LC-MS feature detection and quantification
Summary
Auto-optimized detection and quantification of metabolite features from LC-MS1 spectra, producing a feature table with dimensions (features × samples) and peak intensity statistics. This skill bridges raw mzML/NetCDF data to downstream metabolite annotation and functional analysis.
When to use
You have raw LC-MS data (mzML or equivalent format) from a metabolomics experiment and need to extract a reproducible, quantified feature table with intensity measurements before conducting metabolite identification or statistical analysis. Use this when you require automated peak picking with parameter optimization rather than manual threshold tuning.
When NOT to use
- Input is already a processed feature table or quantification matrix
- Data is from targeted MS assays with predefined compound lists (use targeted quantification instead)
- Raw spectra lack sufficient chromatographic resolution or signal-to-noise for reliable peak detection
Inputs
- Raw LC-MS spectral data in mzML or NetCDF format
- Sample metadata (optional, for batch annotation)
Outputs
- Feature table (features × samples matrix)
- Peak intensity values and summary statistics (mean, median, std dev, min, max)
- Feature metadata including m/z and retention time
How to apply
Load raw LC-MS data (mzML/NetCDF format) into MetaboAnalystR 4.0 and execute the unified LC-MS workflow, which applies auto-optimized feature detection via ultra-fast parameter optimization for peak picking. The workflow generates a feature table by assigning detected peaks to features and computing peak intensity values across all samples. Extract table dimensions (number of features as rows, number of samples as columns) and compute summary statistics on intensity distributions (mean, median, standard deviation, min, max). The auto-optimization approach improves quantification accuracy and detection of high-quality MS features compared to fixed-parameter methods.
Related tools
Evaluation signals
- Feature table has non-zero dimensions (number of features > 0, number of samples ≥ 2)
- Intensity values are numeric, positive, and within instrument detection range
- Summary statistics (mean, median, std dev) are internally consistent and match raw peak intensities
- Retention time and m/z values are within expected ranges for LC-MS analysis
- Results match or exceed reference benchmark: >10% more high-quality features detected compared to legacy methods
Limitations
- Auto-optimization performance depends on data quality; low signal-to-noise or poor chromatographic resolution reduce feature detection sensitivity
- Parameter optimization assumes adequate chemical diversity in the sample; highly specialized or limited metabolite sets may not optimize effectively
- Feature table does not include compound identities; annotation requires downstream MS/MS deconvolution and spectral matching
- Batch effects or signal drift across runs are not corrected in the feature detection step; separate batch correction workflows are recommended
Evidence
- [readme] an auto-optimized feature detection and quantification module for LC-MS1 spectra processing: "an auto-optimized feature detection and quantification module for LC-MS1 spectra processing"
- [readme] ultra-fast parameter optimization for peak picking, automated batch effect correction: "ultra-fast parameter optimization for peak picking, automated batch effect correction, and improved pathway activity prediction"
- [readme] MetaboAnalystR 4.0 can accurately detect and identify > 10% more high-quality MS and MS/MS features: "MetaboAnalystR 4.0 can accurately detect and identify > 10% more high-quality MS and MS/MS features"
- [other] Extract feature table dimensions (number of rows as features, number of columns as samples) and compute summary statistics on peak intensity values (mean, median, standard deviation, min, max): "Extract feature table dimensions (number of rows as features, number of columns as samples) and compute summary statistics on peak intensity values (mean, median, standard deviation, min, max)"
1---2name: lcms-feature-detection-and-quantification-23description: Use when you have raw LC-MS data (mzML or equivalent format) from a metabolomics experiment and need to extract a reproducible, quantified feature table with intensity measurements before conducting metabolite identification or statistical analysis.4license: CC-BY-4.05---67# LC-MS feature detection and quantification89## Summary1011Auto-optimized detection and quantification of metabolite features from LC-MS1 spectra, producing a feature table with dimensions (features × samples) and peak intensity statistics. This skill bridges raw mzML/NetCDF data to downstream metabolite annotation and functional analysis.1213## When to use1415You have raw LC-MS data (mzML or equivalent format) from a metabolomics experiment and need to extract a reproducible, quantified feature table with intensity measurements before conducting metabolite identification or statistical analysis. Use this when you require automated peak picking with parameter optimization rather than manual threshold tuning.1617## When NOT to use1819- Input is already a processed feature table or quantification matrix20- Data is from targeted MS assays with predefined compound lists (use targeted quantification instead)21- Raw spectra lack sufficient chromatographic resolution or signal-to-noise for reliable peak detection2223## Inputs2425- Raw LC-MS spectral data in mzML or NetCDF format26- Sample metadata (optional, for batch annotation)2728## Outputs2930- Feature table (features × samples matrix)31- Peak intensity values and summary statistics (mean, median, std dev, min, max)32- Feature metadata including m/z and retention time3334## How to apply3536Load raw LC-MS data (mzML/NetCDF format) into MetaboAnalystR 4.0 and execute the unified LC-MS workflow, which applies auto-optimized feature detection via ultra-fast parameter optimization for peak picking. The workflow generates a feature table by assigning detected peaks to features and computing peak intensity values across all samples. Extract table dimensions (number of features as rows, number of samples as columns) and compute summary statistics on intensity distributions (mean, median, standard deviation, min, max). The auto-optimization approach improves quantification accuracy and detection of high-quality MS features compared to fixed-parameter methods.3738## Related tools3940- **MetaboAnalystR** (Executes auto-optimized LC-MS1 feature detection, quantification, and unified workflow for peak picking and intensity matrix generation) — https://github.com/xia-lab/MetaboAnalystR4142## Evaluation signals4344- Feature table has non-zero dimensions (number of features > 0, number of samples ≥ 2)45- Intensity values are numeric, positive, and within instrument detection range46- Summary statistics (mean, median, std dev) are internally consistent and match raw peak intensities47- Retention time and m/z values are within expected ranges for LC-MS analysis48- Results match or exceed reference benchmark: >10% more high-quality features detected compared to legacy methods4950## Limitations5152- Auto-optimization performance depends on data quality; low signal-to-noise or poor chromatographic resolution reduce feature detection sensitivity53- Parameter optimization assumes adequate chemical diversity in the sample; highly specialized or limited metabolite sets may not optimize effectively54- Feature table does not include compound identities; annotation requires downstream MS/MS deconvolution and spectral matching55- Batch effects or signal drift across runs are not corrected in the feature detection step; separate batch correction workflows are recommended5657## Evidence5859- [readme] an auto-optimized feature detection and quantification module for LC-MS1 spectra processing: "an auto-optimized feature detection and quantification module for LC-MS1 spectra processing"60- [readme] ultra-fast parameter optimization for peak picking, automated batch effect correction: "ultra-fast parameter optimization for peak picking, automated batch effect correction, and improved pathway activity prediction"61- [readme] MetaboAnalystR 4.0 can accurately detect and identify > 10% more high-quality MS and MS/MS features: "MetaboAnalystR 4.0 can accurately detect and identify > 10% more high-quality MS and MS/MS features"62- [other] Extract feature table dimensions (number of rows as features, number of columns as samples) and compute summary statistics on peak intensity values (mean, median, standard deviation, min, max): "Extract feature table dimensions (number of rows as features, number of columns as samples) and compute summary statistics on peak intensity values (mean, median, standard deviation, min, max)"
HolobiomicsLab 