Retention time clustering alignment

retention-time-clustering-alignment

Summary

Group putatively identical metabolic features detected across multiple LC-MS samples by clustering on retention time within specified tolerance windows, as a preliminary step before m/z-based refinement in multi-sample metabolomics alignment. This skill consolidates redundant or near-duplicate feature detections into unified feature identities for downstream annotation.

When to use

You have extracted feature tables (via MS1 peak picking, MS2 recognition, or targeted list extraction) from two or more individual LC-MS samples and need to identify which features represent the same metabolite across samples before generating a unified, sample-aligned feature table for adduct and metabolite annotation.

When NOT to use

• Input is already an aligned feature table from a prior alignment run; re-clustering will introduce redundancy or degrade existing alignments.
• Single-sample analysis where no cross-sample consolidation is needed; retention time clustering is designed for multi-sample workflows.
• Full-scan or DIA (data-independent acquisition) datasets where retention time variability may exceed typical tolerance windows; consider broader tolerance or alternative alignment strategies.

Inputs

• Per-sample feature tables (CSV or data frame format with columns: m/z, retention time (seconds), rtmin, rtmax, intensity)
• Retention time tolerance window (seconds; typical range 10–30 s)
• m/z tolerance (ppm; typical range 5–10 ppm)

Outputs

• Unified aligned feature table (standardized CSV or data frame)
• Sample-to-feature coverage matrix (rows = features, columns = samples, entries = intensity or presence/absence)
• Alignment statistics (e.g., number of aligned features, coverage distribution, alignment quality metrics)

How to apply

Load the extracted per-sample feature tables (each containing m/z, retention time, rtmin, rtmax, intensity columns in seconds). Apply retention time-based clustering by grouping features within a specified retention time tolerance window (e.g., ±10–30 seconds, user-configurable). Within each retention time cluster, apply secondary m/z-based clustering to refine grouping and merge features with matching mass-to-charge ratios within ppm tolerance. Generate alignment statistics including the number of aligned features, sample-to-feature coverage matrix (indicating how many samples each feature appears in), and alignment quality metrics. Export the unified aligned feature table in a standardized format (e.g., CSV with columns: aligned_feature_id, mz, rt, sample_count, intensity_per_sample) compatible with JPA's downstream CAMERA annotation and MS2 annotation modules.

Related tools

• JPA (Primary R package implementing the alignment workflow (Part 5: Alignment) that orchestrates retention time and m/z clustering and exports aligned feature tables) — https://github.com/HuanLab/JPA.git
• XCMS (Embedded XCMS functions used within JPA for feature extraction (MS1 peak picking) prior to alignment; XCMS algorithms handle m/z tolerance and peak detection parameters) — https://rdrr.io/bioc/xcms/man/
• CAMERA (Downstream annotation module (Part 6: CAMERA annotation) that uses the aligned feature table to assign adduct annotations and refine metabolite grouping)

Examples

# Assuming featureTable is a per-sample feature table loaded in R via XCMS.featureTable() or custom.featureTable()
# JPA's alignment function (conceptually shown; exact function name from Part 5 of manual):
aligned_ft <- alignment.function(featureTable, rt.tol = 15, mz.ppm = 10)

Evaluation signals

• Aligned feature count matches expected metabolite complexity; each aligned feature should have a single m/z and rt pair (or narrow range) across samples.
• Sample-to-feature coverage matrix shows expected sparsity: high-abundance features should appear in most/all samples; low-abundance features may be sample-specific.
• Retention time deviation within clusters should be ≤ the specified tolerance window (e.g., ±10 s); outliers indicate either misalignment or genuine biological sample-to-sample drift.
• Aligned feature intensity distributions are non-zero across specified samples; zero-intensity entries indicate missing features in certain samples (acceptable if biologically relevant).
• Export file contains all required columns (feature_id, mz, rt, rtmin, rtmax, per-sample intensity) and is readable by downstream CAMERA annotation functions without format errors.

Limitations

• Retention time can vary between samples due to instrument drift, column aging, or differing mobile phase conditions; fixed tolerance windows may miss true alignments in high-drift scenarios or incorrectly merge unrelated features.
• m/z measurement error depends on instrument resolution and calibration; ppm tolerance must be tuned to the mass spectrometer's typical accuracy (e.g., 5 ppm for high-resolution Orbitrap, 10 ppm for lower-resolution instruments).
• Features with identical m/z and rt but different ionization states or adducts may be incorrectly merged; post-alignment CAMERA annotation (Part 6) is required to deconvolute adducts and assign correct molecular identities.
• Clustering performance degrades if per-sample feature extraction parameters (peak picking, noise thresholds, integration settings) are inconsistent across samples; users must standardize extraction parameters before alignment.

Evidence

• [other] Apply retention time-based clustering to group putatively identical features across samples within specified tolerance windows.: "Apply retention time-based clustering to group putatively identical features across samples within specified tolerance windows."
• [other] Apply m/z-based clustering to refine grouping and merge features with matching mass-to-charge ratios.: "Apply m/z-based clustering to refine grouping and merge features with matching mass-to-charge ratios."
• [other] Generate alignment statistics including number of aligned features, sample-to-feature coverage matrix, and alignment quality metrics.: "Generate alignment statistics including number of aligned features, sample-to-feature coverage matrix, and alignment quality metrics."
• [readme] JPA also performs sample alignment, adduct and metabolite annotations.: "JPA also performs sample alignment, adduct and metabolite annotations."
• [readme] for multi-sample analysis, sample alignment is performed after feature extraction. It will be discussed in section 5.: "for multi-sample analysis, sample alignment is performed after feature extraction. It will be discussed in section 5."