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Add neutral loss flag to annotate function
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WassimG committed Sep 23, 2024
1 parent ba24a78 commit 6f4a302
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Showing 5 changed files with 30 additions and 11 deletions.
26 changes: 21 additions & 5 deletions spectrum_fundamentals/annotation/annotation.py
Original file line number Diff line number Diff line change
Expand Up @@ -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.
Expand All @@ -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 = []
Expand All @@ -161,14 +163,22 @@ def annotate_spectra(
unit_mass_tolerance,
fragmentation_method=fragmentation_method,
custom_mods=custom_mods,
annotate_neutral_losses=annotate_neutral_losses
)
if not results:
continue
raw_file_annotations.append(results)
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",
Expand Down Expand Up @@ -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],
]
]:
Expand All @@ -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])
"""
Expand All @@ -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):
Expand All @@ -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.
Expand All @@ -415,20 +429,22 @@ 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"]],
mass_tolerance=mass_tolerance,
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,
Expand All @@ -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(
Expand Down
2 changes: 1 addition & 1 deletion spectrum_fundamentals/fragments.py
Original file line number Diff line number Diff line change
Expand Up @@ -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,
Expand Down
2 changes: 2 additions & 0 deletions spectrum_fundamentals/mod_string.py
Original file line number Diff line number Diff line change
Expand Up @@ -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:
Expand All @@ -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

Expand Down
8 changes: 4 additions & 4 deletions tests/unit_tests/data/spectrum_output.csv
Original file line number Diff line number Diff line change
@@ -1,4 +1,4 @@
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3 changes: 2 additions & 1 deletion tests/unit_tests/data/tmt_spectrum_output.json
Original file line number Diff line number Diff line change
Expand Up @@ -240,5 +240,6 @@
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"removed_peaks": { "0": 0, "1": 0, "2": 1, "3": 0, "4": 0 },
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"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 }
}

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