def plot_psms(psmFile, spectrumFile, plotList = 'currPsms.html',
highResMs2 = False,
dripLearnedMeans = 'dripLearned.means',
dripLearnedCovars = 'dripLearned.covars',
mods = '', ntermMods = '', ctermMods = '',
precursor_filter = False,
high_res_gauss_dist = 0.05):
"""
"""
# initialize arguments for dripExtract
args = dripExtractParams(psmFile, spectrumFile, 'all',
mods, ntermMods, ctermMods,
highResMs2,
dripLearnedMeans, dripLearnedCovars)
mods, varMods = parse_var_mods(mods, True)
ntermMods, ntermVarMods = parse_var_mods(ntermMods, False)
ctermMods, ctermVarMods = parse_var_mods(ctermMods, False)
stde = open('gmtk_err', "w")
# stdo = sys.stdout
stdo = stde
args.precursor_filter = False
args.high_res_gauss_dist = high_res_gauss_dist
if precursor_filter:
args.normalize = 'top300TightSequest'
else:
args.normalize = 'top300Sequest'
# decode DRIP PSMs
t, d, spectra0 = runDripExtract(args, stdo, stde)
# if variable mods, get variable mod string per PSM
if varMods or ntermVarMods or ctermVarMods:
varModDict = psm_var_mods(psmFile)
assert varModDict, "Variable mods specified in enzyme options, but strings denoting variables mods per peptide are not specified in %s, exitting" (psmFile)
spectra, minMz, maxMz, validCharges = load_spectra_minMaxMz(spectrumFile)
# get original intensity values to plot
for sid in spectra0:
spectra[sid].mz = list(spectra0[sid].mz)
mz_vals = set(spectra0[sid].mz)
z = max(spectra0[sid].intensity)
spectra[sid].intensity = [i/z for mz, i in zip(spectra[sid].mz, spectra[sid].intensity)
if mz in mz_vals]
if not highResMs2:
dripMeans = load_drip_means(dripLearnedMeans)
else:
dripMeansSet = set([])
for sid, c in t:
for p in t[sid,c]:
pep = p.peptide
if varMods or ntermVarMods or ctermVarMods:
varModSequence = varModDict[sid, p.peptide]
bNy = interleave_b_y_ions_var_mods(Peptide(pep), c,
mods, ntermMods, ctermMods,
varMods, ntermVarMods, ctermVarMods,
varModSequence)
else:
bNy = interleave_b_y_ions(Peptide(pep), c,
mods, ntermMods, ctermMods)
filter_theoretical_peaks(bNy, minMz, maxMz, high_res_gauss_dist)
dripMeansSet |= set(bNy)
# for i, ion in enumerate(bNy):
# dripMeans[i] = ion
for sid, c in d:
for p in d[sid,c]:
pep = p.peptide
if varMods or ntermVarMods or ctermVarMods:
varModSequence = varModDict[sid, p.peptide]
bNy = interleave_b_y_ions_var_mods(Peptide(pep), c,
mods, ntermMods, ctermMods,
varMods, ntermVarMods, ctermVarMods,
varModSequence)
else:
bNy = interleave_b_y_ions(Peptide(pep), c,
mods, ntermMods, ctermMods)
filter_theoretical_peaks(bNy, minMz, maxMz, high_res_gauss_dist)
dripMeansSet |= set(bNy)
# for i, ion in enumerate(bNy):
# dripMeans[i] = ion
dripMeans = {}
for ind, ion in enumerate(sorted(dripMeansSet)):
dripMeans[ind] = ion
ion_to_index_map = {} # reverse mapping, from ions to indices
for ind in dripMeans:
ion_to_index_map[dripMeans[ind]] = ind
all_psms = []
varModSequence = ''
for sid, c in t:
s = spectra[sid]
for p in t[sid,c]:
p.add_obs_spectrum(s)
p.calculate_drip_features(dripMeans)
if varMods or ntermVarMods or ctermVarMods:
varModSequence = varModDict[sid, p.peptide]
p.calc_by_sets(c,
mods, ntermMods, ctermMods,
highResMs2,
ion_to_index_map,
varMods, ntermVarMods, ctermVarMods,
varModSequence)
all_psms.append(p)
for sid, c in d:
s = spectra[sid]
for p in d[sid,c]:
p.add_obs_spectrum(s)
p.calculate_drip_features(dripMeans)
if varMods or ntermVarMods or ctermVarMods:
varModSequence = varModDict[sid, p.peptide]
p.calc_by_sets(c,
mods, ntermMods, ctermMods,
highResMs2,
ion_to_index_map,
varMods, ntermVarMods, ctermVarMods,
varModSequence)
all_psms.append(p)
fid = open(plotList, "w")
all_psms.sort(key = lambda r: r.score, reverse = True)
for p in all_psms:
if p.kind == 't':
kind = 'target'
elif p.kind == 'd':
kind = 'decoy'
else:
continue
plotName = kind + 'Scan' + str(p.scan) + \
'Charge' + str(p.charge) + \
p.peptide + '.png'
p.plot_drip_viterbi(plotName)
fid.write("<a href=\"%s\">%s Scan %d Charge %d %s</a><br>\n" %
(plotName, kind, p.scan, p.charge, p.peptide))
fid.close()
def plot_psms(psmFile, spectrumFile, plotList = 'currPsms.html',
highResMs2 = False,
dripLearnedMeans = 'dripLearned.means',
dripLearnedCovars = 'dripLearned.covars',
mods = '', ntermMods = '', ctermMods = '',
precursor_filter = False,
high_res_gauss_dist = 0.05):
"""
"""
# initialize arguments for dripExtract
args = dripExtractParams(psmFile, spectrumFile, 'all',
mods, ntermMods, ctermMods,
highResMs2,
dripLearnedMeans, dripLearnedCovars)
mods, varMods = parse_var_mods(mods, True)
ntermMods, ntermVarMods = parse_var_mods(ntermMods, False)
ctermMods, ctermVarMods = parse_var_mods(ctermMods, False)
stde = open('gmtk_err', "w")
# stdo = sys.stdout
stdo = stde
args.precursor_filter = False
args.high_res_gauss_dist = high_res_gauss_dist
if precursor_filter:
args.normalize = 'top300TightSequest'
else:
args.normalize = 'top300Sequest'
# decode DRIP PSMs
t, d, spectra0 = runDripExtract(args, stdo, stde)
# if variable mods, get variable mod string per PSM
if varMods or ntermVarMods or ctermVarMods:
varModDict = psm_var_mods(psmFile)
assert varModDict, "Variable mods specified in enzyme options, but strings denoting variables mods per peptide are not specified in %s, exitting" (psmFile)
spectra, minMz, maxMz, validCharges = load_spectra_minMaxMz(spectrumFile)
# get original intensity values to plot
for sid in spectra0:
spectra[sid].mz = list(spectra0[sid].mz)
mz_vals = set(spectra0[sid].mz)
z = max(spectra0[sid].intensity)
spectra[sid].intensity = [i/z for mz, i in zip(spectra[sid].mz, spectra[sid].intensity)
if mz in mz_vals]
if not highResMs2:
dripMeans = load_drip_means(dripLearnedMeans)
else:
dripMeansSet = set([])
for sid, c in t:
for p in t[sid,c]:
pep = p.peptide
if varMods or ntermVarMods or ctermVarMods:
varModSequence = varModDict[sid, p.peptide]
bNy = interleave_b_y_ions_var_mods(Peptide(pep), c,
mods, ntermMods, ctermMods,
varMods, ntermVarMods, ctermVarMods,
varModSequence)
else:
bNy = interleave_b_y_ions(Peptide(pep), c,
mods, ntermMods, ctermMods)
filter_theoretical_peaks(bNy, minMz, maxMz, high_res_gauss_dist)
dripMeansSet |= set(bNy)
# for i, ion in enumerate(bNy):
# dripMeans[i] = ion
for sid, c in d:
for p in d[sid,c]:
pep = p.peptide
if varMods or ntermVarMods or ctermVarMods:
varModSequence = varModDict[sid, p.peptide]
bNy = interleave_b_y_ions_var_mods(Peptide(pep), c,
mods, ntermMods, ctermMods,
varMods, ntermVarMods, ctermVarMods,
varModSequence)
else:
bNy = interleave_b_y_ions(Peptide(pep), c,
mods, ntermMods, ctermMods)
filter_theoretical_peaks(bNy, minMz, maxMz, high_res_gauss_dist)
dripMeansSet |= set(bNy)
# for i, ion in enumerate(bNy):
# dripMeans[i] = ion
dripMeans = {}
for ind, ion in enumerate(sorted(dripMeansSet)):
dripMeans[ind] = ion
ion_to_index_map = {} # reverse mapping, from ions to indices
for ind in dripMeans:
ion_to_index_map[dripMeans[ind]] = ind
all_psms = []
varModSequence = ''
for sid, c in t:
s = spectra[sid]
for p in t[sid,c]:
p.add_obs_spectrum(s)
p.calculate_drip_features(dripMeans)
if varMods or ntermVarMods or ctermVarMods:
varModSequence = varModDict[sid, p.peptide]
p.calc_by_sets(c,
mods, ntermMods, ctermMods,
highResMs2,
ion_to_index_map,
varMods, ntermVarMods, ctermVarMods,
varModSequence)
all_psms.append(p)
for sid, c in d:
s = spectra[sid]
for p in d[sid,c]:
p.add_obs_spectrum(s)
p.calculate_drip_features(dripMeans)
if varMods or ntermVarMods or ctermVarMods:
varModSequence = varModDict[sid, p.peptide]
p.calc_by_sets(c,
mods, ntermMods, ctermMods,
highResMs2,
ion_to_index_map,
varMods, ntermVarMods, ctermVarMods,
varModSequence)
all_psms.append(p)
fid = open(plotList, "w")
all_psms.sort(key = lambda r: r.score, reverse = True)
for p in all_psms:
if p.kind == 't':
kind = 'target'
elif p.kind == 'd':
kind = 'decoy'
else:
continue
plotName = kind + 'Scan' + str(p.scan) + \
'Charge' + str(p.charge) + \
p.peptide + '.png'
p.plot_drip_viterbi(plotName)
fid.write("<a href=\"%s\">%s Scan %d Charge %d %s</a><br>\n" %
(plotName, kind, p.scan, p.charge, p.peptide))
fid.close()
+ " -inputMasterFile " + args.master_file + " -inputTrainableParameters trained.params -failOnZeroClique F"
# gmtkViterbi command line
vitValsFile = os.path.join(log_dir, 'vitVals.txt')
vitStr = vitStr0 + ' -vitValsFile ' + vitValsFile \
+ ' -of1 ' + spectrum_obs_file \
+ ' -fmt1 flatascii ' \
+ ' -of2 ' + peptide_obs_file \
+ ' -fmt2 flatascii ' \
+ ' -cppCommand ' + cppCommand
# call(shlex.split(vitStr), stdout = sys.stdout, stderr = sys.stdout)
call(shlex.split(vitStr), stdout = stdo, stderr = stde)
# parse output
t,d = ppsm.parse_dripExtract(vitValsFile, os.path.join(output_dir, 'pepDB.txt'))
t = t[sid,c][0]
# calculate insertions and deletions
t.add_obs_spectrum(s0)
t.calculate_drip_features(dripMeans)
t.calc_by_sets(c, mods,
ntermMods, ctermMods, highResMs2,
ion_to_index_map,
varMods, ntermVarMods, ctermVarMods,
varModSequence)
return t
if __name__ == '__main__':
# process input arguments
args = process_args()
targets, decoys, spec_dict = runDripExtract(args)
+ " -inputMasterFile " + args.master_file + " -inputTrainableParameters trained.params -failOnZeroClique F"
# gmtkViterbi command line
vitValsFile = os.path.join(log_dir, 'vitVals.txt')
vitStr = vitStr0 + ' -vitValsFile ' + vitValsFile \
+ ' -of1 ' + spectrum_obs_file \
+ ' -fmt1 flatascii ' \
+ ' -of2 ' + peptide_obs_file \
+ ' -fmt2 flatascii ' \
+ ' -cppCommand ' + cppCommand
# call(shlex.split(vitStr), stdout = sys.stdout, stderr = sys.stdout)
call(shlex.split(vitStr), stdout = stdo, stderr = stde)
# parse output
t,d = ppsm.parse_dripExtract(vitValsFile, os.path.join(output_dir, 'pepDB.txt'))
t = t[sid,c][0]
# calculate insertions and deletions
t.add_obs_spectrum(s0)
t.calculate_drip_features(dripMeans)
t.calc_by_sets(c, mods,
ntermMods, ctermMods, highResMs2,
ion_to_index_map,
varMods, ntermVarMods, ctermVarMods,
varModSequence)
return t
if __name__ == '__main__':
# process input arguments
args = process_args()
targets, decoys, spec_dict = runDripExtract(args)
