def test_modified_cosine_order_of_input_spectrums():
"""Test modified cosine on two spectra in changing order."""
spectrum_1 = Spectrum(mz=numpy.array([100, 150, 200, 300, 500, 510, 1100],
dtype="float"),
intensities=numpy.array(
[700, 200, 100, 1000, 200, 5, 500],
dtype="float"),
metadata={"precursor_mz": 1000.0})
spectrum_2 = Spectrum(
mz=numpy.array([55, 105, 205, 304.5, 494.5, 515.5, 1045],
dtype="float"),
intensities=numpy.array([700, 200, 100, 1000, 200, 5, 500],
dtype="float"),
metadata={"precursor_mz": 1005.0})
norm_spectrum_1 = normalize_intensities(spectrum_1)
norm_spectrum_2 = normalize_intensities(spectrum_2)
modified_cosine = ModifiedCosine(tolerance=2.0)
score_1_2, n_matches_1_2 = modified_cosine.pair(norm_spectrum_1,
norm_spectrum_2)
score_2_1, n_matches_2_1 = modified_cosine.pair(norm_spectrum_2,
norm_spectrum_1)
assert score_1_2 == score_2_1, "Expected that the order of the arguments would not matter."
assert n_matches_1_2 == n_matches_2_1, "Expected that the order of the arguments would not matter."
def test_modified_cosine_with_mass_shifted_and_unshifted_matches():
"""Test modified cosine on two spectra with mass set shift.
In this example 5 peak pairs are possible, but only 3 should be selected (every peak
can only be counted once!)"""
spectrum_1 = Spectrum(mz=numpy.array([100, 110, 200, 300, 400, 500, 600],
dtype="float"),
intensities=numpy.array([100, 50, 1, 80, 1, 1, 50],
dtype="float"),
metadata={"precursor_mz": 1000.0})
spectrum_2 = Spectrum(mz=numpy.array([110, 200, 300, 310, 700, 800],
dtype="float"),
intensities=numpy.array([100, 1, 90, 90, 1, 100],
dtype="float"),
metadata={"precursor_mz": 1010.0})
modified_cosine = ModifiedCosine()
score = modified_cosine.pair(spectrum_1, spectrum_2)
spec1 = spectrum_1.peaks.intensities
spec2 = spectrum_2.peaks.intensities
peak_pairs_multiplied = spec1[0] * spec2[0] + spec1[3] * spec2[3] + spec1[
2] * spec2[1]
expected_score = peak_pairs_multiplied / numpy.sqrt(
numpy.sum(spec1**2) * numpy.sum(spec2**2))
assert score["score"] == pytest.approx(
expected_score, 0.00001), "Expected different cosine score."
assert score["matches"] == 3, "Expected 3 matching peaks."
def test_modified_cosine_with_mass_shift(peaks, tolerance, masses,
expected_matches):
"""Test modified cosine on two spectra with mass shift."""
builder = SpectrumBuilder()
spectrum_1 = builder.with_mz(peaks[0][0]).with_intensities(
peaks[0][1]).with_metadata(metadata={
"precursor_mz": masses[0]
}).build()
spectrum_2 = builder.with_mz(peaks[1][0]).with_intensities(
peaks[1][1]).with_metadata(metadata={
"precursor_mz": masses[1]
}).build()
norm_spectrum_1 = normalize_intensities(spectrum_1)
norm_spectrum_2 = normalize_intensities(spectrum_2)
if tolerance is None:
modified_cosine = ModifiedCosine()
else:
modified_cosine = ModifiedCosine(tolerance=tolerance)
score = modified_cosine.pair(norm_spectrum_1, norm_spectrum_2)
expected_score = compute_expected_score(norm_spectrum_1, norm_spectrum_2,
expected_matches)
assert score["score"] == pytest.approx(
expected_score, 0.0001), "Expected different cosine score."
assert score["matches"] == len(
expected_matches), "Expected differnt number of matching peaks."
def create_dummy_scores_symmetric_modified_cosine():
spectrums = create_dummy_spectrums()
# Create Scores object by calculating dice scores
similarity_measure = ModifiedCosine()
scores = calculate_scores(spectrums, spectrums, similarity_measure)
return scores
def test_modified_cosine_with_mass_shift_5_no_matches_expected():
"""Test modified cosine on two spectra with no expected matches."""
spectrum_1 = Spectrum(mz=numpy.array([100, 200, 300], dtype="float"),
intensities=numpy.array([10, 10, 500], dtype="float"),
metadata={"precursor_mz": 1000.0})
spectrum_2 = Spectrum(mz=numpy.array([120, 220, 320], dtype="float"),
intensities=numpy.array([10, 10, 500], dtype="float"),
metadata={"precursor_mz": 1005})
norm_spectrum_1 = normalize_intensities(spectrum_1)
norm_spectrum_2 = normalize_intensities(spectrum_2)
modified_cosine = ModifiedCosine(tolerance=1.0)
score = modified_cosine.pair(norm_spectrum_1, norm_spectrum_2)
assert score["score"] == pytest.approx(0.0, 1e-5), "Expected different modified cosine score."
assert score["matches"] == 0, "Expected 0 matching peaks."
def test_modified_cosine_without_precursor_mz():
"""Test without precursor-m/z. Should raise assertion error."""
spectrum_1 = Spectrum(mz=numpy.array([100, 150, 200, 300, 500, 510, 1100], dtype="float"),
intensities=numpy.array([700, 200, 100, 1000, 200, 5, 500], dtype="float"))
spectrum_2 = Spectrum(mz=numpy.array([100, 140, 190, 300, 490, 510, 1090], dtype="float"),
intensities=numpy.array([700, 200, 100, 1000, 200, 5, 500], dtype="float"))
norm_spectrum_1 = normalize_intensities(spectrum_1)
norm_spectrum_2 = normalize_intensities(spectrum_2)
modified_cosine = ModifiedCosine()
with pytest.raises(AssertionError) as msg:
modified_cosine.pair(norm_spectrum_1, norm_spectrum_2)
expected_message = "Precursor_mz missing. Apply 'add_precursor_mz' filter first."
assert str(msg.value) == expected_message
def test_modified_cosine_with_mass_shift_5_tolerance_2():
"""Test modified cosine on two spectra with mass set shift and tolerance."""
spectrum_1 = Spectrum(mz=numpy.array([100, 200, 299, 300, 301, 500, 510], dtype="float"),
intensities=numpy.array([10, 10, 500, 100, 200, 20, 100], dtype="float"),
metadata={"precursor_mz": 1000.0})
spectrum_2 = Spectrum(mz=numpy.array([105, 205, 305, 306, 505, 517], dtype="float"),
intensities=numpy.array([10, 10, 500, 100, 20, 100], dtype="float"),
metadata={"precursor_mz": 1005})
norm_spectrum_1 = normalize_intensities(spectrum_1)
norm_spectrum_2 = normalize_intensities(spectrum_2)
modified_cosine = ModifiedCosine(tolerance=2.0)
score = modified_cosine.pair(norm_spectrum_1, norm_spectrum_2)
assert score["score"] == pytest.approx(0.96788, 0.0001), "Expected different modified cosine score."
assert score["matches"] == 6, "Expected 6 matching peaks."
def test_modified_cosine_with_mass_shift_5():
"""Test modified cosine on two spectra with mass set shift."""
spectrum_1 = Spectrum(mz=numpy.array([100, 150, 200, 300, 500, 510, 1100], dtype="float"),
intensities=numpy.array([700, 200, 100, 1000, 200, 5, 500], dtype="float"),
metadata={"precursor_mz": 1000.0})
spectrum_2 = Spectrum(mz=numpy.array([55, 105, 205, 304.5, 494.5, 515.5, 1045], dtype="float"),
intensities=numpy.array([700, 200, 100, 1000, 200, 5, 500], dtype="float"),
metadata={"precursor_mz": 1005.0})
norm_spectrum_1 = normalize_intensities(spectrum_1)
norm_spectrum_2 = normalize_intensities(spectrum_2)
modified_cosine = ModifiedCosine()
score = modified_cosine.pair(norm_spectrum_1, norm_spectrum_2)
assert score["score"] == pytest.approx(0.081966, 0.0001), "Expected different cosine score."
assert score["matches"] == 2, "Expected 2 matching peaks."
def test_modified_cosine_precursor_mz_as_invalid_string():
"""Test modified cosine on two spectra with precursor_mz given as string."""
spectrum_1 = Spectrum(mz=np.array([100, 200, 300], dtype="float"),
intensities=np.array([10, 10, 500], dtype="float"),
metadata={"precursor_mz": 1000.0})
spectrum_2 = Spectrum(mz=np.array([120, 220, 320], dtype="float"),
intensities=np.array([10, 10, 500], dtype="float"),
metadata={"precursor_mz": "mz 1005.0"})
norm_spectrum_1 = normalize_intensities(spectrum_1)
norm_spectrum_2 = normalize_intensities(spectrum_2)
modified_cosine = ModifiedCosine(tolerance=1.0)
with pytest.raises(AssertionError) as msg:
_ = modified_cosine.pair(norm_spectrum_1, norm_spectrum_2)
expected_message = "Precursor_mz missing. Apply 'add_precursor_mz' filter first."
assert str(msg.value) == expected_message
def test_modified_cosine_precursor_mz_as_string(caplog):
"""Test modified cosine on two spectra with precursor_mz given as string."""
spectrum_1 = Spectrum(mz=np.array([100, 200, 300], dtype="float"),
intensities=np.array([10, 10, 500], dtype="float"),
metadata={"precursor_mz": 1000.0},
metadata_harmonization=False)
spectrum_2 = Spectrum(mz=np.array([120, 220, 320], dtype="float"),
intensities=np.array([10, 10, 500], dtype="float"),
metadata={"precursor_mz": "1005.0"},
metadata_harmonization=False)
norm_spectrum_1 = normalize_intensities(spectrum_1)
norm_spectrum_2 = normalize_intensities(spectrum_2)
modified_cosine = ModifiedCosine(tolerance=1.0)
score = modified_cosine.pair(norm_spectrum_1, norm_spectrum_2)
assert score["score"] == pytest.approx(
0.0, 1e-5), "Expected different modified cosine score."
assert score["matches"] == 0, "Expected 0 matching peaks."
expected_msg = "Precursor_mz must be of type int or float. Apply 'add_precursor_mz' filter first."
assert expected_msg in caplog.text, "Expected different log message"
def library_matching(
documents_query: List[SpectrumDocument],
documents_library: List[SpectrumDocument],
model: BaseTopicModel,
presearch_based_on: List[str] = ["precursor_mz", "spec2vec-top10"],
ignore_non_annotated: bool = True,
include_scores=["spec2vec", "cosine", "modcosine"],
intensity_weighting_power: float = 0.5,
allowed_missing_percentage: float = 0,
cosine_tol: float = 0.005,
min_matches: int = 6,
mass_tolerance: float = 2.0,
mass_tolerance_type: str = "ppm"):
"""Selecting potential spectra matches with spectra library.
Suitable candidates will be selected by 1) top_n Spec2Vec similarity, and 2)
same precursor mass (within given mz_ppm tolerance(s)).
For later matching routines, additional scores (cosine, modified cosine)
are added as well.
Args:
--------
documents_query:
List containing all spectrum documents that should be queried against the library.
documents_library:
List containing all library spectrum documents.
model:
Pretrained word2Vec model.
presearch_based_on:
List with strings to specify which measures to use for the presearch.
This can include 'precursor_mz', 'spec2vec-topX',
ignore_non_annotated: bool, optional
If True, only annotated spectra will be considered for matching.
Default = True.
cosine_tol: float, optional
Set tolerance for the cosine and modified cosine score. Default = 0.005
mass_tolerance
Specify tolerance for a mass match.
mass_toleramce_type
Chose between "ppm" (relative) and "Dalton" (absolute) tolerance type.
"""
# Initializations
found_matches = []
m_mass_matches = None
m_spec2vec_similarities = None
m_modcos_similarities = None
def get_metadata(documents):
metadata = []
for doc in documents:
metadata.append(doc._obj.get("smiles"))
return metadata
library_spectra_metadata = get_metadata(documents_library)
if ignore_non_annotated:
# Get array of all ids for spectra with smiles
library_ids = np.asarray(
[i for i, x in enumerate(library_spectra_metadata) if x])
else:
library_ids = np.arange(len(documents_library))
allowed_presearch_type = ["precursor_mz", "spec2vec-top", "modcos-top"]
msg = "Presearch must include one of: " + ", ".join(allowed_presearch_type)
assert np.any([(x in y) for x in allowed_presearch_type
for y in presearch_based_on]), msg
# 1. Search for top-n Spec2Vec matches ------------------------------------
if np.any(["spec2vec" in x for x in presearch_based_on]):
top_n = int([
x.split("top")[1] for x in presearch_based_on if "spec2vec" in x
][0])
print(f"Pre-selection includes spec2vec top {top_n}.")
spec2vec = Spec2Vec(
model=model,
intensity_weighting_power=intensity_weighting_power,
allowed_missing_percentage=allowed_missing_percentage,
progress_bar=True)
m_spec2vec_similarities = spec2vec.matrix(
[documents_library[i] for i in library_ids], documents_query)
# Select top_n similarity values:
selection_spec2vec = np.argpartition(m_spec2vec_similarities,
-top_n,
axis=0)[-top_n:, :]
else:
selection_spec2vec = np.empty((0, len(documents_query)), dtype="int")
# 2. Search for precursor_mz based matches ---------------------------------
if "precursor_mz" in presearch_based_on:
print(
f"Pre-selection includes mass matches within {mass_tolerance} {mass_tolerance_type}."
)
mass_matching = PrecursorMzMatch(tolerance=mass_tolerance,
tolerance_type=mass_tolerance_type)
m_mass_matches = mass_matching.matrix(
[documents_library[i]._obj for i in library_ids],
[x._obj for x in documents_query])
selection_massmatch = []
for i in range(len(documents_query)):
selection_massmatch.append(np.where(m_mass_matches[:, i] == 1)[0])
else:
selection_massmatch = np.empty((len(documents_query), 0), dtype="int")
# 3. Search for top-n modified cosine matches ------------------------------------
if np.any(["modcos" in x for x in presearch_based_on]):
top_n = int([
x.split("top")[1] for x in presearch_based_on if "modcos" in x
][0])
print(f"Pre-selection includes modified cosine top {top_n}.")
modcos = ModifiedCosine(tolerance=cosine_tol)
n_rows = len(library_ids)
n_cols = len(documents_query)
m_modcos_similarities = np.zeros([n_rows, n_cols], dtype=np.float64)
m_modcos_matches = np.zeros([n_rows, n_cols], dtype=np.float64)
for i_ref, reference in enumerate(
tqdm([documents_library[i]._obj for i in library_ids])):
for i_query, query in enumerate([x._obj for x in documents_query]):
score = modcos.pair(reference, query)
m_modcos_similarities[i_ref][i_query] = score[0]
m_modcos_matches[i_ref][i_query] = score[1]
# Select top_n similarity values:
m_modcos_selected = m_modcos_similarities.copy()
m_modcos_selected[m_modcos_matches < min_matches] = 0
selection_modcos = np.argpartition(m_modcos_selected, -top_n,
axis=0)[-top_n:, :]
else:
selection_modcos = np.empty((0, len(documents_query)), dtype="int")
# 4. Combine found matches ------------------------------------------------
if "cosine" in include_scores:
print("Calculate cosine score for selected candidates.")
if "modcosine" in include_scores:
print("Calculate modified cosine score for selected candidates.")
for i in tqdm(range(len(documents_query))):
s2v_top_ids = selection_spec2vec[:, i]
mass_match_ids = selection_massmatch[i]
modcos_ids = selection_modcos[:, i]
all_match_ids = np.unique(
np.concatenate((s2v_top_ids, mass_match_ids, modcos_ids)))
if len(all_match_ids) > 0:
if "cosine" in include_scores:
# Get cosine score for found matches
cosine_similarity = CosineGreedy(tolerance=cosine_tol)
cosine_scores = []
for match_id in library_ids[all_match_ids]:
cosine_scores.append(
cosine_similarity.pair(
documents_library[match_id]._obj,
documents_query[i]._obj))
else:
cosine_scores = len(all_match_ids) * ["not calculated"]
if m_modcos_similarities is not None:
mod_cosine_scores0 = [
x for x in m_modcos_similarities[all_match_ids, i]
]
mod_cosine_scores1 = [
x for x in m_modcos_matches[all_match_ids, i]
]
mod_cosine_scores = list(
zip(mod_cosine_scores0, mod_cosine_scores1))
elif "modcosine" in include_scores:
# Get modified cosine score for found matches
mod_cosine_similarity = ModifiedCosine(tolerance=cosine_tol)
mod_cosine_scores = []
for match_id in library_ids[all_match_ids]:
mod_cosine_scores.append(
mod_cosine_similarity.pair(
documents_library[match_id]._obj,
documents_query[i]._obj))
else:
mod_cosine_scores = len(all_match_ids) * ["not calculated"]
matches_df = pd.DataFrame(
{
"cosine_score": [x["score"] for x in cosine_scores],
"cosine_matches": [x["matches"] for x in cosine_scores],
"mod_cosine_score":
[x["score"] for x in mod_cosine_scores],
"mod_cosine_matches":
[x["matches"] for x in mod_cosine_scores]
},
index=library_ids[all_match_ids])
if m_mass_matches is not None:
matches_df["mass_match"] = m_mass_matches[all_match_ids, i]
if m_spec2vec_similarities is not None:
matches_df["s2v_score"] = m_spec2vec_similarities[
all_match_ids, i]
elif "spec2vec" in include_scores:
spec2vec_similarity = Spec2Vec(
model=model,
intensity_weighting_power=intensity_weighting_power,
allowed_missing_percentage=allowed_missing_percentage)
spec2vec_scores = []
for match_id in library_ids[all_match_ids]:
spec2vec_scores.append(
spec2vec_similarity.pair(documents_library[match_id],
documents_query[i]))
matches_df["s2v_score"] = spec2vec_scores
found_matches.append(matches_df.fillna(0))
else:
found_matches.append([])
return found_matches
print("normalising intensities")
# Apply filters to clean and enhance each spectrum
spectrums = []
for spectrum in file:
spectrum = default_filters(spectrum)
# Scale peak intensities to maximum of 1
spectrum = normalize_intensities(spectrum)
print(spectrum.get('precursor_mz'))
spectrums.append(spectrum)
scores = calculate_scores(
references=spectrums,
queries=spectrums,
similarity_function=ModifiedCosine(tolerance=args.fragment_tolerance))
spectra_matches = convert.convert_scores(scores)
spectra_list = []
for s in spectrums:
new = convert.convert_spectrum(s)
spectra_list.append(new)
else:
from msmolnet import read_mgf as mgf
input_mgf = f'{args.input}.mgf'
print(f"reading file {input_mgf}")
spectra_list = mgf.read_mgf(input_mgf)
if (args.library):
def main(argv):
parser = argparse.ArgumentParser(
description="Compute MSP similarity scores")
parser.add_argument("-f",
dest="default_filters",
action='store_true',
help="Apply default filters")
parser.add_argument("-n",
dest="normalize_intensities",
action='store_true',
help="Normalize intensities.")
parser.add_argument("-s",
dest="symmetric",
action='store_true',
help="Computation is symmetric.")
parser.add_argument("--ref",
dest="references_filename",
type=str,
help="Path to reference MSP library.")
parser.add_argument("queries_filename",
type=str,
help="Path to query spectra.")
parser.add_argument("similarity_metric",
type=str,
help='Metric to use for matching.')
parser.add_argument("tolerance",
type=float,
help="Tolerance to use for peak matching.")
parser.add_argument(
"mz_power",
type=float,
help="The power to raise mz to in the cosine function.")
parser.add_argument(
"intensity_power",
type=float,
help="The power to raise intensity to in the cosine function.")
parser.add_argument("output_filename_scores",
type=str,
help="Path where to store the output .csv scores.")
parser.add_argument("output_filename_matches",
type=str,
help="Path where to store the output .csv matches.")
args = parser.parse_args()
queries_spectra = list(load_from_msp(args.queries_filename))
if args.symmetric:
reference_spectra = []
else:
reference_spectra = list(load_from_msp(args.references_filename))
if args.default_filters is True:
print("Applying default filters...")
queries_spectra = list(map(default_filters, queries_spectra))
reference_spectra = list(map(default_filters, reference_spectra))
if args.normalize_intensities is True:
print("Normalizing intensities...")
queries_spectra = list(map(normalize_intensities, queries_spectra))
reference_spectra = list(map(normalize_intensities, reference_spectra))
if args.similarity_metric == 'CosineGreedy':
similarity_metric = CosineGreedy(args.tolerance, args.mz_power,
args.intensity_power)
elif args.similarity_metric == 'CosineHungarian':
similarity_metric = CosineHungarian(args.tolerance, args.mz_power,
args.intensity_power)
elif args.similarity_metric == 'ModifiedCosine':
similarity_metric = ModifiedCosine(args.tolerance, args.mz_power,
args.intensity_power)
reference_spectra = list(map(add_precursor_mz, reference_spectra))
queries_spectra = list(map(add_precursor_mz, queries_spectra))
else:
return -1
print("Calculating scores...")
scores = calculate_scores(
references=queries_spectra if args.symmetric else reference_spectra,
queries=queries_spectra,
similarity_function=similarity_metric,
is_symmetric=args.symmetric)
write_outputs(args, scores)
return 0
def main(argv):
parser = argparse.ArgumentParser(
description="Compute MSP similarity scores")
parser.add_argument("references_filename",
type=str,
help="Path to reference MSP library.")
parser.add_argument("queries_filename",
type=str,
help="Path to query spectra.")
parser.add_argument("similarity_metric",
type=str,
help='Metric to use for matching.')
parser.add_argument("output_filename_scores",
type=str,
help="Path where to store the output .csv scores.")
parser.add_argument("output_filename_matches",
type=str,
help="Path where to store the output .csv matches.")
parser.add_argument("tolerance",
type=float,
help="Tolerance to use for peak matching.")
parser.add_argument(
"mz_power",
type=float,
help="The power to raise mz to in the cosine function.")
parser.add_argument(
"intensity_power",
type=float,
help="The power to raise intensity to in the cosine function.")
args = parser.parse_args()
reference_spectra = load_from_msp(args.references_filename)
queries_spectra = load_from_msp(args.queries_filename)
if args.similarity_metric == 'CosineGreedy':
similarity_metric = CosineGreedy(args.tolerance, args.mz_power,
args.intensity_power)
elif args.similarity_metric == 'CosineHungarian':
similarity_metric = CosineHungarian(args.tolerance, args.mz_power,
args.intensity_power)
elif args.similarity_metric == 'ModifiedCosine':
similarity_metric = ModifiedCosine(args.tolerance, args.mz_power,
args.intensity_power)
reference_spectra = map(add_precursor_mz, reference_spectra)
queries_spectra = map(add_precursor_mz, queries_spectra)
else:
return -1
scores = calculate_scores(
references=list(reference_spectra),
queries=list(queries_spectra),
similarity_function=similarity_metric,
)
query_names = [spectra.metadata['name'] for spectra in scores.queries]
reference_names = [
spectra.metadata['name'] for spectra in scores.references
]
# Write scores to dataframe
dataframe_scores = DataFrame(
data=[entry["score"] for entry in scores.scores],
index=reference_names,
columns=query_names)
dataframe_scores.to_csv(args.output_filename_scores, sep=';')
# Write number of matches to dataframe
dataframe_matches = DataFrame(
data=[entry["matches"] for entry in scores.scores],
index=reference_names,
columns=query_names)
dataframe_matches.to_csv(args.output_filename_matches, sep=';')
return 0
def main(argv):
parser = argparse.ArgumentParser(
description="Compute MSP similarity scores")
parser.add_argument("-s",
dest="symmetric",
action='store_true',
help="Computation is symmetric.")
parser.add_argument("--ref",
dest="references_filename",
type=str,
help="Path to reference spectra library.")
parser.add_argument("--ref_format",
dest="references_format",
type=str,
help="Reference spectra library file format.")
parser.add_argument("queries_filename",
type=str,
help="Path to query spectra.")
parser.add_argument("queries_format",
type=str,
help="Query spectra file format.")
parser.add_argument("similarity_metric",
type=str,
help='Metric to use for matching.')
parser.add_argument("tolerance",
type=float,
help="Tolerance to use for peak matching.")
parser.add_argument(
"mz_power",
type=float,
help="The power to raise mz to in the cosine function.")
parser.add_argument(
"intensity_power",
type=float,
help="The power to raise intensity to in the cosine function.")
parser.add_argument("output_filename_scores",
type=str,
help="Path where to store the output .tsv scores.")
parser.add_argument("output_filename_matches",
type=str,
help="Path where to store the output .tsv matches.")
args = parser.parse_args()
if args.queries_format == 'msp':
queries_spectra = list(load_from_msp(args.queries_filename))
elif args.queries_format == 'mgf':
queries_spectra = list(load_from_mgf(args.queries_filename))
else:
raise ValueError(
f'File format {args.queries_format} not supported for query spectra.'
)
if args.symmetric:
reference_spectra = []
else:
if args.references_format == 'msp':
reference_spectra = list(load_from_msp(args.references_filename))
elif args.references_format == 'mgf':
reference_spectra = list(load_from_mgf(args.references_filename))
else:
raise ValueError(
f'File format {args.references_format} not supported for reference spectra library.'
)
if args.similarity_metric == 'CosineGreedy':
similarity_metric = CosineGreedy(args.tolerance, args.mz_power,
args.intensity_power)
elif args.similarity_metric == 'CosineHungarian':
similarity_metric = CosineHungarian(args.tolerance, args.mz_power,
args.intensity_power)
elif args.similarity_metric == 'ModifiedCosine':
similarity_metric = ModifiedCosine(args.tolerance, args.mz_power,
args.intensity_power)
reference_spectra = list(map(convert_precursor_mz, reference_spectra))
queries_spectra = list(map(convert_precursor_mz, queries_spectra))
else:
return -1
print("Calculating scores...")
scores = calculate_scores(
references=queries_spectra if args.symmetric else reference_spectra,
queries=queries_spectra,
similarity_function=similarity_metric,
is_symmetric=args.symmetric)
write_outputs(args, scores)
return 0
def library_matching(documents_query: List[SpectrumDocument],
documents_library: List[SpectrumDocument],
model,
presearch_based_on=["parentmass", "spec2vec-top10"],
ignore_non_annotated: bool = True,
include_scores=["spec2vec", "cosine", "modcosine"],
intensity_weighting_power: float = 0.5,
allowed_missing_percentage: float = 0,
cosine_tol: float = 0.005,
mass_tolerance: float = 1.0):
"""Selecting potential spectra matches with spectra library.
Suitable candidates will be selected by 1) top_n Spec2Vec similarity, and 2)
same precursor mass (within given mz_ppm tolerance(s)).
For later matching routines, additional scores (cosine, modified cosine)
are added as well.
Args:
--------
documents_query:
List containing all spectrum documents that should be queried against the library.
documents_library:
List containing all library spectrum documents.
model:
Pretrained word2Vec model.
top_n: int, optional
Number of entries witht the top_n highest Spec2Vec scores to keep as
found matches. Default = 10.
ignore_non_annotated: bool, optional
If True, only annotated spectra will be considered for matching.
Default = True.
cosine_tol: float, optional
Set tolerance for the cosine and modified cosine score. Default = 0.005
mass_tolerance
Specify tolerance for a parentmass match.
"""
# Initializations
found_matches = []
m_spec2vec_similarities = None
def get_metadata(documents):
metadata = []
for doc in documents:
metadata.append(doc._obj.get("smiles"))
return metadata
library_spectra_metadata = get_metadata(documents_library)
if ignore_non_annotated:
# Get array of all ids for spectra with smiles
library_ids = np.asarray(
[i for i, x in enumerate(library_spectra_metadata) if x])
else:
library_ids = np.arange(len(documents_library))
# 1. Search for top-n Spec2Vec matches ------------------------------------
if np.any(["spec2vec" in x for x in presearch_based_on]):
top_n = int([
x.split("top")[1] for x in presearch_based_on if "spec2vec" in x
][0])
print("Pre-selection includes spec2vec top {}.".format(top_n))
spec2vec = Spec2VecParallel(
model=model,
intensity_weighting_power=intensity_weighting_power,
allowed_missing_percentage=allowed_missing_percentage)
m_spec2vec_similarities = spec2vec(
[documents_library[i] for i in library_ids], documents_query)
# Select top_n similarity values:
selection_spec2vec = np.argpartition(m_spec2vec_similarities,
-top_n,
axis=0)[-top_n:, :]
else:
selection_spec2vec = np.empty((0, len(documents_query)), dtype="int")
# 2. Search for parent mass based matches ---------------------------------
if "parentmass" in presearch_based_on:
mass_matching = ParentmassMatchParallel(mass_tolerance)
m_mass_matches = mass_matching(
[documents_library[i]._obj for i in library_ids],
[x._obj for x in documents_query])
else:
m_mass_matches = np.empty((0, len(documents_query)), dtype="int")
# 3. Combine found matches ------------------------------------------------
for i in range(len(documents_query)):
s2v_top_ids = selection_spec2vec[:, i]
mass_match_ids = np.where(m_mass_matches[:, i] == 1)[0]
all_match_ids = np.unique(np.concatenate(
(s2v_top_ids, mass_match_ids)))
if len(all_match_ids) > 0:
# Get cosine score for found matches
cosine_similarity = CosineGreedyNumba(tolerance=cosine_tol)
cosine_scores = []
for match_id in library_ids[all_match_ids]:
cosine_scores.append(
cosine_similarity(documents_library[match_id]._obj,
documents_query[i]._obj))
# Get modified cosine score for found matches
mod_cosine_similarity = ModifiedCosine(tolerance=cosine_tol)
mod_cosine_scores = []
for match_id in library_ids[all_match_ids]:
mod_cosine_scores.append(
mod_cosine_similarity(documents_library[match_id]._obj,
documents_query[i]._obj))
matches_df = pd.DataFrame(
{
"mass_match": m_mass_matches[all_match_ids, i],
"cosine_score": [x[0] for x in cosine_scores],
"cosine_matches": [x[1] for x in cosine_scores],
"mod_cosine_score": [x[0] for x in mod_cosine_scores],
"mod_cosine_matches": [x[1] for x in mod_cosine_scores]
},
index=library_ids[all_match_ids])
if m_spec2vec_similarities is not None:
matches_df["s2v_score"] = m_spec2vec_similarities[
all_match_ids, i]
elif "spec2vec" in include_scores:
spec2vec_similarity = Spec2Vec(
model=model,
intensity_weighting_power=intensity_weighting_power,
allowed_missing_percentage=allowed_missing_percentage)
spec2vec_scores = []
for match_id in library_ids[all_match_ids]:
spec2vec_scores.append(
spec2vec_similarity(documents_library[match_id]._obj,
documents_query[i]._obj))
matches_df["s2v_score"] = spec2vec_scores
found_matches.append(matches_df.fillna(0))
else:
found_matches.append([])
return found_matches
# 'similarities_unique_inchikey_spec2vec_librarymodel.npy')
# if not os.path.exists(sims_out):
# spec2vec_similarity = Spec2Vec(model, intensity_weighting_power=0.5)
# similarity_matrix = spec2vec_similarity.matrix(
# uniq_documents_processed, uniq_documents_processed,
# is_symmetric=True)
# np.save(sims_out, similarity_matrix)
# else:
# similarity_matrix = np.load(sims_out)
# classical mod cosine similarities
mod_cos_sims_out = os.path.join(
cmd.output_dir, "similarities_unique_inchikey_mod_cosine.npy")
if not os.path.exists(mod_cos_sims_out):
similarity_measure = ModifiedCosine(tolerance=0.005,
mz_power=0,
intensity_power=1.0)
mod_cos_similarity = similarity_measure.matrix(
uniq_spectrums_classical,
uniq_spectrums_classical,
is_symmetric=True)
np.save(mod_cos_sims_out, mod_cos_similarity)
else:
mod_cos_similarity = np.load(mod_cos_sims_out)
# # md s2v similarities
# md_sims_out = os.path.join(
# cmd.output_dir,
# 'similarities_unique_inchikey_mds_spec2vec_librarymodel.npy')
# if not os.path.exists(md_sims_out):
# md_spec2vec_similarity = Spec2Vec(model_mds,
