4
+ − 1 from __future__ import division
+ − 2 import csv
+ − 3 from enum import Enum
+ − 4 import re
+ − 5 import sys
+ − 6 import numpy as np
+ − 7 import pandas as pd
+ − 8 import itertools as it
+ − 9 import scipy.stats as st
+ − 10 import lxml.etree as ET
+ − 11 import math
+ − 12 import utils.general_utils as utils
+ − 13 from PIL import Image
+ − 14 import os
+ − 15 import argparse
+ − 16 import pyvips
+ − 17 from typing import Tuple, Union, Optional, List, Dict
+ − 18
+ − 19 ERRORS = []
+ − 20 ########################## argparse ##########################################
+ − 21 ARGS :argparse.Namespace
+ − 22 def process_args() -> argparse.Namespace:
+ − 23 """
+ − 24 Interfaces the script of a module with its frontend, making the user's choices for various parameters available as values in code.
+ − 25
+ − 26 Args:
+ − 27 args : Always obtained (in file) from sys.argv
+ − 28
+ − 29 Returns:
+ − 30 Namespace : An object containing the parsed arguments
+ − 31 """
+ − 32 parser = argparse.ArgumentParser(
+ − 33 usage = "%(prog)s [options]",
+ − 34 description = "process some value's genes to create a comparison's map.")
+ − 35
+ − 36 #General:
+ − 37 parser.add_argument(
+ − 38 '-td', '--tool_dir',
+ − 39 type = str,
+ − 40 required = True,
+ − 41 help = 'your tool directory')
+ − 42
+ − 43 parser.add_argument('-on', '--control', type = str)
+ − 44 parser.add_argument('-ol', '--out_log', help = "Output log")
+ − 45
+ − 46 #Computation details:
+ − 47 parser.add_argument(
+ − 48 '-co', '--comparison',
+ − 49 type = str,
+ − 50 default = '1vs1',
+ − 51 choices = ['manyvsmany', 'onevsrest', 'onevsmany'])
+ − 52
+ − 53 parser.add_argument(
+ − 54 '-pv' ,'--pValue',
+ − 55 type = float,
+ − 56 default = 0.1,
+ − 57 help = 'P-Value threshold (default: %(default)s)')
+ − 58
+ − 59 parser.add_argument(
+ − 60 '-fc', '--fChange',
+ − 61 type = float,
+ − 62 default = 1.5,
+ − 63 help = 'Fold-Change threshold (default: %(default)s)')
+ − 64
+ − 65 parser.add_argument(
+ − 66 "-ne", "--net",
+ − 67 type = utils.Bool("net"), default = False,
+ − 68 help = "choose if you want net enrichment for RPS")
+ − 69
+ − 70 parser.add_argument(
+ − 71 '-op', '--option',
+ − 72 type = str,
+ − 73 choices = ['datasets', 'dataset_class'],
+ − 74 help='dataset or dataset and class')
+ − 75
+ − 76 #RAS:
+ − 77 parser.add_argument(
+ − 78 "-ra", "--using_RAS",
+ − 79 type = utils.Bool("using_RAS"), default = True,
+ − 80 help = "choose whether to use RAS datasets.")
+ − 81
+ − 82 parser.add_argument(
+ − 83 '-id', '--input_data',
+ − 84 type = str,
+ − 85 help = 'input dataset')
+ − 86
+ − 87 parser.add_argument(
+ − 88 '-ic', '--input_class',
+ − 89 type = str,
+ − 90 help = 'sample group specification')
+ − 91
+ − 92 parser.add_argument(
+ − 93 '-ids', '--input_datas',
+ − 94 type = str,
+ − 95 nargs = '+',
+ − 96 help = 'input datasets')
+ − 97
+ − 98 parser.add_argument(
+ − 99 '-na', '--names',
+ − 100 type = str,
+ − 101 nargs = '+',
+ − 102 help = 'input names')
+ − 103
+ − 104 #RPS:
+ − 105 parser.add_argument(
+ − 106 "-rp", "--using_RPS",
+ − 107 type = utils.Bool("using_RPS"), default = False,
+ − 108 help = "choose whether to use RPS datasets.")
+ − 109
+ − 110 parser.add_argument(
+ − 111 '-idr', '--input_data_rps',
+ − 112 type = str,
+ − 113 help = 'input dataset rps')
+ − 114
+ − 115 parser.add_argument(
+ − 116 '-icr', '--input_class_rps',
+ − 117 type = str,
+ − 118 help = 'sample group specification rps')
+ − 119
+ − 120 parser.add_argument(
+ − 121 '-idsr', '--input_datas_rps',
+ − 122 type = str,
+ − 123 nargs = '+',
+ − 124 help = 'input datasets rps')
+ − 125
+ − 126 parser.add_argument(
+ − 127 '-nar', '--names_rps',
+ − 128 type = str,
+ − 129 nargs = '+',
+ − 130 help = 'input names rps')
+ − 131
+ − 132 #Output:
+ − 133 parser.add_argument(
+ − 134 "-gs", "--generate_svg",
+ − 135 type = utils.Bool("generate_svg"), default = True,
+ − 136 help = "choose whether to use RAS datasets.")
+ − 137
+ − 138 parser.add_argument(
+ − 139 "-gp", "--generate_pdf",
+ − 140 type = utils.Bool("generate_pdf"), default = True,
+ − 141 help = "choose whether to use RAS datasets.")
+ − 142
+ − 143 parser.add_argument(
+ − 144 '-cm', '--custom_map',
+ − 145 type = str,
+ − 146 help='custom map to use')
+ − 147
+ − 148 parser.add_argument(
+ − 149 '-mc', '--choice_map',
+ − 150 type = utils.Model, default = utils.Model.HMRcore,
+ − 151 choices = [utils.Model.HMRcore, utils.Model.ENGRO2, utils.Model.Custom])
+ − 152
+ − 153 args :argparse.Namespace = parser.parse_args()
+ − 154 if args.using_RAS and not args.using_RPS: args.net = False
+ − 155
+ − 156 return args
+ − 157
+ − 158 ############################ dataset input ####################################
+ − 159 def read_dataset(data :str, name :str) -> pd.DataFrame:
+ − 160 """
+ − 161 Tries to read the dataset from its path (data) as a tsv and turns it into a DataFrame.
+ − 162
+ − 163 Args:
+ − 164 data : filepath of a dataset (from frontend input params or literals upon calling)
+ − 165 name : name associated with the dataset (from frontend input params or literals upon calling)
+ − 166
+ − 167 Returns:
+ − 168 pd.DataFrame : dataset in a runtime operable shape
+ − 169
+ − 170 Raises:
+ − 171 sys.exit : if there's no data (pd.errors.EmptyDataError) or if the dataset has less than 2 columns
+ − 172 """
+ − 173 try:
+ − 174 dataset = pd.read_csv(data, sep = '\t', header = 0, engine='python')
+ − 175 except pd.errors.EmptyDataError:
+ − 176 sys.exit('Execution aborted: wrong format of ' + name + '\n')
+ − 177 if len(dataset.columns) < 2:
+ − 178 sys.exit('Execution aborted: wrong format of ' + name + '\n')
+ − 179 return dataset
+ − 180
+ − 181 ############################ dataset name #####################################
+ − 182 def name_dataset(name_data :str, count :int) -> str:
+ − 183 """
+ − 184 Produces a unique name for a dataset based on what was provided by the user. The default name for any dataset is "Dataset", thus if the user didn't change it this function appends f"_{count}" to make it unique.
+ − 185
+ − 186 Args:
+ − 187 name_data : name associated with the dataset (from frontend input params)
+ − 188 count : counter from 1 to make these names unique (external)
+ − 189
+ − 190 Returns:
+ − 191 str : the name made unique
+ − 192 """
+ − 193 if str(name_data) == 'Dataset':
+ − 194 return str(name_data) + '_' + str(count)
+ − 195 else:
+ − 196 return str(name_data)
+ − 197
+ − 198 ############################ map_methods ######################################
+ − 199 FoldChange = Union[float, int, str] # Union[float, Literal[0, "-INF", "INF"]]
+ − 200 def fold_change(avg1 :float, avg2 :float) -> FoldChange:
+ − 201 """
+ − 202 Calculates the fold change between two gene expression values.
+ − 203
+ − 204 Args:
+ − 205 avg1 : average expression value from one dataset avg2 : average expression value from the other dataset
+ − 206
+ − 207 Returns:
+ − 208 FoldChange :
+ − 209 0 : when both input values are 0
+ − 210 "-INF" : when avg1 is 0
+ − 211 "INF" : when avg2 is 0
+ − 212 float : for any other combination of values
+ − 213 """
+ − 214 if avg1 == 0 and avg2 == 0:
+ − 215 return 0
+ − 216 elif avg1 == 0:
+ − 217 return '-INF'
+ − 218 elif avg2 == 0:
+ − 219 return 'INF'
+ − 220 else: # (threshold_F_C - 1) / (abs(threshold_F_C) + 1) con threshold_F_C > 1
+ − 221 return (avg1 - avg2) / (abs(avg1) + abs(avg2))
+ − 222
+ − 223 def fix_style(l :str, col :Optional[str], width :str, dash :str) -> str:
+ − 224 """
+ − 225 Produces a "fixed" style string to assign to a reaction arrow in the SVG map, assigning style properties to the corresponding values passed as input params.
+ − 226
+ − 227 Args:
+ − 228 l : current style string of an SVG element
+ − 229 col : new value for the "stroke" style property
+ − 230 width : new value for the "stroke-width" style property
+ − 231 dash : new value for the "stroke-dasharray" style property
+ − 232
+ − 233 Returns:
+ − 234 str : the fixed style string
+ − 235 """
+ − 236 tmp = l.split(';')
+ − 237 flag_col = False
+ − 238 flag_width = False
+ − 239 flag_dash = False
+ − 240 for i in range(len(tmp)):
+ − 241 if tmp[i].startswith('stroke:'):
+ − 242 tmp[i] = 'stroke:' + col
+ − 243 flag_col = True
+ − 244 if tmp[i].startswith('stroke-width:'):
+ − 245 tmp[i] = 'stroke-width:' + width
+ − 246 flag_width = True
+ − 247 if tmp[i].startswith('stroke-dasharray:'):
+ − 248 tmp[i] = 'stroke-dasharray:' + dash
+ − 249 flag_dash = True
+ − 250 if not flag_col:
+ − 251 tmp.append('stroke:' + col)
+ − 252 if not flag_width:
+ − 253 tmp.append('stroke-width:' + width)
+ − 254 if not flag_dash:
+ − 255 tmp.append('stroke-dasharray:' + dash)
+ − 256 return ';'.join(tmp)
+ − 257
+ − 258 # The type of d values is collapsed, losing precision, because the dict containst lists instead of tuples, please fix!
+ − 259 def fix_map(d :Dict[str, List[Union[float, FoldChange]]], core_map :ET.ElementTree, threshold_P_V :float, threshold_F_C :float, max_z_score :float) -> ET.ElementTree:
+ − 260 """
+ − 261 Edits the selected SVG map based on the p-value and fold change data (d) and some significance thresholds also passed as inputs.
+ − 262
+ − 263 Args:
+ − 264 d : dictionary mapping a p-value and a fold-change value (values) to each reaction ID as encoded in the SVG map (keys)
+ − 265 core_map : SVG map to modify
+ − 266 threshold_P_V : threshold for a p-value to be considered significant
+ − 267 threshold_F_C : threshold for a fold change value to be considered significant
+ − 268 max_z_score : highest z-score (absolute value)
+ − 269
+ − 270 Returns:
+ − 271 ET.ElementTree : the modified core_map
+ − 272
+ − 273 Side effects:
+ − 274 core_map : mut
+ − 275 """
+ − 276 maxT = 12
+ − 277 minT = 2
+ − 278 grey = '#BEBEBE'
+ − 279 blue = '#6495ed'
+ − 280 red = '#ecac68'
+ − 281 for el in core_map.iter():
+ − 282 el_id = str(el.get('id'))
+ − 283 if el_id.startswith('R_'):
+ − 284 tmp = d.get(el_id[2:])
+ − 285 if tmp != None:
+ − 286 p_val :float = tmp[0]
+ − 287 f_c = tmp[1]
+ − 288 z_score = tmp[2]
+ − 289 if p_val < threshold_P_V:
+ − 290 if not isinstance(f_c, str):
+ − 291 if abs(f_c) < ((threshold_F_C - 1) / (abs(threshold_F_C) + 1)): #
+ − 292 col = grey
+ − 293 width = str(minT)
+ − 294 else:
+ − 295 if f_c < 0:
+ − 296 col = blue
+ − 297 elif f_c > 0:
+ − 298 col = red
+ − 299 width = str(max((abs(z_score) * maxT) / max_z_score, minT))
+ − 300 else:
+ − 301 if f_c == '-INF':
+ − 302 col = blue
+ − 303 elif f_c == 'INF':
+ − 304 col = red
+ − 305 width = str(maxT)
+ − 306 dash = 'none'
+ − 307 else:
+ − 308 dash = '5,5'
+ − 309 col = grey
+ − 310 width = str(minT)
+ − 311 el.set('style', fix_style(el.get('style', ""), col, width, dash))
+ − 312 return core_map
+ − 313
+ − 314 def getElementById(reactionId :str, metabMap :ET.ElementTree) -> utils.Result[ET.Element, utils.Result.ResultErr]:
+ − 315 """
+ − 316 Finds any element in the given map with the given ID. ID uniqueness in an svg file is recommended but
+ − 317 not enforced, if more than one element with the exact ID is found only the first will be returned.
+ − 318
+ − 319 Args:
+ − 320 reactionId (str): exact ID of the requested element.
+ − 321 metabMap (ET.ElementTree): metabolic map containing the element.
+ − 322
+ − 323 Returns:
+ − 324 utils.Result[ET.Element, ResultErr]: result of the search, either the first match found or a ResultErr.
+ − 325 """
+ − 326 return utils.Result.Ok(
+ − 327 f"//*[@id=\"{reactionId}\"]").map(
+ − 328 lambda xPath : metabMap.xpath(xPath)[0]).mapErr(
+ − 329 lambda _ : utils.Result.ResultErr(f"No elements with ID \"{reactionId}\" found in map"))
+ − 330 # ^^^ we shamelessly ignore the contents of the IndexError, it offers nothing to the user.
+ − 331
+ − 332 def styleMapElement(element :ET.Element, styleStr :str) -> None:
+ − 333 currentStyles :str = element.get("style", "")
+ − 334 if re.search(r";stroke:[^;]+;stroke-width:[^;]+;stroke-dasharray:[^;]+$", currentStyles):
+ − 335 currentStyles = ';'.join(currentStyles.split(';')[:-3])
+ − 336
+ − 337 element.set("style", currentStyles + styleStr)
+ − 338
+ − 339 class ReactionDirection(Enum):
+ − 340 Unknown = ""
+ − 341 Direct = "_F"
+ − 342 Inverse = "_B"
+ − 343
+ − 344 @classmethod
+ − 345 def fromDir(cls, s :str) -> "ReactionDirection":
+ − 346 # vvv as long as there's so few variants I actually condone the if spam:
+ − 347 if s == ReactionDirection.Direct.value: return ReactionDirection.Direct
+ − 348 if s == ReactionDirection.Inverse.value: return ReactionDirection.Inverse
+ − 349 return ReactionDirection.Unknown
+ − 350
+ − 351 @classmethod
+ − 352 def fromReactionId(cls, reactionId :str) -> "ReactionDirection":
+ − 353 return ReactionDirection.fromDir(reactionId[-2:])
+ − 354
+ − 355 def getArrowBodyElementId(reactionId :str) -> str:
+ − 356 if reactionId.endswith("_RV"): reactionId = reactionId[:-3] #TODO: standardize _RV
+ − 357 elif ReactionDirection.fromReactionId(reactionId) is not ReactionDirection.Unknown: reactionId = reactionId[:-2]
+ − 358 return f"R_{reactionId}"
+ − 359
+ − 360 def getArrowHeadElementId(reactionId :str) -> Tuple[str, str]:
+ − 361 """
+ − 362 We attempt extracting the direction information from the provided reaction ID, if unsuccessful we provide the IDs of both directions.
+ − 363
+ − 364 Args:
+ − 365 reactionId : the provided reaction ID.
+ − 366
+ − 367 Returns:
+ − 368 Tuple[str, str]: either a single str ID for the correct arrow head followed by an empty string or both options to try.
+ − 369 """
+ − 370 if reactionId.endswith("_RV"): reactionId = reactionId[:-3] #TODO: standardize _RV
+ − 371 elif ReactionDirection.fromReactionId(reactionId) is not ReactionDirection.Unknown: return reactionId[:-3:-1] + reactionId[:-2], ""
+ − 372 return f"F_{reactionId}", f"B_{reactionId}"
+ − 373
+ − 374 class ArrowColor(Enum):
+ − 375 """
+ − 376 Encodes possible arrow colors based on their meaning in the enrichment process.
+ − 377 """
+ − 378 Invalid = "#BEBEBE" # gray, fold-change under treshold
+ − 379 UpRegulated = "#ecac68" # red, up-regulated reaction
+ − 380 DownRegulated = "#6495ed" # blue, down-regulated reaction
+ − 381
+ − 382 UpRegulatedInv = "#FF0000"
+ − 383 # ^^^ different shade of red (actually orange), up-regulated net value for a reversible reaction with
+ − 384 # conflicting enrichment in the two directions.
+ − 385
+ − 386 DownRegulatedInv = "#0000FF"
+ − 387 # ^^^ different shade of blue (actually purple), down-regulated net value for a reversible reaction with
+ − 388 # conflicting enrichment in the two directions.
+ − 389
+ − 390 @classmethod
+ − 391 def fromFoldChangeSign(cls, foldChange :float, *, useAltColor = False) -> "ArrowColor":
+ − 392 colors = (cls.DownRegulated, cls.DownRegulatedInv) if foldChange < 0 else (cls.UpRegulated, cls.UpRegulatedInv)
+ − 393 return colors[useAltColor]
+ − 394
+ − 395 def __str__(self) -> str: return self.value
+ − 396
+ − 397 class Arrow:
+ − 398 """
+ − 399 Models the properties of a reaction arrow that change based on enrichment.
+ − 400 """
+ − 401 MIN_W = 2
+ − 402 MAX_W = 12
+ − 403
+ − 404 def __init__(self, width :int, col: ArrowColor, *, isDashed = False) -> None:
+ − 405 """
+ − 406 (Private) Initializes an instance of Arrow.
+ − 407
+ − 408 Args:
+ − 409 width : width of the arrow, ideally to be kept within Arrow.MIN_W and Arrow.MAX_W (not enforced).
+ − 410 col : color of the arrow.
+ − 411 isDashed : whether the arrow should be dashed, meaning the associated pValue resulted not significant.
+ − 412
+ − 413 Returns:
+ − 414 None : practically, a Arrow instance.
+ − 415 """
+ − 416 self.w = width
+ − 417 self.col = col
+ − 418 self.dash = isDashed
+ − 419
+ − 420 def applyTo(self, reactionId :str, metabMap :ET.ElementTree, styleStr :str) -> None:
+ − 421 if getElementById(reactionId, metabMap).map(lambda el : styleMapElement(el, styleStr)).isErr:
+ − 422 ERRORS.append(reactionId)
+ − 423
+ − 424 def styleReactionElements(self, metabMap :ET.ElementTree, reactionId :str, *, mindReactionDir = True) -> None:
+ − 425 # If We're dealing with RAS data or in general don't care about the direction of the reaction we only style the arrow body
+ − 426 if not mindReactionDir:
+ − 427 return self.applyTo(getArrowBodyElementId(reactionId), metabMap, self.toStyleStr())
+ − 428
+ − 429 # Now we style the arrow head(s):
+ − 430 idOpt1, idOpt2 = getArrowHeadElementId(reactionId)
+ − 431 self.applyTo(idOpt1, metabMap, self.toStyleStr(downSizedForTips = True))
+ − 432 if idOpt2: self.applyTo(idOpt2, metabMap, self.toStyleStr(downSizedForTips = True))
+ − 433
+ − 434 def getMapReactionId(self, reactionId :str, mindReactionDir :bool) -> str:
+ − 435 """
+ − 436 Computes the reaction ID as encoded in the map for a given reaction ID from the dataset.
+ − 437
+ − 438 Args:
+ − 439 reactionId: the reaction ID, as encoded in the dataset.
+ − 440 mindReactionDir: if True forward (F_) and backward (B_) directions will be encoded in the result.
+ − 441
+ − 442 Returns:
+ − 443 str : the ID of an arrow's body or tips in the map.
+ − 444 """
+ − 445 # we assume the reactionIds also don't encode reaction dir if they don't mind it when styling the map.
+ − 446 if not mindReactionDir: return "R_" + reactionId
+ − 447
+ − 448 #TODO: this is clearly something we need to make consistent in RPS
+ − 449 return (reactionId[:-3:-1] + reactionId[:-2]) if reactionId[:-2] in ["_F", "_B"] else f"F_{reactionId}" # "Pyr_F" --> "F_Pyr"
+ − 450
+ − 451 def toStyleStr(self, *, downSizedForTips = False) -> str:
+ − 452 """
+ − 453 Collapses the styles of this Arrow into a str, ready to be applied as part of the "style" property on an svg element.
+ − 454
+ − 455 Returns:
+ − 456 str : the styles string.
+ − 457 """
+ − 458 width = self.w
+ − 459 if downSizedForTips: width *= 0.8
+ − 460 return f";stroke:{self.col};stroke-width:{width};stroke-dasharray:{'5,5' if self.dash else 'none'}"
+ − 461
+ − 462 # vvv These constants could be inside the class itself a static properties, but python
+ − 463 # was built by brainless organisms so here we are!
+ − 464 INVALID_ARROW = Arrow(Arrow.MIN_W, ArrowColor.Invalid)
+ − 465 INSIGNIFICANT_ARROW = Arrow(Arrow.MIN_W, ArrowColor.Invalid, isDashed = True)
+ − 466
+ − 467 def applyRpsEnrichmentToMap(rpsEnrichmentRes :Dict[str, Union[Tuple[float, FoldChange], Tuple[float, FoldChange, float, float]]], metabMap :ET.ElementTree, maxNumericZScore :float) -> None:
+ − 468 """
+ − 469 Applies RPS enrichment results to the provided metabolic map.
+ − 470
+ − 471 Args:
+ − 472 rpsEnrichmentRes : RPS enrichment results.
+ − 473 metabMap : the metabolic map to edit.
+ − 474 maxNumericZScore : biggest finite z-score value found.
+ − 475
+ − 476 Side effects:
+ − 477 metabMap : mut
+ − 478
+ − 479 Returns:
+ − 480 None
+ − 481 """
+ − 482 for reactionId, values in rpsEnrichmentRes.items():
+ − 483 pValue = values[0]
+ − 484 foldChange = values[1]
+ − 485 z_score = values[2]
+ − 486
+ − 487 if isinstance(foldChange, str): foldChange = float(foldChange)
+ − 488 if pValue >= ARGS.pValue: # pValue above tresh: dashed arrow
+ − 489 INSIGNIFICANT_ARROW.styleReactionElements(metabMap, reactionId)
+ − 490 continue
+ − 491
+ − 492 if abs(foldChange) < (ARGS.fChange - 1) / (abs(ARGS.fChange) + 1):
+ − 493 INVALID_ARROW.styleReactionElements(metabMap, reactionId)
+ − 494 continue
+ − 495
+ − 496 width = Arrow.MAX_W
+ − 497 if not math.isinf(foldChange):
+ − 498 try: width = max(abs(z_score * Arrow.MAX_W) / maxNumericZScore, Arrow.MIN_W)
+ − 499 except ZeroDivisionError: pass
+ − 500
+ − 501 if not reactionId.endswith("_RV"): # RV stands for reversible reactions
+ − 502 Arrow(width, ArrowColor.fromFoldChangeSign(foldChange)).styleReactionElements(metabMap, reactionId)
+ − 503 continue
+ − 504
+ − 505 reactionId = reactionId[:-3] # Remove "_RV"
+ − 506
+ − 507 inversionScore = (values[3] < 0) + (values[4] < 0) # Compacts the signs of averages into 1 easy to check score
+ − 508 if inversionScore == 2: foldChange *= -1
+ − 509 # ^^^ Style the inverse direction with the opposite sign netValue
+ − 510
+ − 511 # If the score is 1 (opposite signs) we use alternative colors vvv
+ − 512 arrow = Arrow(width, ArrowColor.fromFoldChangeSign(foldChange, useAltColor = inversionScore == 1))
+ − 513
+ − 514 # vvv These 2 if statements can both be true and can both happen
+ − 515 if ARGS.net: # style arrow head(s):
+ − 516 arrow.styleReactionElements(metabMap, reactionId + ("_B" if inversionScore == 2 else "_F"))
+ − 517
+ − 518 if not ARGS.using_RAS: # style arrow body
+ − 519 arrow.styleReactionElements(metabMap, reactionId, mindReactionDir = False)
+ − 520
+ − 521 ############################ split class ######################################
+ − 522 def split_class(classes :pd.DataFrame, resolve_rules :Dict[str, List[float]]) -> Dict[str, List[List[float]]]:
+ − 523 """
+ − 524 Generates a :dict that groups together data from a :DataFrame based on classes the data is related to.
+ − 525
+ − 526 Args:
+ − 527 classes : a :DataFrame of only string values, containing class information (rows) and keys to query the resolve_rules :dict
+ − 528 resolve_rules : a :dict containing :float data
+ − 529
+ − 530 Returns:
+ − 531 dict : the dict with data grouped by class
+ − 532
+ − 533 Side effects:
+ − 534 classes : mut
+ − 535 """
+ − 536 class_pat :Dict[str, List[List[float]]] = {}
+ − 537 for i in range(len(classes)):
+ − 538 classe :str = classes.iloc[i, 1]
+ − 539 if pd.isnull(classe): continue
+ − 540
+ − 541 l :List[List[float]] = []
+ − 542 for j in range(i, len(classes)):
+ − 543 if classes.iloc[j, 1] == classe:
+ − 544 pat_id :str = classes.iloc[j, 0]
+ − 545 tmp = resolve_rules.get(pat_id, None)
+ − 546 if tmp != None:
+ − 547 l.append(tmp)
+ − 548 classes.iloc[j, 1] = None
+ − 549
+ − 550 if l:
+ − 551 class_pat[classe] = list(map(list, zip(*l)))
+ − 552 continue
+ − 553
+ − 554 utils.logWarning(
+ − 555 f"Warning: no sample found in class \"{classe}\", the class has been disregarded", ARGS.out_log)
+ − 556
+ − 557 return class_pat
+ − 558
+ − 559 ############################ conversion ##############################################
+ − 560 #conversion from svg to png
+ − 561 def svg_to_png_with_background(svg_path :utils.FilePath, png_path :utils.FilePath, dpi :int = 72, scale :int = 1, size :Optional[float] = None) -> None:
+ − 562 """
+ − 563 Internal utility to convert an SVG to PNG (forced opaque) to aid in PDF conversion.
+ − 564
+ − 565 Args:
+ − 566 svg_path : path to SVG file
+ − 567 png_path : path for new PNG file
+ − 568 dpi : dots per inch of the generated PNG
+ − 569 scale : scaling factor for the generated PNG, computed internally when a size is provided
+ − 570 size : final effective width of the generated PNG
+ − 571
+ − 572 Returns:
+ − 573 None
+ − 574 """
+ − 575 if size:
+ − 576 image = pyvips.Image.new_from_file(svg_path.show(), dpi=dpi, scale=1)
+ − 577 scale = size / image.width
+ − 578 image = image.resize(scale)
+ − 579 else:
+ − 580 image = pyvips.Image.new_from_file(svg_path.show(), dpi=dpi, scale=scale)
+ − 581
+ − 582 white_background = pyvips.Image.black(image.width, image.height).new_from_image([255, 255, 255])
+ − 583 white_background = white_background.affine([scale, 0, 0, scale])
+ − 584
+ − 585 if white_background.bands != image.bands:
+ − 586 white_background = white_background.extract_band(0)
+ − 587
+ − 588 composite_image = white_background.composite2(image, 'over')
+ − 589 composite_image.write_to_file(png_path.show())
+ − 590
+ − 591 #funzione unica, lascio fuori i file e li passo in input
+ − 592 #conversion from png to pdf
+ − 593 def convert_png_to_pdf(png_file :utils.FilePath, pdf_file :utils.FilePath) -> None:
+ − 594 """
+ − 595 Internal utility to convert a PNG to PDF to aid from SVG conversion.
+ − 596
+ − 597 Args:
+ − 598 png_file : path to PNG file
+ − 599 pdf_file : path to new PDF file
+ − 600
+ − 601 Returns:
+ − 602 None
+ − 603 """
+ − 604 image = Image.open(png_file.show())
+ − 605 image = image.convert("RGB")
+ − 606 image.save(pdf_file.show(), "PDF", resolution=100.0)
+ − 607
+ − 608 #function called to reduce redundancy in the code
+ − 609 def convert_to_pdf(file_svg :utils.FilePath, file_png :utils.FilePath, file_pdf :utils.FilePath) -> None:
+ − 610 """
+ − 611 Converts the SVG map at the provided path to PDF.
+ − 612
+ − 613 Args:
+ − 614 file_svg : path to SVG file
+ − 615 file_png : path to PNG file
+ − 616 file_pdf : path to new PDF file
+ − 617
+ − 618 Returns:
+ − 619 None
+ − 620 """
+ − 621 svg_to_png_with_background(file_svg, file_png)
+ − 622 try:
+ − 623 convert_png_to_pdf(file_png, file_pdf)
+ − 624 print(f'PDF file {file_pdf.filePath} successfully generated.')
+ − 625
+ − 626 except Exception as e:
+ − 627 raise utils.DataErr(file_pdf.show(), f'Error generating PDF file: {e}')
+ − 628
+ − 629 ############################ map ##############################################
+ − 630 def buildOutputPath(dataset1Name :str, dataset2Name = "rest", *, details = "", ext :utils.FileFormat) -> utils.FilePath:
+ − 631 """
+ − 632 Builds a FilePath instance from the names of confronted datasets ready to point to a location in the
+ − 633 "result/" folder, used by this tool for output files in collections.
+ − 634
+ − 635 Args:
+ − 636 dataset1Name : _description_
+ − 637 dataset2Name : _description_. Defaults to "rest".
+ − 638 details : _description_
+ − 639 ext : _description_
+ − 640
+ − 641 Returns:
+ − 642 utils.FilePath : _description_
+ − 643 """
+ − 644 # This function returns a util data structure but is extremely specific to this module.
+ − 645 # RAS also uses collections as output and as such might benefit from a method like this, but I'd wait
+ − 646 # TODO: until a third tool with multiple outputs appears before porting this to utils.
+ − 647 return utils.FilePath(
+ − 648 f"{dataset1Name}_vs_{dataset2Name}" + (f" ({details})" if details else ""),
+ − 649 # ^^^ yes this string is built every time even if the form is the same for the same 2 datasets in
+ − 650 # all output files: I don't care, this was never the performance bottleneck of the tool and
+ − 651 # there is no other net gain in saving and re-using the built string.
+ − 652 ext,
+ − 653 prefix = "result")
+ − 654
+ − 655 FIELD_NOT_AVAILABLE = '/'
+ − 656 def writeToCsv(rows: List[list], fieldNames :List[str], outPath :utils.FilePath) -> None:
+ − 657 fieldsAmt = len(fieldNames)
+ − 658 with open(outPath.show(), "w", newline = "") as fd:
+ − 659 writer = csv.DictWriter(fd, fieldnames = fieldNames, delimiter = '\t')
+ − 660 writer.writeheader()
+ − 661
+ − 662 for row in rows:
+ − 663 sizeMismatch = fieldsAmt - len(row)
+ − 664 if sizeMismatch > 0: row.extend([FIELD_NOT_AVAILABLE] * sizeMismatch)
+ − 665 writer.writerow({ field : data for field, data in zip(fieldNames, row) })
+ − 666
+ − 667 OldEnrichedScores = Dict[str, List[Union[float, FoldChange]]] #TODO: try to use Tuple whenever possible
+ − 668 def writeTabularResult(enrichedScores : OldEnrichedScores, ras_enrichment: bool, outPath :utils.FilePath) -> None:
+ − 669 fieldNames = ["ids", "P_Value", "fold change"]
+ − 670 if not ras_enrichment: fieldNames.extend(["average_1", "average_2"])
+ − 671
+ − 672 writeToCsv([ [reactId] + values for reactId, values in enrichedScores.items() ], fieldNames, outPath)
+ − 673
+ − 674 def temp_thingsInCommon(tmp :Dict[str, List[Union[float, FoldChange]]], core_map :ET.ElementTree, max_z_score :float, dataset1Name :str, dataset2Name = "rest", ras_enrichment = True) -> None:
+ − 675 # this function compiles the things always in common between comparison modes after enrichment.
+ − 676 # TODO: organize, name better.
+ − 677 writeTabularResult(tmp, ras_enrichment, buildOutputPath(dataset1Name, dataset2Name, details = "Tabular Result", ext = utils.FileFormat.TSV))
+ − 678
+ − 679 if ras_enrichment:
+ − 680 fix_map(tmp, core_map, ARGS.pValue, ARGS.fChange, max_z_score)
+ − 681 return
+ − 682
+ − 683 for reactId, enrichData in tmp.items(): tmp[reactId] = tuple(enrichData)
+ − 684 applyRpsEnrichmentToMap(tmp, core_map, max_z_score)
+ − 685
+ − 686 def computePValue(dataset1Data: List[float], dataset2Data: List[float]) -> Tuple[float, float]:
+ − 687 """
+ − 688 Computes the statistical significance score (P-value) of the comparison between coherent data
+ − 689 from two datasets. The data is supposed to, in both datasets:
+ − 690 - be related to the same reaction ID;
+ − 691 - be ordered by sample, such that the item at position i in both lists is related to the
+ − 692 same sample or cell line.
+ − 693
+ − 694 Args:
+ − 695 dataset1Data : data from the 1st dataset.
+ − 696 dataset2Data : data from the 2nd dataset.
+ − 697
+ − 698 Returns:
+ − 699 tuple: (P-value, Z-score)
+ − 700 - P-value from a Kolmogorov-Smirnov test on the provided data.
+ − 701 - Z-score of the difference between means of the two datasets.
+ − 702 """
+ − 703 # Perform Kolmogorov-Smirnov test
+ − 704 ks_statistic, p_value = st.ks_2samp(dataset1Data, dataset2Data)
+ − 705
+ − 706 # Calculate means and standard deviations
+ − 707 mean1 = np.mean(dataset1Data)
+ − 708 mean2 = np.mean(dataset2Data)
+ − 709 std1 = np.std(dataset1Data, ddof=1)
+ − 710 std2 = np.std(dataset2Data, ddof=1)
+ − 711
+ − 712 n1 = len(dataset1Data)
+ − 713 n2 = len(dataset2Data)
+ − 714
+ − 715 # Calculate Z-score
+ − 716 z_score = (mean1 - mean2) / np.sqrt((std1**2 / n1) + (std2**2 / n2))
+ − 717
+ − 718 return p_value, z_score
+ − 719
+ − 720 def compareDatasetPair(dataset1Data :List[List[float]], dataset2Data :List[List[float]], ids :List[str]) -> Tuple[Dict[str, List[Union[float, FoldChange]]], float]:
+ − 721 #TODO: the following code still suffers from "dumbvarnames-osis"
+ − 722 tmp :Dict[str, List[Union[float, FoldChange]]] = {}
+ − 723 count = 0
+ − 724 max_z_score = 0
+ − 725
+ − 726 for l1, l2 in zip(dataset1Data, dataset2Data):
+ − 727 reactId = ids[count]
+ − 728 count += 1
+ − 729 if not reactId: continue # we skip ids that have already been processed
+ − 730
+ − 731 try: #TODO: identify the source of these errors and minimize code in the try block
+ − 732 reactDir = ReactionDirection.fromReactionId(reactId)
+ − 733 # Net score is computed only for reversible reactions when user wants it on arrow tips or when RAS datasets aren't used
+ − 734 if (ARGS.net or not ARGS.using_RAS) and reactDir is not ReactionDirection.Unknown:
+ − 735 try: position = ids.index(reactId[:-1] + ('B' if reactDir is ReactionDirection.Direct else 'F'))
+ − 736 except ValueError: continue # we look for the complementary id, if not found we skip
+ − 737
+ − 738 nets1 = np.subtract(l1, dataset1Data[position])
+ − 739 nets2 = np.subtract(l2, dataset2Data[position])
+ − 740
+ − 741 p_value, z_score = computePValue(nets1, nets2)
+ − 742 avg1 = sum(nets1) / len(nets1)
+ − 743 avg2 = sum(nets2) / len(nets2)
+ − 744 net = fold_change(avg1, avg2)
+ − 745
+ − 746 if math.isnan(net): continue
+ − 747 tmp[reactId[:-1] + "RV"] = [p_value, net, z_score, avg1, avg2]
+ − 748
+ − 749 # vvv complementary directional ids are set to None once processed if net is to be applied to tips
+ − 750 if ARGS.net:
+ − 751 ids[position] = None
+ − 752 continue
+ − 753
+ − 754 # fallthrough is intended, regular scores need to be computed when tips aren't net but RAS datasets aren't used
+ − 755 p_value, z_score = computePValue(l1, l2)
+ − 756 avg = fold_change(sum(l1) / len(l1), sum(l2) / len(l2))
+ − 757 if not isinstance(z_score, str) and max_z_score < abs(z_score): max_z_score = abs(z_score)
+ − 758 tmp[reactId] = [float(p_value), avg, z_score]
+ − 759
+ − 760 except (TypeError, ZeroDivisionError): continue
+ − 761
+ − 762 return tmp, max_z_score
+ − 763
+ − 764 def computeEnrichment(metabMap :ET.ElementTree, class_pat :Dict[str, List[List[float]]], ids :List[str], *, fromRAS = True) -> None:
+ − 765 """
+ − 766 Compares clustered data based on a given comparison mode and applies enrichment-based styling on the
+ − 767 provided metabolic map.
+ − 768
+ − 769 Args:
+ − 770 metabMap : SVG map to modify.
+ − 771 class_pat : the clustered data.
+ − 772 ids : ids for data association.
+ − 773 fromRAS : whether the data to enrich consists of RAS scores.
+ − 774
+ − 775 Returns:
+ − 776 None
+ − 777
+ − 778 Raises:
+ − 779 sys.exit : if there are less than 2 classes for comparison
+ − 780
+ − 781 Side effects:
+ − 782 metabMap : mut
+ − 783 ids : mut
+ − 784 """
+ − 785 class_pat = { k.strip() : v for k, v in class_pat.items() }
+ − 786 #TODO: simplfy this stuff vvv and stop using sys.exit (raise the correct utils error)
+ − 787 if (not class_pat) or (len(class_pat.keys()) < 2): sys.exit('Execution aborted: classes provided for comparisons are less than two\n')
+ − 788
+ − 789 if ARGS.comparison == "manyvsmany":
+ − 790 for i, j in it.combinations(class_pat.keys(), 2):
+ − 791 #TODO: these 2 functions are always called in pair and in this order and need common data,
+ − 792 # some clever refactoring would be appreciated.
+ − 793 comparisonDict, max_z_score = compareDatasetPair(class_pat.get(i), class_pat.get(j), ids)
+ − 794 temp_thingsInCommon(comparisonDict, metabMap, max_z_score, i, j, fromRAS)
+ − 795
+ − 796 elif ARGS.comparison == "onevsrest":
+ − 797 for single_cluster in class_pat.keys():
+ − 798 t :List[List[List[float]]] = []
+ − 799 for k in class_pat.keys():
+ − 800 if k != single_cluster:
+ − 801 t.append(class_pat.get(k))
+ − 802
+ − 803 rest :List[List[float]] = []
+ − 804 for i in t:
+ − 805 rest = rest + i
+ − 806
+ − 807 comparisonDict, max_z_score = compareDatasetPair(class_pat.get(single_cluster), rest, ids)
+ − 808 temp_thingsInCommon(comparisonDict, metabMap, max_z_score, single_cluster, fromRAS)
+ − 809
+ − 810 elif ARGS.comparison == "onevsmany":
+ − 811 controlItems = class_pat.get(ARGS.control)
+ − 812 for otherDataset in class_pat.keys():
+ − 813 if otherDataset == ARGS.control: continue
+ − 814
+ − 815 comparisonDict, max_z_score = compareDatasetPair(controlItems, class_pat.get(otherDataset), ids)
+ − 816 temp_thingsInCommon(comparisonDict, metabMap, max_z_score, ARGS.control, otherDataset, fromRAS)
+ − 817
+ − 818 def createOutputMaps(dataset1Name :str, dataset2Name :str, core_map :ET.ElementTree) -> None:
+ − 819 svgFilePath = buildOutputPath(dataset1Name, dataset2Name, details = "SVG Map", ext = utils.FileFormat.SVG)
+ − 820 utils.writeSvg(svgFilePath, core_map)
+ − 821
+ − 822 if ARGS.generate_pdf:
+ − 823 pngPath = buildOutputPath(dataset1Name, dataset2Name, details = "PNG Map", ext = utils.FileFormat.PNG)
+ − 824 pdfPath = buildOutputPath(dataset1Name, dataset2Name, details = "PDF Map", ext = utils.FileFormat.PDF)
+ − 825 convert_to_pdf(svgFilePath, pngPath, pdfPath)
+ − 826
+ − 827 if not ARGS.generate_svg: os.remove(svgFilePath.show())
+ − 828
+ − 829 ClassPat = Dict[str, List[List[float]]]
+ − 830 def getClassesAndIdsFromDatasets(datasetsPaths :List[str], datasetPath :str, classPath :str, names :List[str]) -> Tuple[List[str], ClassPat]:
+ − 831 # TODO: I suggest creating dicts with ids as keys instead of keeping class_pat and ids separate,
+ − 832 # for the sake of everyone's sanity.
+ − 833 class_pat :ClassPat = {}
+ − 834 if ARGS.option == 'datasets':
+ − 835 num = 1 #TODO: the dataset naming function could be a generator
+ − 836 for path, name in zip(datasetsPaths, names):
+ − 837 name = name_dataset(name, num)
+ − 838 resolve_rules_float, ids = getDatasetValues(path, name)
+ − 839 if resolve_rules_float != None:
+ − 840 class_pat[name] = list(map(list, zip(*resolve_rules_float.values())))
+ − 841
+ − 842 num += 1
+ − 843
+ − 844 elif ARGS.option == "dataset_class":
+ − 845 classes = read_dataset(classPath, "class")
+ − 846 classes = classes.astype(str)
+ − 847
+ − 848 resolve_rules_float, ids = getDatasetValues(datasetPath, "Dataset Class (not actual name)")
+ − 849 if resolve_rules_float != None: class_pat = split_class(classes, resolve_rules_float)
+ − 850
+ − 851 return ids, class_pat
+ − 852 #^^^ TODO: this could be a match statement over an enum, make it happen future marea dev with python 3.12! (it's why I kept the ifs)
+ − 853
+ − 854 #TODO: create these damn args as FilePath objects
+ − 855 def getDatasetValues(datasetPath :str, datasetName :str) -> Tuple[ClassPat, List[str]]:
+ − 856 """
+ − 857 Opens the dataset at the given path and extracts the values (expected nullable numerics) and the IDs.
+ − 858
+ − 859 Args:
+ − 860 datasetPath : path to the dataset
+ − 861 datasetName (str): dataset name, used in error reporting
+ − 862
+ − 863 Returns:
+ − 864 Tuple[ClassPat, List[str]]: values and IDs extracted from the dataset
+ − 865 """
+ − 866 dataset = read_dataset(datasetPath, datasetName)
+ − 867 IDs = pd.Series.tolist(dataset.iloc[:, 0].astype(str))
+ − 868
+ − 869 dataset = dataset.drop(dataset.columns[0], axis = "columns").to_dict("list")
+ − 870 return { id : list(map(utils.Float("Dataset values, not an argument"), values)) for id, values in dataset.items() }, IDs
+ − 871
+ − 872 ############################ MAIN #############################################
+ − 873 def main() -> None:
+ − 874 """
+ − 875 Initializes everything and sets the program in motion based on the fronted input arguments.
+ − 876
+ − 877 Returns:
+ − 878 None
+ − 879
+ − 880 Raises:
+ − 881 sys.exit : if a user-provided custom map is in the wrong format (ET.XMLSyntaxError, ET.XMLSchemaParseError)
+ − 882 """
+ − 883
+ − 884 global ARGS
+ − 885 ARGS = process_args()
+ − 886
+ − 887 if os.path.isdir('result') == False: os.makedirs('result')
+ − 888
+ − 889 core_map :ET.ElementTree = ARGS.choice_map.getMap(
+ − 890 ARGS.tool_dir,
+ − 891 utils.FilePath.fromStrPath(ARGS.custom_map) if ARGS.custom_map else None)
+ − 892 # TODO: ^^^ ugly but fine for now, the argument is None if the model isn't custom because no file was given.
+ − 893 # getMap will None-check the customPath and panic when the model IS custom but there's no file (good). A cleaner
+ − 894 # solution can be derived from my comment in FilePath.fromStrPath
+ − 895
+ − 896 if ARGS.using_RAS:
+ − 897 ids, class_pat = getClassesAndIdsFromDatasets(ARGS.input_datas, ARGS.input_data, ARGS.input_class, ARGS.names)
+ − 898 computeEnrichment(core_map, class_pat, ids)
+ − 899
+ − 900 if ARGS.using_RPS:
+ − 901 ids, class_pat = getClassesAndIdsFromDatasets(ARGS.input_datas_rps, ARGS.input_data_rps, ARGS.input_class_rps, ARGS.names_rps)
+ − 902 computeEnrichment(core_map, class_pat, ids, fromRAS = False)
+ − 903
+ − 904 # create output files: TODO: this is the same comparison happening in "maps", find a better way to organize this
+ − 905 if ARGS.comparison == "manyvsmany":
+ − 906 for i, j in it.combinations(class_pat.keys(), 2): createOutputMaps(i, j, core_map)
+ − 907 return
+ − 908
+ − 909 if ARGS.comparison == "onevsrest":
+ − 910 for single_cluster in class_pat.keys(): createOutputMaps(single_cluster, "rest", core_map)
+ − 911 return
+ − 912
+ − 913 for otherDataset in class_pat.keys():
+ − 914 if otherDataset != ARGS.control: createOutputMaps(i, j, core_map)
+ − 915
+ − 916 if not ERRORS: return
+ − 917 utils.logWarning(
+ − 918 f"The following reaction IDs were mentioned in the dataset but weren't found in the map: {ERRORS}",
+ − 919 ARGS.out_log)
+ − 920
+ − 921 print('Execution succeded')
+ − 922
+ − 923 ###############################################################################
+ − 924 if __name__ == "__main__":
+ − 925 main()