chromatographic-peak-detection-with-prominence-control
Summary
Detect chromatographic peaks in mass-spectrometry intensity arrays using local maxima and dynamic prominence thresholds, applied to composite or individual mass tracks after baseline/noise removal and optional smoothing. This approach prioritizes signal selectivity and reduces false positives by tuning prominence relative to noise level and peak height.
When to use
Apply this skill after constructing baseline-corrected mass tracks (either composite across samples or per-sample) when you need to identify individual chromatographic peaks for feature extraction in LC-MS or GC-MS metabolomics workflows. Use it specifically when you have intensity arrays with known noise characteristics and want to avoid over-detection of shallow peaks or under-detection due to fixed, global prominence thresholds.
When NOT to use
- Input mass track has already been peak-detected and feature-summarized—do not re-apply peak detection to a feature table or aggregated peak list.
- Mass track exhibits extreme baseline drift or multiplicative noise that cannot be adequately corrected by the audit step (detrend, baseline subtraction); consider preprocessing first.
- You require detection of peaks in noisy, low-resolution data where Gaussian fitting is unreliable; this skill assumes sufficient chromatographic resolution to justify prominence-based filtering.
Inputs
- aligned mass track (intensity array, full retention-time length, single m/z value)
- retention-time calibration dictionaries (rt_cal_dict per sample, if per-sample tracks)
- parameters: min_peak_height (integer, default ~5000–10000 counts), min_timepoints (integer, default 25 scans), noise_threshold_ppm (float, default 0.01 or 1%)
Outputs
- detected peaks (list of dicts with scan indices, intensity, m/z, retention time, Gaussian fit metrics)
- peak quality metrics: cSelectivity (float, chromatographic selectivity), SNR (float, signal-to-noise ratio), goodness_fitting (float, Gaussian R²)
- filtered peak list (subset meeting SNR > 2 and Gaussian fit thresholds)
How to apply
Begin by auditing each mass track: check intensity ceiling (cap at 1E8), detect low-intensity tracks (median < 1e3), and apply detrend if median > 10× min_peak_height or >50% of points exceed the height threshold. Compute baseline and noise from bottom-signal quartiles; apply moving-average smoothing if noise > 1% of max intensity AND max intensity < 10× min_peak_height. Subtract baseline+noise to isolate positive regions and split the track into segments. For each segment, invoke scipy.signal.find_peaks with a dynamic prominence parameter set to the maximum of: (1) 1/3 × min_peak_height, (2) noise level, and (3) 5% of segment max intensity. Use min_peak_height and min_timepoints (default 25 scans) as additional window controls. Finally, filter detected peaks by requiring goodness-of-fit to a Gaussian model, chromatographic selectivity (cSelectivity), and SNR > 2.
Related tools
- scipy.signal.find_peaks (core local-maxima detection engine; invoked once per mass track segment with dynamic prominence parameter) — https://scipy.org
- scipy.signal.detrend (removes polynomial or mean baseline drift from mass track before peak detection) — https://scipy.org
- asari (peaks.audit_mass_track) (validates and conditions mass track intensity ceiling, rescaling, smoothing, baseline/noise computation before peak detection) — https://github.com/shuzhao-li/asari
- asari (peaks.stats_detect_elution_peaks) (orchestrates the full peak-detection workflow including find_peaks invocation with prominence and height controls) — https://github.com/shuzhao-li/asari
- asari (peaks.evaluate_gaussian_peak_on_intensity_list) (fits Gaussian model to detected peak and computes goodness_fitting metric for quality filtering) — https://github.com/shuzhao-li/asari
- asari (peaks._peaks_cSelectivity_stats) (computes chromatographic selectivity (cSelectivity) metric to filter peaks for specificity) — https://github.com/shuzhao-li/asari
- asari (peaks.compute_noise_by_flanks) (estimates noise level from signal flanks for use in dynamic prominence calculation) — https://github.com/shuzhao-li/asari
Examples
from asari.peaks import stats_detect_elution_peaks; detected = stats_detect_elution_peaks(mass_track, min_peak_height=5000, min_timepoints=25, noise=150.0)
Evaluation signals
- Verify that scipy.signal.find_peaks is invoked exactly once per composite mass track (or once per segment within a per-sample track), not N times—log or count function calls.
- Confirm that all reported peaks include three required metrics: cSelectivity (float), SNR (float ≥ 2), and Gaussian goodness_fitting (R² or equivalent)—audit output JSON/CSV schema.
- Cross-check that peaks with SNR ≤ 2 or failing Gaussian fit are excluded from the final peak list—compare against full candidate peak set before filtering.
- Validate that dynamic prominence was computed for each segment as max(1/3 × min_peak_height, noise level, 5% × segment max intensity)—spot-check a few segments in logs or intermediate outputs.
- Confirm that baseline and noise estimates are derived from bottom-signal quartiles and that detrend/smoothing was applied only when audit conditions are met (median, thresholds)—trace audit_mass_track output.
Limitations
- Gaussian fitting assumes peaks are approximately bell-shaped; highly asymmetric or multi-modal peaks may not fit well and will be incorrectly filtered.
- Dynamic prominence depends on accurate noise estimation from flanks; in extremely noisy or low-intensity tracks (median < 1e3), noise computation may be unreliable and prominence thresholds may be too high or too low.
- The default min_timepoints = 25 scans assumes typical chromatographic resolution; for very fast gradients or high-frequency sampling, this threshold may prune valid narrow peaks.
- Detrending and smoothing are conditional on audit thresholds; if a mass track exhibits complex baseline drift not captured by the median or 10× min_peak_height rule, these corrections may be skipped, leaving residual artifacts.
- SNR computation and cSelectivity depend on sample registration and cross-sample alignment quality; misaligned or poorly calibrated retention-time dictionaries will degrade selectivity filtering.
Evidence
- [methods] Asari uses a simple local maxima method (scipy.signal.find_peaks), with prominence control: "Asari uses a simple local maxima method (scipy.signal.find_peaks), with prominence control"
- [other] Apply scipy.signal.find_peaks on each segment with dynamic prominence (max of 1/3 min_peak_height, noise level, and 5% of segment max intensity): "Apply scipy.signal.find_peaks on each segment with dynamic prominence (max of 1/3 min_peak_height, noise level, and 5% of segment max intensity), using min_peak_height and min_timepoints (default 25"
- [other] Audit each composite mass track (peaks.audit_mass_track): check max intensity ceiling (1E8), apply rescaling if needed, detect low-intensity tracks (median < 1e3), perform detrend if median > 10× min_peak_height and >50% of points exceed threshold: "Audit each composite mass track (peaks.audit_mass_track): check max intensity ceiling (1E8), apply rescaling if needed, detect low-intensity tracks (median < 1e3), perform detrend if median > 10×"
- [other] Evaluate detected peaks for gaussian fit (goodness_fitting), chromatographic selectivity (cSelectivity), and signal-to-noise ratio (SNR > 2), retaining only peaks meeting thresholds.: "Evaluate detected peaks for gaussian fit (goodness_fitting), chromatographic selectivity (cSelectivity), and signal-to-noise ratio (SNR > 2), retaining only peaks meeting thresholds."
- [intro] Statistics guided peak dection, based on local maxima and prominence, selective use of smoothing: "Statistics guided peak dection, based on local maxima and prominence, selective use of smoothing"
1---2name: chromatographic-peak-detection-with-prominence-control3description: Use when after constructing baseline-corrected mass tracks (either composite across samples or per-sample) when you need to identify individual chromatographic peaks for feature extraction in LC-MS or GC-MS metabolomics workflows.4license: CC-BY-4.05---67# chromatographic-peak-detection-with-prominence-control89## Summary1011Detect chromatographic peaks in mass-spectrometry intensity arrays using local maxima and dynamic prominence thresholds, applied to composite or individual mass tracks after baseline/noise removal and optional smoothing. This approach prioritizes signal selectivity and reduces false positives by tuning prominence relative to noise level and peak height.1213## When to use1415Apply this skill after constructing baseline-corrected mass tracks (either composite across samples or per-sample) when you need to identify individual chromatographic peaks for feature extraction in LC-MS or GC-MS metabolomics workflows. Use it specifically when you have intensity arrays with known noise characteristics and want to avoid over-detection of shallow peaks or under-detection due to fixed, global prominence thresholds.1617## When NOT to use1819- Input mass track has already been peak-detected and feature-summarized—do not re-apply peak detection to a feature table or aggregated peak list.20- Mass track exhibits extreme baseline drift or multiplicative noise that cannot be adequately corrected by the audit step (detrend, baseline subtraction); consider preprocessing first.21- You require detection of peaks in noisy, low-resolution data where Gaussian fitting is unreliable; this skill assumes sufficient chromatographic resolution to justify prominence-based filtering.2223## Inputs2425- aligned mass track (intensity array, full retention-time length, single m/z value)26- retention-time calibration dictionaries (rt_cal_dict per sample, if per-sample tracks)27- parameters: min_peak_height (integer, default ~5000–10000 counts), min_timepoints (integer, default 25 scans), noise_threshold_ppm (float, default 0.01 or 1%)2829## Outputs3031- detected peaks (list of dicts with scan indices, intensity, m/z, retention time, Gaussian fit metrics)32- peak quality metrics: cSelectivity (float, chromatographic selectivity), SNR (float, signal-to-noise ratio), goodness_fitting (float, Gaussian R²)33- filtered peak list (subset meeting SNR > 2 and Gaussian fit thresholds)3435## How to apply3637Begin by auditing each mass track: check intensity ceiling (cap at 1E8), detect low-intensity tracks (median < 1e3), and apply detrend if median > 10× min_peak_height or >50% of points exceed the height threshold. Compute baseline and noise from bottom-signal quartiles; apply moving-average smoothing if noise > 1% of max intensity AND max intensity < 10× min_peak_height. Subtract baseline+noise to isolate positive regions and split the track into segments. For each segment, invoke scipy.signal.find_peaks with a dynamic prominence parameter set to the maximum of: (1) 1/3 × min_peak_height, (2) noise level, and (3) 5% of segment max intensity. Use min_peak_height and min_timepoints (default 25 scans) as additional window controls. Finally, filter detected peaks by requiring goodness-of-fit to a Gaussian model, chromatographic selectivity (cSelectivity), and SNR > 2.3839## Related tools4041- **scipy.signal.find_peaks** (core local-maxima detection engine; invoked once per mass track segment with dynamic prominence parameter) — https://scipy.org42- **scipy.signal.detrend** (removes polynomial or mean baseline drift from mass track before peak detection) — https://scipy.org43- **asari (peaks.audit_mass_track)** (validates and conditions mass track intensity ceiling, rescaling, smoothing, baseline/noise computation before peak detection) — https://github.com/shuzhao-li/asari44- **asari (peaks.stats_detect_elution_peaks)** (orchestrates the full peak-detection workflow including find_peaks invocation with prominence and height controls) — https://github.com/shuzhao-li/asari45- **asari (peaks.evaluate_gaussian_peak_on_intensity_list)** (fits Gaussian model to detected peak and computes goodness_fitting metric for quality filtering) — https://github.com/shuzhao-li/asari46- **asari (peaks.__peaks_cSelectivity_stats_)** (computes chromatographic selectivity (cSelectivity) metric to filter peaks for specificity) — https://github.com/shuzhao-li/asari47- **asari (peaks.compute_noise_by_flanks)** (estimates noise level from signal flanks for use in dynamic prominence calculation) — https://github.com/shuzhao-li/asari4849## Examples5051```52from asari.peaks import stats_detect_elution_peaks; detected = stats_detect_elution_peaks(mass_track, min_peak_height=5000, min_timepoints=25, noise=150.0)53```5455## Evaluation signals5657- Verify that scipy.signal.find_peaks is invoked exactly once per composite mass track (or once per segment within a per-sample track), not N times—log or count function calls.58- Confirm that all reported peaks include three required metrics: cSelectivity (float), SNR (float ≥ 2), and Gaussian goodness_fitting (R² or equivalent)—audit output JSON/CSV schema.59- Cross-check that peaks with SNR ≤ 2 or failing Gaussian fit are excluded from the final peak list—compare against full candidate peak set before filtering.60- Validate that dynamic prominence was computed for each segment as max(1/3 × min_peak_height, noise level, 5% × segment max intensity)—spot-check a few segments in logs or intermediate outputs.61- Confirm that baseline and noise estimates are derived from bottom-signal quartiles and that detrend/smoothing was applied only when audit conditions are met (median, thresholds)—trace audit_mass_track output.6263## Limitations6465- Gaussian fitting assumes peaks are approximately bell-shaped; highly asymmetric or multi-modal peaks may not fit well and will be incorrectly filtered.66- Dynamic prominence depends on accurate noise estimation from flanks; in extremely noisy or low-intensity tracks (median < 1e3), noise computation may be unreliable and prominence thresholds may be too high or too low.67- The default min_timepoints = 25 scans assumes typical chromatographic resolution; for very fast gradients or high-frequency sampling, this threshold may prune valid narrow peaks.68- Detrending and smoothing are conditional on audit thresholds; if a mass track exhibits complex baseline drift not captured by the median or 10× min_peak_height rule, these corrections may be skipped, leaving residual artifacts.69- SNR computation and cSelectivity depend on sample registration and cross-sample alignment quality; misaligned or poorly calibrated retention-time dictionaries will degrade selectivity filtering.7071## Evidence7273- [methods] Asari uses a simple local maxima method (scipy.signal.find_peaks), with prominence control: "Asari uses a simple local maxima method (scipy.signal.find_peaks), with prominence control"74- [other] Apply scipy.signal.find_peaks on each segment with dynamic prominence (max of 1/3 min_peak_height, noise level, and 5% of segment max intensity): "Apply scipy.signal.find_peaks on each segment with dynamic prominence (max of 1/3 min_peak_height, noise level, and 5% of segment max intensity), using min_peak_height and min_timepoints (default 25"75- [other] Audit each composite mass track (peaks.audit_mass_track): check max intensity ceiling (1E8), apply rescaling if needed, detect low-intensity tracks (median < 1e3), perform detrend if median > 10× min_peak_height and >50% of points exceed threshold: "Audit each composite mass track (peaks.audit_mass_track): check max intensity ceiling (1E8), apply rescaling if needed, detect low-intensity tracks (median < 1e3), perform detrend if median > 10×"76- [other] Evaluate detected peaks for gaussian fit (goodness_fitting), chromatographic selectivity (cSelectivity), and signal-to-noise ratio (SNR > 2), retaining only peaks meeting thresholds.: "Evaluate detected peaks for gaussian fit (goodness_fitting), chromatographic selectivity (cSelectivity), and signal-to-noise ratio (SNR > 2), retaining only peaks meeting thresholds."77- [intro] Statistics guided peak dection, based on local maxima and prominence, selective use of smoothing: "Statistics guided peak dection, based on local maxima and prominence, selective use of smoothing"
HolobiomicsLab