Composite Mass Track Construction

composite-mass-track-construction

Summary

Construct a single composite mass track by element-wise summation of intensity values across aligned mass tracks from all samples at each m/z value, enabling efficient single-pass peak detection instead of per-sample detection. This reduces computational cost from O(N) to O(1) peak-detection algorithm invocations while preserving signal fidelity across the cohort.

When to use

When processing a multi-sample LC-MS metabolomics project after mass-track extraction and retention-time calibration have been applied to all individual samples, and you need to detect peaks across the entire cohort. This approach is especially valuable when N > 10 samples, as it avoids redundant peak-detection invocations and enables detection of low-abundance features visible only when signal is pooled across samples.

When NOT to use

• When input samples have drastically different ionization efficiency or sample loads (pooling may obscure sample-specific rare peaks or introduce bias toward dominant samples).
• When retentiontime miscalibration across samples is severe (misaligned scans will blur the composite track and lower peak definition).
• When peak detection must preserve sample-level spatial information or variance (composite pooling loses per-sample intensity and signal heterogeneity).

Inputs

• aligned mass tracks from all samples (one mass track per m/z per sample)
• retention-time calibration dictionaries (rt_cal_dict) mapping sample scan numbers to reference coordinates
• reference sample scan-number range (defines synchronized coordinate system)
• min_peak_height threshold (default tunable, used for dynamic prominence calculation)
• min_timepoints for peak width control (default 25 scans)

Outputs

• composite mass track (intensity array of full retention-time length, summed across all N samples)
• detected peaks on composite track with metrics: m/z, retention time, intensity, prominence, gaussian fit goodness (goodness_fitting), chromatographic selectivity (cSelectivity), signal-to-noise ratio (SNR)
• audit report on composite track (max intensity value, rescaling applied, detrending applied, baseline and noise estimates)

How to apply

After extracting aligned mass tracks from each sample and obtaining retention-time calibration dictionaries (rt_cal_dict), synchronize scan-number indices across all samples by applying rt_cal_dict to map each sample's scan numbers to a reference sample coordinate system. For each unique m/z value in the aligned mass grid, sum the intensity arrays element-wise across all samples to produce a single composite mass track of full retention-time length. Audit the composite track using peaks.audit_mass_track (check intensity ceiling at 1E8, apply rescaling if needed, detect low-intensity tracks with median < 1e3, detrend if median > 10× min_peak_height and >50% of points exceed threshold, compute baseline and noise from bottom-signal quartiles, apply moving-average smoothing when noise > 1% of max intensity and max intensity < 10× min_peak_height). Subtract the baseline and noise filter to isolate positive regions, splitting the track into segments. Then apply scipy.signal.find_peaks to each segment once, using dynamic prominence (max of 1/3 min_peak_height, noise level, and 5% of segment max intensity) and min_timepoints (default 25 scans) for window control, rather than calling find_peaks N times on individual samples.

Related tools

• scipy.signal.find_peaks (Applied once per composite track segment to detect local maxima with prominence control, replacing N per-sample invocations) — https://docs.scipy.org/doc/scipy/reference/generated/scipy.signal.find_peaks.html
• scipy.signal.detrend (Applied conditionally during composite mass-track audit to remove low-frequency drift when median intensity > 10× min_peak_height and >50% of points exceed threshold) — https://docs.scipy.org/doc/scipy/reference/generated/scipy.signal.detrend.html
• asari peaks.audit_mass_track (Inspects composite track intensity range, applies rescaling, detects low-signal regions, computes baseline and noise estimates, and applies smoothing per configured thresholds before peak detection) — https://github.com/shuzhao-li-lab/asari
• asari chromatograms.rt_lowess_calibration (Generates retention-time calibration dictionaries used to synchronize scan indices across samples prior to composite track construction) — https://github.com/shuzhao-li-lab/asari

Evaluation signals

• Verify that scipy.signal.find_peaks is invoked exactly once per composite track (or once per segment within a composite track), not N times, by instrumenting or logging the call stack.
• Confirm that the composite track intensity array has length equal to the full reference retention-time span (number of scans in reference sample) and contains element-wise sums of all aligned input tracks.
• Check that detected peaks include all four required metrics: goodness_fitting (gaussian fit), cSelectivity (chromatographic selectivity), SNR > 2 (signal-to-noise ratio), and min_peak_height threshold met.
• Validate that reported peak retention times fall within the range of scans present in all or nearly all input samples (indicating genuine pooled signal, not noise from misaligned outliers).
• Audit log should show that audit_mass_track executed once per composite track with reported baseline, noise, and detrending decisions.

Limitations

• Composite pooling assumes that retention time is sufficiently synchronized across samples via rt_cal_dict; large inter-sample drift will blur peaks and reduce sensitivity.
• Pooling assumes similar sample loading and ionization efficiency across the cohort; highly unbalanced samples may dominate the composite signal and obscure low-abundance metabolites in weaker samples.
• The single composite peak list does not preserve per-sample signal heterogeneity; downstream abundance tables must be extracted by mapping detected peaks back to individual sample mass tracks via mass-to-feature association, which requires the original sample-level data.
• Very low-abundance metabolites visible in only 1–2 samples may be diluted below detection threshold in the composite track if other samples have zero signal; per-sample detection may recover these features, trading computational cost for sensitivity.
• Baseline and noise estimation are computed once from the composite pooled signal; samples with anomalous noise or drift may skew these estimates and lead to suboptimal thresholds for the composite detection step.

Evidence

• [other] Asari implements peak detection using scipy.signal.find_peaks on a composite map constructed from summed mass tracks across all samples, reducing the number of peak-detection algorithm calls from N (one per sample) to one (composite), thereby improving scalability and computational efficiency.: "Asari implements peak detection using scipy.signal.find_peaks on a composite map constructed from summed mass tracks across all samples, reducing the number of peak-detection algorithm calls from N"
• [other] Sum intensity values element-wise across all samples for each unique m/z value in the mass grid, producing composite mass tracks as full-length intensity arrays.: "Sum intensity values element-wise across all samples for each unique m/z value in the mass grid, producing composite mass tracks as full-length intensity arrays."
• [other] Synchronize scan-number indices across samples by applying rt_cal_dict to map each sample's scan numbers to reference sample coordinates.: "Synchronize scan-number indices across samples by applying rt_cal_dict to map each sample's scan numbers to reference sample coordinates."
• [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, compute baseline and noise from bottom-signal quartiles, apply smoothing (moving average) when noise > 1% of max intensity and max intensity < 10× min_peak_height.: "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×"
• [readme] Peak detection on a composite map instead of repeated on individual samples: "Peak detection on a composite map instead of repeated on individual samples"