4
+ − 1 from __future__ import division
+ − 2 # galaxy complains this ^^^ needs to be at the very beginning of the file, for some reason.
+ − 3 import sys
+ − 4 import argparse
+ − 5 import collections
+ − 6 import pandas as pd
+ − 7 import pickle as pk
+ − 8 import utils.general_utils as utils
+ − 9 import utils.rule_parsing as ruleUtils
+ − 10 from typing import Union, Optional, List, Dict, Tuple, TypeVar
+ − 11
+ − 12 ERRORS = []
+ − 13 ########################## argparse ##########################################
+ − 14 ARGS :argparse.Namespace
+ − 15 def process_args() -> argparse.Namespace:
+ − 16 """
+ − 17 Processes command-line arguments.
+ − 18
+ − 19 Args:
+ − 20 args (list): List of command-line arguments.
+ − 21
+ − 22 Returns:
+ − 23 Namespace: An object containing parsed arguments.
+ − 24 """
+ − 25 parser = argparse.ArgumentParser(
+ − 26 usage = '%(prog)s [options]',
+ − 27 description = "process some value's genes to create a comparison's map.")
+ − 28
+ − 29 parser.add_argument(
+ − 30 '-rs', '--rules_selector',
+ − 31 type = utils.Model, default = utils.Model.HMRcore, choices = list(utils.Model),
+ − 32 help = 'chose which type of dataset you want use')
+ − 33
+ − 34 parser.add_argument("-rl", "--rule_list", type = str,
+ − 35 help = "path to input file with custom rules, if provided")
+ − 36
+ − 37 parser.add_argument("-rn", "--rules_name", type = str, help = "custom rules name")
+ − 38 # ^ I need this because galaxy converts my files into .dat but I need to know what extension they were in
+ − 39
+ − 40 parser.add_argument(
+ − 41 '-n', '--none',
+ − 42 type = utils.Bool("none"), default = True,
+ − 43 help = 'compute Nan values')
+ − 44
+ − 45 parser.add_argument(
+ − 46 '-td', '--tool_dir',
+ − 47 type = str,
+ − 48 required = True, help = 'your tool directory')
+ − 49
+ − 50 parser.add_argument(
+ − 51 '-ol', '--out_log',
+ − 52 type = str,
+ − 53 help = "Output log")
+ − 54
+ − 55 parser.add_argument(
+ − 56 '-in', '--input', #id è diventato in
+ − 57 type = str,
+ − 58 help = 'input dataset')
+ − 59
+ − 60 parser.add_argument(
+ − 61 '-ra', '--ras_output',
+ − 62 type = str,
+ − 63 required = True, help = 'ras output')
+ − 64
+ − 65 return parser.parse_args()
+ − 66
+ − 67 ############################ dataset input ####################################
+ − 68 def read_dataset(data :str, name :str) -> pd.DataFrame:
+ − 69 """
+ − 70 Read a dataset from a CSV file and return it as a pandas DataFrame.
+ − 71
+ − 72 Args:
+ − 73 data (str): Path to the CSV file containing the dataset.
+ − 74 name (str): Name of the dataset, used in error messages.
+ − 75
+ − 76 Returns:
+ − 77 pandas.DataFrame: DataFrame containing the dataset.
+ − 78
+ − 79 Raises:
+ − 80 pd.errors.EmptyDataError: If the CSV file is empty.
+ − 81 sys.exit: If the CSV file has the wrong format, the execution is aborted.
+ − 82 """
+ − 83 try:
+ − 84 dataset = pd.read_csv(data, sep = '\t', header = 0, engine='python')
+ − 85 except pd.errors.EmptyDataError:
+ − 86 sys.exit('Execution aborted: wrong format of ' + name + '\n')
+ − 87 if len(dataset.columns) < 2:
+ − 88 sys.exit('Execution aborted: wrong format of ' + name + '\n')
+ − 89 return dataset
+ − 90
+ − 91 ############################ load id e rules ##################################
+ − 92 def load_id_rules(reactions :Dict[str, Dict[str, List[str]]]) -> Tuple[List[str], List[Dict[str, List[str]]]]:
+ − 93 """
+ − 94 Load IDs and rules from a dictionary of reactions.
+ − 95
+ − 96 Args:
+ − 97 reactions (dict): A dictionary where keys are IDs and values are rules.
+ − 98
+ − 99 Returns:
+ − 100 tuple: A tuple containing two lists, the first list containing IDs and the second list containing rules.
+ − 101 """
+ − 102 ids, rules = [], []
+ − 103 for key, value in reactions.items():
+ − 104 ids.append(key)
+ − 105 rules.append(value)
+ − 106 return (ids, rules)
+ − 107
+ − 108 ############################ check_methods ####################################
+ − 109 def gene_type(l :str, name :str) -> str:
+ − 110 """
+ − 111 Determine the type of gene ID.
+ − 112
+ − 113 Args:
+ − 114 l (str): The gene identifier to check.
+ − 115 name (str): The name of the dataset, used in error messages.
+ − 116
+ − 117 Returns:
+ − 118 str: The type of gene ID ('hugo_id', 'ensembl_gene_id', 'symbol', or 'entrez_id').
+ − 119
+ − 120 Raises:
+ − 121 sys.exit: If the gene ID type is not supported, the execution is aborted.
+ − 122 """
+ − 123 if check_hgnc(l):
+ − 124 return 'hugo_id'
+ − 125 elif check_ensembl(l):
+ − 126 return 'ensembl_gene_id'
+ − 127 elif check_symbol(l):
+ − 128 return 'symbol'
+ − 129 elif check_entrez(l):
+ − 130 return 'entrez_id'
+ − 131 else:
+ − 132 sys.exit('Execution aborted:\n' +
+ − 133 'gene ID type in ' + name + ' not supported. Supported ID'+
+ − 134 'types are: HUGO ID, Ensemble ID, HUGO symbol, Entrez ID\n')
+ − 135
+ − 136 def check_hgnc(l :str) -> bool:
+ − 137 """
+ − 138 Check if a gene identifier follows the HGNC format.
+ − 139
+ − 140 Args:
+ − 141 l (str): The gene identifier to check.
+ − 142
+ − 143 Returns:
+ − 144 bool: True if the gene identifier follows the HGNC format, False otherwise.
+ − 145 """
+ − 146 if len(l) > 5:
+ − 147 if (l.upper()).startswith('HGNC:'):
+ − 148 return l[5:].isdigit()
+ − 149 else:
+ − 150 return False
+ − 151 else:
+ − 152 return False
+ − 153
+ − 154 def check_ensembl(l :str) -> bool:
+ − 155 """
+ − 156 Check if a gene identifier follows the Ensembl format.
+ − 157
+ − 158 Args:
+ − 159 l (str): The gene identifier to check.
+ − 160
+ − 161 Returns:
+ − 162 bool: True if the gene identifier follows the Ensembl format, False otherwise.
+ − 163 """
+ − 164 return l.upper().startswith('ENS')
+ − 165
+ − 166
+ − 167 def check_symbol(l :str) -> bool:
+ − 168 """
+ − 169 Check if a gene identifier follows the symbol format.
+ − 170
+ − 171 Args:
+ − 172 l (str): The gene identifier to check.
+ − 173
+ − 174 Returns:
+ − 175 bool: True if the gene identifier follows the symbol format, False otherwise.
+ − 176 """
+ − 177 if len(l) > 0:
+ − 178 if l[0].isalpha() and l[1:].isalnum():
+ − 179 return True
+ − 180 else:
+ − 181 return False
+ − 182 else:
+ − 183 return False
+ − 184
+ − 185 def check_entrez(l :str) -> bool:
+ − 186 """
+ − 187 Check if a gene identifier follows the Entrez ID format.
+ − 188
+ − 189 Args:
+ − 190 l (str): The gene identifier to check.
+ − 191
+ − 192 Returns:
+ − 193 bool: True if the gene identifier follows the Entrez ID format, False otherwise.
+ − 194 """
+ − 195 if len(l) > 0:
+ − 196 return l.isdigit()
+ − 197 else:
+ − 198 return False
+ − 199
+ − 200 ############################ gene #############################################
+ − 201 def data_gene(gene: pd.DataFrame, type_gene: str, name: str, gene_custom: Optional[Dict[str, str]]) -> Dict[str, str]:
+ − 202 """
+ − 203 Process gene data to ensure correct formatting and handle duplicates.
+ − 204
+ − 205 Args:
+ − 206 gene (DataFrame): DataFrame containing gene data.
+ − 207 type_gene (str): Type of gene data (e.g., 'hugo_id', 'ensembl_gene_id', 'symbol', 'entrez_id').
+ − 208 name (str): Name of the dataset.
+ − 209 gene_custom (dict or None): Custom gene data dictionary if provided.
+ − 210
+ − 211 Returns:
+ − 212 dict: A dictionary containing gene data with gene IDs as keys and corresponding values.
+ − 213 """
+ − 214 args = process_args()
+ − 215 for i in range(len(gene)):
+ − 216 tmp = gene.iloc[i, 0]
+ − 217 gene.iloc[i, 0] = tmp.strip().split('.')[0]
+ − 218
+ − 219 gene_dup = [item for item, count in
+ − 220 collections.Counter(gene[gene.columns[0]]).items() if count > 1]
+ − 221 pat_dup = [item for item, count in
+ − 222 collections.Counter(list(gene.columns)).items() if count > 1]
+ − 223
+ − 224 if gene_dup:
+ − 225 if gene_custom == None:
+ − 226 if args.rules_selector == 'HMRcore':
+ − 227 gene_in_rule = pk.load(open(args.tool_dir + '/local/pickle files/HMRcore_genes.p', 'rb'))
+ − 228
+ − 229 elif args.rules_selector == 'Recon':
+ − 230 gene_in_rule = pk.load(open(args.tool_dir + '/local/pickle files/Recon_genes.p', 'rb'))
+ − 231
+ − 232 elif args.rules_selector == 'ENGRO2':
+ − 233 gene_in_rule = pk.load(open(args.tool_dir + '/local/pickle files/ENGRO2_genes.p', 'rb'))
47
+ − 234 print(f"{args.tool_dir}'/local/pickle files/ENGRO2_genes.p'")
46
+ − 235 utils.logWarning(f"{args.tool_dir}'/local/pickle files/ENGRO2_genes.p'", ARGS.out_log)
45
+ − 236 print(args.rules_selector)
4
+ − 237 gene_in_rule = gene_in_rule.get(type_gene)
+ − 238
+ − 239 else:
+ − 240 gene_in_rule = gene_custom
+ − 241 tmp = []
+ − 242 for i in gene_dup:
+ − 243 if gene_in_rule.get(i) == 'ok':
+ − 244 tmp.append(i)
+ − 245 if tmp:
+ − 246 sys.exit('Execution aborted because gene ID '
+ − 247 +str(tmp)+' in '+name+' is duplicated\n')
+ − 248
+ − 249 if pat_dup: utils.logWarning(f"Warning: duplicated label\n{pat_dup} in {name}", ARGS.out_log)
+ − 250 return (gene.set_index(gene.columns[0])).to_dict()
+ − 251
+ − 252 ############################ resolve ##########################################
+ − 253 def replace_gene_value(l :str, d :str) -> Tuple[Union[int, float], list]:
+ − 254 """
+ − 255 Replace gene identifiers with corresponding values from a dictionary.
+ − 256
+ − 257 Args:
+ − 258 l (str): String of gene identifier.
+ − 259 d (str): String corresponding to its value.
+ − 260
+ − 261 Returns:
+ − 262 tuple: A tuple containing two lists: the first list contains replaced values, and the second list contains any errors encountered during replacement.
+ − 263 """
+ − 264 tmp = []
+ − 265 err = []
+ − 266 while l:
+ − 267 if isinstance(l[0], list):
+ − 268 tmp_rules, tmp_err = replace_gene_value(l[0], d)
+ − 269 tmp.append(tmp_rules)
+ − 270 err.extend(tmp_err)
+ − 271 else:
+ − 272 value = replace_gene(l[0], d)
+ − 273 tmp.append(value)
+ − 274 if value == None:
+ − 275 err.append(l[0])
+ − 276 l = l[1:]
+ − 277 return (tmp, err)
+ − 278
+ − 279 def replace_gene(l :str, d :str) -> Union[int, float]:
+ − 280 """
+ − 281 Replace a single gene identifier with its corresponding value from a dictionary.
+ − 282
+ − 283 Args:
+ − 284 l (str): Gene identifier to replace.
+ − 285 d (str): String corresponding to its value.
+ − 286
+ − 287 Returns:
+ − 288 float/int: Corresponding value from the dictionary if found, None otherwise.
+ − 289
+ − 290 Raises:
+ − 291 sys.exit: If the value associated with the gene identifier is not valid.
+ − 292 """
+ − 293 if l =='and' or l == 'or':
+ − 294 return l
+ − 295 else:
+ − 296 value = d.get(l, None)
+ − 297 if not(value == None or isinstance(value, (int, float))):
+ − 298 sys.exit('Execution aborted: ' + value + ' value not valid\n')
+ − 299 return value
+ − 300
+ − 301 T = TypeVar("T", bound = Optional[Union[int, float]])
+ − 302 def computes(val1 :T, op :str, val2 :T, cn :bool) -> T:
+ − 303 """
+ − 304 Compute the RAS value between two value and an operator ('and' or 'or').
+ − 305
+ − 306 Args:
+ − 307 val1(Optional(Union[float, int])): First value.
+ − 308 op (str): Operator ('and' or 'or').
+ − 309 val2(Optional(Union[float, int])): Second value.
+ − 310 cn (bool): Control boolean value.
+ − 311
+ − 312 Returns:
+ − 313 Optional(Union[float, int]): Result of the computation.
+ − 314 """
+ − 315 if val1 != None and val2 != None:
+ − 316 if op == 'and':
+ − 317 return min(val1, val2)
+ − 318 else:
+ − 319 return val1 + val2
+ − 320 elif op == 'and':
+ − 321 if cn is True:
+ − 322 if val1 != None:
+ − 323 return val1
+ − 324 elif val2 != None:
+ − 325 return val2
+ − 326 else:
+ − 327 return None
+ − 328 else:
+ − 329 return None
+ − 330 else:
+ − 331 if val1 != None:
+ − 332 return val1
+ − 333 elif val2 != None:
+ − 334 return val2
+ − 335 else:
+ − 336 return None
+ − 337
+ − 338 # ris should be Literal[None] but Literal is not supported in Python 3.7
+ − 339 def control(ris, l :List[Union[int, float, list]], cn :bool) -> Union[bool, int, float]: #Union[Literal[False], int, float]:
+ − 340 """
+ − 341 Control the format of the expression.
+ − 342
+ − 343 Args:
+ − 344 ris: Intermediate result.
+ − 345 l (list): Expression to control.
+ − 346 cn (bool): Control boolean value.
+ − 347
+ − 348 Returns:
+ − 349 Union[Literal[False], int, float]: Result of the control.
+ − 350 """
+ − 351 if len(l) == 1:
+ − 352 if isinstance(l[0], (float, int)) or l[0] == None:
+ − 353 return l[0]
+ − 354 elif isinstance(l[0], list):
+ − 355 return control(None, l[0], cn)
+ − 356 else:
+ − 357 return False
+ − 358 elif len(l) > 2:
+ − 359 return control_list(ris, l, cn)
+ − 360 else:
+ − 361 return False
+ − 362
+ − 363 def control_list(ris, l :List[Optional[Union[float, int, list]]], cn :bool) -> Optional[bool]: #Optional[Literal[False]]:
+ − 364 """
+ − 365 Control the format of a list of expressions.
+ − 366
+ − 367 Args:
+ − 368 ris: Intermediate result.
+ − 369 l (list): List of expressions to control.
+ − 370 cn (bool): Control boolean value.
+ − 371
+ − 372 Returns:
+ − 373 Optional[Literal[False]]: Result of the control.
+ − 374 """
+ − 375 while l:
+ − 376 if len(l) == 1:
+ − 377 return False
+ − 378 elif (isinstance(l[0], (float, int)) or
+ − 379 l[0] == None) and l[1] in ['and', 'or']:
+ − 380 if isinstance(l[2], (float, int)) or l[2] == None:
+ − 381 ris = computes(l[0], l[1], l[2], cn)
+ − 382 elif isinstance(l[2], list):
+ − 383 tmp = control(None, l[2], cn)
+ − 384 if tmp is False:
+ − 385 return False
+ − 386 else:
+ − 387 ris = computes(l[0], l[1], tmp, cn)
+ − 388 else:
+ − 389 return False
+ − 390 l = l[3:]
+ − 391 elif l[0] in ['and', 'or']:
+ − 392 if isinstance(l[1], (float, int)) or l[1] == None:
+ − 393 ris = computes(ris, l[0], l[1], cn)
+ − 394 elif isinstance(l[1], list):
+ − 395 tmp = control(None,l[1], cn)
+ − 396 if tmp is False:
+ − 397 return False
+ − 398 else:
+ − 399 ris = computes(ris, l[0], tmp, cn)
+ − 400 else:
+ − 401 return False
+ − 402 l = l[2:]
+ − 403 elif isinstance(l[0], list) and l[1] in ['and', 'or']:
+ − 404 if isinstance(l[2], (float, int)) or l[2] == None:
+ − 405 tmp = control(None, l[0], cn)
+ − 406 if tmp is False:
+ − 407 return False
+ − 408 else:
+ − 409 ris = computes(tmp, l[1], l[2], cn)
+ − 410 elif isinstance(l[2], list):
+ − 411 tmp = control(None, l[0], cn)
+ − 412 tmp2 = control(None, l[2], cn)
+ − 413 if tmp is False or tmp2 is False:
+ − 414 return False
+ − 415 else:
+ − 416 ris = computes(tmp, l[1], tmp2, cn)
+ − 417 else:
+ − 418 return False
+ − 419 l = l[3:]
+ − 420 else:
+ − 421 return False
+ − 422 return ris
+ − 423
+ − 424 ResolvedRules = Dict[str, List[Optional[Union[float, int]]]]
+ − 425 def resolve(genes: Dict[str, str], rules: List[str], ids: List[str], resolve_none: bool, name: str) -> Tuple[Optional[ResolvedRules], Optional[list]]:
+ − 426 """
+ − 427 Resolve rules using gene data to compute scores for each rule.
+ − 428
+ − 429 Args:
+ − 430 genes (dict): Dictionary containing gene data with gene IDs as keys and corresponding values.
+ − 431 rules (list): List of rules to resolve.
+ − 432 ids (list): List of IDs corresponding to the rules.
+ − 433 resolve_none (bool): Flag indicating whether to resolve None values in the rules.
+ − 434 name (str): Name of the dataset.
+ − 435
+ − 436 Returns:
+ − 437 tuple: A tuple containing resolved rules as a dictionary and a list of gene IDs not found in the data.
+ − 438 """
+ − 439 resolve_rules = {}
+ − 440 not_found = []
+ − 441 flag = False
+ − 442 for key, value in genes.items():
+ − 443 tmp_resolve = []
+ − 444 for i in range(len(rules)):
+ − 445 tmp = rules[i]
+ − 446 if tmp:
+ − 447 tmp, err = replace_gene_value(tmp, value)
+ − 448 if err:
+ − 449 not_found.extend(err)
+ − 450 ris = control(None, tmp, resolve_none)
+ − 451 if ris is False or ris == None:
+ − 452 tmp_resolve.append(None)
+ − 453 else:
+ − 454 tmp_resolve.append(ris)
+ − 455 flag = True
+ − 456 else:
+ − 457 tmp_resolve.append(None)
+ − 458 resolve_rules[key] = tmp_resolve
+ − 459
+ − 460 if flag is False:
+ − 461 utils.logWarning(
+ − 462 f"Warning: no computable score (due to missing gene values) for class {name}, the class has been disregarded",
+ − 463 ARGS.out_log)
+ − 464
+ − 465 return (None, None)
+ − 466
+ − 467 return (resolve_rules, list(set(not_found)))
+ − 468 ############################ create_ras #######################################
+ − 469 def create_ras(resolve_rules: Optional[ResolvedRules], dataset_name: str, rules: List[str], ids: List[str], file: str) -> None:
+ − 470 """
+ − 471 Create a RAS (Reaction Activity Score) file from resolved rules.
+ − 472
+ − 473 Args:
+ − 474 resolve_rules (dict): Dictionary containing resolved rules.
+ − 475 dataset_name (str): Name of the dataset.
+ − 476 rules (list): List of rules.
+ − 477 file (str): Path to the output RAS file.
+ − 478
+ − 479 Returns:
+ − 480 None
+ − 481 """
+ − 482 if resolve_rules is None:
+ − 483 utils.logWarning(f"Couldn't generate RAS for current dataset: {dataset_name}", ARGS.out_log)
+ − 484
+ − 485 for geni in resolve_rules.values():
+ − 486 for i, valori in enumerate(geni):
+ − 487 if valori == None:
+ − 488 geni[i] = 'None'
+ − 489
+ − 490 output_ras = pd.DataFrame.from_dict(resolve_rules)
+ − 491
+ − 492 output_ras.insert(0, 'Reactions', ids)
+ − 493 output_to_csv = pd.DataFrame.to_csv(output_ras, sep = '\t', index = False)
+ − 494
+ − 495 text_file = open(file, "w")
+ − 496
+ − 497 text_file.write(output_to_csv)
+ − 498 text_file.close()
+ − 499
+ − 500 ################################- NEW RAS COMPUTATION -################################
+ − 501 Expr = Optional[Union[int, float]]
+ − 502 Ras = Expr
+ − 503 def ras_for_cell_lines(dataset: pd.DataFrame, rules: Dict[str, ruleUtils.OpList]) -> Dict[str, Dict[str, Ras]]:
+ − 504 """
+ − 505 Generates the RAS scores for each cell line found in the dataset.
+ − 506
+ − 507 Args:
+ − 508 dataset (pd.DataFrame): Dataset containing gene values.
+ − 509 rules (dict): The dict containing reaction ids as keys and rules as values.
+ − 510
+ − 511 Side effects:
+ − 512 dataset : mut
+ − 513
+ − 514 Returns:
+ − 515 dict: A dictionary where each key corresponds to a cell line name and each value is a dictionary
+ − 516 where each key corresponds to a reaction ID and each value is its computed RAS score.
+ − 517 """
+ − 518 ras_values_by_cell_line = {}
+ − 519 dataset.set_index(dataset.columns[0], inplace=True)
+ − 520 # Considera tutte le colonne tranne la prima in cui ci sono gli hugo quindi va scartata
+ − 521 for cell_line_name in dataset.columns[1:]:
+ − 522 cell_line = dataset[cell_line_name].to_dict()
+ − 523 ras_values_by_cell_line[cell_line_name]= get_ras_values(rules, cell_line)
+ − 524 return ras_values_by_cell_line
+ − 525
+ − 526 def get_ras_values(value_rules: Dict[str, ruleUtils.OpList], dataset: Dict[str, Expr]) -> Dict[str, Ras]:
+ − 527 """
+ − 528 Computes the RAS (Reaction Activity Score) values for each rule in the given dict.
+ − 529
+ − 530 Args:
+ − 531 value_rules (dict): A dictionary where keys are reaction ids and values are OpLists.
+ − 532 dataset : gene expression data of one cell line.
+ − 533
+ − 534 Returns:
+ − 535 dict: A dictionary where keys are reaction ids and values are the computed RAS values for each rule.
+ − 536 """
+ − 537 return {key: ras_op_list(op_list, dataset) for key, op_list in value_rules.items()}
+ − 538
+ − 539 def get_gene_expr(dataset :Dict[str, Expr], name :str) -> Expr:
+ − 540 """
+ − 541 Extracts the gene expression of the given gene from a cell line dataset.
+ − 542
+ − 543 Args:
+ − 544 dataset : gene expression data of one cell line.
+ − 545 name : gene name.
+ − 546
+ − 547 Returns:
+ − 548 Expr : the gene's expression value.
+ − 549 """
+ − 550 expr = dataset.get(name, None)
+ − 551 if expr is None: ERRORS.append(name)
+ − 552
+ − 553 return expr
+ − 554
+ − 555 def ras_op_list(op_list: ruleUtils.OpList, dataset: Dict[str, Expr]) -> Ras:
+ − 556 """
+ − 557 Computes recursively the RAS (Reaction Activity Score) value for the given OpList, considering the specified flag to control None behavior.
+ − 558
+ − 559 Args:
+ − 560 op_list (OpList): The OpList representing a rule with gene values.
+ − 561 dataset : gene expression data of one cell line.
+ − 562
+ − 563 Returns:
+ − 564 Ras: The computed RAS value for the given OpList.
+ − 565 """
+ − 566 op = op_list.op
+ − 567 ras_value :Ras = None
+ − 568 if not op: return get_gene_expr(dataset, op_list[0])
+ − 569 if op is ruleUtils.RuleOp.AND and not ARGS.none and None in op_list: return None
+ − 570
+ − 571 for i in range(len(op_list)):
+ − 572 item = op_list[i]
+ − 573 if isinstance(item, ruleUtils.OpList):
+ − 574 item = ras_op_list(item, dataset)
+ − 575
+ − 576 else:
+ − 577 item = get_gene_expr(dataset, item)
+ − 578
+ − 579 if item is None:
+ − 580 if op is ruleUtils.RuleOp.AND and not ARGS.none: return None
+ − 581 continue
+ − 582
+ − 583 if ras_value is None:
+ − 584 ras_value = item
+ − 585 else:
+ − 586 ras_value = ras_value + item if op is ruleUtils.RuleOp.OR else min(ras_value, item)
+ − 587
+ − 588 return ras_value
+ − 589
+ − 590 def save_as_tsv(rasScores: Dict[str, Dict[str, Ras]], reactions :List[str]) -> None:
+ − 591 """
+ − 592 Save computed ras scores to the given path, as a tsv file.
+ − 593
+ − 594 Args:
+ − 595 rasScores : the computed ras scores.
+ − 596 path : the output tsv file's path.
+ − 597
+ − 598 Returns:
+ − 599 None
+ − 600 """
+ − 601 for scores in rasScores.values(): # this is actually a lot faster than using the ootb dataframe metod, sadly
+ − 602 for reactId, score in scores.items():
+ − 603 if score is None: scores[reactId] = "None"
+ − 604
+ − 605 output_ras = pd.DataFrame.from_dict(rasScores)
+ − 606 output_ras.insert(0, 'Reactions', reactions)
+ − 607 output_ras.to_csv(ARGS.ras_output, sep = '\t', index = False)
+ − 608
+ − 609 ############################ MAIN #############################################
+ − 610 #TODO: not used but keep, it will be when the new translator dicts will be used.
+ − 611 def translateGene(geneName :str, encoding :str, geneTranslator :Dict[str, Dict[str, str]]) -> str:
+ − 612 """
+ − 613 Translate gene from any supported encoding to HugoID.
+ − 614
+ − 615 Args:
+ − 616 geneName (str): the name of the gene in its current encoding.
+ − 617 encoding (str): the encoding.
+ − 618 geneTranslator (Dict[str, Dict[str, str]]): the dict containing all supported gene names
+ − 619 and encodings in the current model, mapping each to the corresponding HugoID encoding.
+ − 620
+ − 621 Raises:
+ − 622 ValueError: When the gene isn't supported in the model.
+ − 623
+ − 624 Returns:
+ − 625 str: the gene in HugoID encoding.
+ − 626 """
+ − 627 supportedGenesInEncoding = geneTranslator[encoding]
+ − 628 if geneName in supportedGenesInEncoding: return supportedGenesInEncoding[geneName]
+ − 629 raise ValueError(f"Gene \"{geneName}\" non trovato, verifica di star utilizzando il modello corretto!")
+ − 630
+ − 631 def load_custom_rules() -> Dict[str, ruleUtils.OpList]:
+ − 632 """
+ − 633 Opens custom rules file and extracts the rules. If the file is in .csv format an additional parsing step will be
+ − 634 performed, significantly impacting the runtime.
+ − 635
+ − 636 Returns:
+ − 637 Dict[str, ruleUtils.OpList] : dict mapping reaction IDs to rules.
+ − 638 """
+ − 639 datFilePath = utils.FilePath.fromStrPath(ARGS.rule_list) # actual file, stored in galaxy as a .dat
+ − 640
+ − 641 try: filenamePath = utils.FilePath.fromStrPath(ARGS.rules_name) # file's name in input, to determine its original ext
+ − 642 except utils.PathErr as err:
+ − 643 raise utils.PathErr(filenamePath, f"Please make sure your file's name is a valid file path, {err.msg}")
+ − 644
+ − 645 if filenamePath.ext is utils.FileFormat.PICKLE: return utils.readPickle(datFilePath)
+ − 646
+ − 647 # csv rules need to be parsed, those in a pickle format are taken to be pre-parsed.
+ − 648 return { line[0] : ruleUtils.parseRuleToNestedList(line[1]) for line in utils.readCsv(datFilePath) }
+ − 649
+ − 650 def main() -> None:
+ − 651 """
+ − 652 Initializes everything and sets the program in motion based on the fronted input arguments.
+ − 653
+ − 654 Returns:
+ − 655 None
+ − 656 """
+ − 657 # get args from frontend (related xml)
+ − 658 global ARGS
+ − 659 ARGS = process_args()
44
+ − 660 print(ARGS.rules_selector)
4
+ − 661 # read dataset
+ − 662 dataset = read_dataset(ARGS.input, "dataset")
+ − 663 dataset.iloc[:, 0] = (dataset.iloc[:, 0]).astype(str)
+ − 664
+ − 665 # remove versioning from gene names
+ − 666 dataset.iloc[:, 0] = dataset.iloc[:, 0].str.split('.').str[0]
+ − 667
+ − 668 # handle custom models
+ − 669 model :utils.Model = ARGS.rules_selector
+ − 670 if model is utils.Model.Custom:
+ − 671 rules = load_custom_rules()
+ − 672 reactions = list(rules.keys())
+ − 673
+ − 674 save_as_tsv(ras_for_cell_lines(dataset, rules), reactions)
+ − 675 if ERRORS: utils.logWarning(
+ − 676 f"The following genes are mentioned in the rules but don't appear in the dataset: {ERRORS}",
+ − 677 ARGS.out_log)
+ − 678
+ − 679 return
+ − 680
+ − 681 # This is the standard flow of the ras_generator program, for non-custom models.
+ − 682 name = "RAS Dataset"
+ − 683 type_gene = gene_type(dataset.iloc[0, 0], name)
+ − 684
+ − 685 rules = model.getRules(ARGS.tool_dir)
+ − 686 genes = data_gene(dataset, type_gene, name, None)
+ − 687 ids, rules = load_id_rules(rules.get(type_gene))
+ − 688
+ − 689 resolve_rules, err = resolve(genes, rules, ids, ARGS.none, name)
+ − 690 create_ras(resolve_rules, name, rules, ids, ARGS.ras_output)
+ − 691
+ − 692 if err: utils.logWarning(
+ − 693 f"Warning: gene(s) {err} not found in class \"{name}\", " +
+ − 694 "the expression level for this gene will be considered NaN",
+ − 695 ARGS.out_log)
+ − 696
+ − 697 print("Execution succeded")
+ − 698
+ − 699 ###############################################################################
+ − 700 if __name__ == "__main__":
+ − 701 main()