Directional Effect Assignment Across Studies

directional-effect-assignment-across-studies

Summary

Assigns directional votes (+1 for up-regulation, −1 for down-regulation, 0 for no trend) to each metabolite compound within individual studies, then sums votes across all studies to produce a qualitative consensus measure. This vote-counting approach enables metabolomic meta-analysis when standard deviation or variance data are unavailable.

When to use

Apply this skill when you have harmonized metabolite results from multiple independent studies, each with fold-change or trend classification data but no variance/standard deviation, and you need to produce a qualitative consensus on directionality for each compound. Vote-counting is appropriate when raw individual-level data are unavailable and you wish to combine directional signals rather than compute weighted effect sizes.

When NOT to use

• Raw individual-level metabolomics data are available; use quantitative meta-analysis (weighted p-value and fold-change combination) instead to leverage full statistical power.
• Continuous effect sizes with confidence intervals or standard errors are reported; vote-counting discards magnitude information.
• You require statistical significance testing or confidence intervals around the consensus estimate; vote-counting is purely descriptive and ordinal.

Inputs

• Harmonized metabolite dataset with columns: compound identifier, fold-change or trend classification, and study reference
• CSV, XLS/XLSX, or TXT file formatted for amanida_read with mode='qual'

Outputs

• Vote-counting table with compound identifier, total vote score (sum of directional votes), and vote distribution (counts of +1, −1, 0 votes)
• Vote plot visualization (bar chart restricted to ≤30 compounds for readability)
• Explore plot showing vote distribution and reports by trend with optional filtering for consistency

How to apply

For each compound in each study, assign a vote based on the reported fold-change or trend classification: assign +1 if fold-change > 1 or positive trend is reported, −1 if fold-change < 1 or negative trend is reported, and 0 if no significant trend or change is observed. Aggregate votes by summing across all studies for each compound to produce a final vote count. Optionally divide by the number of studies reporting on that compound to normalize the consensus score. This method requires only the compound identifier, fold-change (or trend label), and study reference; missing data are ignored during computation. Report the vote distribution (count of +1, −1, and 0 votes) alongside the total vote score to show consistency of direction across studies.

Related tools

• amanida (R package that implements vote-counting via amanida_vote() and compute_amanida() functions; performs harmonized metabolite meta-analysis combining quantitative and qualitative methods) — https://github.com/mariallr/amanida
• amanida_read (Function within amanida package to load and parse harmonized metabolite datasets in CSV, XLS/XLSX, or TXT format with mode='qual' for qualitative analysis) — https://github.com/mariallr/amanida
• webchem (R package used by amanida to harmonize compound identifiers and retrieve PubChem IDs for duplicate checking before vote-counting)
• check_names (Function within amanida to standardize compound identifiers by converting chemical names, InChI, InChIKey, and SMILES to PubChem IDs and detecting duplicates) — https://github.com/mariallr/amanida
• vote_plot (Visualization function in amanida to display vote-counting results as a bar plot with optional subsetting by vote count threshold) — https://github.com/mariallr/amanida
• explore_plot (Visualization function in amanida to show vote distribution and reports stratified by trend, enabling detection of consistency or discrepancies across studies) — https://github.com/mariallr/amanida

Examples

library(amanida); coln = c('Compound Name', 'Behaviour', 'References'); data_votes <- amanida_read('dataset.csv', mode = 'qual', coln, separator = ';'); vote_result <- amanida_vote(data_votes); vote_plot(vote_result)

Evaluation signals

• Total vote score for each compound equals the algebraic sum of +1 (up-regulation), −1 (down-regulation), and 0 (no trend) votes across all studies
• Sum of vote counts (+1s, −1s, and 0s) equals the total number of studies reporting on each compound
• Vote-counting results are restricted to ≤30 compounds in vote_plot output and ≤25 compounds in explore_plot output for readability
• No votes are assigned to missing data; compounds with no reported trend are assigned 0 votes within each study
• Qualitative consensus direction (majority vote direction) aligns with the sign of the final vote score (positive = net up-regulation; negative = net down-regulation; zero = no consensus)

Limitations

• Vote-counting discards information about effect magnitude (fold-change values > 1 or < 1 are treated identically); use quantitative meta-analysis if magnitude matters.
• The method assumes all studies are equally weighted regardless of sample size or statistical power; study size weighting is not incorporated in vote-counting as it is in amanida's quantitative Fisher p-value combination.
• Vote-counting cannot detect or report statistical significance or confidence intervals; results are ordinal descriptive measures only.
• Missing data are silently ignored, which may bias results if missingness is not random across studies or compounds.
• Compounds with discrepancies (both up- and down-regulated across studies) will have vote scores near zero, masking heterogeneity; use explore_plot to identify such inconsistencies.
• Negative fold-change values are internally transformed to positive (1/value) before vote assignment, which may obscure the original direction if not documented.

Evidence

• [intro] Finding on vote assignment rule: "votes are +1 for up-regulation, -1 for down-regulation and 0 if no trend"
• [intro] Workflow step: assign votes per compound: "Assign votes per compound per study: +1 for up-regulation (fold-change > 1 or positive trend), −1 for down-regulation (fold-change < 1 or negative trend), 0 for no significant trend"
• [intro] Workflow step: aggregate votes across studies: "Sum votes across all studies for each compound to produce final vote counts"
• [intro] Vote output specification: "Generate vote-counting table with compound identifier, total vote score, and vote distribution (count of +1, −1, 0 votes)"
• [intro] Rationale for qualitative meta-analysis: "Amanida also computes qualitative meta-analysis performing a vote-counting for compounds, including the option of only using identifier and trend labels"
• [readme] Vote-counting computation in README: "Compound vote-counting: votes are +1 for up-regulation, -1 for down-regulation and 0 if no trend. The total votes are divided by the number of reports"
• [intro] Vote plot readability constraint: "output is restricted to 30 compounds to facilitate the readability"
• [intro] Explore plot readability constraint: "output is restricted to 25 compounds to facilitate the readability"