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comparison Marea/ras_generator.py @ 46:5d5d01ef1d68 draft

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author bimib
date Wed, 22 Jan 2020 11:50:54 -0500
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45:7aa966c488a4 46:5d5d01ef1d68
1 from __future__ import division
2 import sys
3 import pandas as pd
4 import itertools as it
5 import scipy.stats as st
6 import collections
7 import lxml.etree as ET
8 import pickle as pk
9 import math
10 import os
11 import argparse
12 from svglib.svglib import svg2rlg
13 from reportlab.graphics import renderPDF
14
15 ########################## argparse ##########################################
16
17 def process_args(args):
18 parser = argparse.ArgumentParser(usage = '%(prog)s [options]',
19 description = 'process some value\'s'+
20 ' genes to create a comparison\'s map.')
21 parser.add_argument('-rs', '--rules_selector',
22 type = str,
23 default = 'HMRcore',
24 choices = ['HMRcore', 'Recon', 'Custom'],
25 help = 'chose which type of dataset you want use')
26 parser.add_argument('-cr', '--custom',
27 type = str,
28 help='your dataset if you want custom rules')
29 parser.add_argument('-na', '--names',
30 type = str,
31 nargs = '+',
32 help = 'input names')
33 parser.add_argument('-n', '--none',
34 type = str,
35 default = 'true',
36 choices = ['true', 'false'],
37 help = 'compute Nan values')
38 parser.add_argument('-pv' ,'--pValue',
39 type = float,
40 default = 0.05,
41 help = 'P-Value threshold (default: %(default)s)')
42 parser.add_argument('-fc', '--fChange',
43 type = float,
44 default = 1.5,
45 help = 'Fold-Change threshold (default: %(default)s)')
46 parser.add_argument('-td', '--tool_dir',
47 type = str,
48 required = True,
49 help = 'your tool directory')
50 parser.add_argument('-op', '--option',
51 type = str,
52 choices = ['datasets', 'dataset_class', 'datasets_rasonly'],
53 help='dataset or dataset and class')
54 parser.add_argument('-ol', '--out_log',
55 help = "Output log")
56 parser.add_argument('-ids', '--input_datas',
57 type = str,
58 nargs = '+',
59 help = 'input datasets')
60 parser.add_argument('-id', '--input_data',
61 type = str,
62 help = 'input dataset')
63 parser.add_argument('-ic', '--input_class',
64 type = str,
65 help = 'sample group specification')
66 parser.add_argument('-cm', '--custom_map',
67 type = str,
68 help = 'custom map')
69 parser.add_argument('-yn', '--yes_no',
70 type = str,
71 choices = ['yes', 'no'],
72 help = 'if make or not custom map')
73 parser.add_argument('-gs', '--generate_svg',
74 type = str,
75 default = 'true',
76 choices = ['true', 'false'],
77 help = 'generate svg map')
78 parser.add_argument('-gp', '--generate_pdf',
79 type = str,
80 default = 'true',
81 choices = ['true', 'false'],
82 help = 'generate pdf map')
83 parser.add_argument('-gr', '--generate_ras',
84 type = str,
85 default = 'true',
86 choices = ['true', 'false'],
87 help = 'generate reaction activity score')
88 parser.add_argument('-sr', '--single_ras_file',
89 type = str,
90 help = 'file that will contain ras')
91
92 args = parser.parse_args()
93 return args
94
95 ########################### warning ###########################################
96
97 def warning(s):
98 args = process_args(sys.argv)
99 with open(args.out_log, 'a') as log:
100 log.write(s)
101
102 ############################ dataset input ####################################
103
104 def read_dataset(data, name):
105 try:
106 dataset = pd.read_csv(data, sep = '\t', header = 0, engine='python')
107 except pd.errors.EmptyDataError:
108 sys.exit('Execution aborted: wrong format of ' + name + '\n')
109 if len(dataset.columns) < 2:
110 sys.exit('Execution aborted: wrong format of ' + name + '\n')
111 return dataset
112
113 ############################ dataset name #####################################
114
115 def name_dataset(name_data, count):
116 if str(name_data) == 'Dataset':
117 return str(name_data) + '_' + str(count)
118 else:
119 return str(name_data)
120
121 ############################ load id e rules ##################################
122
123 def load_id_rules(reactions):
124 ids, rules = [], []
125 for key, value in reactions.items():
126 ids.append(key)
127 rules.append(value)
128 return (ids, rules)
129
130 ############################ check_methods ####################################
131
132 def gene_type(l, name):
133 if check_hgnc(l):
134 return 'hugo_id'
135 elif check_ensembl(l):
136 return 'ensembl_gene_id'
137 elif check_symbol(l):
138 return 'symbol'
139 elif check_entrez(l):
140 return 'entrez_id'
141 else:
142 sys.exit('Execution aborted:\n' +
143 'gene ID type in ' + name + ' not supported. Supported ID'+
144 'types are: HUGO ID, Ensemble ID, HUGO symbol, Entrez ID\n')
145
146 def check_hgnc(l):
147 if len(l) > 5:
148 if (l.upper()).startswith('HGNC:'):
149 return l[5:].isdigit()
150 else:
151 return False
152 else:
153 return False
154
155 def check_ensembl(l):
156 if len(l) == 15:
157 if (l.upper()).startswith('ENS'):
158 return l[4:].isdigit()
159 else:
160 return False
161 else:
162 return False
163
164 def check_symbol(l):
165 if len(l) > 0:
166 if l[0].isalpha() and l[1:].isalnum():
167 return True
168 else:
169 return False
170 else:
171 return False
172
173 def check_entrez(l):
174 if len(l) > 0:
175 return l.isdigit()
176 else:
177 return False
178
179 def check_bool(b):
180 if b == 'true':
181 return True
182 elif b == 'false':
183 return False
184
185 ############################ resolve_methods ##################################
186
187 def replace_gene_value(l, d):
188 tmp = []
189 err = []
190 while l:
191 if isinstance(l[0], list):
192 tmp_rules, tmp_err = replace_gene_value(l[0], d)
193 tmp.append(tmp_rules)
194 err.extend(tmp_err)
195 else:
196 value = replace_gene(l[0], d)
197 tmp.append(value)
198 if value == None:
199 err.append(l[0])
200 l = l[1:]
201 return (tmp, err)
202
203
204 def replace_gene(l, d):
205 if l =='and' or l == 'or':
206 return l
207 else:
208 value = d.get(l, None)
209 if not(value == None or isinstance(value, (int, float))):
210 sys.exit('Execution aborted: ' + value + ' value not valid\n')
211 return value
212
213 def computes(val1, op, val2, cn):
214 if val1 != None and val2 != None:
215 if op == 'and':
216 return min(val1, val2)
217 else:
218 return val1 + val2
219 elif op == 'and':
220 if cn is True:
221 if val1 != None:
222 return val1
223 elif val2 != None:
224 return val2
225 else:
226 return None
227 else:
228 return None
229 else:
230 if val1 != None:
231 return val1
232 elif val2 != None:
233 return val2
234 else:
235 return None
236
237 def control(ris, l, cn):
238 if len(l) == 1:
239 if isinstance(l[0], (float, int)) or l[0] == None:
240 return l[0]
241 elif isinstance(l[0], list):
242 return control(None, l[0], cn)
243 else:
244 return False
245 elif len(l) > 2:
246 return control_list(ris, l, cn)
247 else:
248 return False
249
250 def control_list(ris, l, cn):
251 while l:
252 if len(l) == 1:
253 return False
254 elif (isinstance(l[0], (float, int)) or
255 l[0] == None) and l[1] in ['and', 'or']:
256 if isinstance(l[2], (float, int)) or l[2] == None:
257 ris = computes(l[0], l[1], l[2], cn)
258 elif isinstance(l[2], list):
259 tmp = control(None, l[2], cn)
260 if tmp is False:
261 return False
262 else:
263 ris = computes(l[0], l[1], tmp, cn)
264 else:
265 return False
266 l = l[3:]
267 elif l[0] in ['and', 'or']:
268 if isinstance(l[1], (float, int)) or l[1] == None:
269 ris = computes(ris, l[0], l[1], cn)
270 elif isinstance(l[1], list):
271 tmp = control(None,l[1], cn)
272 if tmp is False:
273 return False
274 else:
275 ris = computes(ris, l[0], tmp, cn)
276 else:
277 return False
278 l = l[2:]
279 elif isinstance(l[0], list) and l[1] in ['and', 'or']:
280 if isinstance(l[2], (float, int)) or l[2] == None:
281 tmp = control(None, l[0], cn)
282 if tmp is False:
283 return False
284 else:
285 ris = computes(tmp, l[1], l[2], cn)
286 elif isinstance(l[2], list):
287 tmp = control(None, l[0], cn)
288 tmp2 = control(None, l[2], cn)
289 if tmp is False or tmp2 is False:
290 return False
291 else:
292 ris = computes(tmp, l[1], tmp2, cn)
293 else:
294 return False
295 l = l[3:]
296 else:
297 return False
298 return ris
299
300 ############################ map_methods ######################################
301
302 def fold_change(avg1, avg2):
303 if avg1 == 0 and avg2 == 0:
304 return 0
305 elif avg1 == 0:
306 return '-INF'
307 elif avg2 == 0:
308 return 'INF'
309 else:
310 return math.log(avg1 / avg2, 2)
311
312 def fix_style(l, col, width, dash):
313 tmp = l.split(';')
314 flag_col = False
315 flag_width = False
316 flag_dash = False
317 for i in range(len(tmp)):
318 if tmp[i].startswith('stroke:'):
319 tmp[i] = 'stroke:' + col
320 flag_col = True
321 if tmp[i].startswith('stroke-width:'):
322 tmp[i] = 'stroke-width:' + width
323 flag_width = True
324 if tmp[i].startswith('stroke-dasharray:'):
325 tmp[i] = 'stroke-dasharray:' + dash
326 flag_dash = True
327 if not flag_col:
328 tmp.append('stroke:' + col)
329 if not flag_width:
330 tmp.append('stroke-width:' + width)
331 if not flag_dash:
332 tmp.append('stroke-dasharray:' + dash)
333 return ';'.join(tmp)
334
335 def fix_map(d, core_map, threshold_P_V, threshold_F_C, max_F_C):
336 maxT = 12
337 minT = 2
338 grey = '#BEBEBE'
339 blue = '#0000FF'
340 red = '#E41A1C'
341 for el in core_map.iter():
342 el_id = str(el.get('id'))
343 if el_id.startswith('R_'):
344 tmp = d.get(el_id[2:])
345 if tmp != None:
346 p_val = tmp[0]
347 f_c = tmp[1]
348 if p_val < threshold_P_V:
349 if not isinstance(f_c, str):
350 if abs(f_c) < math.log(threshold_F_C, 2):
351 col = grey
352 width = str(minT)
353 else:
354 if f_c < 0:
355 col = blue
356 elif f_c > 0:
357 col = red
358 width = str(max((abs(f_c) * maxT) / max_F_C, minT))
359 else:
360 if f_c == '-INF':
361 col = blue
362 elif f_c == 'INF':
363 col = red
364 width = str(maxT)
365 dash = 'none'
366 else:
367 dash = '5,5'
368 col = grey
369 width = str(minT)
370 el.set('style', fix_style(el.get('style'), col, width, dash))
371 return core_map
372
373 ############################ make recon #######################################
374
375 def check_and_doWord(l):
376 tmp = []
377 tmp_genes = []
378 count = 0
379 while l:
380 if count >= 0:
381 if l[0] == '(':
382 count += 1
383 tmp.append(l[0])
384 l.pop(0)
385 elif l[0] == ')':
386 count -= 1
387 tmp.append(l[0])
388 l.pop(0)
389 elif l[0] == ' ':
390 l.pop(0)
391 else:
392 word = []
393 while l:
394 if l[0] in [' ', '(', ')']:
395 break
396 else:
397 word.append(l[0])
398 l.pop(0)
399 word = ''.join(word)
400 tmp.append(word)
401 if not(word in ['or', 'and']):
402 tmp_genes.append(word)
403 else:
404 return False
405 if count == 0:
406 return (tmp, tmp_genes)
407 else:
408 return False
409
410 def brackets_to_list(l):
411 tmp = []
412 while l:
413 if l[0] == '(':
414 l.pop(0)
415 tmp.append(resolve_brackets(l))
416 else:
417 tmp.append(l[0])
418 l.pop(0)
419 return tmp
420
421 def resolve_brackets(l):
422 tmp = []
423 while l[0] != ')':
424 if l[0] == '(':
425 l.pop(0)
426 tmp.append(resolve_brackets(l))
427 else:
428 tmp.append(l[0])
429 l.pop(0)
430 l.pop(0)
431 return tmp
432
433 def priorityAND(l):
434 tmp = []
435 flag = True
436 while l:
437 if len(l) == 1:
438 if isinstance(l[0], list):
439 tmp.append(priorityAND(l[0]))
440 else:
441 tmp.append(l[0])
442 l = l[1:]
443 elif l[0] == 'or':
444 tmp.append(l[0])
445 flag = False
446 l = l[1:]
447 elif l[1] == 'or':
448 if isinstance(l[0], list):
449 tmp.append(priorityAND(l[0]))
450 else:
451 tmp.append(l[0])
452 tmp.append(l[1])
453 flag = False
454 l = l[2:]
455 elif l[1] == 'and':
456 tmpAnd = []
457 if isinstance(l[0], list):
458 tmpAnd.append(priorityAND(l[0]))
459 else:
460 tmpAnd.append(l[0])
461 tmpAnd.append(l[1])
462 if isinstance(l[2], list):
463 tmpAnd.append(priorityAND(l[2]))
464 else:
465 tmpAnd.append(l[2])
466 l = l[3:]
467 while l:
468 if l[0] == 'and':
469 tmpAnd.append(l[0])
470 if isinstance(l[1], list):
471 tmpAnd.append(priorityAND(l[1]))
472 else:
473 tmpAnd.append(l[1])
474 l = l[2:]
475 elif l[0] == 'or':
476 flag = False
477 break
478 if flag == True: #when there are only AND in list
479 tmp.extend(tmpAnd)
480 elif flag == False:
481 tmp.append(tmpAnd)
482 return tmp
483
484 def checkRule(l):
485 if len(l) == 1:
486 if isinstance(l[0], list):
487 if checkRule(l[0]) is False:
488 return False
489 elif len(l) > 2:
490 if checkRule2(l) is False:
491 return False
492 else:
493 return False
494 return True
495
496 def checkRule2(l):
497 while l:
498 if len(l) == 1:
499 return False
500 elif isinstance(l[0], list) and l[1] in ['and', 'or']:
501 if checkRule(l[0]) is False:
502 return False
503 if isinstance(l[2], list):
504 if checkRule(l[2]) is False:
505 return False
506 l = l[3:]
507 elif l[1] in ['and', 'or']:
508 if isinstance(l[2], list):
509 if checkRule(l[2]) is False:
510 return False
511 l = l[3:]
512 elif l[0] in ['and', 'or']:
513 if isinstance(l[1], list):
514 if checkRule(l[1]) is False:
515 return False
516 l = l[2:]
517 else:
518 return False
519 return True
520
521 def do_rules(rules):
522 split_rules = []
523 err_rules = []
524 tmp_gene_in_rule = []
525 for i in range(len(rules)):
526 tmp = list(rules[i])
527 if tmp:
528 tmp, tmp_genes = check_and_doWord(tmp)
529 tmp_gene_in_rule.extend(tmp_genes)
530 if tmp is False:
531 split_rules.append([])
532 err_rules.append(rules[i])
533 else:
534 tmp = brackets_to_list(tmp)
535 if checkRule(tmp):
536 split_rules.append(priorityAND(tmp))
537 else:
538 split_rules.append([])
539 err_rules.append(rules[i])
540 else:
541 split_rules.append([])
542 if err_rules:
543 warning('Warning: wrong format rule in ' + str(err_rules) + '\n')
544 return (split_rules, list(set(tmp_gene_in_rule)))
545
546 def make_recon(data):
547 try:
548 import cobra as cb
549 import warnings
550 with warnings.catch_warnings():
551 warnings.simplefilter('ignore')
552 recon = cb.io.read_sbml_model(data)
553 react = recon.reactions
554 rules = [react[i].gene_reaction_rule for i in range(len(react))]
555 ids = [react[i].id for i in range(len(react))]
556 except cb.io.sbml3.CobraSBMLError:
557 try:
558 data = (pd.read_csv(data, sep = '\t', dtype = str, engine='python')).fillna('')
559 if len(data.columns) < 2:
560 sys.exit('Execution aborted: wrong format of '+
561 'custom datarules\n')
562 if not len(data.columns) == 2:
563 warning('Warning: more than 2 columns in custom datarules.\n' +
564 'Extra columns have been disregarded\n')
565 ids = list(data.iloc[:, 0])
566 rules = list(data.iloc[:, 1])
567 except pd.errors.EmptyDataError:
568 sys.exit('Execution aborted: wrong format of custom datarules\n')
569 except pd.errors.ParserError:
570 sys.exit('Execution aborted: wrong format of custom datarules\n')
571 split_rules, tmp_genes = do_rules(rules)
572 gene_in_rule = {}
573 for i in tmp_genes:
574 gene_in_rule[i] = 'ok'
575 return (ids, split_rules, gene_in_rule)
576
577 ############################ gene #############################################
578
579 def data_gene(gene, type_gene, name, gene_custom):
580 args = process_args(sys.argv)
581 for i in range(len(gene)):
582 tmp = gene.iloc[i, 0]
583 if tmp.startswith(' ') or tmp.endswith(' '):
584 gene.iloc[i, 0] = (tmp.lstrip()).rstrip()
585 gene_dup = [item for item, count in
586 collections.Counter(gene[gene.columns[0]]).items() if count > 1]
587 pat_dup = [item for item, count in
588 collections.Counter(list(gene.columns)).items() if count > 1]
589
590 if gene_dup:
591 if gene_custom == None:
592 if args.rules_selector == 'HMRcore':
593 gene_in_rule = pk.load(open(args.tool_dir +
594 '/local/HMRcore_genes.p', 'rb'))
595 elif args.rules_selector == 'Recon':
596 gene_in_rule = pk.load(open(args.tool_dir +
597 '/local/Recon_genes.p', 'rb'))
598 gene_in_rule = gene_in_rule.get(type_gene)
599 else:
600 gene_in_rule = gene_custom
601 tmp = []
602 for i in gene_dup:
603 if gene_in_rule.get(i) == 'ok':
604 tmp.append(i)
605 if tmp:
606 sys.exit('Execution aborted because gene ID '
607 +str(tmp)+' in '+name+' is duplicated\n')
608 if pat_dup:
609 warning('Warning: duplicated label\n' + str(pat_dup) + 'in ' + name +
610 '\n')
611
612 return (gene.set_index(gene.columns[0])).to_dict()
613
614 ############################ resolve ##########################################
615
616 def resolve(genes, rules, ids, resolve_none, name):
617 resolve_rules = {}
618 names_array = []
619 not_found = []
620 flag = False
621 for key, value in genes.items():
622 tmp_resolve = []
623 for i in range(len(rules)):
624 tmp = rules[i]
625 if tmp:
626 tmp, err = replace_gene_value(tmp, value)
627 if err:
628 not_found.extend(err)
629 ris = control(None, tmp, resolve_none)
630 if ris is False or ris == None:
631 tmp_resolve.append(None)
632 else:
633 tmp_resolve.append(ris)
634 flag = True
635 else:
636 tmp_resolve.append(None)
637 resolve_rules[key] = tmp_resolve
638 if flag is False:
639 warning('Warning: no computable score (due to missing gene values)' +
640 'for class ' + name + ', the class has been disregarded\n')
641 return (None, None)
642 return (resolve_rules, list(set(not_found)))
643
644 ############################ split class ######################################
645
646 def split_class(classes, resolve_rules):
647 class_pat = {}
648 for i in range(len(classes)):
649 classe = classes.iloc[i, 1]
650 if not pd.isnull(classe):
651 l = []
652 for j in range(i, len(classes)):
653 if classes.iloc[j, 1] == classe:
654 pat_id = classes.iloc[j, 0]
655 tmp = resolve_rules.get(pat_id, None)
656 if tmp != None:
657 l.append(tmp)
658 classes.iloc[j, 1] = None
659 if l:
660 class_pat[classe] = list(map(list, zip(*l)))
661 else:
662 warning('Warning: no sample found in class ' + classe +
663 ', the class has been disregarded\n')
664 return class_pat
665
666 ############################ create_ras #######################################
667
668 def create_ras (resolve_rules, dataset_name, single_ras, rules, ids):
669
670 if resolve_rules == None:
671 warning("Couldn't generate RAS for current dataset: " + dataset_name)
672
673 for geni in resolve_rules.values():
674 for i, valori in enumerate(geni):
675 if valori == None:
676 geni[i] = 'None'
677
678 output_ras = pd.DataFrame.from_dict(resolve_rules)
679
680 output_ras.insert(0, 'Reactions', ids)
681 output_to_csv = pd.DataFrame.to_csv(output_ras, sep = '\t', index = False)
682
683 if (single_ras):
684 args = process_args(sys.argv)
685 text_file = open(args.single_ras_file, "w")
686 else:
687 text_file = open("ras/Reaction_Activity_Score_Of_" + dataset_name + ".tsv", "w")
688
689 text_file.write(output_to_csv)
690 text_file.close()
691
692 ############################ map ##############################################
693
694 def maps(core_map, class_pat, ids, threshold_P_V, threshold_F_C, create_svg, create_pdf):
695 args = process_args(sys.argv)
696 if (not class_pat) or (len(class_pat.keys()) < 2):
697 sys.exit('Execution aborted: classes provided for comparisons are ' +
698 'less than two\n')
699 for i, j in it.combinations(class_pat.keys(), 2):
700 tmp = {}
701 count = 0
702 max_F_C = 0
703 for l1, l2 in zip(class_pat.get(i), class_pat.get(j)):
704 try:
705 stat_D, p_value = st.ks_2samp(l1, l2)
706 avg = fold_change(sum(l1) / len(l1), sum(l2) / len(l2))
707 if not isinstance(avg, str):
708 if max_F_C < abs(avg):
709 max_F_C = abs(avg)
710 tmp[ids[count]] = [float(p_value), avg]
711 count += 1
712 except (TypeError, ZeroDivisionError):
713 count += 1
714 tab = 'result/' + i + '_vs_' + j + ' (Tabular Result).tsv'
715 tmp_csv = pd.DataFrame.from_dict(tmp, orient = "index")
716 tmp_csv = tmp_csv.reset_index()
717 header = ['ids', 'P_Value', 'Log2(fold change)']
718 tmp_csv.to_csv(tab, sep = '\t', index = False, header = header)
719
720 if create_svg or create_pdf:
721 if args.rules_selector == 'HMRcore' or (args.rules_selector == 'Custom'
722 and args.yes_no == 'yes'):
723 fix_map(tmp, core_map, threshold_P_V, threshold_F_C, max_F_C)
724 file_svg = 'result/' + i + '_vs_' + j + ' (SVG Map).svg'
725 with open(file_svg, 'wb') as new_map:
726 new_map.write(ET.tostring(core_map))
727
728
729 if create_pdf:
730 file_pdf = 'result/' + i + '_vs_' + j + ' (PDF Map).pdf'
731 renderPDF.drawToFile(svg2rlg(file_svg), file_pdf)
732
733 if not create_svg:
734 #Ho utilizzato il file svg per generare il pdf,
735 #ma l'utente non ne ha richiesto il ritorno, quindi
736 #lo elimino
737 os.remove('result/' + i + '_vs_' + j + ' (SVG Map).svg')
738
739 return None
740
741 ############################ MAIN #############################################
742
743 def main():
744 args = process_args(sys.argv)
745
746 create_svg = check_bool(args.generate_svg)
747 create_pdf = check_bool(args.generate_pdf)
748 generate_ras = check_bool(args.generate_ras)
749
750 os.makedirs('result')
751
752 if generate_ras:
753 os.makedirs('ras')
754
755 if args.rules_selector == 'HMRcore':
756 recon = pk.load(open(args.tool_dir + '/local/HMRcore_rules.p', 'rb'))
757 elif args.rules_selector == 'Recon':
758 recon = pk.load(open(args.tool_dir + '/local/Recon_rules.p', 'rb'))
759 elif args.rules_selector == 'Custom':
760 ids, rules, gene_in_rule = make_recon(args.custom)
761
762 resolve_none = check_bool(args.none)
763
764 class_pat = {}
765
766 if args.option == 'datasets_rasonly':
767 name = "RAS Dataset"
768 dataset = read_dataset(args.input_datas[0],"dataset")
769
770 dataset.iloc[:, 0] = (dataset.iloc[:, 0]).astype(str)
771
772 type_gene = gene_type(dataset.iloc[0, 0], name)
773
774 if args.rules_selector != 'Custom':
775 genes = data_gene(dataset, type_gene, name, None)
776 ids, rules = load_id_rules(recon.get(type_gene))
777 elif args.rules_selector == 'Custom':
778 genes = data_gene(dataset, type_gene, name, gene_in_rule)
779
780 resolve_rules, err = resolve(genes, rules, ids, resolve_none, name)
781
782 create_ras(resolve_rules, name, True, rules, ids)
783
784 if err != None and err:
785 warning('Warning: gene\n' + str(err) + '\nnot found in class '
786 + name + ', the expression level for this gene ' +
787 'will be considered NaN\n')
788
789 print('execution succeded')
790 return None
791
792
793 elif args.option == 'datasets':
794 num = 1
795 for i, j in zip(args.input_datas, args.names):
796
797 name = name_dataset(j, num)
798 dataset = read_dataset(i, name)
799
800 dataset.iloc[:, 0] = (dataset.iloc[:, 0]).astype(str)
801
802 type_gene = gene_type(dataset.iloc[0, 0], name)
803
804 if args.rules_selector != 'Custom':
805 genes = data_gene(dataset, type_gene, name, None)
806 ids, rules = load_id_rules(recon.get(type_gene))
807 elif args.rules_selector == 'Custom':
808 genes = data_gene(dataset, type_gene, name, gene_in_rule)
809
810
811 resolve_rules, err = resolve(genes, rules, ids, resolve_none, name)
812
813 if generate_ras:
814 create_ras(resolve_rules, name, False, rules, ids)
815
816 if err != None and err:
817 warning('Warning: gene\n' + str(err) + '\nnot found in class '
818 + name + ', the expression level for this gene ' +
819 'will be considered NaN\n')
820 if resolve_rules != None:
821 class_pat[name] = list(map(list, zip(*resolve_rules.values())))
822 num += 1
823 elif args.option == 'dataset_class':
824 name = 'RNAseq'
825 dataset = read_dataset(args.input_data, name)
826 dataset.iloc[:, 0] = (dataset.iloc[:, 0]).astype(str)
827 type_gene = gene_type(dataset.iloc[0, 0], name)
828 classes = read_dataset(args.input_class, 'class')
829 if not len(classes.columns) == 2:
830 warning('Warning: more than 2 columns in class file. Extra' +
831 'columns have been disregarded\n')
832 classes = classes.astype(str)
833 if args.rules_selector != 'Custom':
834 genes = data_gene(dataset, type_gene, name, None)
835 ids, rules = load_id_rules(recon.get(type_gene))
836 elif args.rules_selector == 'Custom':
837 genes = data_gene(dataset, type_gene, name, gene_in_rule)
838 resolve_rules, err = resolve(genes, rules, ids, resolve_none, name)
839 if err != None and err:
840 warning('Warning: gene\n'+str(err)+'\nnot found in class '
841 + name + ', the expression level for this gene ' +
842 'will be considered NaN\n')
843 if resolve_rules != None:
844 class_pat = split_class(classes, resolve_rules)
845 if generate_ras:
846 create_ras(resolve_rules, name, False, rules, ids)
847
848
849 if args.rules_selector == 'Custom':
850 if args.yes_no == 'yes':
851 try:
852 core_map = ET.parse(args.custom_map)
853 except (ET.XMLSyntaxError, ET.XMLSchemaParseError):
854 sys.exit('Execution aborted: custom map in wrong format')
855 elif args.yes_no == 'no':
856 core_map = ET.parse(args.tool_dir + '/local/HMRcoreMap.svg')
857 else:
858 core_map = ET.parse(args.tool_dir+'/local/HMRcoreMap.svg')
859
860 maps(core_map, class_pat, ids, args.pValue, args.fChange, create_svg, create_pdf)
861
862 print('Execution succeded')
863
864 return None
865
866 ###############################################################################
867
868 if __name__ == "__main__":
869 main()