Fold Change Calculation Metabolomics

fold-change-calculation-metabolomics

Summary

Fold-change (FC) marker identification is a method for ranking metabolic features by their abundance ratio between sample groups in a feature-by-sample metabolomic matrix. It is used to rapidly identify differential metabolites without statistical hypothesis testing, suitable for initial screening and biomarker discovery.

When to use

Apply this skill when you have a feature-by-sample metabolomic intensity matrix (finalData) with corresponding sample group labels (finalLabel), and you need to identify metabolic markers ranked by their fold-change between two or more groups. Use FC ranking when you prioritize simplicity and speed over statistical rigor, or as a first-pass filter before applying more stringent methods (t-test, OPLS-DA, SVM-RFE).

When NOT to use

• Data is already a ranked marker list or pre-filtered feature table; fold-change calculation is redundant.
• Sample groups have >2 levels and you need multi-level pairwise or global comparison; consider OPLS-DA, SAM, or Kruskal–Wallis instead.
• Input matrix contains missing values or is unnormalized; preprocessing (imputation, normalization) must precede fold-change calculation.
• Your goal is statistical inference with p-values and confidence intervals; fold-change alone provides no distributional test—use t-test or Wilcoxon rank sum.

Inputs

• finalData: numeric matrix (features × samples) containing normalized metabolite abundances
• finalLabel: character or factor vector (length = ncol(finalData)) assigning each sample to a group

Outputs

• MarkerResult object containing FC_table: data frame with metabolite identifiers, fold-change values, and statistical rankings

How to apply

Load the MarkerData object containing a metabolite abundance matrix (finalData, rows = features/metabolites, columns = samples) and sample group assignments (finalLabel). Call Marker_Identify(finalData, finalLabel, method='FC') in R to compute fold-change values between sample groups using the fold-change algorithm. The function returns a MarkerResult object with an FC_table containing metabolite identifiers, computed fold-change values, and rankings ordered by magnitude. Extract and inspect the FC_table to verify metabolites are ranked by fold-change; optionally save to CSV/TSV for downstream annotation or pathway enrichment.

Related tools

• LargeMetabo (R package implementing Marker_Identify() function with 13 marker identification strategies including FC method) — https://github.com/LargeMetabo/LargeMetabo
• R (Runtime environment (≥3.5.0) for executing Marker_Identify() and post-processing FC results)
• ggplot2 (Visualization of ranked fold-change values and distribution across metabolites)

Examples

finalData <- MarkerData$finalData
finalLabel <- MarkerData$finalLabel
MarkerResult <- Marker_Identify(finalData, finalLabel, method = "FC")
head(MarkerResult$FC_table)

Evaluation signals

• FC_table columns include metabolite identifier, fold-change numeric value, and rank; no missing or NaN entries in fold-change column.
• Fold-change values are non-negative; ranked metabolites show expected monotonic ordering by absolute FC magnitude.
• Number of rows in FC_table equals number of rows in finalData (all features ranked).
• When saved to CSV/TSV and re-loaded, fold-change values and rankings are preserved; no data corruption in serialization.
• Top-ranked metabolites (high FC) show expected biological consistency with known group differences (e.g., disease vs. control phenotype).

Limitations

• Fold-change is sensitive to normalization and baseline choice; biased fold-changes can result from improper preprocessing or small denominator values in low-abundance features.
• No statistical test (p-value, confidence interval) is provided; fold-change alone cannot distinguish true signal from noise or account for variance across replicates.
• For >2 sample groups, fold-change is typically computed pairwise; no built-in multi-level or global ranking strategy is specified in the LargeMetabo documentation.
• Fold-change can be inflated by single outlier samples; robust alternatives (e.g., median fold-change, trimmed means) are not mentioned.

Evidence

• [other] Marker_Identify() with method='FC' accepts finalData and finalLabel, returns MarkerResult with FC_table: "The Marker_Identify() function with method='FC' accepts a data matrix (finalData) and corresponding sample labels (finalLabel), and returns a MarkerResult object containing an FC_table with ranked"
• [other] Workflow: Call Marker_Identify, extract and structure FC_table, save as delimited file: "Call Marker_Identify(MarkerData, method='FC') to compute fold-change values between sample groups using the fold-change algorithm. 3. Extract and structure the resulting FC_table containing"
• [readme] 13 marker identification strategies include fold change (FC) method: "In the marker identification step, there are 13 popular strategies to identify metabolic markers for the given dataset. These strategies include fold change (FC), partial least squares discrimination"
• [readme] Example R invocation of Marker_Identify with FC method: "finalData <- MarkerData$finalData finalLabel <- MarkerData$finalLabel MarkerResult <- Marker_Identify(finalData, finalLabel, method = "FC") MarkerResult$FC_table[1:5,]"
• [readme] Data input format: feature-by-sample matrix with sample labels: "Before data integration, the csv files containing a feature-by-sample matrix should be prepared in advance. Each dataset (csv file) contains five essential columns providing the information of mass,"