Reconstruct the peak recognition utility identifying peaks from feature data
Summary
Peak recognition detects and flags local maxima in breath spectrometry feature data (retention time or m/z dimension) to classify volatile organic compounds. This utility assigns confidence scores based on signal intensity and prominence thresholds, enabling prioritization of high-quality features for downstream analysis.
When to use
Apply this skill after feature extraction and alignment when you have a numerical feature table (CSV or dataframe) with intensity values across retention time or m/z dimensions, and you need to identify and rank peaks by signal quality and prominence rather than relying on all extracted features equally.
When NOT to use
- Input is already a list of validated, manually curated peaks—peak detection would be redundant.
- No consistent signal intensity variation exists across retention time or m/z (e.g., flat or noise-only spectra).
- Peak confidence scoring is not required; all extracted features are equally trusted.
Inputs
- Feature table (CSV or dataframe) with m/z values and intensity columns
- Aligned feature table with sample-wise intensity values
- Numerical intensity signal array (retention time or m/z indexed)
Outputs
- Labelled peak output file (CSV or JSON) with peak indices, m/z values, intensity values, and confidence flags
- Peak detection report with signal intensity, prominence scores, and threshold decisions
How to apply
Load the aligned or single-sample feature table as a numerical array or dataframe. Apply a peak detection algorithm (e.g., topological or Gaussian-based) to identify local maxima in the intensity signal across the retention time or m/z dimension. Assign confidence scores or binary peak flags to each detected peak by comparing signal intensity and prominence against configurable thresholds (e.g., signal-to-noise ratio, peak width). Export the labelled peak output—including peak indices, intensity values, and confidence flags—in a structured format (CSV or JSON). This approach reduces false positives by filtering noise that lacks consistent breath-like peak morphology.
Related tools
Evaluation signals
- Peak indices and m/z values are correctly mapped to feature table rows; no out-of-bounds indices.
- Confidence scores or flags are assigned only to local maxima (intensity > neighbouring values); no spurious detections on flat or monotonic regions.
- Intensity and prominence values in the output match the source feature table; exported CSV/JSON parses without schema errors.
- Peaks flagged as high-confidence show higher signal-to-noise ratios and consistency (e.g., lower relative standard deviation) than low-confidence or unflagged peaks.
- Output includes all peaks above the specified threshold; no features are silently omitted.
Limitations
- Peak detection algorithm performance depends on configurable thresholds (intensity, prominence); suboptimal thresholds may produce false positives or false negatives.
- Noisy or low-resolution spectra may yield ambiguous peak boundaries; requires upstream quality filtering (e.g., RSD-based control) to be effective.
- Overlapping or poorly resolved peaks in high-density m/z or retention-time regions may be misclassified or merged into single detections.
- Peak recognition does not perform compound identification; confidence scores reflect signal morphology only, not chemical annotation.
Evidence
- [other] Apply peak detection algorithm to identify local maxima in the feature signal across the retention time or m/z dimension.: "Apply peak detection algorithm to identify local maxima in the feature signal across the retention time or m/z dimension."
- [other] Assign confidence scores or peak flags to each detected peak based on signal intensity and prominence thresholds.: "Assign confidence scores or peak flags to each detected peak based on signal intensity and prominence thresholds."
- [other] Generate and save labelled peak output with peak indices, intensity values, and flags in a structured format (CSV or JSON).: "Generate and save labelled peak output with peak indices, intensity values, and flags in a structured format (CSV or JSON)."
- [readme] * Peak recognition: "* Peak recognition"
- [readme] BreathXplorer is a bioinformatic solution to process breath data generated from HRMS analysis. It contains a suite of functions, including feature extraction, feature alignment, and breath recognition.: "BreathXplorer is a bioinformatic solution to process breath data generated from HRMS analysis. It contains a suite of functions, including feature extraction, feature alignment, and breath"
1---2name: breath-volatile-peak-classification3description: Use when after feature extraction and alignment when you have a numerical feature table (CSV or dataframe) with intensity values across retention time or m/z dimensions, and you need to identify and rank peaks by signal quality and prominence rather than relying on all extracted features equally.4license: CC-BY-4.05---67# Reconstruct the peak recognition utility identifying peaks from feature data89## Summary1011Peak recognition detects and flags local maxima in breath spectrometry feature data (retention time or m/z dimension) to classify volatile organic compounds. This utility assigns confidence scores based on signal intensity and prominence thresholds, enabling prioritization of high-quality features for downstream analysis.1213## When to use1415Apply this skill after feature extraction and alignment when you have a numerical feature table (CSV or dataframe) with intensity values across retention time or m/z dimensions, and you need to identify and rank peaks by signal quality and prominence rather than relying on all extracted features equally.1617## When NOT to use1819- Input is already a list of validated, manually curated peaks—peak detection would be redundant.20- No consistent signal intensity variation exists across retention time or m/z (e.g., flat or noise-only spectra).21- Peak confidence scoring is not required; all extracted features are equally trusted.2223## Inputs2425- Feature table (CSV or dataframe) with m/z values and intensity columns26- Aligned feature table with sample-wise intensity values27- Numerical intensity signal array (retention time or m/z indexed)2829## Outputs3031- Labelled peak output file (CSV or JSON) with peak indices, m/z values, intensity values, and confidence flags32- Peak detection report with signal intensity, prominence scores, and threshold decisions3334## How to apply3536Load the aligned or single-sample feature table as a numerical array or dataframe. Apply a peak detection algorithm (e.g., topological or Gaussian-based) to identify local maxima in the intensity signal across the retention time or m/z dimension. Assign confidence scores or binary peak flags to each detected peak by comparing signal intensity and prominence against configurable thresholds (e.g., signal-to-noise ratio, peak width). Export the labelled peak output—including peak indices, intensity values, and confidence flags—in a structured format (CSV or JSON). This approach reduces false positives by filtering noise that lacks consistent breath-like peak morphology.3738## Related tools3940- **BreathXplorer** (Python package providing peak recognition utility as part of breath spectrometry workflow) — https://github.com/wykswr/breathXplorer4142## Evaluation signals4344- Peak indices and m/z values are correctly mapped to feature table rows; no out-of-bounds indices.45- Confidence scores or flags are assigned only to local maxima (intensity > neighbouring values); no spurious detections on flat or monotonic regions.46- Intensity and prominence values in the output match the source feature table; exported CSV/JSON parses without schema errors.47- Peaks flagged as high-confidence show higher signal-to-noise ratios and consistency (e.g., lower relative standard deviation) than low-confidence or unflagged peaks.48- Output includes all peaks above the specified threshold; no features are silently omitted.4950## Limitations5152- Peak detection algorithm performance depends on configurable thresholds (intensity, prominence); suboptimal thresholds may produce false positives or false negatives.53- Noisy or low-resolution spectra may yield ambiguous peak boundaries; requires upstream quality filtering (e.g., RSD-based control) to be effective.54- Overlapping or poorly resolved peaks in high-density m/z or retention-time regions may be misclassified or merged into single detections.55- Peak recognition does not perform compound identification; confidence scores reflect signal morphology only, not chemical annotation.5657## Evidence5859- [other] Apply peak detection algorithm to identify local maxima in the feature signal across the retention time or m/z dimension.: "Apply peak detection algorithm to identify local maxima in the feature signal across the retention time or m/z dimension."60- [other] Assign confidence scores or peak flags to each detected peak based on signal intensity and prominence thresholds.: "Assign confidence scores or peak flags to each detected peak based on signal intensity and prominence thresholds."61- [other] Generate and save labelled peak output with peak indices, intensity values, and flags in a structured format (CSV or JSON).: "Generate and save labelled peak output with peak indices, intensity values, and flags in a structured format (CSV or JSON)."62- [readme] * [Peak recognition](#peak-recognition): "* [Peak recognition](#peak-recognition)"63- [readme] BreathXplorer is a bioinformatic solution to process breath data generated from HRMS analysis. It contains a suite of functions, including feature extraction, feature alignment, and breath recognition.: "BreathXplorer is a bioinformatic solution to process breath data generated from HRMS analysis. It contains a suite of functions, including feature extraction, feature alignment, and breath"
HolobiomicsLab