def test_remove_peaks_around_precursor_mz_no_params():
"""Using defaults with precursor mz present."""
mz = numpy.array([10, 20, 30, 40], dtype="float")
intensities = numpy.array([0, 1, 10, 100], dtype="float")
spectrum_in = Spectrum(mz=mz, intensities=intensities)
spectrum_in.set("precursor_mz", 60.)
spectrum = remove_peaks_around_precursor_mz(spectrum_in)
assert spectrum == spectrum_in, "Expected no changes."
def test_require_precursor_mz_pass():
"""Test with correct precursor mz present."""
mz = numpy.array([10, 20, 30, 40], dtype="float")
intensities = numpy.array([0, 1, 10, 100], dtype="float")
spectrum_in = Spectrum(mz=mz, intensities=intensities)
spectrum_in.set("precursor_mz", 60.)
spectrum = require_precursor_mz(spectrum_in)
assert spectrum == spectrum_in, "Expected no changes."
def test_require_precursor_mz_fail_because_below_zero():
"""Test if spectrum is None when precursor_mz < 0"""
mz = numpy.array([10, 20, 30, 40], dtype="float")
intensities = numpy.array([0, 1, 10, 100], dtype="float")
spectrum_in = Spectrum(mz=mz, intensities=intensities)
spectrum_in.set("precursor_mz", -3.5)
spectrum = require_precursor_mz(spectrum_in)
assert spectrum is None, "Expected spectrum to be None."
def _assign_ri_value(spectrum: Spectrum, value: Data.RetentionIndexType):
"""Assign RI value to Spectrum object
Args:
spectrum (Spectrum): Spectrum to add RI to
value (Data.RetentionIndexType): RI to be added to Spectrum
"""
if value is not None:
retention_index = ('%f' % float(value)).rstrip('0').rstrip('.')
spectrum.set(key='retentionindex', value=retention_index)
def test_if_spectrum_is_cloned():
"""Test if filter is correctly cloning the input spectrum."""
mz = numpy.array([], dtype="float")
intensities = numpy.array([], dtype="float")
spectrum_in = Spectrum(mz=mz, intensities=intensities)
spectrum_in.set("precursor_mz", 1.)
spectrum = remove_peaks_around_precursor_mz(spectrum_in)
spectrum.set("testfield", "test")
assert not spectrum_in.get(
"testfield"), "Expected input spectrum to remain unchanged."
def test_remove_peaks_around_precursor_mz_tolerance_20():
"""Set mz_tolerance to 20."""
mz = numpy.array([10, 20, 30, 40], dtype="float")
intensities = numpy.array([0, 1, 10, 100], dtype="float")
spectrum_in = Spectrum(mz=mz, intensities=intensities)
spectrum_in.set("precursor_mz", 60.)
spectrum = remove_peaks_around_precursor_mz(spectrum_in, mz_tolerance=20)
assert len(spectrum.peaks) == 3, "Expected 3 peaks to remain."
assert spectrum.peaks.mz.tolist() == [
10., 20., 30.
], "Expected different peaks to remain."
def naive_decoys(spectra, initial_fragments, fragments, no_peaks_for_spectra):
decoy_spectra = []
no_fragments = len(fragments)
for i in range(len(spectra)):
spectrum = spectra[i]
decoy_intensities = []
decoy_mzs = []
#Adding initial (precursor) peak.
initial_fragment = initial_fragments[i, :]
decoy_intensities.append(initial_fragment[1])
decoy_mzs.append(initial_fragment[0])
#Add remaining fragment ions randomly
desired_no_peaks = no_peaks_for_spectra[i]
draws = np.random.randint(no_fragments, size=desired_no_peaks - 1)
for draw in draws:
fragment = fragments[draw, :]
mz = fragment[0]
if mz_check(mz, decoy_mzs, spectrum, 5):
decoy_intensities.append(fragment[1])
decoy_mzs.append(mz)
order = np.argsort(decoy_mzs)
decoy_spectrum = Spectrum(
intensities=(np.array(decoy_intensities))[order],
mz=(np.array((decoy_mzs)))[order])
parentmass = spectrum.get("parent_mass") if spectrum.get(
"parent_mass") else get_parent_mass(spectrum)
decoy_spectrum.set("parent_mass", parentmass)
decoy_spectra.append(decoy_spectrum)
return decoy_spectra
def spectrum_based_decoys(spectra, initial_fragments, fragments,
sorted_fragments, first_fragments,
no_peaks_for_spectra):
decoy_spectra = []
fragment_mzs = sorted_fragments[:, 0]
for i in range(len(spectra)):
spectrum = spectra[i]
spectrum_peaks = np.arange(first_fragments[i], first_fragments[i + 1])
desired_no_peaks = no_peaks_for_spectra[i]
decoy_intensities = []
decoy_mzs = []
#Adding initial (precursor) peak.
initial_fragment = initial_fragments[i, :]
decoy_intensities.append(initial_fragment[1])
decoy_mzs.append(initial_fragment[0])
#Adding peaks based on peaks already added to the decoy spectrum.
fragment_candidates = []
j = 1
while j != desired_no_peaks:
#Find fragments within mz range
mz = decoy_mzs[j - 1]
tolerance = (mz * 5) / (10**6)
lower_mz = mz - tolerance
upper_mz = mz + tolerance
lower_index = np.searchsorted(fragment_mzs, lower_mz, side="left")
upper_index = np.searchsorted(fragment_mzs, upper_mz, side="right")
indices = np.arange(lower_index, upper_index)
#Adding 5 or less fragments ions to fragment candidates
#Fragments are stored by their indices in fragments
#First get spectra which have previously added fragment within our p.p.m. tolerance (5)
#Then randomly add 5 fragments from these spectra
#When there are less than 5 suitable spectra, randomly add fragments from suitable spectra
suitable_spectra = np.unique(
sorted_fragments[indices,
2]) #Get unique suitable spectra indices
if len(suitable_spectra) >= 5:
spectra_draws = np.random.choice(suitable_spectra,
5,
replace=False)
for index in spectra_draws:
index = int(index)
peak_draw = np.random.choice(no_peaks_for_spectra[index])
fragment_draw = first_fragments[index] + peak_draw
fragment_candidates.append(fragment_draw)
else:
spectra_draws = np.random.choice(
suitable_spectra, 5,
replace=True) #Note that each spectra must have >=5 peaks
for index in spectra_draws:
index = int(index)
peak_draw = np.random.choice(no_peaks_for_spectra[index])
fragment_draw = first_fragments[index] + peak_draw
fragment_candidates.append(fragment_draw)
#Add fragment from fragment candidates for decoy spectrum.
#Only add fragment if it is not within 5 p.p.m. of previously added fragments
#and not greater than the precursor mz of the spectrum being decoyed.
#If chosen fragment is not suitable, remove fragment from fragment_candidates_clone
#and search again.
search_attempts = 0
fragment_found = False
no_candidates = len(fragment_candidates)
fragment_candidates_clone = fragment_candidates
while search_attempts != no_candidates and not fragment_found:
choice = np.random.choice(fragment_candidates_clone)
fragment_candidate = fragments[choice, :]
candidate_mz = fragment_candidate[0]
if mz_check(candidate_mz, decoy_mzs, spectrum, 5):
decoy_intensities.append(fragment_candidate[1])
decoy_mzs.append(candidate_mz)
fragment_found = True
j += 1
else:
fragment_candidates_clone.remove(choice)
search_attempts += 1
#If fragment search is over and a suitable fragment candidate isn't found, then
#add random fragment from spectrum being decoyed
if not fragment_found:
choice = np.random.choice(spectrum_peaks)
fragment_candidate = fragments[choice, :]
decoy_intensities.append(fragment_candidate[1])
decoy_mzs.append(fragment_candidate[0])
j += 1
print(
"Suitable fragment candidate not found. Random fragment from real spectrum added."
)
order = np.argsort(decoy_mzs)
decoy_spectrum = Spectrum(
intensities=(np.array(decoy_intensities))[order],
mz=(np.array((decoy_mzs)))[order])
parentmass = spectrum.get("parent_mass") if spectrum.get(
"parent_mass") else get_parent_mass(spectrum)
decoy_spectrum.set("parent_mass", parentmass)
decoy_spectra.append(decoy_spectrum)
return decoy_spectra
