-
Notifications
You must be signed in to change notification settings - Fork 29
/
AltAnalyze.py
executable file
·8857 lines (8160 loc) · 531 KB
/
AltAnalyze.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
###AltAnalyze
#Copyright 2005-2008 J. David Gladstone Institutes, San Francisco California
#Author Nathan Salomonis - nsalomonis@gmail.com
#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.
import math
#import pkg_resources
#import distutils
from stats_scripts import statistics
import sys, string
import os.path
import unique
import update
import UI
import copy
import export; reload(export)
import ExpressionBuilder; reload(ExpressionBuilder)
from build_scripts import ExonAnalyze_module; reload(ExonAnalyze_module)
from import_scripts import ExonAnnotate_module; reload(ExonAnnotate_module)
from import_scripts import ResultsExport_module
from build_scripts import FeatureAlignment
import GO_Elite
import time
import webbrowser
import random
import traceback
import shutil
try:
import multiprocessing as mlp
except Exception:
mlp=None
print 'Note: Multiprocessing not supported for this verison python.'
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
try:
from PIL import Image as PIL_Image
try: import ImageTk
except Exception: from PIL import ImageTk
import PIL._imaging
import PIL._imagingft
except Exception:
print traceback.format_exc()
pass #print 'Python Imaging Library not installed... using default PNG viewer'
use_Tkinter = 'no'
debug_mode = 'no'
analysis_start_time = time.time()
command_args = string.join(sys.argv,' ')
if len(sys.argv[1:])>1 and '-' in command_args and '--GUI' not in command_args:
runningCommandLine = True
else:
runningCommandLine = False
def filepath(filename):
fn = unique.filepath(filename)
return fn
def read_directory(sub_dir):
dir_list = unique.read_directory(sub_dir)
dir_list2 = [] #add in code to prevent folder names from being included
for entry in dir_list:
if entry[-4:] == ".txt" or entry[-4:] == ".csv" or entry[-4:] == ".TXT":
dir_list2.append(entry)
return dir_list2
def eliminate_redundant_dict_values(database):
db1={}
for key in database:
list = unique.unique(database[key])
list.sort()
db1[key] = list
return db1
def makeUnique(item):
db1={}; list1=[]; k=0
for i in item:
try: db1[i]=[]
except TypeError: db1[tuple(i)]=[]; k=1
for i in db1:
if k==0: list1.append(i)
else: list1.append(list(i))
list1.sort()
return list1
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 returnLargeGlobalVars():
### Prints all large global variables retained in memory (taking up space)
all = [var for var in globals() if (var[:2], var[-2:]) != ("__", "__")]
for var in all:
try:
if len(globals()[var])>500:
print var, len(globals()[var])
except Exception: null=[]
def clearObjectsFromMemory(db_to_clear):
db_keys={}
try:
for key in db_to_clear: db_keys[key]=[]
except Exception:
for key in db_to_clear: del key ### if key is a list
for key in db_keys:
try: del db_to_clear[key]
except Exception:
try:
for i in key: del i ### For lists of tuples
except Exception: del key ### For plain lists
def importGeneric(filename):
fn=filepath(filename); key_db = {}
for line in open(fn,'rU').xreadlines():
data = cleanUpLine(line)
t = string.split(data,'\t')
key_db[t[0]] = t[1:]
return key_db
def importGenericFiltered(filename,filter_db):
fn=filepath(filename); key_db = {}
for line in open(fn,'rU').xreadlines():
data = cleanUpLine(line)
t = string.split(data,'\t')
key = t[0]
if key in filter_db: key_db[key] = t[1:]
return key_db
def importGenericFilteredDBList(filename,filter_db):
fn=filepath(filename); key_db = {}
for line in open(fn,'rU').xreadlines():
data = cleanUpLine(line)
t = string.split(data,'\t')
try:
null=filter_db[t[0]]
try: key_db[t[0]].append(t[1])
except KeyError: key_db[t[0]] = [t[1]]
except Exception: null=[]
return key_db
def importGenericDBList(filename):
fn=filepath(filename); key_db = {}
for line in open(fn,'rU').xreadlines():
data = cleanUpLine(line)
t = string.split(data,'\t')
try: key_db[t[0]].append(t[1])
except KeyError: key_db[t[0]] = [t[1]]
return key_db
def importExternalDBList(filename):
fn=filepath(filename); key_db = {}
for line in open(fn,'rU').xreadlines():
data = cleanUpLine(line)
t = string.split(data,'\t')
try: key_db[t[0]].append(t[1:])
except Exception: key_db[t[0]] = [t[1:]]
return key_db
def FindDir(dir,term):
dir_list = unique.read_directory(dir)
dir_list2=[]
dir_list.sort()
for i in dir_list:
if term == i: dir_list2.append(i)
if len(dir_list2)==0:
for i in dir_list:
if term in i: dir_list2.append(i)
dir_list2.sort(); dir_list2.reverse()
if len(dir_list2)>0: return dir_list2[0]
else: return ''
def openFile(file_dir):
if runningCommandLine:
pass
elif os.name == 'nt':
try: os.startfile('"'+file_dir+'"')
except Exception: os.system('open "'+file_dir+'"')
elif 'darwin' in sys.platform: os.system('open "'+file_dir+'"')
elif 'linux' in sys.platform: os.system('xdg-open "'+file_dir+'"')
def openCytoscape(parent_dir,application_dir,application_name):
cytoscape_dir = FindDir(parent_dir,application_dir); cytoscape_dir = filepath(parent_dir+'/'+cytoscape_dir)
app_dir = FindDir(cytoscape_dir,application_name)
app_dir = cytoscape_dir+'/'+app_dir
if 'linux' in sys.platform:
app_dir = app_dir
app_dir2 = cytoscape_dir+'/Cytoscape'
try: createCytoscapeDesktop(cytoscape_dir)
except Exception: null=[]
dir_list = unique.read_directory('/usr/bin/') ### Check to see that JAVA is installed
if 'java' not in dir_list: print 'Java not referenced in "usr/bin/. If not installed,\nplease install and re-try opening Cytoscape'
try:
jar_path = cytoscape_dir+'/cytoscape.jar'
main_path = cytoscape_dir+'/cytoscape.CyMain'
plugins_path = cytoscape_dir+'/plugins'
os.system('java -Dswing.aatext=true -Xss5M -Xmx512M -jar '+jar_path+' '+main_path+' -p '+plugins_path+' &')
print 'Cytoscape jar opened:',jar_path
except Exception:
print 'OS command to open Java failed.'
try:
try: openFile(app_dir2); print 'Cytoscape opened:',app_dir2
except Exception:
os.chmod(app_dir,0777)
openFile(app_dir2)
except Exception:
try: openFile(app_dir)
except Exception:
os.chmod(app_dir,0777)
openFile(app_dir)
else:
try: openFile(app_dir)
except Exception:
os.chmod(app_dir,0777)
openFile(app_dir)
def createCytoscapeDesktop(cytoscape_dir):
cyto_ds_output = cytoscape_dir+'/Cytoscape.desktop'
data = export.ExportFile(cyto_ds_output)
cytoscape_desktop = cytoscape_dir+'/Cytoscape'; #cytoscape_desktop = '/hd3/home/nsalomonis/Cytoscape_v2.6.1/Cytoscape'
cytoscape_png = cytoscape_dir+ '/.install4j/Cytoscape.png'; #cytoscape_png = '/hd3/home/nsalomonis/Cytoscape_v2.6.1/.install4j/Cytoscape.png'
data.write('[Desktop Entry]'+'\n')
data.write('Type=Application'+'\n')
data.write('Name=Cytoscape'+'\n')
data.write('Exec=/bin/sh "'+cytoscape_desktop+'"'+'\n')
data.write('Icon='+cytoscape_png+'\n')
data.write('Categories=Application;'+'\n')
data.close()
########### Parse Input Annotations ###########
def ProbesetCalls(array_type,probeset_class,splice_event,constitutive_call,external_exonid):
include_probeset = 'yes'
if array_type == 'AltMouse':
exonid = splice_event
if filter_probesets_by == 'exon':
if '-' in exonid or '|' in exonid: ###Therfore the probeset represents an exon-exon junction or multi-exon probeset
include_probeset = 'no'
if filter_probesets_by != 'exon':
if '|' in exonid: include_probeset = 'no'
if constitutive_call == 'yes': include_probeset = 'yes'
else:
if avg_all_for_ss == 'yes' and (probeset_class == 'core' or len(external_exonid)>2): constitutive_call = 'yes'
#if len(splice_event)>2 and constitutive_call == 'yes' and avg_all_for_ss == 'no': constitutive_call = 'no'
if constitutive_call == 'no' and len(splice_event)<2 and len(external_exonid)<2: ###otherwise these are interesting probesets to keep
if filter_probesets_by != 'full':
if filter_probesets_by == 'extended':
if probeset_class == 'full': include_probeset = 'no'
elif filter_probesets_by == 'core':
if probeset_class != 'core': include_probeset = 'no'
return include_probeset,constitutive_call
def EvidenceOfAltSplicing(slicing_annot):
splice_annotations = ["ntron","xon","strangeSplice","Prime","3","5","C-term"]; as_call = 0
splice_annotations2 = ["ntron","assette","strangeSplice","Prime","3","5"]
for annot in splice_annotations:
if annot in slicing_annot: as_call = 1
if as_call == 1:
if "C-term" in slicing_annot and ("N-" in slicing_annot or "Promoter" in slicing_annot):
as_call = 0
for annot in splice_annotations2:
if annot in slicing_annot: as_call = 1
elif "bleed" in slicing_annot and ("N-" in slicing_annot or "Promoter" in slicing_annot):
as_call = 0
for annot in splice_annotations2:
if annot in slicing_annot: as_call = 1
return as_call
########### Begin Analyses ###########
class SplicingAnnotationData:
def ArrayType(self):
self._array_type = array_type
return self._array_type
def Probeset(self): return self._probeset
def setProbeset(self,probeset): self._probeset = probeset
def ExonID(self): return self._exonid
def setDisplayExonID(self,exonid): self._exonid = exonid
def GeneID(self): return self._geneid
def Symbol(self):
symbol = ''
if self.GeneID() in annotate_db:
y = annotate_db[self.GeneID()]
symbol = y.Symbol()
return symbol
def ExternalGeneID(self): return self._external_gene
def ProbesetType(self):
###e.g. Exon, junction, constitutive(gene)
return self._probeset_type
def GeneStructure(self): return self._block_structure
def SecondaryExonID(self): return self._block_exon_ids
def setSecondaryExonID(self,ids): self._block_exon_ids = ids
def setLocationData(self, chromosome, strand, probeset_start, probeset_stop):
self._chromosome = chromosome; self._strand = strand
self._start = probeset_start; self._stop = probeset_stop
def LocationSummary(self):
location = self.Chromosome()+':'+self.ProbeStart()+'-'+self.ProbeStop()+'('+self.Strand()+')'
return location
def Chromosome(self): return self._chromosome
def Strand(self): return self._strand
def ProbeStart(self): return self._start
def ProbeStop(self): return self._stop
def ProbesetClass(self):
###e.g. core, extendended, full
return self._probest_class
def ExternalExonIDs(self): return self._external_exonids
def ExternalExonIDList(self):
external_exonid_list = string.split(self.ExternalExonIDs(),'|')
return external_exonid_list
def Constitutive(self): return self._constitutive_status
def setTranscriptCluster(self,secondary_geneid): self._secondary_geneid = secondary_geneid
def setNovelExon(self,novel_exon): self._novel_exon = novel_exon
def NovelExon(self): return self._novel_exon
def SecondaryGeneID(self): return self._secondary_geneid
def setExonRegionID(self,exon_region): self._exon_region = exon_region
def ExonRegionID(self): return self._exon_region
def SplicingEvent(self):
splice_event = self._splicing_event
if len(splice_event)!=0:
if splice_event[0] == '|': splice_event = splice_event[1:]
return splice_event
def SplicingCall(self): return self._splicing_call
def SpliceJunctions(self): return self._splice_junctions
def Delete(self): del self
def Report(self):
output = self.ArrayType() +'|'+ self.ExonID() +'|'+ self.ExternalGeneID()
return output
def __repr__(self): return self.Report()
class AltMouseData(SplicingAnnotationData):
def __init__(self,affygene,exons,ensembl,block_exon_ids,block_structure,probe_type_call):
self._geneid = affygene; self._external_gene = ensembl; self._exonid = exons; self._secondary_geneid = ensembl
self._probeset_type = probe_type_call; self._block_structure = block_structure; self._block_exon_ids = block_exon_ids
self._external_exonids = 'NA';
self._constitutive_status = 'no'
self._splicing_event = ''
self._secondary_geneid = 'NA'
self._exon_region = ''
if self._probeset_type == 'gene': self._constitutive_status = 'yes'
else: self._constitutive_status = 'no'
class AffyExonSTData(SplicingAnnotationData):
def __init__(self,ensembl_gene_id,exon_id,ens_exon_ids, constitutive_call_probeset, exon_region, splicing_event, splice_junctions, splicing_call):
self._geneid = ensembl_gene_id; self._external_gene = ensembl_gene_id; self._exonid = exon_id
self._constitutive_status = constitutive_call_probeset#; self._start = probeset_start; self._stop = probeset_stop
self._external_exonids = ens_exon_ids; #self._secondary_geneid = transcript_cluster_id#; self._chromosome = chromosome; self._strand = strand
self._exon_region=exon_region; self._splicing_event=splicing_event; self._splice_junctions=splice_junctions; self._splicing_call = splicing_call
if self._exonid[0] == 'U': self._probeset_type = 'UTR'
elif self._exonid[0] == 'E': self._probeset_type = 'exonic'
elif self._exonid[0] == 'I': self._probeset_type = 'intronic'
class AffyExonSTDataAbbreviated(SplicingAnnotationData):
def __init__(self,ensembl_gene_id,exon_id,splicing_call):
self._geneid = ensembl_gene_id; self._exonid = exon_id; self._splicing_call = splicing_call
def importSplicingAnnotations(array_type,Species,probeset_type,avg_ss_for_all,root_dir):
global filter_probesets_by; filter_probesets_by = probeset_type
global species; species = Species; global avg_all_for_ss; avg_all_for_ss = avg_ss_for_all; global exon_db; exon_db={}
global summary_data_db; summary_data_db={}; global remove_intronic_junctions; remove_intronic_junctions = 'no'
if array_type == 'RNASeq':
probeset_annotations_file = root_dir+'AltDatabase/'+species+'/'+array_type+'/'+species+'_Ensembl_junctions.txt'
else: probeset_annotations_file = 'AltDatabase/'+species+'/'+array_type+'/'+species+'_Ensembl_probesets.txt'
filtered_arrayids={};filter_status='no'
constitutive_probeset_db,exon_db,genes_being_analyzed = importSplicingAnnotationDatabase(probeset_annotations_file,array_type,filtered_arrayids,filter_status)
return exon_db, constitutive_probeset_db
def importSplicingAnnotationDatabase(filename,array_type,filtered_arrayids,filter_status):
begin_time = time.time()
probesets_included_by_new_evidence = 0; export_exon_regions = 'yes'
if 'fake' in array_type: array_type = string.replace(array_type,'-fake',''); original_arraytype = 'RNASeq'
else: original_arraytype = array_type
if filter_status == 'no': global gene_transcript_cluster_db; gene_transcript_cluster_db={}; gene_transcript_cluster_db2={}; global last_exon_region_db; last_exon_region_db = {}
else: new_exon_db={}
fn=filepath(filename)
last_gene = ' '; last_exon_region = ''
constitutive_probeset_db = {}; constitutive_gene = {}
count = 0; x = 0; constitutive_original = {}
#if filter_status == 'yes': exon_db = {}
if array_type == 'AltMouse':
for line in open(fn,'rU').xreadlines():
probeset_data = cleanUpLine(line) #remove endline
probeset,affygene,exons,transcript_num,transcripts,probe_type_call,ensembl,block_exon_ids,block_structure,comparison_info = string.split(probeset_data,'\t')
###note: currently exclude comparison_info since not applicable for existing analyses
if x == 0: x = 1
else:
if exons[-1] == '|': exons = exons[0:-1]
if affygene[-1] == '|': affygene = affygene[0:-1]; constitutive_gene[affygene]=[]
if probe_type_call == 'gene': constitutive_call = 'yes' #looked through the probe annotations and the gene seems to be the most consistent constitutive feature
else: constitutive_call = 'no'
include_call,constitutive_call = ProbesetCalls(array_type,'',exons,constitutive_call,'')
if include_call == 'yes':
probe_data = AltMouseData(affygene,exons,ensembl,block_exon_ids,block_structure,probe_type_call) #this used to just have affygene,exon in the values (1/17/05)
exon_db[probeset] = probe_data
if filter_status == 'yes': new_exon_db[probeset] = probe_data
if constitutive_call == 'yes': constitutive_probeset_db[probeset] = affygene
genes_being_analyzed = constitutive_gene
else:
for line in open(fn,'rU').xreadlines():
probeset_data = cleanUpLine(line) #remove endline
if x == 0: x = 1
else:
try: probeset_id, exon_id, ensembl_gene_id, transcript_cluster_id, chromosome, strand, probeset_start, probeset_stop, affy_class, constitutive_call_probeset, external_exonid, ens_const_exons, exon_region, exon_region_start, exon_region_stop, splicing_event, splice_junctions = string.split(probeset_data,'\t')
except Exception: print probeset_data;force_error
if affy_class == 'free': affy_class = 'full' ### Don't know what the difference is
include_call,constitutive_call = ProbesetCalls(array_type,affy_class,splicing_event,constitutive_call_probeset,external_exonid)
#if 'ENSG00000163904:E11.5' in probeset_id: print probeset_data
#print array_type,affy_class,splicing_event,constitutive_call_probeset,external_exonid,constitutive_call,include_call;kill
if array_type == 'junction' and '.' not in exon_id: exon_id = string.replace(exon_id,'-','.'); exon_region = string.replace(exon_region,'-','.')
if ensembl_gene_id != last_gene: new_gene = 'yes'
else: new_gene = 'no'
if filter_status == 'no' and new_gene == 'yes':
if '.' in exon_id: ### Exclude junctions
if '-' not in last_exon_region and 'E' in last_exon_region: last_exon_region_db[last_gene] = last_exon_region
else: last_exon_region_db[last_gene] = last_exon_region
last_gene = ensembl_gene_id
if len(exon_region)>1: last_exon_region = exon_region ### some probeset not linked to an exon region
###Record the transcript clusters assoicated with each gene to annotate the results later on
if constitutive_call_probeset!=constitutive_call: probesets_included_by_new_evidence +=1#; print probeset_id,[splicing_event],[constitutive_call_probeset];kill
proceed = 'no'; as_call = 0
if array_type == 'RNASeq' or array_type == 'junction': include_call = 'yes' ### Constitutive expression is not needed
if remove_intronic_junctions == 'yes':
if 'E' not in exon_id: include_call = 'no' ### Remove junctions that only have splice-sites within an intron or UTR
if include_call == 'yes' or constitutive_call == 'yes':
#if proceed == 'yes':
as_call = EvidenceOfAltSplicing(splicing_event)
if filter_status == 'no':
probe_data = AffyExonSTDataAbbreviated(ensembl_gene_id, exon_id, as_call)
if array_type != 'RNASeq':
probe_data.setTranscriptCluster(transcript_cluster_id)
try:
if export_exon_regions == 'yes':
probe_data.setExonRegionID(exon_region)
except Exception: null=[]
else:
probe_data = AffyExonSTData(ensembl_gene_id, exon_id, external_exonid, constitutive_call, exon_region, splicing_event, splice_junctions, as_call)
probe_data.setLocationData(chromosome, strand, probeset_start, probeset_stop)
if array_type != 'RNASeq':
probe_data.setTranscriptCluster(transcript_cluster_id)
else:
probe_data.setNovelExon(affy_class)
if filter_status == 'yes':
try: ### saves memory
null = filtered_arrayids[probeset_id]
new_exon_db[probeset_id] = probe_data
except KeyError: null = []
else: exon_db[probeset_id] = probe_data
if constitutive_call == 'yes' and filter_status == 'no': ###only perform function when initially running
constitutive_probeset_db[probeset_id] = ensembl_gene_id
try: constitutive_gene[ensembl_gene_id].append(probeset_id)
except Exception: constitutive_gene[ensembl_gene_id] = [probeset_id]
###Only consider transcript clusters that make up the constitutive portion of the gene or that are alternatively regulated
if array_type != 'RNASeq':
try: gene_transcript_cluster_db[ensembl_gene_id].append(transcript_cluster_id)
except KeyError: gene_transcript_cluster_db[ensembl_gene_id] = [transcript_cluster_id]
if constitutive_call_probeset == 'yes' and filter_status == 'no': ###only perform function when initially running
try: constitutive_original[ensembl_gene_id].append(probeset_id)
except KeyError: constitutive_original[ensembl_gene_id] = [probeset_id]
if array_type != 'RNASeq':
try: gene_transcript_cluster_db2[ensembl_gene_id].append(transcript_cluster_id)
except KeyError: gene_transcript_cluster_db2[ensembl_gene_id] = [transcript_cluster_id]
###If no constitutive probesets for a gene as a result of additional filtering (removing all probesets associated with a splice event), add these back
original_probesets_add = 0; genes_being_analyzed = {}
for gene in constitutive_gene: genes_being_analyzed[gene]=[]
for gene in constitutive_original:
if gene not in constitutive_gene:
genes_being_analyzed[gene] = [gene]
constitutive_gene[gene]=[]
original_probesets_add +=1
gene_transcript_cluster_db[gene] = gene_transcript_cluster_db2[gene]
for probeset in constitutive_original[gene]: constitutive_probeset_db[probeset] = gene
#if array_type == 'junction' or array_type == 'RNASeq':
### Added the below in 1.16!!!
### If no constitutive probesets for a gene assigned, assign all gene probesets
for probeset in exon_db:
gene = exon_db[probeset].GeneID()
proceed = 'no'
exonid = exon_db[probeset].ExonID()
### Rather than add all probesets, still filter based on whether the probeset is in an annotated exon
if 'E' in exonid and 'I' not in exonid and '_' not in exonid: proceed = 'yes'
if proceed == 'yes':
if gene not in constitutive_gene:
constitutive_probeset_db[probeset] = gene
genes_being_analyzed[gene] = [gene]
### DO NOT ADD TO constitutive_gene SINCE WE WANT ALL mRNA ALIGNING EXONS/JUNCTIONS TO BE ADDED!!!!
#constitutive_gene[gene]=[]
gene_transcript_cluster_db = eliminate_redundant_dict_values(gene_transcript_cluster_db)
#if affygene == 'ENSMUSG00000023089': print [abs(fold_change_log)],[log_fold_cutoff];kill
if array_type == 'RNASeq':
import RNASeq
try: last_exon_region_db = RNASeq.importExonAnnotations(species,'distal-exon','')
except Exception: null=[]
constitutive_original=[]; constitutive_gene=[]
#clearObjectsFromMemory(exon_db); constitutive_probeset_db=[];genes_being_analyzed=[] ### used to evaluate how much memory objects are taking up
#print 'remove_intronic_junctions:',remove_intronic_junctions
#print constitutive_gene['ENSMUSG00000031170'];kill ### Determine if avg_ss_for_all is working
if original_arraytype == 'RNASeq': id_name = 'exon/junction IDs'
else: id_name = 'array IDs'
print len(exon_db),id_name,'stored as instances of SplicingAnnotationData in memory'
#print len(constitutive_probeset_db),'array IDs stored as constititive'
#print probesets_included_by_new_evidence, 'array IDs were re-annotated as NOT constitutive based on mRNA evidence'
if array_type != 'AltMouse': print original_probesets_add, 'genes not viewed as constitutive as a result of filtering',id_name,'based on splicing evidence, added back'
end_time = time.time(); time_diff = int(end_time-begin_time)
#print filename,"import finished in %d seconds" % time_diff
if filter_status == 'yes': return new_exon_db
else:
summary_data_db['gene_assayed'] = len(genes_being_analyzed)
try: exportDenominatorGenes(genes_being_analyzed)
except Exception: null=[]
return constitutive_probeset_db,exon_db,genes_being_analyzed
def exportDenominatorGenes(genes_being_analyzed):
goelite_output = root_dir+'GO-Elite/denominator/AS.denominator.txt'
goelite_data = export.ExportFile(goelite_output)
systemcode = 'En'
goelite_data.write("GeneID\tSystemCode\n")
for gene in genes_being_analyzed:
if array_type == 'AltMouse':
try: gene = annotate_db[gene].ExternalGeneID()
except KeyError: null = []
goelite_data.write(gene+'\t'+systemcode+'\n')
try: goelite_data.close()
except Exception: null=[]
def performExpressionAnalysis(filename,constitutive_probeset_db,exon_db,annotate_db,dataset_name):
#if analysis_method == 'splicing-index': returnLargeGlobalVars();kill ### used to ensure all large global vars from the reciprocal junction analysis have been cleared from memory
#returnLargeGlobalVars()
"""import list of expression values for arrayids and calculates statistics"""
global fold_dbase; global original_conditions; global normalization_method
stats_dbase = {}; fold_dbase={}; ex_db={}; si_db=[]; bad_row_import = {}; count=0
global array_group_name_db; array_group_name_db = {}
global array_group_db; array_group_db = {};
global array_raw_group_values; array_raw_group_values = {}; global original_array_names; original_array_names=[]
global max_replicates; global equal_replicates; global array_group_list
array_index_list = [] ###Use this list for permutation analysis
fn=filepath(filename); line_num = 1
for line in open(fn,'rU').xreadlines():
data = cleanUpLine(line); t = string.split(data,'\t'); probeset = t[0]
if t[0]== '#': null=[] ### Don't import line
elif line_num == 1:
line_num += 1 #makes this value null for the next loop of actual array data
###Below ocucrs if the data is raw opposed to precomputed
if ':' in t[1]:
array_group_list = []; x=0 ###gives us an original index value for each entry in the group
for entry in t[1:]:
original_array_names.append(entry)
aa = string.split(entry,':')
try: array_group,array_name = aa
except Exception: array_name = string.join(aa[1:],':'); array_group = aa[0]
try:
array_group_db[array_group].append(x)
array_group_name_db[array_group].append(array_name)
except KeyError:
array_group_db[array_group] = [x]
array_group_name_db[array_group] = [array_name]
### below only occurs with a new group addition
array_group_list.append(array_group) #use this to generate comparisons in the below linked function
x += 1
else:
#try: print data_type
#except Exception,exception:
#print exception
#print traceback.format_exc()
print_out = 'The AltAnalyze filtered expression file "'+filename+'" is not propperly formatted.\n Review formatting requirements if this file was created by another application.\n'
print_out += "\nFirst line\n"+line
try: UI.WarningWindow(print_out,'Exit'); print print_out
except Exception: print print_out
badExit()
else:
#if probeset in exon_db:
#if exon_db[probeset].GeneID() == 'ENSG00000139970':
###Use the index values from above to assign each expression value to a new database
temp_group_array = {}
line_num+=1
for group in array_group_db:
if count == 0: array_index_list.append(array_group_db[group])
for array_index in array_group_db[group]:
try: exp_val = float(t[array_index+1])
except Exception:
if 'Gene_ID' not in line: bad_row_import[probeset]=line; exp_val = 1
###appended is the numerical expression value for each array in the group (temporary array)
try: temp_group_array[group].append(exp_val) #add 1 since probeset is the first column
except KeyError: temp_group_array[group] = [exp_val]
if count == 0: array_index_list.sort(); count = 1
####store the group database within the probeset database entry
try:
null = exon_db[probeset] ###To conserve memory, don't store any probesets not used for downstream analyses (e.g. not linked to mRNAs)
#if 'ENSG00000139970' in probeset:
#print [max_exp]
#print t[1:];kill
#max_exp = max(map(float, t[1:]))
#if len(array_raw_group_values)>10000: break
#if max_exp>math.log(70,2):
array_raw_group_values[probeset] = temp_group_array
except KeyError:
#print probeset
pass
print len(array_raw_group_values), 'sequence identifiers imported out of', line_num-1
if len(bad_row_import)>0:
print len(bad_row_import), "Rows with an unexplained import error processed and deleted."
print "Example row:"; x=0
for i in bad_row_import:
if x==0: print bad_row_import[i]
try: del array_raw_group_values[i]
except Exception: null=[]
x+=1
### If no gene expression reporting probesets were imported, update constitutive_probeset_db to include all mRNA aligning probesets
cs_genedb={}; missing_genedb={}; addback_genedb={}; rnaseq_cs_gene_db={}
for probeset in constitutive_probeset_db:
gene = constitutive_probeset_db[probeset]
#if gene == 'ENSG00000185008': print [probeset]
try:
null=array_raw_group_values[probeset]; cs_genedb[gene]=[]
if gene == probeset: rnaseq_cs_gene_db[gene]=[] ### If RPKM normalization used, use the gene expression values already calculated
except Exception: missing_genedb[gene]=[] ### Collect possible that are missing from constitutive database (verify next)
for gene in missing_genedb:
try: null=cs_genedb[gene]
except Exception: addback_genedb[gene]=[]
for probeset in array_raw_group_values:
try:
gene = exon_db[probeset].GeneID()
try:
null=addback_genedb[gene]
if 'I' not in probeset and 'U' not in probeset: ### No intron or UTR containing should be used for constitutive expression
null=string.split(probeset,':')
if len(null)<3: ### No trans-gene junctions should be used for constitutive expression
constitutive_probeset_db[probeset]=gene
except Exception: null=[]
except Exception: null=[]
for probeset in constitutive_probeset_db:
gene = constitutive_probeset_db[probeset]
#if gene == 'ENSG00000185008': print [[probeset]]
### Only examine values for associated exons when determining RNASeq constitutive expression (when exon data is present)
normalization_method = 'raw'
if array_type == 'RNASeq':
junction_count=0; constitutive_probeset_db2={}
for uid in constitutive_probeset_db:
if '-' in uid: junction_count+=1
if len(rnaseq_cs_gene_db)>0: ### If filtered RPKM gene-level expression data present, use this instead (and only this)
normalization_method = 'RPKM'
constitutive_probeset_db={} ### Re-set this database
for gene in rnaseq_cs_gene_db:
constitutive_probeset_db[gene]=gene
elif junction_count !=0 and len(constitutive_probeset_db) != junction_count:
### occurs when there is a mix of junction and exon IDs
for uid in constitutive_probeset_db:
if '-' not in uid: constitutive_probeset_db2[uid] = constitutive_probeset_db[uid]
constitutive_probeset_db = constitutive_probeset_db2; constitutive_probeset_db2=[]
"""
for probeset in constitutive_probeset_db:
gene = constitutive_probeset_db[probeset]
if gene == 'ENSG00000185008': print [probeset]
"""
###Build all putative splicing events
global alt_junction_db; global exon_dbase; global critical_exon_db; critical_exon_db={}
if array_type == 'AltMouse' or ((array_type == 'junction' or array_type == 'RNASeq') and explicit_data_type == 'null'):
### Applies to reciprocal junction analyses only
if array_type == 'AltMouse':
alt_junction_db,critical_exon_db,exon_dbase,exon_inclusion_db,exon_db = ExonAnnotate_module.identifyPutativeSpliceEvents(exon_db,constitutive_probeset_db,array_raw_group_values,agglomerate_inclusion_probesets,onlyAnalyzeJunctions)
print 'Number of Genes with Examined Splice Events:',len(alt_junction_db)
elif (array_type == 'junction' or array_type == 'RNASeq') and explicit_data_type == 'null':
from build_scripts import JunctionArray
alt_junction_db,critical_exon_db,exon_dbase,exon_inclusion_db,exon_db = JunctionArray.getPutativeSpliceEvents(species,array_type,exon_db,agglomerate_inclusion_probesets,root_dir)
print 'Number of Genes with Examined Splice Events:',len(alt_junction_db)
#alt_junction_db=[]; critical_exon_db=[]; exon_dbase=[]; exon_inclusion_db=[]
if agglomerate_inclusion_probesets == 'yes':
array_raw_group_values = agglomerateInclusionProbesets(array_raw_group_values,exon_inclusion_db)
exon_inclusion_db=[]
### For datasets with high memory requirements (RNASeq), filter the current and new databases
### Begin this function after agglomeration to ensure agglomerated probesets are considered
reciprocal_probesets = {}
if array_type == 'junction' or array_type == 'RNASeq':
for affygene in alt_junction_db:
for event in alt_junction_db[affygene]:
reciprocal_probesets[event.InclusionProbeset()]=[]
reciprocal_probesets[event.ExclusionProbeset()]=[]
not_evalutated={}
for probeset in array_raw_group_values:
try: null=reciprocal_probesets[probeset]
except Exception:
### Don't remove constitutive probesets
try: null=constitutive_probeset_db[probeset]
except Exception: not_evalutated[probeset]=[]
#print 'Removing',len(not_evalutated),'exon/junction IDs not evaulated for splicing'
for probeset in not_evalutated:
del array_raw_group_values[probeset]
###Check to see if we have precomputed expression data or raw to be analyzed
x=0; y=0; array_raw_group_values2={}; probesets_to_delete=[] ### Record deleted probesets
if len(array_raw_group_values)==0:
print_out = "No genes were considered 'Expressed' based on your input options. Check to make sure that the right species database is indicated and that the right data format has been selected (e.g., non-log versus log expression)."
try: UI.WarningWindow(print_out,'Exit')
except Exception: print print_out; print "Exiting program"
badExit()
elif len(array_raw_group_values)>0:
###array_group_list should already be unique and correctly sorted (see above)
for probeset in array_raw_group_values:
data_lists=[]
for group_name in array_group_list:
data_list = array_raw_group_values[probeset][group_name] ###nested database entry access - baseline expression
if global_addition_factor > 0: data_list = addGlobalFudgeFactor(data_list,'log')
data_lists.append(data_list)
if len(array_group_list)==2:
data_list1 = data_lists[0]; data_list2 = data_lists[-1]; avg1 = statistics.avg(data_list1); avg2 = statistics.avg(data_list2)
log_fold = avg2 - avg1
try:
#t,df,tails = statistics.ttest(data_list1,data_list2,2,3) #unpaired student ttest, calls p_value function
#t = abs(t); df = round(df) #Excel doesn't recognize fractions in a DF
#p = statistics.t_probability(t,df)
p = statistics.runComparisonStatistic(data_list1,data_list2,probability_statistic)
if p == -1:
if len(data_list1)>1 and len(data_list2)>1:
print_out = "The probability statistic selected ("+probability_statistic+") is not compatible with the\nexperimental design. Please consider an alternative statistic or correct the problem.\nExiting AltAnalyze."
try: UI.WarningWindow(print_out,'Exit')
except Exception: print print_out; print "Exiting program"
badExit()
else: p = 1
except Exception: p = 1
fold_dbase[probeset] = [0]; fold_dbase[probeset].append(log_fold)
stats_dbase[probeset]=[avg1]; stats_dbase[probeset].append(p)
###replace entries with the two lists for later permutation analysis
if p == -1: ### should by p == 1: Not sure why this filter was here, but mistakenly removes probesets where there is just one array for each group
del fold_dbase[probeset];del stats_dbase[probeset]; probesets_to_delete.append(probeset); x += 1
if x == 1: print 'Bad data detected...', data_list1, data_list2
elif (avg1 < expression_threshold and avg2 < expression_threshold and p > p_threshold) and array_type != 'RNASeq': ### Inserted a filtering option to exclude small variance, low expreession probesets
del fold_dbase[probeset];del stats_dbase[probeset]; probesets_to_delete.append(probeset); x += 1
else: array_raw_group_values2[probeset] = [data_list1,data_list2]
else: ###Non-junction analysis can handle more than 2 groups
index=0
for data_list in data_lists:
try: array_raw_group_values2[probeset].append(data_list)
except KeyError: array_raw_group_values2[probeset] = [data_list]
if len(array_group_list)>2: ### Thus, there is some variance for this probeset
### Create a complete stats_dbase containing all fold changes
if index==0:
avg_baseline = statistics.avg(data_list); stats_dbase[probeset] = [avg_baseline]
else:
avg_exp = statistics.avg(data_list)
log_fold = avg_exp - avg_baseline
try: fold_dbase[probeset].append(log_fold)
except KeyError: fold_dbase[probeset] = [0,log_fold]
index+=1
if array_type == 'RNASeq': id_name = 'exon/junction IDs'
else: id_name = 'array IDs'
array_raw_group_values = array_raw_group_values2; array_raw_group_values2=[]
print x, id_name,"excluded prior to analysis... predicted not detected"
global original_avg_const_exp_db; global original_fold_dbase
global avg_const_exp_db; global permute_lists; global midas_db
if len(array_raw_group_values)>0:
adj_fold_dbase, nonlog_NI_db, conditions, gene_db, constitutive_gene_db, constitutive_fold_change, original_avg_const_exp_db = constitutive_exp_normalization(fold_dbase,stats_dbase,exon_db,constitutive_probeset_db)
stats_dbase=[] ### No longer needed after this point
original_fold_dbase = fold_dbase; avg_const_exp_db = {}; permute_lists = []; y = 0; original_conditions = conditions; max_replicates,equal_replicates = maxReplicates()
gene_expression_diff_db = constitutive_expression_changes(constitutive_fold_change,annotate_db) ###Add in constitutive fold change filter to assess gene expression for ASPIRE
while conditions > y:
avg_const_exp_db = constitutive_exp_normalization_raw(gene_db,constitutive_gene_db,array_raw_group_values,exon_db,y,avg_const_exp_db); y+=1
#print len(avg_const_exp_db),constitutive_gene_db['ENSMUSG00000054850']
###Export Analysis Results for external splicing analysis (e.g. MiDAS format)
if run_MiDAS == 'yes' and normalization_method != 'RPKM': ### RPKM has negative values which will crash MiDAS
status = ResultsExport_module.exportTransitResults(array_group_list,array_raw_group_values,array_group_name_db,avg_const_exp_db,adj_fold_dbase,exon_db,dataset_name,apt_location)
print "Finished exporting input data for MiDAS analysis"
try: midas_db = ResultsExport_module.importMidasOutput(dataset_name)
except Exception: midas_db = {} ### Occurs if there are not enough samples to calculate a MiDAS p-value
else: midas_db = {}
###Provides all pairwise permuted group comparisons
if array_type == 'AltMouse' or ((array_type == 'junction' or array_type == 'RNASeq') and explicit_data_type == 'null'):
permute_lists = statistics.permute_arrays(array_index_list)
### Now remove probesets from the analysis that were used to evaluate gene expression
for probeset in constitutive_probeset_db:
try: null = reciprocal_probesets[probeset]
except Exception:
try: del array_raw_group_values[probeset]
except Exception: null=[]
not_evalutated=[]; reciprocal_probesets=[]
constitutive_probeset_db=[]
### Above, all conditions were examined when more than 2 are present... change this so that only the most extreeem are analyzed further
if len(array_group_list)>2 and analysis_method == 'splicing-index' and (array_type == 'exon' or array_type == 'gene' or explicit_data_type != 'null'): ### USED FOR MULTIPLE COMPARISONS
print 'Calculating splicing-index values for multiple group comparisons (please be patient)...',
"""
if len(midas_db)==0:
print_out = 'Warning!!! MiDAS failed to run for multiple groups. Please make\nsure there are biological replicates present for your groups.\nAltAnalyze requires replicates for multi-group (more than two) analyses.'
try: UI.WarningWindow(print_out,'Exit')
except Exception: print print_out; print "Exiting program"
badExit()"""
if filter_for_AS == 'yes':
for probeset in exon_db:
as_call = exon_db[probeset].SplicingCall()
if as_call == 0:
try: del nonlog_NI_db[probeset]
except KeyError: null=[]
if export_NI_values == 'yes':
export_exon_regions = 'yes'
### Currently, we don't deal with raw adjusted expression values, just group, so just export the values for each group
summary_output = root_dir+'AltResults/RawSpliceData/'+species+'/'+analysis_method+'/'+dataset_name[:-1]+'.txt'
print "Exporting all normalized intensities to:\n"+summary_output
adjoutput = export.ExportFile(summary_output)
title = string.join(['Gene\tExonID\tprobesetID']+original_array_names,'\t')+'\n'; adjoutput.write(title)
### Pick which data lists have the most extreem values using the NI_dbase (adjusted folds for each condition)
original_increment = int(len(nonlog_NI_db)/20); increment = original_increment; interaction = 0
for probeset in nonlog_NI_db:
if interaction == increment: increment+=original_increment; print '*',
interaction +=1
geneid = exon_db[probeset].GeneID(); ed = exon_db[probeset]
index=0; NI_list=[] ### Add the group_name to each adj fold value
for NI in nonlog_NI_db[probeset]:
NI_list.append((NI,index)); index+=1 ### setup to sort for the extreeme adj folds and get associated group_name using the index
raw_exp_vals = array_raw_group_values[probeset]
adj_exp_lists={} ### Store the adjusted expression values for each group
if geneid in avg_const_exp_db:
k=0; gi=0; adj_exp_vals = []
for exp_list in raw_exp_vals:
for exp in exp_list:
adj_exp_val = exp-avg_const_exp_db[geneid][k]
try: adj_exp_lists[gi].append(adj_exp_val)
except Exception: adj_exp_lists[gi] = [adj_exp_val]
if export_NI_values == 'yes': adj_exp_vals.append(str(adj_exp_val))
k+=1
gi+=1
if export_NI_values == 'yes':
#print geneid+'-'+probeset, adj_exp_val, [ed.ExonID()];kill
if export_exon_regions == 'yes':
try: ### Thid will only work if ExonRegionID is stored in the abreviated AffyExonSTData object - useful in comparing results between arrays (exon-region centric)
if (array_type == 'exon' or array_type == 'gene') or '-' not in ed.ExonID(): ### only include exon entries not junctions
exon_regions = string.split(ed.ExonRegionID(),'|')
for er in exon_regions:
if len(er)>0: er = er
else:
try: er = ed.ExonID()
except Exception: er = 'NA'
ev = string.join([geneid+'\t'+er+'\t'+probeset]+adj_exp_vals,'\t')+'\n'
if len(filtered_probeset_db)>0:
if probeset in filtered_probeset_db: adjoutput.write(ev) ### This is used when we want to restrict to only probesets known to already by changed
else: adjoutput.write(ev)
except Exception:
ev = string.join([geneid+'\t'+'NA'+'\t'+probeset]+adj_exp_vals,'\t')+'\n'; adjoutput.write(ev)
NI_list.sort()
examine_pairwise_comparisons = 'yes'
if examine_pairwise_comparisons == 'yes':
k1=0; k2=0; filtered_NI_comps = []
NI_list_rev = list(NI_list); NI_list_rev.reverse()
NI1,index1 = NI_list[k1]; NI2,index2 = NI_list_rev[k2]; abs_SI = abs(math.log(NI1/NI2,2))
if abs_SI<alt_exon_logfold_cutoff:
### Indicates that no valid matches were identified - hence, exit loop and return an NI_list with no variance
NI_list = [NI_list[0],NI_list[0]]
else:
### Indicates that no valid matches were identified - hence, exit loop and return an NI_list with no variance
constit_exp1 = original_avg_const_exp_db[geneid][index1]
constit_exp2 = original_avg_const_exp_db[geneid][index2]
ge_fold = constit_exp2-constit_exp1
#print 'original',abs_SI,k1,k2, ge_fold, constit_exp1, constit_exp2
if abs(ge_fold) < log_fold_cutoff: filtered_NI_comps.append([abs_SI,k1,k2])
else:
for i1 in NI_list:
k2=0
for i2 in NI_list_rev:
NI1,index1 = i1; NI2,index2 = i2; abs_SI = abs(math.log(NI1/NI2,2))
#constit_exp1 = original_avg_const_exp_db[geneid][index1]
#constit_exp2 = original_avg_const_exp_db[geneid][index2]
#ge_fold = constit_exp2-constit_exp1
#if abs(ge_fold) < log_fold_cutoff: filtered_NI_comps.append([abs_SI,k1,k2])
#print k1,k2, i1, i2, abs_SI, abs(ge_fold), log_fold_cutoff, alt_exon_logfold_cutoff
if abs_SI<alt_exon_logfold_cutoff: break
else:
constit_exp1 = original_avg_const_exp_db[geneid][index1]
constit_exp2 = original_avg_const_exp_db[geneid][index2]
ge_fold = constit_exp2-constit_exp1
if abs(ge_fold) < log_fold_cutoff:
filtered_NI_comps.append([abs_SI,k1,k2])
#if k1 == 49 or k1 == 50 or k1 == 51: print probeset, abs_SI, k1, k2, abs(ge_fold),log_fold_cutoff, index1, index2, NI1, NI2, constit_exp1,constit_exp2
k2+=1
k1+=1
if len(filtered_NI_comps)>0:
#print filtered_NI_comps
#print NI_list_rev
#print probeset,geneid
#print len(filtered_NI_comps)
#print original_avg_const_exp_db[geneid]
filtered_NI_comps.sort()
si,k1,k2 = filtered_NI_comps[-1]
NI_list = [NI_list[k1],NI_list_rev[k2]]
"""
NI1,index1 = NI_list[0]; NI2,index2 = NI_list[-1]
constit_exp1 = original_avg_const_exp_db[geneid][index1]
constit_exp2 = original_avg_const_exp_db[geneid][index2]
ge_fold = constit_exp2-constit_exp1
print probeset, si, ge_fold, NI_list"""
#print k1,k2;sys.exit()
index1 = NI_list[0][1]; index2 = NI_list[-1][1]
nonlog_NI_db[probeset] = [NI_list[0][0],NI_list[-1][0]] ### Update the values of this dictionary
data_list1 = array_raw_group_values[probeset][index1]; data_list2 = array_raw_group_values[probeset][index2]
avg1 = statistics.avg(data_list1); avg2 = statistics.avg(data_list2); log_fold = avg2 - avg1
group_name1 = array_group_list[index1]; group_name2 = array_group_list[index2]
try:
#t,df,tails = statistics.ttest(data_list1,data_list2,2,3) #unpaired student ttest, calls p_value function
#t = abs(t); df = round(df); ttest_exp_p = statistics.t_probability(t,df)
ttest_exp_p = statistics.runComparisonStatistic(data_list1,data_list2,probability_statistic)
except Exception: ttest_exp_p = 1
fold_dbase[probeset] = [0]; fold_dbase[probeset].append(log_fold)
if ttest_exp_p == -1: del fold_dbase[probeset]; probesets_to_delete.append(probeset); x += 1
elif avg1 < expression_threshold and avg2 < expression_threshold and (ttest_exp_p > p_threshold and ttest_exp_p != 1): ### Inserted a filtering option to exclude small variance, low expreession probesets
del fold_dbase[probeset]; probesets_to_delete.append(probeset); x += 1
else:
constit_exp1 = original_avg_const_exp_db[geneid][index1]
constit_exp2 = original_avg_const_exp_db[geneid][index2]
ge_fold = constit_exp2-constit_exp1
normInt1 = (avg1-constit_exp1); normInt2 = (avg2-constit_exp2)
adj_fold = normInt2 - normInt1
splicing_index = -1*adj_fold; abs_splicing_index = abs(splicing_index)
#print probeset, splicing_index, ge_fold, index1, index2
#normIntList1 = adj_exp_lists[index1]; normIntList2 = adj_exp_lists[index2]
all_nI=[]
for g_index in adj_exp_lists: all_nI.append(adj_exp_lists[g_index])
try: normIntensityP = statistics.OneWayANOVA(all_nI) #[normIntList1,normIntList2] ### This stays an ANOVA independent of the algorithm choosen since groups number > 2
except Exception: normIntensityP = 'NA'
if (normInt1*normInt2)<0: opposite_SI_log_mean = 'yes'
else: opposite_SI_log_mean = 'no'
abs_log_ratio = abs(ge_fold)
if probeset in midas_db:
try: midas_p = float(midas_db[probeset])
except ValueError: midas_p = 'NA'
else: midas_p = 'NA'
#if 'ENSG00000059588' in geneid: print probeset, splicing_index, constit_exp1, constit_exp2, ge_fold,group_name2+'_vs_'+group_name1, index1, index2
if abs_splicing_index>alt_exon_logfold_cutoff and (midas_p < p_threshold or midas_p == 'NA'): #and abs_log_ratio>1 and ttest_exp_p<0.05: ###and ge_threshold_count==2
exonid = ed.ExonID(); critical_exon_list = [1,[exonid]]
ped = ProbesetExpressionData(avg1, avg2, log_fold, adj_fold, ttest_exp_p, group_name2+'_vs_'+group_name1)
sid = ExonData(splicing_index,probeset,critical_exon_list,geneid,normInt1,normInt2,normIntensityP,opposite_SI_log_mean)
sid.setConstitutiveExpression(constit_exp1); sid.setConstitutiveFold(ge_fold); sid.setProbesetExpressionData(ped)
si_db.append((splicing_index,sid))
else:
### Also record the data for probesets that are excluded... Used by DomainGraph
eed = ExcludedExonData(splicing_index,geneid,normIntensityP)
ex_db[probeset] = eed
if array_type == 'RNASeq': id_name = 'exon/junction IDs'
else: id_name = 'array IDs'
print len(si_db),id_name,"with evidence of Alternative expression"
original_fold_dbase = fold_dbase; si_db.sort()
summary_data_db['denominator_exp_events']=len(nonlog_NI_db)
del avg_const_exp_db; del gene_db; del constitutive_gene_db; gene_expression_diff_db={}
if export_NI_values == 'yes': adjoutput.close()