forked from nsalomonis/altanalyze
-
Notifications
You must be signed in to change notification settings - Fork 0
/
mappfinder.py
executable file
·1153 lines (1032 loc) · 58.6 KB
/
mappfinder.py
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
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
628
629
630
631
632
633
634
635
636
637
638
639
640
641
642
643
644
645
646
647
648
649
650
651
652
653
654
655
656
657
658
659
660
661
662
663
664
665
666
667
668
669
670
671
672
673
674
675
676
677
678
679
680
681
682
683
684
685
686
687
688
689
690
691
692
693
694
695
696
697
698
699
700
701
702
703
704
705
706
707
708
709
710
711
712
713
714
715
716
717
718
719
720
721
722
723
724
725
726
727
728
729
730
731
732
733
734
735
736
737
738
739
740
741
742
743
744
745
746
747
748
749
750
751
752
753
754
755
756
757
758
759
760
761
762
763
764
765
766
767
768
769
770
771
772
773
774
775
776
777
778
779
780
781
782
783
784
785
786
787
788
789
790
791
792
793
794
795
796
797
798
799
800
801
802
803
804
805
806
807
808
809
810
811
812
813
814
815
816
817
818
819
820
821
822
823
824
825
826
827
828
829
830
831
832
833
834
835
836
837
838
839
840
841
842
843
844
845
846
847
848
849
850
851
852
853
854
855
856
857
858
859
860
861
862
863
864
865
866
867
868
869
870
871
872
873
874
875
876
877
878
879
880
881
882
883
884
885
886
887
888
889
890
891
892
893
894
895
896
897
898
899
900
901
902
903
904
905
906
907
908
909
910
911
912
913
914
915
916
917
918
919
920
921
922
923
924
925
926
927
928
929
930
931
932
933
934
935
936
937
938
939
940
941
942
943
944
945
946
947
948
949
950
951
952
953
954
955
956
957
958
959
960
961
962
963
964
965
966
967
968
969
970
971
972
973
974
975
976
977
978
979
980
981
982
983
984
985
986
987
988
989
990
991
992
993
994
995
996
997
998
999
1000
###mappfinder
#Copyright 2005-2008 J. David Gladstone Institutes, San Francisco California
#Author Nathan Salomonis - [email protected]
#Permission is hereby granted, free of charge, to any person obtaining a copy
#of this software and associated documentation files (the "Software"), to deal
#in the Software without restriction, including without limitation the rights
#to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
#copies of the Software, and to permit persons to whom the Software is furnished
#to do so, subject to the following conditions:
#THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED,
#INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A
#PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT
#HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION
#OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE
#SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
"""This module contains methods for performing over-representation analysis (ORA) on input gene
lists provided by the user relative to denominator gene lists for nested Gene Ontology relationships
and WikiPathway biological pathways. These methods include a permutation based analysis and multiple
hypthesis correction."""
import sys, string
import os.path, platform
import unique
import math
import time
import gene_associations; reload(gene_associations)
try: from import_scripts import OBO_import
except Exception: pass
import GO_Elite
from stats_scripts import statistics
import random
import UI
import export; reload(export)
import re
from stats_scripts import fishers_exact_test
import traceback
import warnings
try:
from scipy import stats
except Exception:
pass ### scipy is not required but is used as a faster implementation of Fisher Exact Test when present
################# Parse directory files
def filepath(filename):
fn = unique.filepath(filename)
return fn
def read_directory(sub_dir):
try:
dir_list = unique.read_directory(sub_dir)
except Exception:
dir_list=[] ### Directory does not exist
dir_list2 = []
###Code to prevent folder names from being included
for entry in dir_list:
if entry[-4:] == ".txt" or entry[-4:] == ".csv": dir_list2.append(entry)
return dir_list2
def readDirText(sub_dir):
if sub_dir == None or sub_dir == '':
dir_list2 = [] ### For automatically assigned denominators
elif '.txt' in sub_dir:
dir_list2 = [export.findFilename(sub_dir)] ### for pooled analyses - analyze only the specific file direction provided
else:
dir_list = unique.read_directory(sub_dir); dir_list2 = []
###Code to prevent folder names from being included
for entry in dir_list:
if entry[-4:] == ".txt" and '._' not in entry: dir_list2.append(entry)
return dir_list2
###### Classes ######
class GrabFiles:
def setdirectory(self,value):
if '.txt' in value:
value = export.findParentDir(value) ### For pooled analyses where only one file is submitted
self.data = value
def display(self):
print self.data
def directory(self):
return self.data
def searchdirectory(self,search_term):
#self is an instance while self.data is the value of the instance
all_matching,file_dir,file = gene_associations.getDirectoryFiles(self.data,str(search_term))
#if len(file)<1: print search_term,'not found'
return file_dir,file
def getAllFiles(self,search_term):
#self is an instance while self.data is the value of the instance
all_matching,file_dir,file = gene_associations.getDirectoryFiles(self.data,str(search_term))
#if len(file)<1: print search_term,'not found'
return all_matching
################# Import and Annotate Data
def eliminate_redundant_dict_values(database):
db1={}
for key in database: list = unique.unique(database[key]); list.sort(); db1[key] = list
return db1
def swapKeyValues(db):
swapped={}
for key in db:
values = db[key]
for value in values:
try: swapped[value].append(key)
except KeyError: swapped[value] = [key]
return swapped
def identifyGeneFiles(import_dir,gene_file):
split_name = string.split(gene_file,'.')
e = GrabFiles(); e.setdirectory(import_dir)
dir_files = read_directory(import_dir)
if len(split_name)>2:
prefix_id = split_name[0]+'.'
denominator_file_dir,denominator_file = e.searchdirectory(prefix_id)
else: denominator_file_dir =''
if len(dir_files)==1 or denominator_file_dir=='':
try: denominator_file_dir,denominator_file = e.searchdirectory(dir_files[0])
except IndexError:
print_out = "WARNING: No denominator file included in\nthe GeneQuery/DenominatorGenes directory.\nTo proceed, place all denominator\nIDs in a file in that directory."
if PoolVar: q.put([print_out]); return None
ForceCriticalError(print_out)
return denominator_file_dir
def associateInputSourceWithGene(source_to_gene,source_id_list):
gene_db={}; count_null = 0
for source_id in source_id_list:
try:
gene_ids = source_to_gene[source_id]
for gene_id in gene_ids:
try: gene_db[gene_id].append(source_id)
except KeyError: gene_db[gene_id] = [source_id]
except KeyError: count_null+=1
#print count_null, 'source IDs not imported'
return gene_db
def cleanUpLine(line):
line = string.replace(line,'\n','')
line = string.replace(line,'\c','')
data = string.replace(line,'\r','')
data = string.replace(data,'"','')
return data
def importVersionData(dir):
global OBO_date
program_type,database_dir = unique.whatProgramIsThis(); parent_dir = ''
if program_type == 'AltAnalyze': parent_dir = 'AltDatabase/goelite/'
dir = parent_dir+dir
filename = dir+'version.txt'; fn=filepath(filename)
for line in open(fn,'r').readlines():
data = cleanUpLine(line)
OBO_version, OBO_date = string.split(data,'\t')
return OBO_date
def checkDenominatorMatchesInput(input_gene_list,denominator_gene_list,gene_file):
for id in input_gene_list:
try: null = denominator_gene_list[id] ###this object was changed from a list to a dictionary for efficiency
except KeyError: ###Only occurs if an input ID is NOT found in the denominator
all_alphanumeric = string.join(re.findall(r"\w",id))
print_out = 'Identifier: '+'"'+id+'"'+' not found in Denominator set '+str(len(id))+' '+all_alphanumeric+' '+str(len(input_gene_list))+' '+str(len(denominator_gene_list))+'\n'
print_out = 'WARNING!!! Job stopped... Denominator gene list\ndoes not match the input gene list for\n%s' % gene_file
if PoolVar: q.put([print_out]); return None
ForceCriticalError(print_out)
def formatTime(start_time,end_time):
intgr,decim = string.split(str(end_time-start_time),'.')
### Alternatively, use - round(end_time-start_time,1)
return intgr+'.'+decim[0]
def importGeneData():
### Simple dictionary for verifying ID sets (important when wrong species selected)
program_type,database_dir = unique.whatProgramIsThis()
gene_import_dir = database_dir+'/'+species_code+'/gene/'+mod+'.txt'
fn=filepath(gene_import_dir); gene_annotations={}; x=0
for line in open(fn,'rU').xreadlines():
data = line.strip()
t = string.split(data,'\t')
if x == 0: x = 1
else:
gene_annotations[t[0]] = t[0]
return gene_annotations
def checkCorrectSystemAndSpecies(input_gene_list):
### Verify that the input IDs are the correct system and species
gene_annotations = importGeneData()
missing=True
while missing:
for i in input_gene_list:
try:
null=gene_annotations[i]
missing=False
except Exception: pass
break
return missing
def generateMAPPFinderScores(species_title,species_id,source,mod_db,system_Codes,permute,resources_to_analyze,file_dirs,parent_root,poolVar=False,Q=None,Multi=None):
global mappfinder_output_dir; global custom_sets_folder; global root; root = parent_root
global mapp_to_mod_genes; global ontology_to_mod_genes; global system_codes; system_codes = system_Codes
global q; q=Q; global mlp; mlp = Multi; global display; poolVar = True
criterion_input_folder, criterion_denom_folder, output_dir, custom_sets_folder = file_dirs
previous_denominator_file_dir = ''
ontology_to_mod_genes={}; mapp_to_mod_genes={}; global test; test = 'no'; global PoolVar; PoolVar=poolVar
program_type,database_dir = unique.whatProgramIsThis()
if resources_to_analyze == 'Gene Ontology': resources_to_analyze = 'GeneOntology'
if len(output_dir) == 0: mappfinder_output_dir = 'input/MAPPFinder'
else: mappfinder_output_dir = output_dir + '/GO-Elite_results/CompleteResults/ORA'
denom_folder = criterion_denom_folder
global source_data; source_data = source; global mod; mod = mod_db
global species_code; species_code = species_id
global species_name; species_name = species_title; global gene_to_mapp
global permutations; permutations = permute
global eliminate_redundant_genes; eliminate_redundant_genes = 'yes'
global permuted_z_scores; global ontology_annotations
global original_ontology_z_score_data; global original_mapp_z_score_data
global input_gene_list; global denominator_gene_list
global gene_file; global denom_file_status
global input_count; global denom_count; global gene_annotations
global source_to_gene; global use_FET
if permutations == "FisherExactTest":
use_FET = 'yes' ### Use Fisher's Exact test instead of permutation-based p-values
permutations = 0
else:
use_FET = 'no'
if poolVar: display = False
else: display = True
start_time = time.time()
gene_annotations = gene_associations.importGeneData(species_code,mod)
OBO_date = importVersionData('OBO/')
if len(criterion_input_folder) == 0: import_dir = '/input/GenesToQuery/'+species_code
else: import_dir = criterion_input_folder
m = GrabFiles(); m.setdirectory(import_dir)
import_dir_alt = m.directory()
try: dir_list = readDirText(import_dir) #send a sub_directory to a function to identify all files in a directory
except Exception:
print_out = 'Warning! Input directory location is not a valid folder. Exiting GO-Elite.'
try:
if PoolVar: q.put([print_out]); return None
except Exception: pass
ForceCriticalError(print_out)
try: denom_dir_list = readDirText(criterion_denom_folder)
except Exception:
print_out = 'Warning! Denominator directory location is not a valid folder. Exiting GO-Elite.'
try:
if PoolVar: q.put([print_out]); return None
except Exception: pass
ForceCriticalError(print_out)
if len(dir_list)==0:
error_message = 'No files with the extension ".txt" found in the input directory.'
try:
if PoolVar: q.put([print_out]); return None
except Exception: pass
ForceCriticalError(error_message)
if len(denom_dir_list)==0:
skipDenomImport=True #### No denominator supplied by the user
#error_message = 'No files with the extension ".txt" found in the denominator directory.'
#if PoolVar: q.put([print_out]); return None
#ForceCriticalError(error_message)
else:
skipDenomImport=False
inputs_analyzed=0
for mappfinder_input in dir_list: #loop through each file in the directory
permuted_z_scores={}; original_ontology_z_score_data={}; original_mapp_z_score_data={}
if PoolVar==False:
print 'Performing over-representation analysis (ORA) on',mappfinder_input
gene_file_dir, gene_file = m.searchdirectory(mappfinder_input)
###Import Input gene/source-id lists
input_gene_list,source_data_input,error_message = gene_associations.importUIDsForMAPPFinderQuery(import_dir_alt+'/'+gene_file,system_codes,'no'); input_count = len(input_gene_list)
if 'No results' in error_message:
continue
if 'WARNING!!!' in error_message: ### Warn the user about SwissProt issues when importing the denominator
try:
if PoolVar: q.put([print_out]); return None
except Exception: pass
ForceCriticalError(error_message)
continue
if skipDenomImport==False:
denominator_file_dir = identifyGeneFiles(denom_folder,gene_file) ###input is in input\Genes, denominator in
try:
denominator_file_dir = identifyGeneFiles(denom_folder,gene_file) ###input is in input\Genes, denominator in
denominator_file = string.split(denominator_file_dir,'/')[-1]
if PoolVar==False:
print 'Using:', denominator_file,'for the denominator.'
except Exception:
print_out = "WARNING: No denominator file included in\nthe Denominator directory.\nTo proceed, place all denominator\nIDs in a file in that directory."
try:
if PoolVar: q.put([print_out]); return None
except Exception: pass
ForceCriticalError(print_out)
else:
denominator_file_dir = None
if denominator_file_dir == previous_denominator_file_dir: denom_file_status = 'old'
else: denom_file_status = 'new'
if denom_file_status == 'new':
if skipDenomImport==False:
previous_denominator_file_dir = denominator_file_dir
denominator_gene_list,source_data_denom,error_message = gene_associations.importUIDsForMAPPFinderQuery(denominator_file_dir,system_codes,'no')
denom_count = len(denominator_gene_list)
if 'SwissProt' in error_message and 'WARNING!!!' not in error_message:
if len(input_gene_list)==0:
error_message+='\nNo valid input IDs found. Exiting GO-Elite.'
if PoolVar: q.put([print_out]); return None
ForceCriticalError(error_message)
else:
if PoolVar: q.put([print_out]); return None
ForceCriticalError(error_message)
elif len(error_message)>0:
if PoolVar: q.put([print_out]); return None
ForceCriticalError(error_message)
if len(denominator_gene_list) == len(input_gene_list):
print_out = 'Input and Denominator lists have identical counts.\nPlease load a propper denominator set (containing\nthe input list with all assayed gene IDs) before proceeding.'
if PoolVar: q.put([print_out]); return None
ForceCriticalError(print_out)
original_denominator_gene_list=[]
for id in denominator_gene_list: original_denominator_gene_list.append(id) ###need this to be a valid list not dictionary for permutation analysis
else:
### Pull in all MOD IDs as a surrogate denominator
denominator_gene_list = importGeneData()
original_denominator_gene_list=[]
for id in denominator_gene_list: original_denominator_gene_list.append(id)
denom_count = len(denominator_gene_list)
if len(source_data_input)>0: source_data = source_data_input ###over-ride source_data if a source was identified from the input file
if source_data != mod:
if denom_file_status == 'new':
mod_source = mod+'-'+source_data+'.txt'
try:
gene_to_source_id = gene_associations.getGeneToUid(species_code,mod_source,display=display)
if PoolVar==False:
print mod_source, 'imported'
except Exception:
try:
if mod=='EntrezGene': mod = 'Ensembl'
else: mod = 'EntrezGene'
if PoolVar==False:
print 'The primary system (MOD) has been switched from',mod_db,'to',mod,'\n('+mod_db,'not supported for the %s ID system).' % source_data
mod_source = mod+'-'+source_data+'.txt'
gene_to_source_id = gene_associations.getGeneToUid(species_code,mod_source,display=display)
except Exception:
print_out = "WARNING: The primary gene ID system '"+mod+"'\ndoes not support relationships with '"+ source_data +"'.\nRe-run using a supported primary ID system."
try:
if PoolVar: q.put([print_out]); return None
except Exception: pass
ForceCriticalError(print_out)
source_to_gene = OBO_import.swapKeyValues(gene_to_source_id)
if skipDenomImport==False:
denominator_gene_list = associateInputSourceWithGene(source_to_gene,denominator_gene_list)
### Introduced the below method in version 1.2.1 to improve permutation speed (no longer need to search all source IDs)
### Only includes source ID to gene relationships represented in the denominator file (needed for Affymetrix)
source_to_gene = OBO_import.swapKeyValues(denominator_gene_list)
###Replace input lists with corresponding MOD IDs
input_gene_list = associateInputSourceWithGene(source_to_gene,input_gene_list)
else:
if len(input_gene_list)>3:
missing = checkCorrectSystemAndSpecies(input_gene_list)
if missing:
print_out = "None of the input IDs match the selected mod (%s) or species (%s)." % (mod,species_name)
print_out += '\nVerify the correct ID system is indicated in the input file and the correct species seleced.'
try:
if PoolVar: q.put([print_out]); return None
except Exception: pass
ForceCriticalError(print_out)
if skipDenomImport==False:
checkDenominatorMatchesInput(input_gene_list,denominator_gene_list,gene_file) ###This is for only the associated MOD IDs
gd = GrabFiles(); gd.setdirectory('/'+database_dir+'/'+species_code+'/gene-mapp')
available_genesets = reorganizeResourceList(gd.getAllFiles(mod))
od = GrabFiles(); od.setdirectory('/'+database_dir+'/'+species_code+'/gene-go')
available_ontologies = reorganizeResourceList(od.getAllFiles(mod))
input_gene_count = len(input_gene_list) ###Count number of genes associated with source input IDs
if len(input_gene_list)==0 or len(denominator_gene_list)==0:
if len(input_gene_list)==0:
try:
print_out = 'WARNING!!!! None of the input IDs provided map to genes for '+mappfinder_input+'. Check to make sure the selected species is correct.'
print_out += '\nSelected species: '+species_name
print_out += '\nInput ID system: '+str(source_data_input)
print_out += '\nPrimary ID system (MOD): '+str(mod)
if PoolVar: q.put([print_out]); return None
except Exception:
pass
ForceCriticalError(print_out)
if len(denominator_gene_list)==0:
try:
print_out = 'WARNING!!!! None of the denominator IDs provided map to genes for '+denominator_file_dir+'. Check to make sure the selected species is correct.'
print_out += '\nSelected species: '+species_name
print_out += '\nDenominator ID system: '+str(source)
print_out += '\nPrimary ID system (MOD):'+str(mod)
if PoolVar: q.put([print_out]); return None
ForceCriticalError(print_out)
except Exception:
pass
elif len(available_ontologies) == 0 and len(available_genesets) == 0:
print_out = 'WARNING!!!! No Ontology or GeneSets appear to be available for this species. Please supply and re-analyze.'
try:
if PoolVar: q.put([print_out]); return None
except Exception: pass
ForceCriticalError(print_out)
else:
""" Perform permutation analysis and ORA on available GeneSets or Ontologies"""
inputs_analyzed+=1
global permute_inputs; permute_inputs=[]
if permutations != 0 or use_FET == 'no':
buildPermutationDatabase(original_denominator_gene_list,input_count)
run_status = 0
### Analyzed ontologies
if len(available_ontologies)>0:
if PoolVar==False:
print ' Analyzing input ID list with available ontologies'
for ontology_dir in available_ontologies:
ontology_type = getResourceType(ontology_dir)
permuted_z_scores={}; original_ontology_z_score_data={}
#print ontology_type, resources_to_analyze
if (resources_to_analyze == ontology_type) or (resources_to_analyze == 'all') or (resources_to_analyze == 'both' and ontology_type == 'GeneOntology'):
ontology_annotations = importOntologyAnnotations(species_code,ontology_type)
if ontology_annotations!=None: ### Occurs when the files are named or formatted correctly
status, ontology_to_mod_genes = performOntologyORA(ontology_dir)
run_status += status
### Analyzed gene-sets
if len(available_genesets)>0:
if PoolVar==False:
print ' Analyzing input ID list with available gene-sets'
for geneset_dir in available_genesets:
geneset_type = getResourceType(geneset_dir)
permuted_z_scores={}; original_mapp_z_score_data={}
#print geneset_type, resources_to_analyze;sys.exit()
if geneset_type in resources_to_analyze or resources_to_analyze == 'all' or (resources_to_analyze == 'both' and geneset_type == 'Pathways'):
status, mapp_to_mod_genes = performGeneSetORA(geneset_dir)
run_status += status
if len(custom_sets_folder)>0:
### Hence - Analyze User Supplied GeneSets
permuted_z_scores={}; original_mapp_z_score_data={}
run_status += performGeneSetORA('UserSuppliedAssociations')[0]
permute_inputs=[]; permute_mapp_inputs=[]
ontology_input_gene_count=[]; mapp_input_gene_count=[]
if run_status == 0:
### Returns the number of successfully analyzed gene-set databases
program_type,database_dir = unique.whatProgramIsThis()
print_out = "Warning!!! Either the MOD you have selected: "+mod+"\nis missing the appropriate relationship files necessary to run GO-Elite\nor you have selected an invalid resource to analyze. Either replace\nthe missing MOD files in "+database_dir+'/'+species_code+' sub-directories or\nselect a different MOD at run-time.'
if PoolVar: q.put([print_out]); return None
ForceCriticalError(print_out)
end_time = time.time()
time_diff = formatTime(start_time,end_time)
if PoolVar==False:
print 'ORA analyses finished in %s seconds' % time_diff
try:
q.put([ontology_to_mod_genes, mapp_to_mod_genes,time_diff,mappfinder_input,resources_to_analyze])
except Exception:
q = [ontology_to_mod_genes, mapp_to_mod_genes,time_diff,mappfinder_input,resources_to_analyze]
return q ###Return the MOD genes associated with each GO term and MAPP
def importOntologyAnnotations(species_code,ontology_type):
try:
system_codes,source_types,mod_types = GO_Elite.getSourceData()
verified_nested = OBO_import.verifyNestedFileCreation(species_code,mod_types,ontology_type)
if verified_nested == 'no': force_error
ontology_annotations = OBO_import.importPreviousOntologyAnnotations(ontology_type)
except Exception:
try:
### Occurs when the annotation file isn't built yet - if so try to build
OBO_import.buildNestedOntologyAssociations(species_code,mod_types,ontology_type,display=display)
ontology_annotations = OBO_import.importPreviousOntologyAnnotations(ontology_type)
except Exception:
### Occurs for non-ontologies
#print 'error 2',traceback.format_exc()
ontology_annotations=None
return ontology_annotations
def getResourceType(pathway_dir):
pathway_type = string.split(pathway_dir,'-')[-1][:-4]
if pathway_type == 'MAPP':
pathway_type = 'Pathways'
return pathway_type
def reorganizeResourceList(pathway_list):
### Make sure that WikiPathways and GO are analyzed last, so that gene results are also reported last to GO_Elite.py
add_pathway=[]
pathway_list_reorganized=[]
for pathway in pathway_list:
if '-MAPP.txt' in pathway: add_pathway.append(pathway)
elif '-GeneOntology.txt' in pathway: add_pathway.append(pathway)
else: pathway_list_reorganized.append(pathway)
pathway_list_reorganized+=add_pathway
return pathway_list_reorganized
def ForceCriticalError(print_out):
print '\n'+print_out+'\n'
"""
if len(sys.argv[1:])<2: ### Don't create a Tkinter window if command-line options supplied
try: UI.WarningWindow(print_out,'Error Encountered!'); root.destroy(); GO_Elite.importGOEliteParameters('yes'); #sys.exit()
except Exception: None
else:
#sys.exit()
pass
"""
def buildPermutationDatabase(original_denominator_gene_list,input_count):
if PoolVar==False:
print "Building %d permuted ID sets" % permutations,
global k; k=0; x=0
try: original_increment = int(permutations/10); increment = original_increment
except Exception: null=None
if PoolVar==False:
if permutations!=0: print '*',
start_time = time.time() ### Build Permutation Identifier Database
while x<permutations:
if x == increment and PoolVar==False:
increment+=original_increment; print '*',
try: permute_input_list = random.sample(original_denominator_gene_list,input_count); x+=1
except ValueError:
print_out = 'Input count>Denominator '+str(len(original_denominator_gene_list))+' '+str(input_count)+'\n terminating'
if PoolVar: q.put([print_out]); return None
ForceCriticalError(print_out)
#permute_input_list = random.sample(denominator_gene_list,len(input_gene_list)); x+=1
#permute_input_list = random.shuffle(original_denominator_gene_list); x+=1; permute_input_list = permute_input_list[:input_count]
if source_data!=mod: ###Store the randomly choosen input lists for GenMAPP MAPP Permutation analysis
permute_input_list = associateInputSourceWithGene(source_to_gene,permute_input_list)
if len(permute_input_list)>len(input_gene_list): k+=1
permute_inputs.append(permute_input_list)
end_time = time.time()
time_diff = formatTime(start_time,end_time)
if PoolVar==False:
print 'completed in %s seconds' % time_diff
def swapKeyValues(db):
rev_db = {}
for i, k_list in db.iteritems():
for k in k_list:
try: rev_db[k].append(i)
except Exception: rev_db[k] = [i]
return rev_db
def performGeneSetORA(geneset_dir):
""" Perform over-representation analysis (ORA) on any provided Gene Set """
start_time = time.time()
geneset_type = getResourceType(geneset_dir)
#permuted_z_scores={}; original_mapp_z_score_data={}
if geneset_type == 'Pathways': geneset_type = 'WikiPathways'
### Since MAPP tables can be provided by the user, allow the file to be missing
if geneset_dir == 'UserSuppliedAssociations':
gene_to_mapp = gene_associations.importGeneCustomData(species_code,system_codes,custom_sets_folder,mod)
geneset_type = geneset_dir
else:
try: gene_to_mapp = gene_associations.importGeneMAPPData(species_code,geneset_dir)
except Exception: gene_to_mapp = {}
mapp_to_gene = swapKeyValues(gene_to_mapp)
if len(gene_to_mapp)==0:
return 0, None
else:
###Calculate primary z-scores for GeneSets
#print len(input_gene_list), len(gene_to_mapp)
mapp_to_mod_genes = getGenesInPathway(input_gene_list,gene_to_mapp) ### For summary reporting
mapp_input_gene_count,Rm,input_linked_mapp = countGenesInPathway(input_gene_list,gene_to_mapp,'yes')
mapp_denominator_gene_count,Nm,denom_linked_mapp = countGenesInPathway(denominator_gene_list,gene_to_mapp,'yes')
#print Nm,"unique genes, linked to GeneSets and in dataset and", Rm, "unique GeneSets\n linked genes matching criterion."
#zstart_time = time.time()
try:
#exhaustiveMultiZScores(mapp_input_gene_count,mapp_denominator_gene_count,Nm,Rm,mapp_to_gene,'MAPP')
if PoolVar==False:
try: multiZScores(mapp_input_gene_count,mapp_denominator_gene_count,Nm,Rm,mapp_to_gene,'MAPP')
except Exception: calculateZScores(mapp_input_gene_count,mapp_denominator_gene_count,Nm,Rm,mapp_to_gene,'MAPP')
else:
calculateZScores(mapp_input_gene_count,mapp_denominator_gene_count,Nm,Rm,mapp_to_gene,'MAPP')
except Exception:
calculateZScores(mapp_input_gene_count,mapp_denominator_gene_count,Nm,Rm,mapp_to_gene,'MAPP')
#print len(permuted_z_scores), len(original_mapp_z_score_data)
#print time.time()-zstart_time, 'seconds...';sys.exit()
if use_FET == 'no':
permute_mapp_inputs=[]
###Begin GeneSets Permutation Analysis
try: original_increment = int(permutations/10); increment = original_increment
except Exception: null=None
x=0
if PoolVar==False:
if permutations!=0: print '*',
for permute_input_list in permute_inputs:
if PoolVar==False:
if x == increment: increment+=original_increment; print '*',
x+=1
permute_mapp_input_gene_count,null,null = countGenesInPathway(permute_input_list,gene_to_mapp,'no')
permute_mapp_inputs.append(permute_mapp_input_gene_count)
calculatePermuteZScores(permute_mapp_inputs,mapp_denominator_gene_count,Nm,Rm)
calculatePermuteStats(original_mapp_z_score_data)
adjustPermuteStats(original_mapp_z_score_data)
mapp_headers = formatHeaders(gene_file,input_count,input_linked_mapp,denom_count,denom_linked_mapp,Rm,Nm,'MAPP',OBO_date)
exportPathwayData(original_mapp_z_score_data,gene_file,mapp_headers,geneset_type,'local')
### Export all gene associations (added in version 1.21)
exportPathwayToGeneAssociations(mapp_to_mod_genes,mod,gene_file,gene_annotations,geneset_type,'local')
end_time = time.time()
time_diff = formatTime(start_time,end_time)
if PoolVar==False:
print "Initial results for %s calculated in %s seconds" % (geneset_type,time_diff)
permute_mapp_inputs=[]
return 1, mapp_to_mod_genes
def performOntologyORA(ontology_dir):
""" Perform over-representation analysis (ORA) on any provided Ontology """
start_time = time.time()
ontology_type = getResourceType(ontology_dir)
######### Import Gene-to-Nested-Ontology #########
gene_to_ontology = gene_associations.importGeneToOntologyData(species_code,mod,'nested',ontology_type)
ontology_to_gene = OBO_import.swapKeyValues(gene_to_ontology)
if len(gene_to_ontology)==0:
return 0, None
else:
######### Calculate primary z-scores for GO terms
#a = time.time()
ontology_to_mod_genes = getGenesInPathway(input_gene_list,gene_to_ontology) ### For summary gene reporting
#b = time.time(); print 'a',b-a
ontology_input_gene_count,Rg,input_linked_ontology = countGenesInPathway(input_gene_list,gene_to_ontology,'yes')
#c = time.time(); print 'b',c-b
ontology_denominator_gene_count,Ng,denom_linked_ontology = countGenesInPathway(denominator_gene_list,gene_to_ontology,'yes')
#d = time.time(); print 'c',d-c
#print Ng,"unique genes, linked to GO and in dataset and", Rg, "unique GO linked genes matching criterion."
try:
if PoolVar==False:
multiZScores(ontology_input_gene_count,ontology_denominator_gene_count,Ng,Rg,ontology_to_gene,'Ontology')
else:
calculateZScores(ontology_input_gene_count,ontology_denominator_gene_count,Ng,Rg,ontology_to_gene,'Ontology')
except Exception: calculateZScores(ontology_input_gene_count,ontology_denominator_gene_count,Ng,Rg,ontology_to_gene,'Ontology')
#e = time.time(); print 'd',e-d; sys.exit()
if use_FET == 'no':
###Begining Ontology Permutation Analysis
try: original_increment = int(permutations/10); increment = original_increment
except Exception: null=None
x=0
permute_ontology_inputs=[]
if PoolVar==False:
if permutations!=0: print '*',
for permute_input_list in permute_inputs:
### http://docs.python.org/library/multiprocessing.html
if PoolVar==False:
if x == increment: increment+=original_increment; print '*',
x+=1
permute_ontology_input_gene_count,null,null = countGenesInPathway(permute_input_list,gene_to_ontology,'no'); permute_input_list=[]
permute_ontology_inputs.append(permute_ontology_input_gene_count)
if PoolVar==False:
if permutations !=0: print 'Ontology finished'
calculatePermuteZScores(permute_ontology_inputs,ontology_denominator_gene_count,Ng,Rg)
calculatePermuteStats(original_ontology_z_score_data)
adjustPermuteStats(original_ontology_z_score_data)
go_headers = formatHeaders(gene_file,input_count,input_linked_ontology,denom_count,denom_linked_ontology,Rg,Ng,'Ontology',OBO_date)
exportPathwayData(original_ontology_z_score_data,gene_file,go_headers,ontology_type,'Ontology')
### Export all gene associations (added in version 1.21)
exportPathwayToGeneAssociations(ontology_to_mod_genes,mod,gene_file,gene_annotations,ontology_type,'Ontology')
end_time = time.time()
time_diff = formatTime(start_time,end_time)
if PoolVar==False:
print "Initial results for %s calculated in %s seconds" % (ontology_type,time_diff)
permute_ontology_inputs=[]
return 1, ontology_to_mod_genes
def exportPathwayToGeneAssociations(pathway_to_mod_genes,mod,gene_file,gene_annotations,resource_name,pathway_type):
headers = string.join([mod,'symbol',resource_name],'\t')+'\n'
if resource_name == 'GeneOntology': resource_name = 'GO' ### Makes the output filename compatible with GenMAPP-CS plugin filenames
if resource_name == 'WikiPathways': resource_name = 'local' ### Makes the output filename compatible with GenMAPP-CS plugin filenames
new_file = mappfinder_output_dir+'/'+gene_file[:-4]+'-'+resource_name+'-associations.tab'
data = export.ExportFile(new_file); data.write(headers)
for pathway in pathway_to_mod_genes:
for gene in pathway_to_mod_genes[pathway]:
try: symbol = gene_annotations[gene].Symbol()
except Exception: symbol = ''
if pathway_type == 'Ontology' and ':' not in pathway: pathway = 'GO:'+ pathway
values = string.join([gene,symbol,pathway],'\t')+'\n'
data.write(values)
data.close()
def formatHeaders(gene_file,input_count,input_linked,denom_count,denom_linked,R,N,pathway_type,OBO_date):
headers = []
headers.append('GO-Elite ORA Results')
headers.append('File:')
headers.append('Table:')
if pathway_type == 'Ontology':
headers.append('Database: Based on OBO-Database version: '+OBO_date)
headers.append('colors:')
t = time.localtime(); dt = str(t[1])+'/'+str(t[2])+'/'+str(t[0])
headers.append(dt)
headers.append(species_name)
headers.append('Pvalues = true')
headers.append('Calculation Summary:')
headers.append(str(input_count)+' '+source_data+' source identifiers supplied in the input file:'+gene_file)
headers.append(str(input_linked)+' source identifiers meeting the filter linked to a '+mod+' ID.')
headers.append(str(R)+' genes meeting the criterion linked to a term.')
headers.append(str(denom_count)+' source identifiers in this dataset.')
headers.append(str(denom_linked)+' source identifiers linked to a '+mod+' ID.')
headers.append(str(N)+' Genes linked to a term.')
headers.append('The z score is based on an N of '+str(N)+' and a R of '+str(R)+' distinct genes in all terms.\n')
if use_FET == 'yes': prob = "FisherExactP"
else: prob = "PermuteP"
if pathway_type == 'Ontology':
title = ['Ontology-ID','Ontology Name','Ontology Type','Number Changed','Number Measured','Number in Ontology','Percent Changed','Percent Present','Z Score',prob,'AdjustedP']
title = string.join(title,'\t'); headers.append(title)
else:
title = ['Gene-Set Name','Number Changed','Number Measured','Number in Gene-Set','Percent Changed','Percent Present','Z Score',prob,'AdjustedP']
title = string.join(title,'\t'); headers.append(title)
header_str = string.join(headers,'\n')
return header_str+'\n'
def exportPathwayData(original_pathway_z_score_data,gene_file,headers,resource_name,pathway_type,altOutputDir=None):
if resource_name == 'GeneOntology': resource_name = 'GO' ### Makes the output filename compatible with GenMAPP-CS plugin filenames
if resource_name == 'WikiPathways': resource_name = 'local' ### Makes the output filename compatible with GenMAPP-CS plugin filenames
new_file = mappfinder_output_dir+'/'+gene_file[:-4]+'-'+resource_name+'.txt'
global sort_results
data = export.ExportFile(new_file); data.write(headers); sort_results=[]
#print "Results for",len(original_pathway_z_score_data),"pathways exported to",new_file
for pathway in original_pathway_z_score_data:
zsd=original_pathway_z_score_data[pathway]
try: results = [zsd.Changed(), zsd.Measured(), zsd.InPathway(), zsd.PercentChanged(), zsd.PercentPresent(), zsd.ZScore(), zsd.PermuteP(), zsd.AdjP()]
except AttributeError:
return traceback.format_exc()
#print pathway,len(permuted_z_scores[pathway]);kill
try: ###This is unnecessary, unless using the non-nested GO associations (which can have out of sync GOIDs)
if pathway_type == 'Ontology':
s = ontology_annotations[pathway]
annotations = [s.OntologyID(),s.OntologyTerm(),s.OntologyType()]; results = annotations + results
else:
results = [pathway] + results
results = string.join(results,'\t') + '\n'
sort_results.append([float(zsd.ZScore()),-1/float(zsd.Measured()),results])
except KeyError: null = []
sort_results.sort(); sort_results.reverse()
for values in sort_results:
results = values[2]
data.write(results)
data.close()
def swapKeyValuesTuple(db):
swapped={}
for key in db:
values = tuple(db[key]) ###If the value is not a list, make a list
swapped[values] = [key]
swapped = eliminate_redundant_dict_values(swapped)
return swapped
class ZScoreData:
def __init__(self,pathway,changed,measured,zscore,null_z,in_pathway):
self._pathway = pathway; self._changed = changed; self._measured = measured
self._zscore = zscore; self._null_z = null_z; self._in_pathway = in_pathway
def PathwayID(self): return self._pathway
def Changed(self): return str(int(self._changed))
def Measured(self): return str(int(self._measured))
def InPathway(self): return str(self._in_pathway)
def ZScore(self): return str(self._zscore)
def SetP(self,p): self._permute_p = p
def PermuteP(self): return str(self._permute_p)
def SetAdjP(self,adjp): self._adj_p = adjp
def AdjP(self): return str(self._adj_p)
def setAssociatedIDs(self,ids): self.ids = ids
def AssociatedIDs(self): return self.ids
def PercentChanged(self):
try: pc = float(self.Changed())/float(self.Measured())*100
except Exception: pc = 0
return str(pc)
def PercentPresent(self):
try: pp = float(self.Measured())/float(self.InPathway())*100
except Exception: pp = 0
return str(pp)
def NullZ(self): return self._null_z
def Report(self):
output = self.PathwayID()
return output
def __repr__(self): return self.Report()
def workerExhaustive(queue,pathway,genes_in_pathway,pathway_input_gene_count,pathway_denominator_gene_count,N,R,pathway_type):
permuted_z_scores_instance={}; original_mapp_z_score_data_instance={}; original_ontology_z_score_data_instance={}
""" Exhaustive multiprocessing execution """
try:
n = pathway_denominator_gene_count[pathway]
try: r = pathway_input_gene_count[pathway]
except Exception: r = 0.0000
except Exception: n = 0.0000; r = 0.0000
if n != 0:
try: z = Zscore(r,n,N,R)
except ZeroDivisionError: z = 0.0000
try: null_z = Zscore(0,n,N,R)
except ZeroDivisionError: null_z = 0.000
zsd = ZScoreData(pathway,r,n,z,null_z,genes_in_pathway)
if pathway_type == 'Ontology': original_ontology_z_score_data_instance[pathway] = zsd
else: original_mapp_z_score_data_instance[pathway] = zsd
permuted_z_scores_instance[pathway] = [z]
#if '06878' in pathway: print pathway, z, null_z, r,n, N, R;kill
if use_FET == 'yes':
### Alternatively calculate p using the Fisher's Exact Test
p = FishersExactTest(r,n,R,N)
zsd.SetP(p)
queue.put((permuted_z_scores_instance,original_mapp_z_score_data_instance,original_ontology_z_score_data_instance))
def exhaustiveMultiZScores(pathway_input_gene_count,pathway_denominator_gene_count,N,R,pathway_db,pathway_type):
""" Exhaustive multiprocessing - create a process for each entry in the dictionary for Z-score calcualtion """
procs=list()
queue = mlp.Queue()
for pathway in pathway_db:
genes_in_pathway = len(pathway_db[pathway])
p = mlp.Process(target=workerExhaustive, args=(queue,pathway,genes_in_pathway,pathway_input_gene_count,pathway_denominator_gene_count,N,R,pathway_type))
procs.append(p)
p.start()
permuted_z_scores_list=[]; original_mapp_z_score_data_list=[]; original_ontology_z_score_data_list=[]
for _ in procs:
val = queue.get()
permuted_z_scores_list.append(val[0]); original_mapp_z_score_data_list.append(val[1])
original_ontology_z_score_data_list.append(val[2])
for p in procs:
p.join()
for i in permuted_z_scores_list: permuted_z_scores.update(i)
for i in original_mapp_z_score_data_list: original_mapp_z_score_data.update(i)
for i in original_ontology_z_score_data_list: original_ontology_z_score_data.update(i)
def multiZScores(pathway_input_gene_count,pathway_denominator_gene_count,N,R,pathway_db,pathway_type):
""" Create a finate pool of processes (4) for Z-score calculation """
if mlp.cpu_count() < 3:
processors = mlp.cpu_count()
else: processors = 4
pool = mlp.Pool(processes=processors)
si = (len(pathway_db)/processors)
s = si; b=0
db_ls=[]
if len(pathway_db)<10: forceError ### will si to be zero and an infanite loop
while s<len(pathway_db):
db_ls.append(dict(pathway_db.items()[b:s]))
b+=si; s+=si
db_ls.append(dict(pathway_db.items()[b:s]))
### Create an instance of MultiZscoreWorker (store the variables to save memory)
workerMulti = MultiZscoreWorker(pathway_input_gene_count,pathway_denominator_gene_count,N,R,pathway_type,use_FET)
results = pool.map(workerMulti,db_ls)
pool.close(); pool.join(); pool = None
permuted_z_scores_list=[]; original_mapp_z_score_data_list=[]; original_ontology_z_score_data_list=[]
for (a,b,c) in results:
permuted_z_scores_list.append(a); original_mapp_z_score_data_list.append(b)
original_ontology_z_score_data_list.append(c)
for i in permuted_z_scores_list: permuted_z_scores.update(i)
for i in original_mapp_z_score_data_list: original_mapp_z_score_data.update(i)
for i in original_ontology_z_score_data_list: original_ontology_z_score_data.update(i)
class MultiZscoreWorker:
def __init__(self,pathway_input_gene_count,pathway_denominator_gene_count,N,R,pathway_type,use_FET):
self.pathway_input_gene_count = pathway_input_gene_count
self.pathway_denominator_gene_count = pathway_denominator_gene_count
self.N = N
self.R = R
self.pathway_type = pathway_type
self.use_FET = use_FET
def __call__(self,pathway_db):
N = self.N; R = self.R; use_FET = self.use_FET
zt=0; nzt=0; zsdt=0; rt=0; ft=0
permuted_z_scores_instance={}; original_mapp_z_score_data_instance={}; original_ontology_z_score_data_instance={}
for pathway in pathway_db:
genes_in_pathway = len(pathway_db[pathway])
try:
n = self.pathway_denominator_gene_count[pathway]
#r1 = time.time()
try: r = self.pathway_input_gene_count[pathway]
except Exception: r = 0.0000
#rt += time.time() - r1
except Exception: n = 0.0000; r = 0.0000
if n != 0:
z1 = time.time()
try: z = Zscore(r,n,N,R)
except ZeroDivisionError: z = 0.0000
#zt += time.time() - z1
#nz1 = time.time()
try: null_z = Zscore(0,n,N,R)
except ZeroDivisionError: null_z = 0.000
#nzt+= time.time() - nz1
#zsd1 = time.time()
zsd = ZScoreData(pathway,r,n,z,null_z,genes_in_pathway)
#zsdt+= time.time() - zsd1
if self.pathway_type == 'Ontology': original_ontology_z_score_data_instance[pathway] = zsd
else: original_mapp_z_score_data_instance[pathway] = zsd
permuted_z_scores_instance[pathway] = [z]
#if '06878' in pathway: print pathway, z, null_z, r,n, N, R;kill
if use_FET == 'yes':
### Alternatively calculate p using the Fisher's Exact Test
#ft1 = time.time()
p = FishersExactTest(r,n,R,N)
#ft+= time.time() - ft1
zsd.SetP(p)
#print zt,nzt,zsdt,rt,ft ### Used for efficiency evaluation
return permuted_z_scores_instance,original_mapp_z_score_data_instance, original_ontology_z_score_data_instance
def calculateZScores(pathway_input_gene_count,pathway_denominator_gene_count,N,R,pathway_db,pathway_type):
"""where N is the total number of genes measured:
R is the total number of genes meeting the criterion:
n is the total number of genes in this specific MAPP:
r is the number of genes meeting the criterion in this MAPP: """
for pathway in pathway_db:
try:
n = pathway_denominator_gene_count[pathway]
try: r = pathway_input_gene_count[pathway]
except Exception: r = 0.0000
except Exception: n = 0.0000; r = 0.0000
if n != 0:
try: z = Zscore(r,n,N,R)
except ZeroDivisionError: z = 0.0000
try: null_z = Zscore(0,n,N,R)
except ZeroDivisionError: null_z = 0.000
genes_in_pathway = len(pathway_db[pathway])
zsd = ZScoreData(pathway,r,n,z,null_z,genes_in_pathway)
if pathway_type == 'Ontology': original_ontology_z_score_data[pathway] = zsd
else: original_mapp_z_score_data[pathway] = zsd
permuted_z_scores[pathway] = [z]
#if '06878' in pathway: print pathway, z, null_z, r,n, N, R;kill
if use_FET == 'yes':
### Alternatively calculate p using the Fisher's Exact Test
p = FishersExactTest(r,n,R,N)
zsd.SetP(p)
def Zscore(r,n,N,R):
N=float(N) ### This bring all other values into float space
z = (r - n*(R/N))/math.sqrt(n*(R/N)*(1-(R/N))*(1-((n-1)/(N-1))))
return z
def calculatePermuteZScores(permute_pathway_inputs,pathway_denominator_gene_count,N,R):
for pathway_input_gene_count in permute_pathway_inputs:
for pathway in pathway_input_gene_count:
r = pathway_input_gene_count[pathway]
n = pathway_denominator_gene_count[pathway]
try: z = statistics.zscore(r,n,N,R)
except ZeroDivisionError: z = 0
permuted_z_scores[pathway].append(abs(z))
#if pathway == '0005488':
#a.append(r)
def calculatePermuteStats(original_pathway_z_score_data):
for pathway in original_pathway_z_score_data:
zsd = original_pathway_z_score_data[pathway]
z = abs(permuted_z_scores[pathway][0])
permute_scores = permuted_z_scores[pathway][1:] ###Exclude the true value
nullz = zsd.NullZ()
if abs(nullz) == z: ###Only add the nullz values if they can count towards the p-value (if equal to the original z)
null_z_to_add = permutations - len(permute_scores)
permute_scores+=[abs(nullz)]*null_z_to_add ###Add null_z's in proportion to the amount of times there were not genes found for that pathway
if len(permute_scores)>0: p = permute_p(permute_scores,z)
else: p = 0
#if p>1: p=1
zsd.SetP(p)
def adjustPermuteStats(original_pathway_z_score_data):
#1. Sort ascending the original input p value vector. Call this spval. Keep the original indecies so you can sort back.
#2. Define a new vector called tmp. tmp= spval. tmp will contain the BH p values.
#3. m is the length of tmp (also spval)
#4. i=m-1
#5 tmp[ i ]=min(tmp[i+1], min((m/i)*spval[ i ],1)) - second to last, last, last/second to last
#6. i=m-2
#7 tmp[ i ]=min(tmp[i+1], min((m/i)*spval[ i ],1))
#8 repeat step 7 for m-3, m-4,... until i=1
#9. sort tmp back to the original order of the input p values.
global spval; spval=[]; adj_p_list=[]
for pathway in original_pathway_z_score_data:
zsd = original_pathway_z_score_data[pathway]