From 6f4a3025ec4e80250fb4ebd6134456cbfe4de5ad Mon Sep 17 00:00:00 2001
From: WassimG <wassim.gabriel@gmail.com>
Date: Mon, 23 Sep 2024 16:52:45 +0000
Subject: [PATCH] Add neutral loss flag to annotate function

---
 .../annotation/annotation.py                  | 26 +++++++++++++++----
 spectrum_fundamentals/fragments.py            |  2 +-
 spectrum_fundamentals/mod_string.py           |  2 ++
 tests/unit_tests/data/spectrum_output.csv     |  8 +++---
 .../unit_tests/data/tmt_spectrum_output.json  |  3 ++-
 5 files changed, 30 insertions(+), 11 deletions(-)

diff --git a/spectrum_fundamentals/annotation/annotation.py b/spectrum_fundamentals/annotation/annotation.py
index d71c091..27a65da 100644
--- a/spectrum_fundamentals/annotation/annotation.py
+++ b/spectrum_fundamentals/annotation/annotation.py
@@ -129,6 +129,7 @@ def annotate_spectra(
     unit_mass_tolerance: Optional[str] = None,
     custom_mods: Optional[Dict[str, float]] = None,
     fragmentation_method: str = "HCD",
+    annotate_neutral_losses: Optional[bool] = False
 ) -> pd.DataFrame:
     """
     Annotate a set of spectra.
@@ -149,6 +150,7 @@ def annotate_spectra(
     :param unit_mass_tolerance: unit for the mass tolerance (da or ppm)
     :param fragmentation_method: fragmentation method that was used
     :param custom_mods: mapping of custom UNIMOD string identifiers ('[UNIMOD:xyz]') to their mass
+    :param annotate_neutral_losses: flag to indicate whether to annotate neutral losses or not
     :return: a Pandas DataFrame containing the annotated spectra with meta data
     """
     raw_file_annotations = []
@@ -161,6 +163,7 @@ def annotate_spectra(
             unit_mass_tolerance,
             fragmentation_method=fragmentation_method,
             custom_mods=custom_mods,
+            annotate_neutral_losses=annotate_neutral_losses
         )
         if not results:
             continue
@@ -168,7 +171,14 @@ def annotate_spectra(
     results_df = pd.DataFrame(raw_file_annotations)
 
     if "CROSSLINKER_TYPE" not in index_columns:
-        results_df.columns = ["INTENSITIES", "MZ", "CALCULATED_MASS", "removed_peaks", "count_nl_peaks"]
+        results_df.columns = [
+            "INTENSITIES",
+            "MZ",
+            "CALCULATED_MASS",
+            "removed_peaks",
+            "ANNOTATED_NL_COUNT",
+            "EXPECTED_NL_COUNT",
+        ]
     else:
         results_df.columns = [
             "INTENSITIES_A",
@@ -353,9 +363,10 @@ def parallel_annotate(
     unit_mass_tolerance: Optional[str] = None,
     custom_mods: Optional[Dict[str, float]] = None,
     fragmentation_method: str = "HCD",
+    annotate_neutral_losses: Optional[bool] = False
 ) -> Optional[
     Union[
-        Tuple[np.ndarray, np.ndarray, float, int, int],
+        Tuple[np.ndarray, np.ndarray, float, int, int, int],
         Tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray, float, float, int, int],
     ]
 ]:
@@ -375,6 +386,7 @@ def parallel_annotate(
     :param unit_mass_tolerance: unit for the mass tolerance (da or ppm)
     :param custom_mods: mapping of custom UNIMOD string identifiers ('[UNIMOD:xyz]') to their mass
     :param fragmentation_method: fragmentation method that was used
+    :param annotate_neutral_losses: flag to indicate whether to annotate neutral losses or not
     :return: a tuple containing intensity values (np.ndarray), masses (np.ndarray), calculated mass (float),
              and any removed peaks (List[str])
     """
@@ -389,6 +401,7 @@ def parallel_annotate(
             unit_mass_tolerance,
             fragmentation_method=fragmentation_method,
             custom_mods=custom_mods,
+            add_neutral_losses= annotate_neutral_losses
         )
 
     if (spectrum[index_columns["PEPTIDE_LENGTH_A"]] > 30) or (spectrum[index_columns["PEPTIDE_LENGTH_B"]] > 30):
@@ -405,6 +418,7 @@ def _annotate_linear_spectrum(
     unit_mass_tolerance: Optional[str],
     custom_mods: Optional[Dict[str, float]] = None,
     fragmentation_method: str = "HCD",
+    add_neutral_losses: Optional[bool] = False
 ):
     """
     Annotate a linear peptide spectrum.
@@ -415,13 +429,14 @@ def _annotate_linear_spectrum(
     :param unit_mass_tolerance: Unit for the mass tolerance (da or ppm)
     :param custom_mods: mapping of custom UNIMOD string identifiers ('[UNIMOD:xyz]') to their mass
     :param fragmentation_method: fragmentation method that was used
+    :param add_neutral_losses: flag to indicate whether to annotate neutral losses or not
     :return: Annotated spectrum
     """
     mod_seq_column = "MODIFIED_SEQUENCE"
     if "MODIFIED_SEQUENCE_MSA" in index_columns:
         mod_seq_column = "MODIFIED_SEQUENCE_MSA"
 
-    fragments_meta_data, tmt_n_term, unmod_sequence, calc_mass, _ = initialize_peaks(
+    fragments_meta_data, tmt_n_term, unmod_sequence, calc_mass, expected_nl = initialize_peaks(
         sequence=spectrum[index_columns[mod_seq_column]],
         mass_analyzer=spectrum[index_columns["MASS_ANALYZER"]],
         charge=spectrum[index_columns["PRECURSOR_CHARGE"]],
@@ -429,6 +444,7 @@ def _annotate_linear_spectrum(
         unit_mass_tolerance=unit_mass_tolerance,
         fragmentation_method=fragmentation_method,
         custom_mods=custom_mods,
+        add_neutral_losses= add_neutral_losses
     )
     matched_peaks, count_annotated_nl = match_peaks(
         fragments_meta_data,
@@ -446,13 +462,13 @@ def _annotate_linear_spectrum(
     if len(matched_peaks) == 0:
         intensity = np.full(vec_length, 0.0)
         mass = np.full(vec_length, 0.0)
-        return intensity, mass, calc_mass, 0, 0
+        return intensity, mass, calc_mass, 0, 0, 0
 
     matched_peaks, removed_peaks = handle_multiple_matches(matched_peaks)
     intensities, mass = generate_annotation_matrix(
         matched_peaks, unmod_sequence, spectrum[index_columns["PRECURSOR_CHARGE"]], fragmentation_method
     )
-    return intensities, mass, calc_mass, removed_peaks, count_annotated_nl
+    return intensities, mass, calc_mass, removed_peaks, count_annotated_nl, expected_nl
 
 
 def _annotate_crosslinked_spectrum(
diff --git a/spectrum_fundamentals/fragments.py b/spectrum_fundamentals/fragments.py
index 85c3cff..6af620f 100644
--- a/spectrum_fundamentals/fragments.py
+++ b/spectrum_fundamentals/fragments.py
@@ -213,7 +213,7 @@ def _calculate_nl_score_mass(neutral_loss):
     return score, mass
 
 
-def initialize_peaks(
+def initialize_peaks(  # noqa: C901
     sequence: str,
     mass_analyzer: str,
     charge: int,
diff --git a/spectrum_fundamentals/mod_string.py b/spectrum_fundamentals/mod_string.py
index 0e68f1a..cb282ea 100644
--- a/spectrum_fundamentals/mod_string.py
+++ b/spectrum_fundamentals/mod_string.py
@@ -364,6 +364,7 @@ def add_permutations(
         whether the modification mass was mistakenly picked as the monoisotopic peak. Mainly used for Citrullination.
     :return: list of possible sequence permutations
     """
+    modified_sequence = modified_sequence.replace('UNIMOD','unimod')
     sequence = modified_sequence.replace("[unimod:" + str(unimod_id) + "]", "")
     modifications = len(re.findall("unimod:" + str(unimod_id), modified_sequence))
     if modifications == 0:
@@ -385,6 +386,7 @@ def add_permutations(
             modified_sequence = (
                 modified_sequence[: index + 1] + "[unimod:" + str(unimod_id) + "]" + modified_sequence[index + 1 :]
             )
+        modified_sequence = modified_sequence.replace('unimod','UNIMOD')
         modified_sequences_comb.append(modified_sequence)
     return modified_sequences_comb
 
diff --git a/tests/unit_tests/data/spectrum_output.csv b/tests/unit_tests/data/spectrum_output.csv
index 07f1dfb..e263c56 100644
--- a/tests/unit_tests/data/spectrum_output.csv
+++ b/tests/unit_tests/data/spectrum_output.csv
@@ -1,4 +1,4 @@
-,INTENSITIES,MZ,CALCULATED_MASS,removed_peaks,count_nl_peaks
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diff --git a/tests/unit_tests/data/tmt_spectrum_output.json b/tests/unit_tests/data/tmt_spectrum_output.json
index 67798bd..7d99d64 100644
--- a/tests/unit_tests/data/tmt_spectrum_output.json
+++ b/tests/unit_tests/data/tmt_spectrum_output.json
@@ -240,5 +240,6 @@
         "4": 1445.80325967
     },
     "removed_peaks": { "0": 0, "1": 0, "2": 1, "3": 0, "4": 0 },
-    "count_nl_peaks": { "0": 0, "1": 0, "2": 0, "3": 0, "4": 0 }
+    "ANNOTATED_NL_COUNT": { "0": 0, "1": 0, "2": 0, "3": 0, "4": 0 },
+    "EXPECTED_NL_COUNT": { "0": 0, "1": 0, "2": 0, "3": 0, "4": 0 }
 }