Gaussian peakshape fitting evaluation

Gaussian peakshape fitting and evaluation

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Summary

Evaluates detected elution peaks against expected Gaussian shape to filter for chromatographic quality and reduce false positives. Applied after peak detection to retain only peaks with peakshape scores above a threshold (default > 0.5), ensuring detected features represent genuine metabolites rather than noise or artifacts.

When to use

After peak detection on mass track segments using find_peaks, when you need to distinguish genuine chromatographic peaks from noise-induced false positives or irregular shapes. Particularly important in high-intensity regions where prominence-based detection alone may capture non-Gaussian features or shoulders.

When NOT to use

• Input is already a feature table or pre-filtered peak list — use this skill during initial peak detection, not post-hoc validation.
• Data are from instruments with non-Gaussian elution profiles (e.g. gas chromatography with tailing or fronting, or highly irregular retention behavior) — Gaussian model assumptions may not hold.
• Peak detection has not yet been performed or apex positions are not reliably identified — peakshape evaluation requires a well-localized apex.

Inputs

• detected_peaks (list of peak objects with apex position, intensity window, m/z and retention time)
• mass_track_segment (region of interest: intensity array subset after baseline/noise subtraction)
• peakshape_threshold (numeric, default 0.5)
• snr_threshold (numeric, default 2)

Outputs

• filtered_peaks (peak objects passing peakshape and SNR criteria, with peakshape and SNR scores recorded)
• peak_quality_metrics (table of peakshape, SNR, and apex position for each retained peak)

How to apply

For each detected peak, fit a Gaussian model to the intensity values in a window around the apex position and calculate a peakshape metric (e.g., ratio of observed peak area to fitted Gaussian area, or R² of fit). Retain peaks only if peakshape exceeds the threshold (default 0.5). This filtering is applied in conjunction with SNR thresholds (default SNR > 2) and validates that the apex position lies within the segment boundaries. The rationale is that metabolite peaks follow Gaussian elution profiles in LC; deviations suggest either co-eluting compounds, baseline artifacts, or instrumental noise rather than a single resolved feature.

Related tools

• scipy.signal.find_peaks (Detects local maxima and prominence on mass track; output feeds into peakshape evaluation) — https://docs.scipy.org/doc/scipy/reference/generated/scipy.signal.find_peaks.html
• asari.peaks.evaluate_gaussian_peak_on_intensity_list (Core function implementing Gaussian peakshape fitting and metric calculation) — https://github.com/shuzhao-li/asari
• asari.peaks.detect_evaluate_peaks_on_roi (Orchestrates peak detection and peakshape evaluation on each region of interest) — https://github.com/shuzhao-li/asari
• Python (numpy, scipy.optimize) (Underlying fitting and numerical optimization for Gaussian model)

Examples

from asari.peaks import evaluate_gaussian_peak_on_intensity_list; peakshape = evaluate_gaussian_peak_on_intensity_list(intensity_window, apex_idx); retained = peak if peakshape > 0.5 and snr > 2 else None

Evaluation signals

• Peakshape scores for retained peaks are >= 0.5 and SNR >= 2; rejected peaks are logged with their failure reason
• Apex positions of retained peaks fall strictly within segment boundaries (no boundary violations)
• Peakshape distribution (histogram or summary stats) is unimodal and concentrated in the 0.5–1.0 range, indicating consistent Gaussian fit quality
• Comparison of rejected vs. retained peaks shows that rejected peaks have visibly irregular or multi-modal intensity profiles around the apex
• Reproducibility check: same mass track and parameters re-processed yields identical peakshape scores and the same set of retained peaks

Limitations

• Assumes Gaussian elution profile; real peaks may be slightly tailed or fronted, especially at high intensity or under non-ideal chromatographic conditions.
• Peakshape threshold (default 0.5) is a heuristic; may require tuning for different instrument types, column chemistry, or metabolite classes.
• Co-eluting peaks or shoulder peaks may pass Gaussian fitting if the fit window is too narrow; requires integration with retention time alignment and mass separation.
• Very low-intensity peaks near noise floor may show artificially good peakshape due to stochastic noise that happens to fit a Gaussian; SNR threshold mitigates but does not eliminate this.
• Computational cost scales with number of detected peaks and window size; large feature tables may benefit from vectorized evaluation.

Evidence

• [other] Evaluate detected peaks for Gaussian peakshape, cSelectivity, and signal-to-noise ratio (SNR); retain peaks passing thresholds (default SNR >2, peakshape >0.5).: "Evaluate detected peaks for Gaussian peakshape, cSelectivity, and signal-to-noise ratio (SNR); retain peaks passing thresholds (default SNR >2, peakshape >0.5)."
• [other] Validation: verify that detected peaks have apex positions within segment boundaries, SNR and peakshape values within reported thresholds, and prominence values consistent with local signal characteristics.: "Validation: verify that detected peaks have apex positions within segment boundaries, SNR and peakshape values within reported thresholds, and prominence values consistent with local signal"
• [other] See peaks.evaluate_gaussian_peak_on_intensity_list: "See peaks.evaluate_gaussian_peak_on_intensity_list"