feature-alignment-metabolomics
Summary
Consolidate detected ion peaks from replicate mass-spectrometry injections into unified metabolic features by aligning peaks across the mass-to-charge (m/z) and retention-time dimensions. This skill is essential in untargeted metabolomics workflows to reduce redundancy and generate a quantitative feature abundance matrix suitable for downstream statistical analysis.
When to use
Apply this skill when you have detected multiple ion peaks from replicate injections of the same sample in untargeted metabolomics and need to consolidate them into a single feature table. Specifically, use it after peak detection has identified candidate signals across m/z and retention-time space but before intensity extraction and statistical comparison.
When NOT to use
- Input is already a consolidated feature table with unified features across replicates
- Analysis involves targeted metabolomics with predefined m/z and retention-time windows (use targeted extraction instead)
- Peak detection has not been performed or no replicate injections are available
Inputs
- detected ion peaks (m/z, retention time, intensity) from replicate sample injections
- peak detection output (usually as a feature list with m/z, retention time, and intensity per sample)
Outputs
- feature abundance matrix (rows = features with m/z, retention time, and identifier; columns = sample intensities)
- unified feature table
How to apply
After peak detection identifies ion signals across mass-to-charge and retention-time dimensions in replicate samples, perform peak alignment by matching peaks with similar m/z values (typically within a tolerance of 5–10 ppm) and retention-time windows (typically within 0.1–0.5 min). Group aligned peaks into unified features, resolving inconsistencies in m/z or retention time across replicates by computing consensus values (e.g., median m/z, mean retention time). Validate alignment quality by examining the distribution of within-feature m/z and retention-time variation; peaks that deviate significantly from the feature consensus may indicate misalignment or noise. Once grouping is complete, extract intensity values for each feature across all samples to compile into a feature abundance matrix with rows as features (identified by m/z, retention time, and a unique identifier) and columns as sample intensities.
Related tools
Evaluation signals
- Feature abundance matrix has expected dimensions (number of features × number of samples) with no missing intensity values
- Within-feature m/z variation is below the specified alignment tolerance (typically < 5–10 ppm)
- Within-feature retention-time variation is below the specified alignment window (typically < 0.1–0.5 min)
- Each feature is assigned a unique identifier and possesses a consensus m/z and retention time
- Intensity values are non-negative and show expected sample-to-sample and replicate-to-replicate correlation patterns
Limitations
- Peak alignment quality depends critically on m/z tolerance and retention-time window parameters; narrow tolerances may fragment true features, while wide tolerances may merge distinct metabolites
- Alignment assumes that retention time is reproducible across replicate injections; significant drift or instrumental variation will degrade feature grouping
- Co-eluting metabolites with similar m/z values may be incorrectly merged into a single feature, compromising quantification and identification
- The method does not distinguish between true metabolic features and instrumental artifacts or noise; quality control filtering is required downstream
Evidence
- [other] Perform peak alignment and feature grouping to consolidate peaks from replicate injections into unified features.: "Perform peak alignment and feature grouping to consolidate peaks from replicate injections into unified features."
- [other] Extract intensity values for each feature across all samples and compile into a feature abundance matrix.: "Extract intensity values for each feature across all samples and compile into a feature abundance matrix."
- [other] Output the extracted feature table with rows as features (m/z, retention time, identifier) and columns as sample intensities.: "Output the extracted feature table with rows as features (m/z, retention time, identifier) and columns as sample intensities."
- [readme] It includes the extraction of raw mass data and quality control for the identification of differential metabolic ion peaks.: "It includes the extraction of raw mass data and quality control for the identification of differential metabolic ion peaks."
1---2name: feature-alignment-metabolomics3description: Use when you have detected multiple ion peaks from replicate injections of the same sample in untargeted metabolomics and need to consolidate them into a single feature table.4license: CC-BY-4.05---67# feature-alignment-metabolomics89## Summary1011Consolidate detected ion peaks from replicate mass-spectrometry injections into unified metabolic features by aligning peaks across the mass-to-charge (m/z) and retention-time dimensions. This skill is essential in untargeted metabolomics workflows to reduce redundancy and generate a quantitative feature abundance matrix suitable for downstream statistical analysis.1213## When to use1415Apply this skill when you have detected multiple ion peaks from replicate injections of the same sample in untargeted metabolomics and need to consolidate them into a single feature table. Specifically, use it after peak detection has identified candidate signals across m/z and retention-time space but before intensity extraction and statistical comparison.1617## When NOT to use1819- Input is already a consolidated feature table with unified features across replicates20- Analysis involves targeted metabolomics with predefined m/z and retention-time windows (use targeted extraction instead)21- Peak detection has not been performed or no replicate injections are available2223## Inputs2425- detected ion peaks (m/z, retention time, intensity) from replicate sample injections26- peak detection output (usually as a feature list with m/z, retention time, and intensity per sample)2728## Outputs2930- feature abundance matrix (rows = features with m/z, retention time, and identifier; columns = sample intensities)31- unified feature table3233## How to apply3435After peak detection identifies ion signals across mass-to-charge and retention-time dimensions in replicate samples, perform peak alignment by matching peaks with similar m/z values (typically within a tolerance of 5–10 ppm) and retention-time windows (typically within 0.1–0.5 min). Group aligned peaks into unified features, resolving inconsistencies in m/z or retention time across replicates by computing consensus values (e.g., median m/z, mean retention time). Validate alignment quality by examining the distribution of within-feature m/z and retention-time variation; peaks that deviate significantly from the feature consensus may indicate misalignment or noise. Once grouping is complete, extract intensity values for each feature across all samples to compile into a feature abundance matrix with rows as features (identified by m/z, retention time, and a unique identifier) and columns as sample intensities.3637## Related tools3839- **openNAU** (performs raw mass data extraction, peak detection, peak alignment, and feature grouping for untargeted metabolomics) — https://github.com/zjuRong/openNAU40- **MetaQC** (quality control module within openNAU for validating peak alignment and feature extraction) — https://github.com/zjuRong/openNAU4142## Evaluation signals4344- Feature abundance matrix has expected dimensions (number of features × number of samples) with no missing intensity values45- Within-feature m/z variation is below the specified alignment tolerance (typically < 5–10 ppm)46- Within-feature retention-time variation is below the specified alignment window (typically < 0.1–0.5 min)47- Each feature is assigned a unique identifier and possesses a consensus m/z and retention time48- Intensity values are non-negative and show expected sample-to-sample and replicate-to-replicate correlation patterns4950## Limitations5152- Peak alignment quality depends critically on m/z tolerance and retention-time window parameters; narrow tolerances may fragment true features, while wide tolerances may merge distinct metabolites53- Alignment assumes that retention time is reproducible across replicate injections; significant drift or instrumental variation will degrade feature grouping54- Co-eluting metabolites with similar m/z values may be incorrectly merged into a single feature, compromising quantification and identification55- The method does not distinguish between true metabolic features and instrumental artifacts or noise; quality control filtering is required downstream5657## Evidence5859- [other] Perform peak alignment and feature grouping to consolidate peaks from replicate injections into unified features.: "Perform peak alignment and feature grouping to consolidate peaks from replicate injections into unified features."60- [other] Extract intensity values for each feature across all samples and compile into a feature abundance matrix.: "Extract intensity values for each feature across all samples and compile into a feature abundance matrix."61- [other] Output the extracted feature table with rows as features (m/z, retention time, identifier) and columns as sample intensities.: "Output the extracted feature table with rows as features (m/z, retention time, identifier) and columns as sample intensities."62- [readme] It includes the extraction of raw mass data and quality control for the identification of differential metabolic ion peaks.: "It includes the extraction of raw mass data and quality control for the identification of differential metabolic ion peaks."
HolobiomicsLab