolite names, and numeric values. For each metabolite, fit a repeated measures ANOVA model with the grouping variable as a fixed effect and subject ID as a random effect (blocking factor). Extract the F-statistic and p-value from each model's ANOVA table. Rank all metabolites in ascending order by p-value (or descending by F-statistic) to identify those with the strongest evidence of group differences. The ranking prioritizes metabolites most likely to reflect true biological effects rather than random variation. Export the results as a CSV table with columns for metabolite name, F-statistic, p-value, and rank for downstream interpretation.3940## Related tools4142- **MeTEor** (R Shiny application implementing repeated measures ANOVA, mixed ANOVA, and Friedman test for longitudinal metabolomics feature ranking and visualization) — https://github.com/scibiome/meteor43- **R** (Statistical computing environment for model fitting and ANOVA table extraction)44- **tidyverse** (Data wrangling and long-format manipulation prior to model fitting)4546## Examples4748```49library(tidyverse); library(MeTEor); data(example_data); long_data <- pivot_longer(example_data, cols=-c(id, time, group), names_to='metabolite', values_to='value'); anova_results <- long_data %>% group_by(metabolite) %>% do(broom::tidy(aov(value ~ group + Error(id/group), data=.))) %>% arrange(p.value)50```5152## Evaluation signals5354- ANOVA output includes valid F-statistics and p-values for all metabolites; no missing or infinite values.55- Ranked table is sorted in ascending order by p-value with no ties or duplicate ranks unless metabolites have identical p-values.56- P-value distribution shows expected right-skew with most metabolites near p=1 and a subset with p<0.05 for statistical significance.57- Model convergence: each fitted repeated measures ANOVA model converges without singular fit warnings or zero variance components.58- Subject ID is correctly specified as a random effect blocking factor; F-statistics reflect within-subject variation and between-group effects, not subject-level confounding.5960## Limitations6162- Repeated measures ANOVA assumes sphericity (homogeneity of variance across time differences); violations may inflate Type I error. Consider Mauchly's test or Greenhouse–Geisser correction.63- Unbalanced designs (missing measurements for some subjects at some time points) complicate analysis; balance across subjects and time is preferred.64- P-value ranking alone does not correct for multiple testing across metabolites; family-wise error rate control (e.g., Bonferroni, FDR) should be applied downstream.65- The method assumes metabolite values are normally distributed or can be approximated as such after imputation; highly skewed distributions may require transformation.66- Small sample sizes (few subjects) reduce statistical power; repeated measures ANOVA is most robust with ≥10–15 subjects per group.6768## Evidence6970- [other] Repeated measures ANOVA with grouping and random effects specification: "For each metabolite, fit a repeated measures ANOVA model with the grouping variable as the fixed effect and id as the random effect (subject)."71- [other] Long-format input data structure: "Load the imputed long-format metabolomics dataset (with id, time, categorical grouping variable, metabolite names, and numeric values)."72- [other] Ranking and export of results: "Rank all metabolites by p-value (ascending) or F-statistic (descending) to prioritize those with strongest evidence of difference across groups. Export the ranked feature table as a CSV file with"73- [readme] MeTEor implements repeated measures ANOVA for metabolomics: "Statistical models: Linear Mixed Models, Repeated Measures ANOVA, Mixed ANOVA, Friedman test"74- [other] Data format requirement for analysis: "The data format is being transformed from wide to long format to make it compatible with MeTEor."
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