Trend Classification From Fold Change

Trend classification from fold-change

Summary

Assign directional trend labels (+1 for up-regulation, −1 for down-regulation, 0 for no trend) to metabolite compounds based on fold-change thresholds and statistical significance. This classification enables qualitative vote-counting meta-analysis in metabolomics studies where raw variance data are unavailable.

When to use

When you have harmonized metabolomics data with fold-change and p-value columns from multiple studies and need to assign trend categories prior to vote-counting meta-analysis. Specifically, use this skill when comparing metabolite behaviour across studies but lack standard deviation or variance estimates required by traditional weighted meta-analysis methods.

When NOT to use

• Input data lack fold-change or p-value columns — standard trend classification requires both magnitude and significance information.
• Analysis goal is quantitative meta-analysis combining effect sizes and confidence intervals — use weighted Fisher's method or logarithmic fold-change combination instead.
• Metabolite identifiers are not harmonized to a common nomenclature (e.g., PubChem ID) — perform identifier harmonization via check_names before classification to avoid spurious trend splits across synonym variants.

Inputs

• Harmonized metabolite dataset with columns: compound identifier, fold-change (numeric), statistical significance (p-value or significance flag)
• Study-level fold-change and trend classification data

Outputs

• Trend-classified metabolite table with vote assignments (+1, −1, 0) per compound per study
• Compound trend vote distribution (count of +1, −1, 0 votes across studies)

How to apply

For each metabolite compound in each study, assign a trend vote based on fold-change magnitude and direction: assign +1 if fold-change > 1 (up-regulation) and statistically significant, −1 if fold-change < 1 (down-regulation) and statistically significant, and 0 if no significant trend is observed. Apply a fold-change cutoff of ≥2 for biological meaningfulness (values below this threshold may represent noise). Handle negative fold-change values by transforming them to positive using the reciprocal (1/value) before comparison. This classification preserves effect direction and allows subsequent aggregation via vote-counting across all studies for each compound, producing a consensus trend measure.

Related tools

• amanida (R package that implements trend classification and vote-counting via amanida_read (data import) and amanida_vote (vote assignment and aggregation) functions; also provides visualization via vote_plot and explore_plot) — https://github.com/mariallr/amanida
• R (Statistical computing environment for executing amanida functions and manipulating fold-change and trend data)
• webchem (R package used upstream (via check_names) to harmonize metabolite identifiers to PubChem ID before trend classification)

Examples

coln = c("Compound Name", "Behaviour", "References"); data_votes <- amanida_read(input_file, mode = "qual", coln, separator = ";"); vote_result <- amanida_vote(data_votes)

Evaluation signals

• Vote distribution table sums to total number of studies per compound (no missing or double-counted studies).
• All vote values are exactly {−1, 0, +1}; no other numeric values appear.
• Compounds with fold-change |log₂(FC)| < 1 (i.e., raw FC < 2 or > 0.5) consistently receive vote = 0 unless overridden by explicit significance threshold.
• Negative fold-change values (e.g., −0.8) are transformed to reciprocals (1/0.8 = 1.25) before comparison; no negative fold-changes remain in the classified output.
• Vote plot and explore plot outputs restrict to ≤30 and ≤25 compounds respectively (per output readability constraints); verify the limitation is applied when generating visualizations.

Limitations

• Vote-counting is qualitative and loses magnitude information — two compounds with fold-changes of 1.1 and 10 both receive vote = +1 if statistically significant, conflating weak and strong effects.
• Missing data (studies that did not report a metabolite) are ignored during voting, which may bias consensus toward studies with more complete metabolite coverage.
• No threshold for p-value significance is enforced at the skill level; practitioners must pre-filter data or define p-cutoff externally (typically p < 0.05) before classification.
• Fold-change direction is absolute (1/value transformation); if the original sign convention differs between studies (e.g., some report concentration ratio A/B, others B/A), harmonization must occur before trend classification to avoid spurious sign reversals.

Evidence

• [intro] votes are +1 for up-regulation, -1 for down-regulation and 0 if no trend: "votes are +1 for up-regulation, -1 for down-regulation and 0 if no trend"
• [other] 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.: "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] in case of fold-change we recommend values higher than 2, where it is considered to have biological meaningfulness: "in case of fold-change we recommend values higher than 2, where it is considered to have biological meaningfulness"
• [intro] negative values of fold-change are transformed to positive (1/value): "negative values of fold-change are transformed to positive (1/value)"
• [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.: "Compound vote-counting: votes are +1 for up-regulation, -1 for down-regulation and 0 if no trend"