Meta analysis effect size estimation

Skill HolobiomicsLab/asb-skill-collections/collections/metabolomics/v2/skills/meta-analysis-effect-size-estimation

Curated, evidence-grounded skill and software-tool collections for scientific AI agents, generated by the AgenticScienceBuilder

Install

npx -y skills add HolobiomicsLab/asb-skill-collections --skill meta-analysis-effect-size-estimation

Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.

One thing to look at

14 stars14 stars. Stars are a popularity signal and not a quality one, but at this level it is likely that nobody has read this closely except its author, and you would be relying on your own review.

What its author says it does

Copied from the file, not written here

Use when when you have tabulated results (p-value, fold-change, study size N) from two or more independent metabolomics studies addressing the same research question and need a single meta-analytic estimate of effect size and statistical significance without access to raw data or variance estimates.

The file declares its own license as CC-BY-4.0. That is the author’s claim about this one file, and it is not the same thing as the license GitHub reports for the repository, which is listed with the other numbers below.

SKILL.md

7.7 KB, as published. Nobody here has run it

meta-analysis-effect-size-estimation

License: restricted — no clear open-source license detected for the underlying tool; verify licensing before commercial use or redistribution.

Summary

Combines p-values and fold-changes across multiple metabolomics studies using study-size-weighted statistical methods to produce unified effect size estimates and significance values. This skill enables cross-study comparison in metabolomics where raw data and standard deviations are unavailable.

When to use

When you have tabulated results (p-value, fold-change, study size N) from two or more independent metabolomics studies addressing the same research question and need a single meta-analytic estimate of effect size and statistical significance without access to raw data or variance estimates.

When NOT to use

When raw individual-level data or study-level variance/standard error estimates are available—use traditional meta-analysis methods (e.g., inverse-variance weighting) instead.
When studies report results only as trend direction (up/down) without quantitative p-values or fold-changes—use vote-counting (amanida_vote) as a qualitative alternative.
When combining results from non-metabolomics fields (e.g., genomics, proteomics) that may report effect sizes in different scales or use different standardization conventions.

Inputs

Tabulated metabolomics results table (CSV, XLS, XLSX, or TXT format)
Columns: compound identifier, p-value, fold-change (or log-fold-change), study sample size (N), bibliographic reference
Two or more independent studies with overlapping compound measurements

Outputs

S4 object with @stat slot: meta-analysis results table (compound identifier, combined p-value, combined fold-change, trend direction, N_total)
S4 object with @vote slot: vote-counting qualitative results (optional)
Numerical estimates: meta-analytic p-value, meta-analytic log2(fold-change), total sample size

How to apply

Load the multi-study dataset (identifier, p-value, fold-change, N per study) using amanida_read with mode='quan'. Apply weighted Fisher's method to combine p-values by assigning gamma-distributed weights proportional to each study's sample size N. Transform fold-changes using log2, then compute the study-size-weighted average of log2-transformed fold-changes. Execute compute_amanida to generate the @stat results table, which contains the combined p-value, combined fold-change (with direction: up-, down-, or no trend), and N_total (sum of all study sizes). Use standard cutoffs (p < 0.05 for significance, fold-change > 2 for biological meaningfulness) to interpret results.

Related tools

amanida (R package that performs weighted meta-analysis combining p-values via weighted Fisher's method and fold-changes via log2 transformation and study-size weighting) — https://github.com/mariallr/amanida
webchem (R package used (optionally, via check_names) to harmonize compound identifiers to PubChem IDs before meta-analysis)
R (Statistical computing environment in which amanida functions execute)

Examples

library(amanida); coln = c("Compound Name", "P-value", "Fold-change", "N total", "References"); datafile <- amanida_read("studies.csv", mode = "quan", coln, separator=";"); amanida_result <- compute_amanida(datafile, comp.inf = FALSE); volcano_plot(amanida_result, cutoff = c(0.05, 2))

Evaluation signals

Verify that N_total equals the sum of all input study sample sizes.
Confirm that combined fold-change direction (up/down/none) is consistent with the sign of the weighted average log2(fold-change).
Check that combined p-value is ≤ the most significant (smallest) individual study p-value and ≥ the least significant (largest) individual study p-value.
Ensure no missing or NA values in combined p-value and fold-change columns of @stat table; missing input data should have been skipped during amanida_read import.
Volcano plot should display combined results without duplicate compound entries; coordinates should match combined p-value and log2(fold-change) from @stat table.

Limitations

Requires study sample size (N) for each compound in each study; missing N values are ignored, reducing effective weighting.
Does not account for between-study heterogeneity (I² statistic not computed); high heterogeneity may inflate confidence in combined estimates.
Negative fold-change values are automatically transformed to positive (1/value); users should verify this is appropriate for their data encoding.
Method assumes fold-changes are reported on comparable scales across studies; unnormalized or batch-corrected values may introduce bias.
Weighted Fisher's method is sensitive to the number of combined p-values; combination of many non-significant p-values can yield spurious significance if weights are large.

Evidence

[intro] Quantitative meta-analysis method: "P-value: weighted p-values combination, which is a variant of Fisher's method. A gamma distribution is used to assign non-integral weights proportional to study size to each p-value."
[intro] Fold-change combination method: "Fold-change: [combined using] logarithmic transformation for average with weighting by number of participants."
[intro] Output structure and contents: "In this step you will obtain an S4 object with two tables: adapted meta-analysis acces by amanida_result@stat [and] vote-counting acces by amanida_results@vote"
[readme] Design rationale: non-integral weighting: "amanida performs weighted meta-analysis combining overall results based on statistical significance, relative change and study size without requiring standard deviation"
[readme] Input requirements: "Dataset to analyse must include the following columns: identifier, p-value, fold-change, study size (N) and reference"
[intro] Biological meaningfulness cutoff: "in case of fold-change we recommend values higher than 2, where it is considered to have biological meaningfulness"
[readme] Vote-counting as qualitative complement: "Amanida also computes qualitative meta-analysis performing a vote-counting for compounds, including the option of only using identifier and trend labels"