Spaces:
Sleeping
Sleeping
File size: 15,934 Bytes
eaca1e1 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 |
import re
from os import listdir
from os.path import isfile, join
import numpy as np
from IPython.display import clear_output
from sklearn import preprocessing
from src.snapconfig import config
from src.snaputils import simulatespectra as sim
def read_msps(msp_folder, decoy=False):
msp_files = [join(msp_folder, f) for f in listdir(msp_folder) if
isfile(join(msp_folder, f)) and f.split('.')[-1] == 'msp']
assert len(msp_files) > 0
print('reading {} files'.format(len(msp_files)))
pep_list = []
dataset = []
label = []
for species_id, msp_file in enumerate(msp_files):
print('Reading: {}'.format(msp_file))
tmp_pep_list, tmp_dataset, tmp_labels = read_msp(msp_file, species_id, decoy)
pep_list.extend(tmp_dataset)
dataset.extend(tmp_dataset)
label.extend(tmp_labels)
return pep_list, dataset, label
def read_msp(msp_file, species_id, decoy=False):
"""Read annotated spectra from msp file and return
peptide list, dataset, and labels.
:param decoy:
:param species_id: id of the species
:param msp_file: str
:returns list
"""
f = open(msp_file, "r")
lines = f.readlines()
f.close()
pep_list = []
dataset = []
label = []
# FIXME: config should use only one get_config call.
spec_size = config.get_config(section='input', key='spec_size')
charge = config.get_config(section='input', key='charge')
use_mods = config.get_config(section='input', key='use_mods')
num_species = config.get_config(section='input', key='num_species')
print('len of file: ' + str(len(lines)))
count = 0
limit = 200000
pep = []
spec = []
pep_set = set()
is_name = is_mw = is_num_peaks = False
prev = 0
max_peaks = max_moz = 0
i = 0
while i < len(lines) and limit > 0:
line = lines[i]
i += 1
if line.startswith('Name:'):
name_groups = re.search(r"Name:\s(?P<pep>[a-zA-Z]+)/(?P<charge>\d+)"
r"(?:_(?P<num_mods>\d+)(?P<mods>.*))?", line)
if not name_groups:
continue
pep = name_groups['pep']
l_charge = int(name_groups['charge'])
num_mods = int(name_groups['num_mods'])
if l_charge > charge:
continue
if (use_mods or not num_mods) and pep + str(l_charge) not in pep_set:
pep_set.add(pep + str(l_charge))
is_name = True
else:
continue
if is_name and line.startswith('MW:'):
mass = float(re.findall(r"MW:\s([-+]?[0-9]*\.?[0-9]*)", line)[0])
if round(mass) < spec_size:
is_mw = True
# limit = limit - 1
else:
is_name = is_mw = is_num_peaks = False
continue
if is_name and is_mw and line.startswith('Num peaks:'):
num_peaks = int(re.findall(r"Num peaks:\s([0-9]*\.?[0-9]*)", line)[0])
if num_peaks > max_peaks:
max_peaks = num_peaks
spec = np.zeros(spec_size)
while lines[i] != '\n':
mz_line = lines[i]
i += 1
mz_splits = mz_line.split('\t')
moz, intensity = float(mz_splits[0]), float(mz_splits[1])
if moz > max_moz:
max_moz = moz
spec[round(moz)] += round(intensity)
# for k in range(1, charge + 1):
# spec[-k] = 0
# spec[-l_charge] = 1000.0
spec = np.clip(spec, None, 1000.0)
# spec = preprocessing.scale(spec)
is_num_peaks = True
if is_name and is_mw and is_num_peaks:
is_name = is_mw = is_num_peaks = False
# revPep = pep[0] + pep[1:-1][::-1] + pep[-1]
pep_list.append(pep)
t_spec = sim.get_spectrum(pep)
for k in range(0, charge):
t_spec[k] = 1.0 if k <= l_charge - 1 else 0.0
for k in range(charge, charge + num_species):
t_spec[k] = 1.0 if k - charge == species_id else 0.0
t_spec = preprocessing.scale(t_spec)
if decoy:
revPep = sim.get_rand_mod(pep)
if pep == revPep:
print('decoy is the same. shuffling')
# revPep = ''.join(rand.sample(revPep,len(revPep)))
revPep = sim.get_rand_mod(pep, len(pep))
print(pep)
print(revPep)
rt_spec = sim.get_spectrum(revPep)
#rt_spec = preprocessing.scale(rt_spec)
dataset.append([spec, t_spec, rt_spec])
label.append([1, -1])
else:
dataset.append([spec, t_spec])
label.append([1])
count = count + 1
pep = 0
spec = []
new = int((i / len(lines)) * 100)
if new > prev:
clear_output(wait=True)
print(str(new) + '%')
prev = new
print('max peaks: ' + str(max_peaks))
print('count: ' + str(count))
print('max moz: ' + str(max_moz))
return pep_list, dataset, label
# def read_msp_backup(msp_file, decoy=False):
# """Read annotated spectra from msp file and return
# peptide list, dataset, and labels.
# :param msp_file: str
# :returns list
# """
#
# f = open(msp_file, "r")
# lines = f.readlines()
# f.close()
#
# pep_list = []
# dataset = []
# label = []
#
# # FIXME: config should use only one get_config call.
# spec_size = config.get_config(section='input', key='spec_size')
# charge = config.get_config(section='input', key='charge')
# use_mods = config.get_config(section='input', key='use_mods')
#
# print('len of file: ' + str(len(lines)))
# count = 0
# limit = 200000
# pep = []
# spec = []
# pep_set = set()
# is_name = is_mw = is_num_peaks = False
# prev = 0
# max_peaks = max_moz = 0
# i = 0
# while i < len(lines) and limit > 0:
# line = lines[i]
# i += 1
# splits = line.split(':')
# if (splits[0] == 'Name') and '_' in line:
# split1 = splits[1]
# l_charge = int(split1[split1.find('_') - 1])
# if l_charge != charge and charge > 0: # l_charge == l_charge always true.
# continue
#
# if use_mods or ('(' not in splits[1] and ')' not in splits[1]):
# pep = split1.split('/')[0].lstrip(' ')
#
# if pep not in pep_set:
# pep_set.add(pep)
# else:
# continue
#
# is_name = True
#
# if is_name and splits[0] == 'MW':
# mass = float(splits[1])
# if round(mass) < spec_size:
# is_mw = True
# # limit = limit - 1
# else:
# is_name = is_mw = is_num_peaks = False
# continue
#
# if is_name and is_mw and splits[0] == 'Num peaks':
# num_peaks = int(splits[1])
# if num_peaks > max_peaks:
# max_peaks = num_peaks
#
# spec = np.zeros(spec_size)
# while lines[i] != '\n':
# mz_line = lines[i]
# i += 1
# mz_splits = mz_line.split('\t')
# moz, intensity = float(mz_splits[0]), float(mz_splits[1])
# if moz > max_moz:
# max_moz = moz
# spec[round(moz)] += round(intensity)
#
# spec = np.clip(spec, None, 1000.0)
# spec = preprocessing.scale(spec)
#
# is_num_peaks = True
#
# if is_name and is_mw and is_num_peaks:
# is_name = is_mw = is_num_peaks = False
# # revPep = pep[0] + pep[1:-1][::-1] + pep[-1]
# pep_list.append(pep)
# t_spec = preprocessing.scale(sim.get_spectrum(pep))
# if decoy:
# revPep = sim.get_rand_mod(pep)
# if pep == revPep:
# print('decoy is the same. shuffling')
# # revPep = ''.join(rand.sample(revPep,len(revPep)))
# revPep = sim.get_rand_mod(pep, len(pep))
# print(pep)
# print(revPep)
# rt_spec = preprocessing.scale(sim.get_spectrum(revPep))
# dataset.append([spec, t_spec, rt_spec])
# label.append([1, -1])
# else:
# dataset.append([spec, t_spec])
# label.append([1])
#
# count = count + 1
# pep = 0
# spec = []
# new = int((i / len(lines)) * 100)
# if new > prev:
# clear_output(wait=True)
# print(str(new) + '%')
# prev = new
#
# print('max peaks: ' + str(max_peaks))
# print('count: ' + str(count))
# print('max moz: ' + str(max_moz))
# return pep_list, dataset, label
# def read_msp_with_decoy(msp_file):
# """Read annotated spectra from msp file and return
# data structure along with decoy peptides.
# :param msp_file: str
# :returns list
# """
#
# f = open(msp_file, "r")
# lines = f.readlines()
# f.close()
#
# dataset = []
# label = []
#
# # FIXME: config should use only one get_config call.
# spec_size = config.get_config(section='input', key='spec_size')
# charge = config.get_config(section='input', key='charge')
# use_mods = config.get_config(section='input', key='use_mods')
#
# print('len of file: ' + str(len(lines)))
# count = 0
# limit = 200000
# pep = 0
# spec = []
# is_name = is_mw = is_num_peaks = False
# prev = 0
# max_peaks = max_moz = 0
# i = 0
# while i < len(lines) and limit > 0:
# line = lines[i]
# i += 1
# splits = line.split(':')
# if (splits[0] == 'Name') and '_' in line:
# split1 = splits[1]
# l_charge = int(split1[split1.find('_') - 1])
# if l_charge != charge and charge > 0: # l_charge == l_charge always true.
# continue
# if use_mods:
# pep = split1.split('/')[0].lstrip(' ')
# is_name = True
# elif '(' not in splits[1] and ')' not in splits[1]:
# pep = split1.split('/')[0].lstrip(' ')
# is_name = True
#
# if is_name and splits[0] == 'MW':
# mass = float(splits[1])
# if round(mass) < spec_size:
# is_mw = True
# # limit = limit - 1
# else:
# is_name = is_mw = is_num_peaks = False
# continue
#
# if is_name and is_mw and splits[0] == 'Num peaks':
# num_peaks = int(splits[1])
# if num_peaks > max_peaks:
# max_peaks = num_peaks
#
# spec = np.zeros(spec_size)
# while lines[i] != '\n':
# mz_line = lines[i]
# i += 1
# mz_splits = mz_line.split('\t')
# moz, intensity = float(mz_splits[0]), float(mz_splits[1])
# if moz > max_moz:
# max_moz = moz
# spec[round(moz)] += round(intensity)
#
# spec = np.clip(spec, None, 1000.0)
# spec = preprocessing.scale(spec)
#
# is_num_peaks = True
#
# if is_name and is_mw and is_num_peaks:
# is_name = is_mw = is_num_peaks = False
# # revPep = pep[0] + pep[1:-1][::-1] + pep[-1]
# revPep = sim.get_rand_mod(pep)
# if pep == revPep:
# print('decoy is the same. shuffling')
# # revPep = ''.join(rand.sample(revPep,len(revPep)))
# revPep = sim.get_rand_mod(pep, len(pep))
# print(pep)
# print(revPep)
# t_spec = preprocessing.scale(sim.get_spectrum(pep))
# rt_spec = preprocessing.scale(sim.get_spectrum(revPep))
#
# # TODO: revert this back.
# # dataset.append([spec, t_spec, rt_spec])
# dataset.append([pep, spec, t_spec, rt_spec])
# label.append([1, -1])
#
# count = count + 1
# pep = 0
# spec = []
# new = int((i / len(lines)) * 100)
# if new > prev:
# clear_output(wait=True)
# print(str(new) + '%')
# prev = new
#
# print('max peaks: ' + str(max_peaks))
# print('count: ' + str(count))
# print('max moz: ' + str(max_moz))
# return dataset, label
def read_mgfs(folder_path):
mgf_files = [f for f in listdir(folder_path) if isfile(join(folder_path, f)) and f.split('.')[-1] == 'mgf']
assert len(mgf_files) > 0
spec_size = config.get_config(section='input', key='spec_size')
charge = config.get_config(section='input', key='charge')
spectra = []
masses = []
charges = []
for file in mgf_files:
f = open(join(folder_path, file))
spec_lines = f.readlines()
f.close()
if not spec_lines:
continue
spec = np.zeros(spec_size)
isMass = False
i = 0
'''Read Headers'''
while True:
line = spec_lines[i]
i += 1
splits = line.split('=')
if splits[0].upper() == 'PEPMASS':
masses.append(float(splits[1].split(' ')[0]))
isMass = True
if isMass and splits[0].upper() == 'CHARGE':
l_charge = int(splits[1][0])
if charge and l_charge != charge:
del masses[-1]
isMass = False
isCharge = False
else:
charges.append(l_charge)
isCharge = True
break
'''Read Spectrum'''
while isMass and isCharge and i < len(spec_lines):
line = spec_lines[i]
i += 1
if line != '\n' and 'END IONS' not in line.upper():
splits = line.split(' ')
moz, intensity = float(splits[0]), float(splits[1])
spec[round(moz)] += round(intensity)
elif 'END IONS' in line.upper():
break
if isMass and isCharge:
spec = np.clip(spec, None, 1000.0)
spec = preprocessing.scale(spec)
spectra.append(spec)
return spectra, masses, charges
def read_ms2(file):
f = open(file)
lines = f.readlines()
f.close()
spec_size = config.get_config(section='input', key='spec_size')
charge = config.get_config(section='input', key='charge')
spectra = []
masses = []
charges = []
i = 0
while i < len(lines):
line = lines[i][:-1]
i += 1
splits = line.split('\t')
if splits[0] == 'Z' and (charge <= 0 or float(splits[1]) == charge):
charges.append(float(splits[1]))
masses.append(float(splits[2]))
spec = np.zeros(spec_size)
while i < len(lines):
line = lines[i][:-1] # remove the \n character
i += 1
splits = line.split(' ')
if 'S' in splits[0]:
break
if 'Z' in splits[0]:
continue
moz, intensity = float(splits[0]), float(splits[1])
spec[round(moz)] += round(intensity)
spec = np.clip(spec, None, 1000.0)
spec = preprocessing.scale(spec)
spectra.append(spec)
return spectra, masses, charges
|