Treatment Group Comparison Design

treatment-group-comparison-design

Summary

Design and execute univariate statistical comparisons between treatment groups in metabolomics count data using the omu package. This skill applies t-tests or Welch's test to identify metabolites with significant abundance differences across experimental conditions.

When to use

Apply this skill when you have paired metabolomics count data and metadata with at least two treatment groups (e.g., Strep vs Mock), a clear Factor column identifying group membership, and a research question about which metabolites differ significantly in abundance between treatments. The omu_summary function is appropriate for two-group comparisons; use omu_anova instead if you have >2 groups within a single Factor.

When NOT to use

• Input is already a statistics table or pre-filtered feature set — omu_summary requires raw count data
• You have >2 treatment groups and wish to test all pairwise or omnibus differences — use omu_anova for multi-group Factor analysis
• Metadata lacks a clearly labeled Factor column mapping samples to treatment groups

Inputs

• count_data: metabolomics abundance matrix (rows=metabolites, columns=samples)
• metadata: data frame with Sample column and Factor column(s) indicating group membership
• numerator: character string naming the treatment group
• denominator: character string naming the reference/control group

Outputs

• statistics data frame with columns: padj, log2FoldChange, standard_error, standard_deviation
• one row per metabolite tested

How to apply

Load count data (e.g., c57_nos2KO_mouse_countDF) and metadata (with Sample column and Factor columns) into R using read.metabo or read.csv. Optionally assign hierarchical metabolite class data using assign_hierarchy with identifier='KEGG'. Call omu_summary with parameters: count_data, metadata, numerator (treatment group name), denominator (control/reference group), Factor (metadata column name), response_variable='Metabolite', log_transform=TRUE for normalized abundances, p_adjust='BH' for multiple-testing correction, and test_type='welch' for unequal-variance t-tests. The function returns a statistics data frame with padj, log2FoldChange, standard error, and standard deviation columns. Verify that output contains no missing values in statistical columns and that p-adjusted values are bounded [0,1].

Related tools

• omu_summary (Univariate statistical testing function that performs t-tests or Welch's tests between two treatment groups and returns padj, log2FoldChange, and standard error columns) — https://github.com/connor-reid-tiffany/Omu
• assign_hierarchy (Assigns hierarchical metabolite classification (KEGG, KO_Number, Prokaryote, or Eukaryote) to count data frame prior to statistical testing) — https://github.com/connor-reid-tiffany/Omu
• read.metabo (Wrapper around read.csv that ensures metabolomics data has proper R class structure for input to omu functions) — https://github.com/connor-reid-tiffany/Omu
• omu_anova (Alternative statistical testing function for >2 treatment groups within a single Factor; measures variance across all groups) — https://github.com/connor-reid-tiffany/Omu

Examples

omu_summary(count_data=c57_nos2KO_mouse_countDF, metadata=c57_nos2KO_mouse_metadata, numerator='Strep', denominator='Mock', Factor='Treatment', response_variable='Metabolite', log_transform=TRUE, p_adjust='BH', test_type='welch')

Evaluation signals

• Output data frame has exactly one row per input metabolite and four statistical columns (padj, log2FoldChange, standard_error, standard_deviation)
• All p-adjusted values are in the valid range [0, 1] with no NAs in statistical columns
• log2FoldChange sign is consistent with numerator/denominator direction (positive if numerator > denominator on log2 scale)
• Benjamini–Hochberg adjusted p-values are ≥ unadjusted p-values (monotonicity check)
• Metabolites ranked by padj and log2FoldChange match domain expectation (known biomarkers of treatment should appear in top hits)

Limitations

• omu_summary is designed for two-group comparisons; multi-group Factor analysis requires omu_anova
• The function assumes count data are compositional; log_transform=TRUE is recommended but practitioners must ensure this is appropriate for their platform and preprocessing
• No explicit support for paired/longitudinal designs; samples are treated as independent
• P-value adjustment methods are limited to those available in R; 'BH' is the recommended default but other methods are not documented in the article

Evidence

• [other] Omu supports two univariate statistical models, t test and anova, using the functions omu_summary and anova_function respectively: "Omu supports two univariate statistical models, t test and anova, using the functions omu_summary and anova_function respectively"
• [other] Call omu_summary with count_data, metadata, numerator='Strep', denominator='Mock', Factor='Treatment', response_variable='Metabolite', log_transform=TRUE, p_adjust='BH', and test_type='welch' to compute statistics.: "Call omu_summary with count_data, metadata, numerator='Strep', denominator='Mock', Factor='Treatment', response_variable='Metabolite', log_transform=TRUE, p_adjust='BH', and test_type='welch' to"
• [other] Verify the output data frame contains columns for padj, log2FoldChange, standard error, and standard deviation.: "Verify the output data frame contains columns for padj, log2FoldChange, standard error, and standard deviation"
• [other] To assign hierarchical class data, use the assign_hierarchy function and pick the correct identifier, either 'KEGG', 'KO_Number', 'Prokaryote', or 'Eukaryote': "To assign hierarchical class data, use the assign_hierarchy function and pick the correct identifier, either "KEGG", "KO_Number", "Prokaryote", or "Eukaryote""
• [other] An alternative option to omu_summary is the omu_anova, which can be used to measure the variance of all groups within a factor: "An alternative option to omu_summary is the omu_anova, which can be used to measure the variance of all groups within a factor"