Metabolic Marker Identification

metabolic-marker-identification

Summary

Identifies discriminative metabolic markers from feature-by-sample metabolomic data matrices using statistical and machine-learning strategies. This skill applies one of 13 methods (fold change, t-test, PLS-DA, random forest, SAM, etc.) to rank metabolites by their capacity to distinguish between sample groups, producing ranked marker tables for downstream annotation and pathway analysis.

When to use

After batch effect removal and sample integration, when you have a normalized feature-by-sample abundance matrix (finalData) with corresponding sample group labels (finalLabel), and need to identify which metabolites best discriminate between biological conditions or phenotypes for focused annotation and validation.

When NOT to use

• Input data has not undergone batch effect removal or is not normalized—markers identified will be confounded by technical artifacts rather than true biological signals.
• Sample group labels are missing or inconsistent in length with the number of data columns.
• Number of samples per group is very small (<3) and method requires robust statistical power (e.g., t-test, SAM)—results will have high false-discovery rate.

Inputs

• finalData (feature-by-sample matrix: rows=metabolites/m/z features, columns=samples, values=normalized abundance intensities)
• finalLabel (vector of sample group assignments, length=ncol(finalData))

Outputs

• MarkerResult object containing ranked marker table (e.g., FC_table with metabolite IDs, fold-change values, statistical ranks)
• Delimited marker file (CSV or TSV) with selected metabolites and their discriminative scores

How to apply

Call Marker_Identify(finalData, finalLabel, method=) where strategy is one of: 'FC' (fold change), 't-test', 'Wilcoxon', 'PLS-DA', 'OPLS-DA', 'SAM', 'CFS', 'Relief', 'RF-RFE', 'SVM-RFE', 'Chi-squared', 'entropy', or 'linear model/empirical Bayes'. The function computes discriminative scores (e.g., fold-change ratios, statistical p-values, feature importance ranks) between groups. Select method based on data distribution (parametric vs. non-parametric), sample size, and number of groups. The returned MarkerResult object contains a ranked table (e.g., FC_table) with metabolite identifiers, computed scores, and statistical rankings. Extract and filter the ranked table by appropriate thresholds (e.g., fold-change magnitude, adjusted p-value cutoff) before exporting as delimited file for downstream metabolite annotation.

Related tools

• LargeMetabo (Implements Marker_Identify() function and houses 13 marker identification strategies; orchestrates entire metabolomic workflow from data integration through marker ranking.) — https://github.com/LargeMetabo/LargeMetabo
• FSelector (Provides correlation-based feature selection (CFS) and entropy-based filter methods for marker identification.)
• e1071 (Supports SVM-RFE (support vector machine-recursive feature elimination) strategy.)
• mixOmics (Enables PLS-DA and OPLS-DA marker identification strategies.)
• varSelRF (Implements random forest-recursive feature elimination (RF-RFE) marker selection.)

Examples

finalData <- MarkerData$finalData
finalLabel <- MarkerData$finalLabel
MarkerResult <- Marker_Identify(finalData, finalLabel, method="FC")
MarkerResult$FC_table[1:5,]

Evaluation signals

• MarkerResult object is successfully instantiated and contains a non-empty ranked table with metabolite identifiers, computed scores (fold-change, p-values, or feature importance), and rank positions.
• Ranked table column schema matches expected output (e.g., FC_table contains m/z, metabolite ID, fold-change magnitude, adjusted p-value, and rank order).
• Selected markers show expected separation between sample groups in downstream sample separation plots (e.g., HCA, PCA, PLS-DA score plots)—marked metabolites cluster samples by group better than random features.
• Marker fold-change magnitudes and p-value distributions are consistent with method assumptions (e.g., fold-change scores are positive, p-values fall in [0,1], feature importance scores are normalized).
• Exported marker file is properly delimited (CSV/TSV) with consistent column count and no missing values in core columns.

Limitations

• Performance depends heavily on method choice and data characteristics; no single method is optimal for all metabolomic datasets—parametric tests (t-test) assume normality; non-parametric tests (Wilcoxon, SAM) are robust but may sacrifice power in small samples.
• Markers identified reflect only group discrimination in the current cohort and may not generalize to independent validation cohorts without external validation.
• Multiple testing correction is applied within each method (e.g., FDR adjustment), but comparative performance across all 13 strategies is not systematically benchmarked in the README; practitioners must evaluate method suitability for their biological question.
• Fold-change alone (method='FC') does not account for statistical significance and can highlight spurious markers with large ratios but high variance; should be combined with p-value filtering post-hoc.

Evidence

• [other] The Marker_Identify() function accepts finalData and finalLabel, returns MarkerResult with FC_table: "Call Marker_Identify(finalData, finalLabel, method='FC') to compute fold-change values between sample groups using the fold-change algorithm. 3. Extract and structure the resulting FC_table"
• [readme] 13 marker identification strategies are available in LargeMetabo: "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] Input data structure: feature-by-sample matrix with mass, retention time, intensity columns: "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,"
• [other] Marker table is saved as delimited file after extraction: "Save the FC_table as a delimited file (CSV or TSV format)."
• [readme] Marker identification follows batch removal and precedes annotation: "After data integration, it was essential to remove the unwanted variations among different batches. There are four sample separation methods for visualizing the clustering and separation of different"