HolobiomicsLab computational-efficiency-single-pass-vs-repeated-detection
License: restricted — no clear open-source license detected for the underlying tool; verify licensing before commercial use or redistribution.
Summary
Replace per-sample peak detection with single-pass detection on a composite mass track (summed intensities across all samples) to reduce scipy.signal.find_peaks invocations from N to 1, dramatically improving computational scalability in untargeted LC-MS metabolomics without sacrificing peak quality metrics.
When to use
When processing aligned LC-MS data across multiple samples where the computational bottleneck is repeated peak-detection algorithm calls (one per sample per m/z value). Typical scenario: >10 samples with >1000 m/z values each, where N individual find_peaks invocations dominate runtime. Particularly valuable when samples are aligned to a common retention-time reference via rt_cal_dict calibration dictionaries and when composite signal is strong enough to enable robust peak detection (median intensity >1e3).
When NOT to use
- Input is already a per-sample feature table or pre-aggregated peak list; skip composite construction.
- Samples are poorly aligned or rt_cal_dict calibration fails; composite will be corrupted by misaligned noise.
- Composite signal is too weak (median intensity <1e3) to reliably detect peaks; per-sample detection may recover low-abundance features.
- Peak detection is not a computational bottleneck (e.g., small projects with <5 samples or <100 m/z values per sample).
Inputs
- aligned mass tracks (intensity arrays indexed by m/z) from all N samples
- retention-time calibration dictionaries (rt_cal_dict) mapping per-sample scan numbers to reference coordinates
- mass grid (m/z values common across aligned samples)
- detection parameters: min_peak_height, min_timepoints (default 25 scans), intensity ceiling (1E8), noise/smoothing thresholds
Outputs
- composite mass tracks (summed-intensity arrays per m/z)
- detected peaks with m/z, retention time, intensity, cSelectivity, SNR, gaussian fit metrics
- audit metadata: baseline, noise, smoothing flags, detrend status per composite track
- mapping of detected peaks back to constituent sample contributions (via cmap, mass_grid_mapping)
How to apply
After aligning and calibrating retention times across all samples via rt_cal_dict, synchronize scan-number indices to a reference sample coordinate system. Sum intensity values element-wise across all N samples for each m/z value to construct composite mass tracks (full-length intensity arrays). Audit each composite track: check intensity ceiling (1E8), detect and detrend if median >10× min_peak_height and >50% of points exceed threshold, compute baseline/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, split into segments, and invoke scipy.signal.find_peaks exactly once per segment (not N times) with dynamic prominence (max of 1/3 min_peak_height, noise level, 5% segment max). Filter detected peaks by gaussian fit goodness, chromatographic selectivity (cSelectivity), and SNR >2. This approach leverages increased signal-to-noise in the composite to enable single-pass detection while maintaining per-peak quality metrics traceable back to individual samples.
Related tools
- scipy.signal.find_peaks (Core peak-detection algorithm invoked once per composite mass-track segment; detects local maxima with prominence and min-height control) — https://docs.scipy.org/doc/scipy/reference/generated/scipy.signal.find_peaks.html
- scipy.signal.detrend (Removes polynomial drift from composite mass tracks before peak detection when median intensity >10× min_peak_height) — https://docs.scipy.org/doc/scipy/reference/generated/scipy.signal.detrend.html
- asari.peaks.audit_mass_track (Audits composite track intensity ceiling, noise floor, smoothing necessity, and baseline; gates detrending and smoothing decisions) — https://github.com/shuzhao-li/asari
- asari.peaks.stats_detect_elution_peaks (Invokes find_peaks on detrended/smoothed composite segments with dynamic prominence based on noise and segment max) — https://github.com/shuzhao-li/asari
- asari.peaks.evaluate_gaussian_peak_on_intensity_list (Fits gaussian to each 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 measuring peak isolation and shape consistency) — https://github.com/shuzhao-li/asari
- asari.chromatograms.rt_lowess_calibration (Pre-processing step to construct rt_cal_dict for aligning retention times across samples before composite construction) — https://github.com/shuzhao-li/asari
Examples
```python
from asari.peaks import audit_mass_track, stats_detect_elution_peaks
from scipy.signal import find_peaks
import numpy as np
# Composite mass track from summed intensities across N aligned samples
composite_track = np.sum([sample_track for sample_track in aligned_samples], axis=0)
# Audit and preprocess
audit_result = audit_mass_track(composite_track, min_peak_height=10000)
processed_track = audit_result['processed_track']
# Single-pass peak detection on composite (N=1, not N invocations)
peaks, properties = find_peaks(
processed_track,
height=10000,
prominence=max(3333, audit_result['noise_level'], 0.05*processed_track.max())
)
# Evaluate peak quality
for peak_idx in peaks:
cSelectivity = compute_cSelectivity(processed_track, peak_idx)
snr = compute_SNR(processed_track[peak_idx], audit_result['noise_level'])
if snr > 2 and cSelectivity > 0:
print(f'Peak at scan {peak_idx}: SNR={snr:.2f}, cSelectivity={cSelectivity:.3f}')
## Evaluation signals
- Peak detection invokes find_peaks exactly 1 time per composite mass track (audit log or code profiling confirms N→1 reduction).
- All detected peaks include non-null cSelectivity, SNR, and gaussian fit goodness metrics, verifying per-peak quality evaluation was applied.
- Composite track construction is verified: sum of per-sample intensities at each m/z matches element-wise sum of input mass tracks.
- Retention-time synchronization is correct: rt_cal_dict was applied to map all sample scan numbers to reference coordinates before summation (check alignment audit).
- Runtime is measured: wall-clock time for peak detection on composite is <1/N of wall-clock time for per-sample detection on the same m/z values (where N = number of samples).
- Output feature table reports peaks with cSelectivity >0 (indicating selectivity was evaluated) and SNR ≥2 threshold applied (per documented filter).
## Limitations
- Composite detection relies on strong summed signal; low-abundance features present in few samples may be lost if individual sample SNR is <2 but composite SNR >2 threshold is marginal.
- Retention-time calibration (rt_cal_dict) must succeed for all samples; failure in any sample corrupts the alignment and composite construction.
- Intensity ceiling (1E8) and noise-floor thresholds (median >1e3) are tuned for typical LC-MS data; extreme dynamic range or very noisy samples may require parameter retuning.
- Per-sample abundance variation is invisible in composite detection; peaks present in only 1–2 samples will not be highlighted during composite detection and must be recovered post-hoc if needed.
- Composite approach assumes additive intensity model; ion-suppression effects or sample-dependent chromatographic shifts are averaged out, potentially masking sample-specific phenomena.
## 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.: "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)"
- [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] 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: "check max intensity ceiling (1E8), detect low-intensity tracks (median < 1e3), perform detrend if median > 10× min_peak_height and >50% of points exceed threshold"
- [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), using min_peak_height and min_timepoints (default 25 scans) for window control.: "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)"
- [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] Peak detection on a composite map instead of repeated on individual samples: "Peak detection on a composite map instead of repeated on individual samples"
- [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."