p-value-and-fold-change-computation
Summary
Compute differential analysis summary statistics (p-values, adjusted p-values, and log2 fold changes) from raw metabolomics matrices to quantify metabolite abundance changes between experimental groups. These statistics serve as the foundation for downstream pathway enrichment analysis.
When to use
Apply this skill when you have raw metabolomics data organized as a matrix with metabolites as rows and samples as columns, sample group labels (e.g., 'TK-CMV' and 'K-CMV'), and need to identify which metabolites differ significantly in abundance between two or more experimental groups before performing enrichment analysis.
When NOT to use
- Input data is already a precomputed summary statistics table (p-values, fold changes, etc.) — pass directly to enrichment.
- Samples lack clear group assignments or experimental design is not a simple two-group comparison without covariates.
- Metabolite matrix has not been normalized or batch-corrected; preprocessing should precede differential analysis.
Inputs
- Metabolomics abundance matrix (metabolites × samples)
- Sample group labels ('TK-CMV', 'K-CMV', or equivalent)
- Raw or normalized metabolite intensities
Outputs
- Summary statistics table with metabolite identifiers, p-values, adjusted p-values, and log2 fold changes
- Differential analysis results for enrichment input
How to apply
Load the metabolomics data matrix ensuring metabolites are rows and samples are columns, with samples labeled by group assignment. Call the run_de() function to perform differential analysis, which internally computes p-values testing the null hypothesis of no group difference for each metabolite, calculates adjusted p-values (typically via Benjamini–Hochberg or similar multiple-testing correction), and derives log2 fold changes as the log-ratio of mean abundances between groups. The output is a table of summary statistics (p-values, adjusted p-values, log2fc) for each metabolite that can be filtered by p-value thresholds (e.g., p_value_cutoff = 0.05) and passed to enrichment analysis functions.
Related tools
- run_de() (Core function that accepts metabolomics matrix and group labels, computes p-values, adjusted p-values, and log2 fold changes for differential analysis) — https://github.com/biodatalab/enrichmet
- R (Statistical programming environment in which run_de() is implemented and executed)
Examples
da_out <- run_de(inputData = metabolomics_matrix, groups = c('TK-CMV', 'K-CMV'))
Evaluation signals
- Output table contains one row per metabolite with valid numeric p-values in [0, 1] and adjusted p-values also in [0, 1].
- Adjusted p-values are greater than or equal to raw p-values (multiple-testing correction property).
- Log2 fold changes are numeric, centered near zero for unchanged metabolites, and reflect the direction of change (positive for increased, negative for decreased in treatment vs. control).
- All metabolites from the input matrix appear in the output; no metabolites are silently dropped unless filtered by a specified threshold.
- Results can be successfully ingested by enrichment functions using the p-value and log2fc columns as input.
Limitations
- run_de() is designed for two-group comparisons; multi-group or continuous designs require alternative approaches.
- Function assumes samples are independent; paired or blocked designs are not explicitly handled in the documented workflow.
- No guidance is provided on handling missing values, outliers, or unequal variance between groups within the article or README.
- The article does not specify the underlying statistical test (e.g., t-test, Mann–Whitney, Welch's t-test) or multiple-testing correction method used by run_de().
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 that serve as input for downstream enrichment analysis.: "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"
- [readme] 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.: "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"
- [readme] 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.: "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."
- [other] 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 differential analysis comparing the two groups, generating summary statistics including p-values, adjusted p-values, and log2 fold changes for each metabolite.: "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"
1---2name: p-value-and-fold-change-computation3description: Use when you have raw metabolomics data organized as a matrix with metabolites as rows and samples as columns, sample group labels (e.4license: CC-BY-4.05---67# p-value-and-fold-change-computation89## Summary1011Compute differential analysis summary statistics (p-values, adjusted p-values, and log2 fold changes) from raw metabolomics matrices to quantify metabolite abundance changes between experimental groups. These statistics serve as the foundation for downstream pathway enrichment analysis.1213## When to use1415Apply this skill when you have raw metabolomics data organized as a matrix with metabolites as rows and samples as columns, sample group labels (e.g., 'TK-CMV' and 'K-CMV'), and need to identify which metabolites differ significantly in abundance between two or more experimental groups before performing enrichment analysis.1617## When NOT to use1819- Input data is already a precomputed summary statistics table (p-values, fold changes, etc.) — pass directly to enrichment.20- Samples lack clear group assignments or experimental design is not a simple two-group comparison without covariates.21- Metabolite matrix has not been normalized or batch-corrected; preprocessing should precede differential analysis.2223## Inputs2425- Metabolomics abundance matrix (metabolites × samples)26- Sample group labels ('TK-CMV', 'K-CMV', or equivalent)27- Raw or normalized metabolite intensities2829## Outputs3031- Summary statistics table with metabolite identifiers, p-values, adjusted p-values, and log2 fold changes32- Differential analysis results for enrichment input3334## How to apply3536Load the metabolomics data matrix ensuring metabolites are rows and samples are columns, with samples labeled by group assignment. Call the run_de() function to perform differential analysis, which internally computes p-values testing the null hypothesis of no group difference for each metabolite, calculates adjusted p-values (typically via Benjamini–Hochberg or similar multiple-testing correction), and derives log2 fold changes as the log-ratio of mean abundances between groups. The output is a table of summary statistics (p-values, adjusted p-values, log2fc) for each metabolite that can be filtered by p-value thresholds (e.g., p_value_cutoff = 0.05) and passed to enrichment analysis functions.3738## Related tools3940- **run_de()** (Core function that accepts metabolomics matrix and group labels, computes p-values, adjusted p-values, and log2 fold changes for differential analysis) — https://github.com/biodatalab/enrichmet41- **R** (Statistical programming environment in which run_de() is implemented and executed)4243## Examples4445```46da_out <- run_de(inputData = metabolomics_matrix, groups = c('TK-CMV', 'K-CMV'))47```4849## Evaluation signals5051- Output table contains one row per metabolite with valid numeric p-values in [0, 1] and adjusted p-values also in [0, 1].52- Adjusted p-values are greater than or equal to raw p-values (multiple-testing correction property).53- Log2 fold changes are numeric, centered near zero for unchanged metabolites, and reflect the direction of change (positive for increased, negative for decreased in treatment vs. control).54- All metabolites from the input matrix appear in the output; no metabolites are silently dropped unless filtered by a specified threshold.55- Results can be successfully ingested by enrichment functions using the p-value and log2fc columns as input.5657## Limitations5859- run_de() is designed for two-group comparisons; multi-group or continuous designs require alternative approaches.60- Function assumes samples are independent; paired or blocked designs are not explicitly handled in the documented workflow.61- No guidance is provided on handling missing values, outliers, or unequal variance between groups within the article or README.62- The article does not specify the underlying statistical test (e.g., t-test, Mann–Whitney, Welch's t-test) or multiple-testing correction method used by run_de().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 that serve as input for downstream enrichment analysis.: "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- [readme] 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.: "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"68- [readme] 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.: "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- [other] 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 differential analysis comparing the two groups, generating summary statistics including p-values, adjusted p-values, and log2 fold changes for each metabolite.: "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"
HolobiomicsLab 