Feature Grouping By Molecular Ion

feature-grouping-by-molecular-ion

Summary

Groups detected LC-MS features into clusters sharing the same molecular ion, accounting for isotopic variants (e.g., C13, N15, D) and common adduct forms ([M+H]+, [M+Na]+, [M+NH4]+). This step consolidates redundant representations of the same molecule into single feature clusters, improving downstream quantification and annotation.

When to use

Apply this skill after peak picking and sample alignment have produced an aligned feature table with m/z and retention time coordinates. Use it when you need to reduce feature redundancy caused by naturally occurring isotopologue distributions and variable ionization/adduction chemistry, before gap-filling or final quantification table generation.

When NOT to use

• Input data are already consolidated into a single ion per molecular species (e.g., pre-processed by another tool or manual curation).
• Analysis targets only targeted features with known identity and no need to consolidate redundant ion forms.
• LC-MS data are profile (not centroided); grouping requires accurate m/z measurements from centroided data.

Inputs

• aligned feature table (m/z, retention time, intensity matrix)
• detected LC-MS peaks with mass-to-charge and RT coordinates

Outputs

• grouped feature table with cluster identifiers and group membership
• isotopologue/adduct annotations per feature

How to apply

Load the aligned feature table (output from sample alignment) containing detected m/z and retention time coordinates. Apply a grouping algorithm that clusters features by (1) matching m/z values within a tolerance corresponding to known isotopic mass shifts (C13 ≈ 1.003 Da, N15 ≈ 0.997 Da, D ≈ 1.006 Da) and adduct mass differences ([M+Na]–[M+H] ≈ 21.98 Da, [M+NH4]–[M+H] ≈ 18.03 Da), and (2) enforcing that grouped features share the same or near-identical retention time (allowing for minor drift due to alignment errors). Assign cluster identifiers to each feature and output the grouped feature table with isotopologue/adduct group membership annotations. SLAW applies this as part of its complete workflow after alignment and before gap-filling.

Related tools

• Centwave (peak picking algorithm upstream of grouping; provides initial detected features fed into grouping module) — https://github.com/sneumann/xcms
• SLAW grouping module (implements isotopologue and adduct clustering as part of complete untargeted LC-MS workflow) — https://github.com/zamboni-lab/SLAW
• FeatureFinderMetabo (alternative peak picking algorithm that can feed into grouping step)
• ADAP (alternative peak picking algorithm that can feed into grouping step)

Evaluation signals

• Cluster size distribution: verify that most clusters contain 1–4 members (isotopes + adducts), with outliers flagged for manual inspection.
• Retention time coherence: confirm that all features within a cluster have RT values within expected drift tolerance (typically <0.5 min for aligned data).
• Mass difference validation: spot-check cluster members to verify m/z differences match known isotopic shifts or documented adduct masses.
• Feature redundancy reduction: count total features before and after grouping; expect 20–40% reduction in redundant ions for typical untargeted metabolomics.
• Downstream quantification stability: verify that consolidated features show expected intensity relationships (e.g., C13 isotope peaks ~1–2% of M+0 intensity for natural carbon).

Limitations

• Grouping relies on accurate m/z measurement and peak picking; poor centroiding or peak detection upstream degrades clustering fidelity.
• Algorithm assumes typical isotopic and adduct patterns; unusual ionization modes, non-standard adducts, or multiply-charged ions may not cluster correctly.
• Retention time alignment errors can cause true isotopologues/adducts from the same molecule to be assigned separate clusters if RT drift exceeds tolerance.
• Metabolites with naturally high isotopic abundance (e.g., sulfur-containing compounds with S34) may produce unexpected isotope peak patterns.
• DIA-MS data are not supported by SLAW; workflow is limited to DDA-only LC-MS experiments.

Evidence

• [other] SLAW includes a processing step for grouping of isotopologues and adducts as part of its complete untargeted LC-MS workflow, which operates after peak picking and sample alignment.: "SLAW includes a processing step for grouping of isotopologues and adducts as part of its complete untargeted LC-MS workflow, which operates after peak picking and sample alignment."
• [other] Apply isotopologue and adduct grouping algorithm to cluster features sharing the same molecular ion with mass differences corresponding to isotopic shifts (e.g., C13, N15, D) or common adduct transformations (e.g., [M+H]+, [M+Na]+, [M+NH4]+).: "Apply isotopologue and adduct grouping algorithm to cluster features sharing the same molecular ion with mass differences corresponding to isotopic shifts (e.g., C13, N15, D) or common adduct"
• [other] Load aligned feature table (output from sample alignment step) containing detected m/z and retention time coordinates.: "Load aligned feature table (output from sample alignment step) containing detected m/z and retention time coordinates."
• [readme] Complete processing including peak picking, sample alignment, pick picking, grouping of isotopologues and adducts, gap-filling by data recursion: "Complete processing including peak picking, sample alignment, pick picking, grouping of isotopologues and adducts, gap-filling by data recursion"
• [readme] All data must be centroided and of unique polarity. Centroided mzML can be obtained with ProteoWizard.: "All data must be centroided and of unique polarity. Centroided mzML can be obtained with ProteoWizard."