Peak Detection Signal Processing

peak-detection-signal-processing

Summary

Identify and flag local maxima (peaks) in breath spectrometry feature data by applying peak detection algorithms to signal intensity or m/z dimensions, assigning confidence scores based on signal prominence thresholds. This utility processes extracted features to produce labelled peak indices and intensity values for downstream metabolite identification.

When to use

Apply this skill after feature extraction from mzML/mzXML breath analysis data when you have a numerical array or dataframe of feature intensities across retention time or m/z dimensions and need to identify which features represent genuine volatile organic compound (VOC) signals rather than noise or baseline variation.

When NOT to use

• Input data is already a manually curated or vendor-provided peak list with pre-assigned peak/noise classifications.
• Raw mzML/mzXML spectra have not yet undergone feature extraction; apply feature extraction first.
• Peak detection parameters (intensity threshold, prominence threshold, algorithm type) are unknown and no prior quality control (RSD filtering) has been applied to remove obvious noise.

Inputs

• FeatureSet object (m/z values, scan time, intensity array, relative standard deviation)
• Sample object (aligned feature table with m/z index and sample intensity columns)
• Numerical array or dataframe with feature intensities indexed by m/z or retention time

Outputs

• Labelled peak table (CSV or JSON) with peak indices, m/z values, intensity values, and confidence flags
• Peak flags or confidence scores per detected feature
• Structured output suitable for MS/MS spectra matching or metabolite annotation

How to apply

Load the feature extraction output (FeatureSet or aligned Sample object) as a numerical array indexed by m/z value with intensity columns corresponding to scan time or sample identifiers. Apply a peak detection algorithm to identify local maxima across the retention time or m/z dimension, using signal intensity and prominence thresholds to distinguish true peaks from background. Assign confidence scores or binary peak flags to each detected peak based on the signal-to-noise ratio and prominence relative to adjacent baseline. Export the labelled output as a structured format (CSV or JSON) containing peak indices, m/z values, intensity values, and confidence flags for validation and downstream MS/MS spectra matching or adduct/isotope annotation.

Related tools

• BreathXplorer (Python package providing peak recognition utility component for breath spectrometry feature data; includes peak detection algorithm, confidence scoring, and structured output export) — https://github.com/wykswr/breathXplorer

Evaluation signals

• Detected peaks have m/z values and intensity values within expected biochemical ranges for volatile organic compounds (typically m/z 40–500 in breath analysis).
• Confidence scores or flags are assigned consistently and reproducibly across replicate runs or similar samples.
• Peak indices and intensity values in the output are valid and map correctly back to the input feature array without off-by-one or alignment errors.
• Flagged peaks correspond to local maxima in the original signal; visual inspection or plotting of a subset of peaks against raw intensity traces confirms correct identification.
• Output CSV/JSON schema is valid and contains all required fields (peak indices, m/z, intensity, flags); no missing or null values for detected peaks.

Limitations

• Peak detection algorithm performance depends on signal-to-noise ratio and prominence thresholds; poorly chosen thresholds may miss weak peaks or flag noise as peaks.
• Baseline subtraction or smoothing of the input signal is not described in the README; if the feature extraction output contains strong baseline drift or noise, peak detection sensitivity may be reduced.
• The README does not specify the exact algorithm (e.g., derivative-based, wavelet-based, or statistical) used for peak detection; without access to the source code, reproducibility across different implementations may vary.
• Peak recognition is listed as a utility but not fully documented in the provided README excerpt; detailed parameter documentation, algorithm choice (Topological vs. Gaussian analog), and failure modes are not fully described.

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)."
• [other] Load feature data from the feature extraction output as a numerical array or dataframe.: "Load feature data from the feature extraction output as a numerical array or dataframe."
• [readme] Peak recognition is listed under Utilities alongside MS/MS spectra export as part of the BreathXplorer analytical workflow: "* Peak recognition"