Selectivity Metric Computation Chromatographic

selectivity-metric-computation-chromatographic

License: restricted — no clear open-source license detected for the underlying tool; verify licensing before commercial use or redistribution.

Summary

Compute chromatographic selectivity metrics (cSelectivity) to assess the purity and interference-free nature of detected elution peaks on mass tracks. This metric is essential for filtering low-quality peaks and tracking peak confidence across LC-MS metabolomics workflows.

When to use

Apply this skill after peak detection on composite mass tracks when you need to evaluate whether a detected peak represents a pure, interference-free signal on its m/z channel. Use it as a quality filter during peak evaluation stages, particularly when integrating peaks from different mass regions or assessing peak confidence for annotation workflows. Essential when working with high-resolution LC-MS data where multiple ions may coelute at similar m/z values.

When NOT to use

• Input is already a curated feature table with pre-filtered, high-confidence peaks — selectivity computation is redundant.
• Working with targeted, single-ion monitoring (SIM) or selected reaction monitoring (SRM) where coelution is structurally impossible.
• Peak detection has not yet been performed; selectivity requires detected peak candidates as input.

Inputs

• mass track (intensity array indexed by scan/retention time)
• detected peak positions (apex scan indices)
• peak boundary/region of interest coordinates
• baseline and noise-filtered intensity values

Outputs

• cSelectivity score per peak (0–1 range, higher = purer signal)
• peak quality assessment (pass/fail on selectivity threshold)
• annotated peak list with selectivity metadata

How to apply

After detecting candidate peaks using scipy.signal.find_peaks on a mass track, compute cSelectivity for each peak by analyzing the peak's intensity profile relative to the overall signal on that m/z channel. The selectivity metric quantifies whether the peak stands out cleanly from background and coeluting interference. Retain peaks only if their cSelectivity values exceed quality thresholds (typically cSelectivity >0.5 in asari) alongside SNR and peakshape constraints. This computation is performed during the evaluation phase of detect_evaluate_peaks_on_roi, where peaks are assessed for Gaussian shape, SNR (>2), and selectivity before final feature reporting. The metric helps distinguish genuine chromatographic features from noise or minor shoulders on larger peaks.

Related tools

• scipy.signal.find_peaks (Prerequisite peak detection using local maxima and prominence; selectivity is computed on detected peaks)
• asari.peaks.detect_evaluate_peaks_on_roi (Integration point where cSelectivity is evaluated alongside SNR and peakshape for peak filtering) — https://github.com/shuzhao-li/asari
• asari.peaks.evaluate_gaussian_peak_on_intensity_list (Companion metric evaluation function for peakshape and SNR alongside selectivity) — https://github.com/shuzhao-li/asari

Evaluation signals

• Selectivity values are bounded in [0, 1]; verify no NaN, negative, or >1 values appear in output.
• Peaks passing selectivity threshold (cSelectivity > 0.5) correspond to chromatographically resolved signals; verify by visual inspection of raw mass track intensity profiles around apex.
• Peaks failing selectivity threshold show elevated interference or shoulder patterns; cross-check against baseline-subtracted intensity profiles.
• Feature table filtering on cSelectivity should reduce false positives (noise/interference peaks) without removing genuine low-abundance features; verify via ROC or precision-recall metrics if ground truth is available.
• Selectivity scores should correlate inversely with local background noise and coelution intensity; verify via correlation analysis with SNR and peakshape metrics.

Limitations

• Selectivity computation is sensitive to baseline and noise filter estimation accuracy; poor baseline subtraction inflates apparent selectivity of contaminated peaks.
• The metric may not detect subtle coelution when peaks share very similar retention times and both contribute substantial signal; selectivity alone cannot distinguish unresolved multiplets.
• Selectivity thresholds (e.g., >0.5) are empirically derived and may require tuning for different ionization modes, chromatography types, or instrument configurations.
• High mass resolution alone does not guarantee high selectivity if multiple compounds ionize to the same m/z; selectivity is a chromatographic (retention time domain) purity metric, not a mass accuracy metric.

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)."
• [intro] Tracking peak quality, selectiviy metrics on m/z, chromatography and annotation databases: "Tracking peak quality, selectiviy metrics on m/z, chromatography and annotation databases"
• [other] See peaks.stats_detect_elution_peaks, peaks.detect_evaluate_peaks_on_roi.: "See peaks.stats_detect_elution_peaks, peaks.detect_evaluate_peaks_on_roi."
• [other] For each segment (region of interest), detect peaks using scipy.signal.find_peaks with dynamic prominence and min_peak_height and min_timepoints constraints: "For each segment (region of interest), detect peaks using scipy.signal.find_peaks with dynamic prominence and min_peak_height and min_timepoints constraints on a sliding window"