NEW: annotating real spectra vs calculated one by boopthesnoot · Pull Request #314 · MannLabs/alphabase

boopthesnoot · 2025-04-09T16:01:13Z

@GeorgWa added functionality to compare experimental spectra with calculated ones, so all credit to him.

taken from https://github.com/MannLabs/metaptcm/blob/cbf579b52041d96636dd04c29d4855e5391bd136/metaptcm/utils/annotation.py so @mschwoer already reviewed this

Feel free to suggest a different placement within the package, also added some noqas.

GeorgWa

This module currently contains the conversion of a flat (annotated) library to a dense library. In the meantime we have implemented a more optimized and clean version in alphabase.

See here: create_dense_matrices

GeorgWa · 2025-04-10T08:13:28Z

+# Loss type mappings - these map loss type numeric codes to their string representations
+# Used in annotate.py and flat.py
+
+LOSS_NUMBER_TO_TYPE = {0: "", 17: "_NH3", 18: "_H2O", 98: "_modloss"}


This is now covered in https://github.com/MannLabs/alphabase/blob/main/alphabase/peptide/fragment.py

How much redundency is there between these two modules?

i'll try to minimize redundancy, thank you for pointing it out

GeorgWa · 2025-04-10T08:14:53Z

+    ----------
+    speclib_flat : SpecLibFlat
+        A spectral library flat object containing precursor information.
+    raw_data : MzMLReader | ThermoRawData


I think the most accurate thig would be to use a MsDataBase from alpharaw here as annotation type.

can't use alpharaw, otherwise we get circular dependencies

GeorgWa · 2025-04-10T08:15:31Z

+    )
+
+
+def _get_dense_column(  # noqa: PLR0913


Part of flat -> dense

GeorgWa · 2025-04-10T08:15:39Z

+    return "_".join(items)
+
+
+def _add_frag_column_annotation(


Part of flat -> dense

GeorgWa · 2025-04-10T08:15:56Z

+    )
+
+
+def _assign_to_dense(


Part of flat -> dense

GeorgWa

It's a bit integration work but then we should be good :)

GeorgWa · 2025-04-11T11:55:59Z

+)
+from alphabase.spectral_library.flat import SpecLibFlat
+
+UNANNOTATED_TYPE = 255


can you move this to alphabase/peptide/fragment.py?

GeorgWa · 2025-04-11T11:56:58Z

+    return outlib_flat
+
+
+def calculate_pif(spectrum_peak_df: pd.DataFrame) -> float:


What do you think about moving them into a metrics.py module? similar for gini etc.

GeorgWa · 2025-04-11T11:57:37Z

+    )
+
+
+def _sequence_coverage_metric(flatlib: SpecLibFlat) -> None:


move to metrics.py

GeorgWa · 2025-04-11T11:57:41Z

+    )
+
+
+def _sequence_gini_metric(flatlib: SpecLibFlat) -> None:


move to metrics.py

GeorgWa · 2025-04-11T11:57:47Z

+    )
+
+
+def _normalized_count_metric(flatlib: SpecLibFlat) -> None:


move to metrics.py

GeorgWa · 2025-04-11T11:57:53Z

+    return np.median(valid_values)
+
+
+def _mass_accuracy_metric(flatlib: SpecLibFlat) -> None:


move to metrics.py

GeorgWa · 2025-04-11T12:00:15Z

+            mass_error_ppm=mass_error_ppm,
+        )
+
+        matched_precursor_df.loc[i, "pif"] = calculate_pif(spectrum_peak_df)


Sorry, that I'm asking you to fix my mistakes here :D

could you change the calulate_pif() function so it's structured and applied in the same way as the other metrics?
So it's called after annotation and operates on the precursor df.

GeorgWa · 2025-04-11T12:05:26Z

+    )
+
+
+def add_dense_lib(


what do you think about adding this function directly to the speclib flat object?
so we can call SpecLibFlat.add_dense_lib().
I think this fits the ab pattern better.

GeorgWa · 2025-04-11T12:06:35Z

        # Now if we have a fragment type that is a,b,c we should have the corresponding x,y,z

        corresponding = {"a": "x", "b": "y", "c": "z", "x": "a", "y": "b", "z": "c"}
-        loss_number_to_type = {0: "", 18: "_H2O", 17: "_NH3", 98: "_modloss"}


get_full_charged_types() is deprecated, so there is no need to do any changes here.

GeorgWa · 2025-04-11T12:07:21Z

+# Loss type mappings - these map loss type numeric codes to their string representations
+# Used in annotate.py and flat.py
+
+LOSS_NUMBER_TO_TYPE = {0: "", 17: "_NH3", 18: "_H2O", 98: "_modloss"}


I think this can be removed as it's not needed by the new AB based implementation :).

boopthesnoot requested review from GeorgWa and mschwoer April 9, 2025 16:01

boopthesnoot marked this pull request as draft April 10, 2025 07:26

GeorgWa approved these changes Apr 10, 2025

View reviewed changes

GeorgWa self-requested a review April 11, 2025 11:53

GeorgWa reviewed Apr 11, 2025

View reviewed changes

boopthesnoot force-pushed the annotate_migration branch from ce10314 to 4fcd36a Compare September 17, 2025 09:18

boopthesnoot added 4 commits October 9, 2025 11:50

NEW: annotating real spectra vs calculated one

1ba2463

FIX: circular dependencies

a018f49

FIX: 3.9 typing

231c73d

new: annotating a library with seq and spec coverage

1baf584

boopthesnoot force-pushed the annotate_migration branch from 0c71d9b to 1baf584 Compare October 9, 2025 09:50

boopthesnoot added 2 commits October 9, 2025 11:56

fix: ruff

d54af4f

enh: moved UNANNOTATED_TYPE to fragment.py

c7bd689

boopthesnoot marked this pull request as ready for review October 9, 2025 12:49

boopthesnoot requested review from GeorgWa and jalew188 October 10, 2025 13:14

boopthesnoot added 3 commits November 28, 2025 22:49

fix: saving spec_idx

2a3d8b1

linting

d29634e

fix: _mass_accuracy_metric on flat

cee8362

boopthesnoot merged commit 66dd599 into main Dec 4, 2025
3 checks passed

boopthesnoot deleted the annotate_migration branch December 4, 2025 13:09

		return outlib_flat


		def calculate_pif(spectrum_peak_df: pd.DataFrame) -> float:

		)


		def _sequence_coverage_metric(flatlib: SpecLibFlat) -> None:

		)


		def _normalized_count_metric(flatlib: SpecLibFlat) -> None:

		return np.median(valid_values)


		def _mass_accuracy_metric(flatlib: SpecLibFlat) -> None:

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boopthesnoot commented Apr 9, 2025 •

edited

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