differential-metabolite-abundance-analysis
Summary
Compute differential abundance statistics (p-values, adjusted p-values, log2 fold changes) for metabolites across experimental groups using the run_de() function, which accepts a metabolomics matrix with metabolites as rows and samples as columns labeled with group assignments. This step produces the summary statistics required for downstream pathway enrichment analysis.
When to use
You have raw metabolomics data structured as a matrix with metabolites as rows and samples as columns, samples are annotated with group labels (e.g., 'TK-CMV' and 'K-CMV'), and you need to identify metabolites with significant differences in abundance between two or more experimental groups before performing pathway enrichment or other downstream analyses.
When NOT to use
- Input is already a precomputed summary statistics table (p-values, adjusted p-values, log2 fold changes already calculated) — pass directly to enrichment analysis instead.
- Data are not in the required matrix format (metabolites as rows, samples as columns) — data reformatting is needed before calling run_de().
- Single experimental group or unpaired/unstructured samples without clear group labels — differential analysis requires at least two annotated groups for comparison.
Inputs
- metabolomics data matrix (metabolites × samples) with numeric abundance values
- sample annotation vector with group labels (e.g., 'TK-CMV', 'K-CMV')
Outputs
- differential analysis results table with columns: metabolite identifier, p-value, adjusted p-value, log2 fold change
- summary statistics object suitable for pathway enrichment analysis
How to apply
Load the raw metabolomics data matrix ensuring metabolites are rows and samples are columns, with each sample labeled with its group assignment. Call the run_de() function on this structured input to perform differential analysis, which internally computes p-values, adjusted p-values, and log2 fold changes for each metabolite. The function outputs a differential analysis results table containing metabolite identifiers and their associated statistics. These summary statistics serve as the required input for the enrichmet() enrichment analysis function; the workflow documentation explicitly states 'the workflow should begin with the run_de() function. This step generates the summary statistics required for enrichment, including p-values, adjusted p-values, and log2 fold changes.' Users can optionally supply precomputed summary statistics if differential analysis has been performed elsewhere, but run_de() is the integrated approach for computing them internally.
Related tools
- run_de() (Performs differential abundance analysis on metabolomics matrix, computing p-values, adjusted p-values, and log2 fold changes for each metabolite between experimental groups) — https://github.com/biodatalab/enrichmet
- R (Statistical computing environment for executing the run_de() function and manipulating metabolomics data structures)
- enrichmet() (Accepts run_de() output (da_results parameter) as input for downstream pathway enrichment analysis) — https://github.com/biodatalab/enrichmet
Examples
results <- run_de(metabolomics_matrix = data_matrix, group_labels = c('TK-CMV', 'K-CMV'))
Evaluation signals
- Output table contains one row per input metabolite with non-null p-values (range 0–1) and adjusted p-values (range 0–1), with adjusted p-values ≥ corresponding raw p-values.
- Log2 fold changes have expected direction and magnitude; negative values indicate lower abundance in one group, positive in the other.
- Output table dimensions match input: number of rows equals number of unique metabolites in input matrix.
- No missing values in critical columns (p-value, adjusted p-value, log2fc); any rows with missing statistics indicate potential data quality issues.
- Output is compatible with enrichmet() function by matching expected column names and data types (e.g., da_results parameter accepts the output directly).
Limitations
- run_de() requires properly annotated group labels; missing or inconsistent group annotations will cause function failure.
- The function assumes the two groups being compared are well-defined; complex experimental designs with multiple factors or continuous covariates may require alternative statistical approaches.
- Adjusted p-values depend on the multiple-testing correction method used internally by run_de(); the exact correction procedure is not detailed in the available documentation.
- No explicit filtering by p-value threshold occurs in run_de() itself; filtering to metabolites with p_value_cutoff (e.g., 0.05) is applied downstream during enrichment analysis via the enrichmet() function parameters.
Evidence
- [other] The run_de() function accepts a metabolomics matrix with metabolites as rows and samples as columns, along with group labels ('TK-CMV' and 'K-CMV'), and produces differential analysis output containing p-values, adjusted p-values, and log2 fold changes: "The run_de() function accepts a metabolomics matrix with metabolites as rows and samples as columns, along with group labels ('TK-CMV' and 'K-CMV'), and produces differential analysis output"
- [other] Load the raw metabolomics data matrix and call run_de() to perform differential analysis, generating summary statistics: "Load the raw metabolomics data matrix with metabolites as rows and samples as columns, ensuring samples are labeled with group assignments ('TK-CMV' or 'K-CMV'). Call run_de() function to perform"
- [readme] The workflow should begin with the run_de() function to generate summary statistics required for enrichment: "the workflow should begin with the run_de() function. This step generates the summary statistics required for enrichment, including p-values, adjusted p-values, and log2 fold changes."
- [readme] Results can be supplied in one of two formats: precomputed summary statistics or raw metabolomics data: "These results can be supplied in one of two formats: 1. Precomputed summary statistics 2. Raw metabolomics data, from which the workflow will compute differential analysis results internally"
- [other] Output the differential analysis results table containing metabolite identifiers and their associated statistics: "Output the differential analysis results table containing metabolite identifiers and their associated statistics."
1---2name: differential-metabolite-abundance-analysis3description: Use when you have raw metabolomics data structured as a matrix with metabolites as rows and samples as columns, samples are annotated with group labels (e.4license: CC-BY-4.05---67# differential-metabolite-abundance-analysis89## Summary1011Compute differential abundance statistics (p-values, adjusted p-values, log2 fold changes) for metabolites across experimental groups using the run_de() function, which accepts a metabolomics matrix with metabolites as rows and samples as columns labeled with group assignments. This step produces the summary statistics required for downstream pathway enrichment analysis.1213## When to use1415You have raw metabolomics data structured as a matrix with metabolites as rows and samples as columns, samples are annotated with group labels (e.g., 'TK-CMV' and 'K-CMV'), and you need to identify metabolites with significant differences in abundance between two or more experimental groups before performing pathway enrichment or other downstream analyses.1617## When NOT to use1819- Input is already a precomputed summary statistics table (p-values, adjusted p-values, log2 fold changes already calculated) — pass directly to enrichment analysis instead.20- Data are not in the required matrix format (metabolites as rows, samples as columns) — data reformatting is needed before calling run_de().21- Single experimental group or unpaired/unstructured samples without clear group labels — differential analysis requires at least two annotated groups for comparison.2223## Inputs2425- metabolomics data matrix (metabolites × samples) with numeric abundance values26- sample annotation vector with group labels (e.g., 'TK-CMV', 'K-CMV')2728## Outputs2930- differential analysis results table with columns: metabolite identifier, p-value, adjusted p-value, log2 fold change31- summary statistics object suitable for pathway enrichment analysis3233## How to apply3435Load the raw metabolomics data matrix ensuring metabolites are rows and samples are columns, with each sample labeled with its group assignment. Call the run_de() function on this structured input to perform differential analysis, which internally computes p-values, adjusted p-values, and log2 fold changes for each metabolite. The function outputs a differential analysis results table containing metabolite identifiers and their associated statistics. These summary statistics serve as the required input for the enrichmet() enrichment analysis function; the workflow documentation explicitly states 'the workflow should begin with the run_de() function. This step generates the summary statistics required for enrichment, including p-values, adjusted p-values, and log2 fold changes.' Users can optionally supply precomputed summary statistics if differential analysis has been performed elsewhere, but run_de() is the integrated approach for computing them internally.3637## Related tools3839- **run_de()** (Performs differential abundance analysis on metabolomics matrix, computing p-values, adjusted p-values, and log2 fold changes for each metabolite between experimental groups) — https://github.com/biodatalab/enrichmet40- **R** (Statistical computing environment for executing the run_de() function and manipulating metabolomics data structures)41- **enrichmet()** (Accepts run_de() output (da_results parameter) as input for downstream pathway enrichment analysis) — https://github.com/biodatalab/enrichmet4243## Examples4445```46results <- run_de(metabolomics_matrix = data_matrix, group_labels = c('TK-CMV', 'K-CMV'))47```4849## Evaluation signals5051- Output table contains one row per input metabolite with non-null p-values (range 0–1) and adjusted p-values (range 0–1), with adjusted p-values ≥ corresponding raw p-values.52- Log2 fold changes have expected direction and magnitude; negative values indicate lower abundance in one group, positive in the other.53- Output table dimensions match input: number of rows equals number of unique metabolites in input matrix.54- No missing values in critical columns (p-value, adjusted p-value, log2fc); any rows with missing statistics indicate potential data quality issues.55- Output is compatible with enrichmet() function by matching expected column names and data types (e.g., da_results parameter accepts the output directly).5657## Limitations5859- run_de() requires properly annotated group labels; missing or inconsistent group annotations will cause function failure.60- The function assumes the two groups being compared are well-defined; complex experimental designs with multiple factors or continuous covariates may require alternative statistical approaches.61- Adjusted p-values depend on the multiple-testing correction method used internally by run_de(); the exact correction procedure is not detailed in the available documentation.62- No explicit filtering by p-value threshold occurs in run_de() itself; filtering to metabolites with p_value_cutoff (e.g., 0.05) is applied downstream during enrichment analysis via the enrichmet() function parameters.6364## Evidence6566- [other] The run_de() function accepts a metabolomics matrix with metabolites as rows and samples as columns, along with group labels ('TK-CMV' and 'K-CMV'), and produces differential analysis output containing p-values, adjusted p-values, and log2 fold changes: "The run_de() function accepts a metabolomics matrix with metabolites as rows and samples as columns, along with group labels ('TK-CMV' and 'K-CMV'), and produces differential analysis output"67- [other] Load the raw metabolomics data matrix and call run_de() to perform differential analysis, generating summary statistics: "Load the raw metabolomics data matrix with metabolites as rows and samples as columns, ensuring samples are labeled with group assignments ('TK-CMV' or 'K-CMV'). Call run_de() function to perform"68- [readme] The workflow should begin with the run_de() function to generate summary statistics required for enrichment: "the workflow should begin with the run_de() function. This step generates the summary statistics required for enrichment, including p-values, adjusted p-values, and log2 fold changes."69- [readme] Results can be supplied in one of two formats: precomputed summary statistics or raw metabolomics data: "These results can be supplied in one of two formats: 1. Precomputed summary statistics 2. Raw metabolomics data, from which the workflow will compute differential analysis results internally"70- [other] Output the differential analysis results table containing metabolite identifiers and their associated statistics: "Output the differential analysis results table containing metabolite identifiers and their associated statistics."
