Scan Number Mapping Dictionary Construction

Construct scan-number mapping dictionary from LOWESS retention-time alignment

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

Summary

Export a LOWESS regression function describing retention-time (RT) relationships between a sample and reference into a sparse scan-number mapping dictionary (rt_cal_dict) that records only differing values and constrains extrapolation within sample RT boundaries. This enables efficient, reproducible RT alignment across LC-MS samples.

When to use

After performing LOWESS regression on landmark peak RT pairs between a sample and reference, you need to encode the learned RT transformation as a reusable, memory-efficient lookup table that can be applied during feature alignment without recomputing the regression for every sample.

When NOT to use

• Sample has fewer than ~5–10 high-quality landmark peaks; insufficient data points make LOWESS fitting unreliable.
• RT shift is non-monotonic or exhibits discontinuities; LOWESS may produce invalid mappings that reverse or collapse scan order.
• RT alignment is already complete or sample is already calibrated; constructing a second mapping dictionary would introduce redundant or conflicting transformations.

Inputs

• LOWESS regression function (scipy.signal.lowess output)
• List of landmark peak (sample_RT_number, reference_RT_number) pairs
• Sample RT range (min and max scan number)
• Reference RT range (for boundary extension calculation)

Outputs

• Sparse scan-number mapping dictionary (rt_cal_dict) as JSON or Python dict
• Validation report confirming landmark peak scan number mappings and boundary adherence

How to apply

Given a LOWESS regression function fitted on pairs of (sample_RT_number, reference_RT_number) from high-selectivity landmark peaks (selectivity > 0.99, prominence > 20% of peak height), export the function as a sparse dictionary (rt_cal_dict) that stores only scan numbers where the calibrated RT differs from the uncalibrated scan number. Add 10% extension boundaries at both ends of the RT range to constrain convergence and prevent out-of-bounds extrapolation. Validate the mapping by confirming that landmark peak scan numbers map correctly and that all extrapolated values remain within ±10% extension margins of the sample's original RT boundaries. The resulting rt_cal_dict should stay within sample RT boundaries and be compact enough for efficient storage and lookups during composite map construction.

Related tools

• scipy.signal.lowess (Performs LOWESS regression on landmark peak RT pairs to estimate the smooth, non-parametric RT transformation function) — https://docs.scipy.org/doc/scipy/reference/generated/scipy.signal.lowess.html
• asari (chromatograms.rt_lowess_calibration) (Core function that exports the LOWESS regression function as a sparse rt_cal_dict and enforces boundary constraints) — https://github.com/shuzhao-li/asari
• asari (constructors.set_RT_reference, peaks.quick_detect_unique_elution_peak) (Identifies high-selectivity landmark peaks in reference sample used to seed LOWESS regression) — https://github.com/shuzhao-li/asari
• asari (CompositeMap.calibrate_sample_RT) (Selects good landmark peaks from current sample by applying selectivity criteria and mass track alignment constraints) — https://github.com/shuzhao-li/asari

Examples

from asari.chromatograms import rt_lowess_calibration; rt_cal_dict = rt_lowess_calibration(sample_rts, reference_rts, sample_rt_min, sample_rt_max, extension_pct=0.1)

Evaluation signals

• rt_cal_dict contains only scan numbers where calibrated RT differs from uncalibrated scan number; sparsity should be 50–80% of original scan range.
• All keys and values in rt_cal_dict remain within [sample_RT_min - 10%, sample_RT_max + 10%] bounds; no extrapolation beyond boundaries.
• Landmark peak scan numbers from the LOWESS fit map bidirectionally: applying rt_cal_dict to sample peak scans recovers the corresponding reference peak scans with ≤ 1 scan difference.
• rt_cal_dict can be serialized to JSON and deserialized without loss of precision; round-trip test confirms integer scan numbers.
• Independent validation on a held-out subset of landmark peaks (not used in LOWESS fit) shows RT alignment error < 2 scan numbers.

Limitations

• LOWESS fit quality depends on the number and distribution of landmark peaks; sparse or clustered peaks may produce discontinuous or overly smoothed mappings.
• 10% extension boundary is a heuristic; extreme RT shifts or non-linear instrumental drift may require dataset-specific tuning.
• rt_cal_dict assumes monotonic RT relationship between sample and reference; non-linear or multi-modal RT transformations are not supported and will cause scan reversals.
• Sparse encoding loses information about the underlying LOWESS regression; if fine-grained RT interpolation is needed later, the original regression function must be preserved separately.

Evidence

• [other] Perform LOWESS regression (scipy.signal fitting) on the pairs of (sample_RT_number, reference_RT_number) from the good landmark peaks, adding 10% extension boundaries at both ends to constrain convergence.: "Perform LOWESS regression (scipy.signal fitting) on the pairs of (sample_RT_number, reference_RT_number) from the good landmark peaks, adding 10% extension boundaries at both ends to constrain"
• [other] Export the regression function as a sparse scan-number mapping dictionary (rt_cal_dict) that records only differing values and stays within sample RT boundaries: "Export the regression function as a sparse scan-number mapping dictionary (rt_cal_dict) that records only differing values and stays within sample RT boundaries"
• [other] verify that rt_cal_dict enables accurate RT alignment by confirming landmark peak scan numbers map correctly and that extrapolation remains bounded within ±10% extension margins.: "verify that rt_cal_dict enables accurate RT alignment by confirming landmark peak scan numbers map correctly and that extrapolation remains bounded within ±10% extension margins."
• [other] Identify high-selectivity landmark peaks in the reference sample using criteria: mSelectivity > 0.99, min_peak_height satisfied (default 1e5), prominence > 20% of peak height, and single peak per mass track: "Identify high-selectivity landmark peaks in the reference sample using criteria: mSelectivity > 0.99, min_peak_height satisfied (default 1e5), prominence > 20% of peak height, and single peak per"