Feat isotope-corrected mass error feature

[JemmaLDaniel]

Switch mass error feature to isotope-corrected signed ppm

Summary

Changes MassErrorFeature from computing a raw Dalton mass error to computing a signed mass error in parts per million (ppm) with isotope peak correction. This addresses two limitations of the previous implementation:

1. Isotope peak selection -- when the instrument selects the M+1 or M+2 precursor isotope peak instead of the monoisotopic peak, the old Da error would be off by ~1.003 Da, encouraging the calibrator to penalise correct PSMs. The new implementation evaluates multiple isotope offsets (configurable via isotope_error_range, default (0, 1)) and selects the one with the smallest absolute error.

2. Size normalisation -- a 0.01 Da error means very different things for a 500 Da peptide (20 ppm) vs a 3000 Da peptide (3.3 ppm). The ppm unit normalises for peptide size, giving the calibrator a scale-invariant error measure.

The formula follows the convention used by InstaNovo:

ppm = (mz_theoretical - (mz_measured - isotope * 1.00335 / z)) / mz_measured * 1e6

Changes

• winnow/calibration/features/mass_error.py:
  • Constructor now accepts isotope_error_range: Tuple[int, int] = (0, 1).
  • Computation works in m/z space instead of mass space.
  • For each isotope offset, computes signed ppm error; selects the offset with the smallest absolute value.
  • Output column renamed from mass_error to mass_error_ppm.
  • Reuses CARBON_ISOTOPE_MASS_SHIFT from constants.py.
• tests/calibration/features/test_mass_error.py -- rewritten with tests for: exact monoisotopic match (~0 ppm), formula verification, isotope correction selecting the best offset, sign preservation, invalid peptide handling, and custom residue masses.
• docs/api/features/mass_error.md -- updated to document the new formula, isotope correction, ppm output, and isotope_error_range parameter.

[github-actions]

Coverage Report

File	Stmts	Miss	Cover	Missing
__init__.py	0	0	100%
data_types.py	4	0	100%
calibration
__init__.py	0	0	100%
calibration_features.py	5	0	100%
calibrator.py	91	15	83%	69–70, 72, 106–109, 134–135, 137, 162–163, 167, 194–195
calibration/features
__init__.py	10	0	100%
base.py	8	0	100%
beam.py	47	0	100%
chimeric.py	77	1	98%	198
constants.py	1	0	100%
fragment_match.py	73	1	98%	190
mass_error.py	43	2	95%	85, 89
retention_time.py	77	1	98%	160
sequence.py	19	0	100%
token_score.py	37	1	97%	82
utils.py	114	2	98%	197, 267
compat
__init__.py	0	0	100%
instanovo.py	10	6	40%	12, 14–15, 17, 24–25
datasets
__init__.py	0	0	100%
calibration_dataset.py	109	17	84%	155, 169, 171, 173, 183, 196, 249, 251–252, 258–261, 263–266
data_loaders.py	270	14	94%	23, 189, 220–221, 414, 455, 847, 851, 900, 911, 1023–1024, 1052–1053
interfaces.py	3	0	100%
psm_dataset.py	25	0	100%
fdr
__init__.py	0	0	100%
base.py	58	15	74%	81, 85–86, 91, 98–99, 105, 126, 129–130, 135, 137–138, 144, 186
database_grounded.py	28	1	96%	52
nonparametric.py	25	4	84%	62, 68–69, 72
scripts
__init__.py	0	0	100%
main.py	185	185	0%	8, 10–13, 16–20, 23–24, 26–28, 32, 39, 44, 47, 53, 55–56, 59, 68, 76, 79, 86, 88–90, 92, 94–99, 102, 104–105, 110, 125, 128, 135–141, 144–145, 148, 161–163, 166, 169, 174, 176–178, 180, 182–183, 186–187, 190, 192–193, 195, 197, 199–200, 202, 205–206, 209–210, 213–214, 217–219, 221, 224, 238–240, 242, 244, 249, 251–253, 255–256, 258–260, 265–266, 268–270, 272, 274, 276–277, 281–284, 286–287, 289–290, 292–293, 295, 298, 312–314, 317, 320, 325, 327–329, 331–333, 335–336, 339–340, 343, 345–346, 348, 350, 352–353, 355, 358–359, 365–366, 369–370, 373–374, 377–378, 386–388, 392, 395, 399, 402, 425, 438–439, 442, 464, 476–477, 480, 505, 518–519, 522, 537, 549–550, 553, 565, 577–578, 581, 596, 608–609
utils
__init__.py	4	0	100%
config_formatter.py	53	40	24%	29, 37–38, 40–42, 44, 55, 58–60, 62–63, 66–69, 72–74, 77–78, 80, 91, 102, 113, 127–128, 130–132, 145–147, 150, 153–154, 157–158, 160
config_path.py	76	5	93%	24–26, 117–118
peptide.py	16	0	100%
TOTAL	1468	310	78%

Tests	Skipped	Failures	Errors	Time
321	0 💤	0 ❌	0 🔥	36.070s ⏱️