-
Notifications
You must be signed in to change notification settings - Fork 0
/
clusteringClasses.py
798 lines (725 loc) · 47.8 KB
/
clusteringClasses.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
'''
Created on Aug 26, 2017
@author: PiTav
This is the new version of this script
'''
import functionsForClustering as clust
import numpy
from Bio import Seq
import matplotlib.pyplot as plt
from scipy import stats
class protein(object):
def __init__(self, geneName, speciesOneName, speciesTwoName, speciesOutName, seqHolder, chrPos=None):
self.name = geneName
self.chrPos = chrPos
self.speciesOneName = speciesOneName
self.speciesTwoName = speciesTwoName
self.speciesOutName = speciesOutName
try:
self.firstSeq = seqHolder[speciesOneName].seq
self.secondSeq = seqHolder[speciesTwoName].seq
self.outSeq = seqHolder[speciesOutName].seq
except AttributeError:
try:
self.firstSeq = Seq.Seq(seqHolder[speciesOneName])
self.secondSeq = Seq.Seq(seqHolder[speciesTwoName])
self.outSeq = Seq.Seq(seqHolder[speciesOutName])
except TypeError:
self.firstSeq = seqHolder[speciesOneName]
self.secondSeq = seqHolder[speciesTwoName]
self.outSeq = seqHolder[speciesOutName]
self.seqLength = 0
self.clusteringHolder = dict()
self.clusteringIntronHolder = dict()
self.polarizedClusteringHolder = dict()
self.propertyClusteringHolder = dict()
self.DNLists = None
self.DSLists = None
self.speciesOneDN = None
self.speciesTwoDN = None
self.speciesOneDS = None
self.speciesTwoDS = None
self.propertyClusteringList = dict() # possibly should remove these variables
self.intronPositions = None
self.reasonForFailure = None
self.speciesOnePopHolder = None
self.speciesTwoPopHolder = None
self.speciesOutPopHolder = None
# For this method, the seqHolder should contain a list of strings that are exons in the correct order!
# Must be a list, even if there is only a single exon, otherwise, disaster!
# This only really works if the intron positions are conserved and information is taken from species one
@classmethod
def proteinExons(cls, geneName, speciesOneName, speciesTwoName, speciesOutName, seqHolder, chrPos=None):
# The last one is the end of the gene, so remove:
intronPositions = numpy.cumsum([len(x) for x in seqHolder[speciesOneName]])[:-1]
# Use this to get the introns that occur nicely between codons to be decimal numbers:
betweenCodons = (intronPositions%3 == 0)*0.5
intronPositions//=3
intronPositions = intronPositions.astype(float)
intronPositions -= betweenCodons
tempHolder = dict()
try:
tempHolder[speciesOneName] = ''.join([str(x.seq) for x in seqHolder[speciesOneName]])
tempHolder[speciesTwoName] = ''.join([str(x.seq) for x in seqHolder[speciesTwoName]])
tempHolder[speciesOutName] = ''.join([str(x.seq) for x in seqHolder[speciesOutName]])
except AttributeError:
tempHolder[speciesOneName] = ''.join([str(x) for x in seqHolder[speciesOneName]])
tempHolder[speciesTwoName] = ''.join([str(x) for x in seqHolder[speciesTwoName]])
tempHolder[speciesOutName] = ''.join([str(x) for x in seqHolder[speciesOutName]])
tempHolder = cls(geneName,speciesOneName,speciesTwoName,speciesOutName,tempHolder,chrPos)
tempHolder.intronPositions = intronPositions
return tempHolder
def addIntronPos(self,intronPositions):
# This method assumes that the intron positions are in nucleotide space and indexed like python do
betweenCodons = (intronPositions%3 == 0)*0.5
intronPositions//=3
intronPositions = intronPositions.astype(float)
intronPositions -= betweenCodons
self.intronPositions = intronPositions
def addPopulationData(self,whichSpecies,populationSeqHolder):
# whichSpecies 1 = first species; 2 = second species; 3 = out group
tempHolder = dict()
for currKey in populationSeqHolder:
try:
tempHolder[currKey] = populationSeqHolder[currKey].seq
except AttributeError:
try:
tempHolder[currKey] = Seq.Seq(populationSeqHolder[currKey])
except TypeError:
tempHolder[currKey] = populationSeqHolder[currKey]
if whichSpecies == 1:
self.speciesOnePopHolder = tempHolder
if whichSpecies == 2:
self.speciesTwoPopHolder = tempHolder
if whichSpecies == 3:
self.speciesOutPopHolder = tempHolder
def setDivergenceList(self,DNList,DSList):
self.DNLists = DNList
self.DSLists = DSList
def setPolarizedDivergenceList(self,DNListOne,DSListOne,DNListTwo,DSListTwo):
self.speciesOneDN = DNListOne
self.speciesTwoDN = DNListTwo
self.speciesOneDS = DSListOne
self.speciesTwoDS = DSListTwo
# This is a wrapper function from functionsForClustering
def filterAlignment(self, lenMultipleOfThree = True, minAlnLen = 300,
eachGapMultipleOfThree = True, totalGapLengthMultipleOfThree = True,
gapContentThresh = .2, DNContentThresh = .2, DSContentThresh = 1,
prematureStopCodon = True):
result = clust.filterAlignment(self.firstSeq,self.secondSeq,self.outSeq,
lenMultipleOfThree, minAlnLen, eachGapMultipleOfThree,
totalGapLengthMultipleOfThree, gapContentThresh,
DNContentThresh, DSContentThresh, prematureStopCodon)
if result[0] == -1:
self.reasonForFailure = result[1]
self.firstSeq = None
self.secondSeq = None
self.outSeq = None
return -1
else:
return 1
def processGene(self):
result = clust.variantsNotPolarized(self.firstSeq,self.secondSeq)
self.setDivergenceList(result['DN List'],result['DS List'])
self.seqLength = len(self.firstSeq)
temp_data = clust.clusteringSameMutation(self.DNLists)
temp_norm = clust.analyticalNormSameMut(len(self.DNLists),self.seqLength/3)
self.clusteringHolder['DNDN'] = clusteringObject(temp_data,temp_norm)
temp_data = clust.clusteringSameMutation(self.DSLists)
temp_norm = clust.analyticalNormSameMut(len(self.DSLists),self.seqLength/3)
self.clusteringHolder['DSDS'] = clusteringObject(temp_data,temp_norm)
temp_data = clust.clusteringDifferentMutation(self.DSLists,self.DNLists)
temp_norm = clust.analyticalNormDiffMut(len(self.DSLists),len(self.DNLists),self.seqLength/3)
self.clusteringHolder['DNDS'] = clusteringObject(temp_data,temp_norm)
result = clust.variantsByParsimony(self.firstSeq,self.secondSeq,self.outSeq)
self.setPolarizedDivergenceList(result['Species One DN'],result['Species One DS'],
result['Species Two DN'],result['Species Two DS'])
temp_data = clust.clusteringSameMutation(self.speciesOneDN)
temp_norm = clust.analyticalNormSameMut(len(self.speciesOneDN),self.seqLength/3)
self.polarizedClusteringHolder['DNDN1'] = clusteringObject(temp_data,temp_norm)
temp_data = clust.clusteringSameMutation(self.speciesTwoDN)
temp_norm = clust.analyticalNormSameMut(len(self.speciesTwoDN),self.seqLength/3)
self.polarizedClusteringHolder['DNDN2'] = clusteringObject(temp_data,temp_norm)
temp_data = clust.clusteringDifferentMutation(self.speciesOneDN,self.speciesTwoDN)
temp_norm = clust.analyticalNormDiffMut(len(self.speciesOneDN),len(self.speciesTwoDN),self.seqLength/3)
self.polarizedClusteringHolder['DNDNcross'] = clusteringObject(temp_data,temp_norm)
chargeTable = {'A': 0, 'C': 0, 'E': -1, 'D': -1, 'G': 0, 'F': 0, 'I': 0, 'H': 1,
'K': 1, 'M': 0, 'L': 0, 'N': 0, 'Q': 0, 'P': 0, 'S': 0, 'R': 1,
'T': 0, 'W': 0, 'V': 0, 'Y': 0}
polarityTable = {'A': 1, 'C': 1, 'E': 0, 'D': 0, 'G': 0, 'F': 1, 'I': 1, 'H': -1,
'K': -1, 'M': 1, 'L': 1, 'N': -1, 'Q': 0, 'P': -1, 'S': 0,
'R': -1, 'T': 0, 'W': 1, 'V': 1, 'Y': 1}
sizeTable = {'A': 67, 'C': 86, 'E': 109, 'D': 91, 'G': 48, 'F': 135, 'I': 124,
'H': 118, 'K': 135, 'M': 124, 'L': 124, 'N': 96, 'Q': 114, 'P': 90,
'S': 73, 'R': 148, 'T': 93, 'W': 163, 'V': 105, 'Y': 141}
result = clust.propertyClusteringNew(self.firstSeq,self.secondSeq,self.outSeq,
self.speciesOneDN,self.speciesTwoDN,chargeTable)
self.propertyClusteringHolder['Charge'] = dict([(currKey,result[currKey]) for currKey in result.keys() if ("Inc" not in currKey and "Dec" not in currKey)])
result = clust.propertyClusteringNew(self.firstSeq,self.secondSeq,self.outSeq,
self.speciesOneDN,self.speciesTwoDN,polarityTable)
self.propertyClusteringHolder['Polarity'] = dict([(currKey,result[currKey]) for currKey in result.keys() if ("Inc" not in currKey and "Dec" not in currKey)])
result = clust.propertyClusteringNew(self.firstSeq,self.secondSeq,self.outSeq,
self.speciesOneDN,self.speciesTwoDN,sizeTable)
self.propertyClusteringHolder['Size'] = dict([(currKey,result[currKey]) for currKey in result.keys() if ("Inc" not in currKey and "Dec" not in currKey)])
def processGeneIntrons(self,calculatePolarized = False,num_simulations = 100):
if not calculatePolarized:
if self.DNLists == None:
result = clust.variantsNotPolarized(self.firstSeq,self.secondSeq)
self.setDivergenceList(result['DN List'],result['DS List'])
self.seqLength = len(self.firstSeq)
tempHolder = clust.clusteringSameMutationIntrons(self.DNLists,self.intronPositions,self.seqLength/3,num_simulations)
self.clusteringIntronHolder['DNDN'] = clusteringObject(tempHolder['data'],tempHolder['norm'])
tempHolder = clust.clusteringSameMutationIntrons(self.DSLists,self.intronPositions,self.seqLength/3,num_simulations)
self.clusteringIntronHolder['DSDS'] = clusteringObject(tempHolder['data'],tempHolder['norm'])
#tempHolder = clust.clusteringDifferentMutationIntrons(self.DNLists,self.DSLists,self.intronPositions,self.seqLength/3,num_simulations)
#self.clusteringIntronHolder['DNDS'] = clusteringObject(tempHolder['data'],tempHolder['norm'])
if calculatePolarized:
self.polarizedIntronClusteringHolder = dict()
try:
self.polarizedClusteringHolder
except AttributeError:
result = variantsByParsimony(self.firstSeq,self.secondSeq,self.outSeq)
self.setPolarizedDivergenceList(result['Species One DN'],result['Species One DS'],
result['Species Two DN'],result['Species Two DS'])
tempHolder = clust.clusteringSameMutationIntrons(self.speciesOneDN,self.intronPositions,self.seqLength/3,num_simulations)
self.polarizedIntronClusteringHolder['DNDN1'] = clusteringObject(tempHolder['data'],tempHolder['norm'])
tempHolder = clust.clusteringSameMutationIntrons(self.speciesTwoDN,self.intronPositions,self.seqLength/3,num_simulations)
self.polarizedIntronClusteringHolder['DNDN2'] = clusteringObject(tempHolder['data'],tempHolder['norm'])
#tempHolder = clust.clusteringDifferentMutationIntrons(self.speciesOneDN,self.speciesTwoDN,self.intronPositions,self.seqLength/3,num_simulations)
#self.polarizedIntronClusteringHolder['DNDNcross'] = clusteringObject(tempHolder['data'],tempHolder['norm'])
def removePolymorphicSites(self,whichSpecies,MAFthresh):
# This is not meant to be run alone; it's a helper function for geneClusteringFixedDiff
if whichSpecies == 1:
tempDNList = self.speciesOneDN
tempDSList = self.speciesOneDS
popSeqHolder = self.speciesOnePopHolder
tempSequence = self.firstSeq
if whichSpecies == 2:
tempDNList = self.speciesTwoDN
tempDSList = self.speciesTwoDS
popSeqHolder = self.speciesTwoPopHolder
tempSequence = self.secondSeq
refArrayDN = [str(tempSequence[x:x+3].translate()) for x in tempDNList]
refArrayDS = [str(tempSequence[x:x+3]) for x in tempDSList]
refMatchCountDN = [0 for x in refArrayDN]
notMissingCounterDN = [0 for x in refArrayDN]
refMatchCountDS = [0 for x in refArrayDS]
notMissingCounterDS = [0 for x in refArrayDS]
for currSeq in popSeqHolder:
for i,index in enumerate(tempDNList):
if 'N' not in str(popSeqHolder[currSeq][index:index+3]):
notMissingCounterDN[i] += 1
if str(popSeqHolder[currSeq][index:index+3].translate()) == refArrayDN[i]:
refMatchCountDN[i] += 1
for i,index in enumerate(tempDSList):
if 'N' not in str(popSeqHolder[currSeq][index:index+3]):
notMissingCounterDS[i] += 1
if str(popSeqHolder[currSeq][index:index+3]) == refArrayDS[i]:
refMatchCountDS[i] += 1
tempDNList = [x[2] for x in zip(refMatchCountDN,notMissingCounterDN,tempDNList) if x[1] > 0 and float(x[0])/x[1] > (1 - MAFthresh)]
tempDSList = [x[2] for x in zip(refMatchCountDS,notMissingCounterDS,tempDSList) if x[1] > 0 and float(x[0])/x[1] > (1 - MAFthresh)]
if whichSpecies == 1:
self.speciesOneDN = tempDNList
self.speciesOneDS = tempDSList
if whichSpecies == 2:
self.speciesTwoDN = tempDNList
self.speciesTwoDS = tempDSList
def geneClusteringFixedDiff(self,length = 50, MAFFilter = 0.05, skipVariantFinding = False):
# This can, and should be run alone, if you do something weird and run this and then manually run the other clustering
# functions/methods, you'll have big problems (though if you run processGene after this, that'll overwrite everything)
if not skipVariantFinding:
self.seqLength = len(self.firstSeq)
result = clust.variantsByParsimony(self.firstSeq,self.secondSeq,self.outSeq)
self.setPolarizedDivergenceList(result['Species One DN'],result['Species One DS'],
result['Species Two DN'],result['Species Two DS'])
if self.speciesOnePopHolder != None:
self.removePolymorphicSites(1,MAFFilter)
if self.speciesTwoPopHolder != None:
self.removePolymorphicSites(2,MAFFilter)
temp_data = clust.clusteringSameMutation(self.speciesOneDN)
temp_norm = clust.analyticalNormSameMut(len(self.speciesOneDN),self.seqLength/3)
self.polarizedClusteringHolder['DNDN1'] = clusteringObject(temp_data,temp_norm)
temp_data = clust.clusteringSameMutation(self.speciesTwoDN)
temp_norm = clust.analyticalNormSameMut(len(self.speciesTwoDN),self.seqLength/3)
self.polarizedClusteringHolder['DNDN2'] = clusteringObject(temp_data,temp_norm)
temp_data = clust.clusteringDifferentMutation(self.speciesOneDN,self.speciesTwoDN)
temp_norm = clust.analyticalNormDiffMut(len(self.speciesOneDN),len(self.speciesTwoDN),self.seqLength/3)
self.polarizedClusteringHolder['DNDNcross'] = clusteringObject(temp_data,temp_norm)
return self.geneClustering(length)
def geneClustering(self,length=50):
# This shouldn't really be used if population data is available. The method "geneClusteringFixedDiff"
# removes fixed differences from the alignment
# This must be run after "processGene"
speciesOneClustering = sum(self.polarizedClusteringHolder['DNDN1'].data[1:length+1])
speciesOneNorm = sum(self.polarizedClusteringHolder['DNDN1'].norm[1:length+1])
speciesTwoClustering = sum(self.polarizedClusteringHolder['DNDN2'].data[1:length+1])
speciesTwoNorm = sum(self.polarizedClusteringHolder['DNDN2'].norm[1:length+1])
betweenSpeciesClustering = sum(self.polarizedClusteringHolder['DNDNcross'].data[1:length+1])
betweenSpeciesNorm = sum(self.polarizedClusteringHolder['DNDNcross'].norm[1:length+1])
if speciesOneNorm == 0: speciesOneNorm = 1
if speciesTwoNorm == 0: speciesTwoNorm = 1
if betweenSpeciesNorm == 0: betweenSpeciesNorm = 1
return([speciesOneClustering/speciesOneNorm - betweenSpeciesClustering/betweenSpeciesNorm,
speciesTwoClustering/speciesTwoNorm - betweenSpeciesClustering/betweenSpeciesNorm])
def geneClusteringIntrons(self,length=50):
speciesOneClustering = sum(self.polarizedIntronClusteringHolder['DNDN1'].data[1:length+1])
speciesOneNorm = sum(self.polarizedIntronClusteringHolder['DNDN1'].norm[1:length+1])
speciesTwoClustering = sum(self.polarizedIntronClusteringHolder['DNDN2'].data[1:length+1])
speciesTwoNorm = sum(self.polarizedIntronClusteringHolder['DNDN2'].norm[1:length+1])
betweenSpeciesClustering = sum(self.polarizedIntronClusteringHolder['DNDNcross'].data[1:length+1])
betweenSpeciesNorm = sum(self.polarizedIntronClusteringHolder['DNDNcross'].norm[1:length+1])
if speciesOneNorm == 0: speciesOneNorm = 1
if speciesTwoNorm == 0: speciesTwoNorm = 1
if betweenSpeciesNorm == 0: betweenSpeciesNorm = 1
return([speciesOneClustering/speciesOneNorm - betweenSpeciesClustering/betweenSpeciesNorm,
speciesTwoClustering/speciesTwoNorm - betweenSpeciesClustering/betweenSpeciesNorm])
class clusteringObject(object):
def __init__(self,data,norm):
self.data = data
self.norm = norm
class genomeWideClustering(object):
def __init__(self,speciesOneName,speciesTwoName,speciesOutName,clusteringLength = 500):
#self.totalGeneLength = 0
#self.totalDNMutations = 0
#self.totalDSMutations = 0
#self.totalMutations = 0
self.geneLengthList = []
self.DNMutationList = []
self.DSMutationList = []
self.speciesOneDNMutationList = []
self.speciesTwoDNMutationList = []
self.speciesOneDSMutationList = []
self.speciesTwoDSMutationList = []
self.numberOfGenes = 0
self.speciesOneName = speciesOneName
self.speciesTwoName = speciesTwoName
self.speciesOutName = speciesOutName
self.nonPolarizedDNDNClustering = numpy.zeros(clusteringLength + 1)
self.nonPolarizedDNDSClustering = numpy.zeros(clusteringLength + 1)
self.nonPolarizedDSDSClustering = numpy.zeros(clusteringLength + 1)
self.speciesOneDNDNClustering = numpy.zeros(clusteringLength + 1)
self.speciesTwoDNDNClustering = numpy.zeros(clusteringLength + 1)
self.btwnDNDNClustering = numpy.zeros(clusteringLength + 1)
self.nonPolarizedDNDNClusteringNorm = numpy.zeros(clusteringLength + 1)
self.nonPolarizedDNDSClusteringNorm = numpy.zeros(clusteringLength + 1)
self.nonPolarizedDSDSClusteringNorm = numpy.zeros(clusteringLength + 1)
self.speciesOneDNDNClusteringNorm = numpy.zeros(clusteringLength + 1)
self.speciesTwoDNDNClusteringNorm = numpy.zeros(clusteringLength + 1)
self.btwnDNDNClusteringNorm = numpy.zeros(clusteringLength + 1)
self.speciesOneChargeClusteringComp = numpy.zeros(clusteringLength + 1)
self.speciesOneChargeClusteringRein = numpy.zeros(clusteringLength + 1)
self.speciesOneSizeClusteringComp = numpy.zeros(clusteringLength + 1)
self.speciesOneSizeClusteringRein = numpy.zeros(clusteringLength + 1)
self.speciesOnePolarityClusteringComp = numpy.zeros(clusteringLength + 1)
self.speciesOnePolarityClusteringRein = numpy.zeros(clusteringLength + 1)
self.speciesTwoChargeClusteringComp = numpy.zeros(clusteringLength + 1)
self.speciesTwoChargeClusteringRein = numpy.zeros(clusteringLength + 1)
self.speciesTwoSizeClusteringComp = numpy.zeros(clusteringLength + 1)
self.speciesTwoSizeClusteringRein = numpy.zeros(clusteringLength + 1)
self.speciesTwoPolarityClusteringComp = numpy.zeros(clusteringLength + 1)
self.speciesTwoPolarityClusteringRein = numpy.zeros(clusteringLength + 1)
self.btwnChargeClusteringComp = numpy.zeros(clusteringLength + 1)
self.btwnChargeClusteringRein = numpy.zeros(clusteringLength + 1)
self.btwnSizeClusteringComp = numpy.zeros(clusteringLength + 1)
self.btwnSizeClusteringRein = numpy.zeros(clusteringLength + 1)
self.btwnPolarityClusteringComp = numpy.zeros(clusteringLength + 1)
self.btwnPolarityClusteringRein = numpy.zeros(clusteringLength + 1)
self.nonPolarizedDNDNIntronClustering = numpy.zeros(clusteringLength + 1)
self.nonPolarizedDNDSIntronClustering = numpy.zeros(clusteringLength + 1)
self.nonPolarizedDSDSIntronClustering = numpy.zeros(clusteringLength + 1)
self.nonPolarizedDNDNIntronClusteringNorm = numpy.zeros(clusteringLength + 1)
self.nonPolarizedDNDSIntronClusteringNorm = numpy.zeros(clusteringLength + 1)
self.nonPolarizedDSDSIntronClusteringNorm = numpy.zeros(clusteringLength + 1)
self.nonPolarizedDNDNIntronSimulation = []
self.nonPolarizedDSDSIntronSimulation = []
def parseProteinObject(self,currProtein,parseIntronInfo = False):
self.geneLengthList.append(currProtein.seqLength)
self.DNMutationList.append(len(currProtein.DNLists))
self.DSMutationList.append(len(currProtein.DSLists))
self.speciesOneDNMutationList.append(len(currProtein.speciesOneDN))
self.speciesOneDSMutationList.append(len(currProtein.speciesOneDS))
self.speciesTwoDNMutationList.append(len(currProtein.speciesTwoDN))
self.speciesTwoDSMutationList.append(len(currProtein.speciesTwoDS))
self.numberOfGenes += 1
self.nonPolarizedDNDNClustering += currProtein.clusteringHolder['DNDN'].data
self.nonPolarizedDNDNClusteringNorm += currProtein.clusteringHolder['DNDN'].norm
self.nonPolarizedDSDSClustering += currProtein.clusteringHolder['DSDS'].data
self.nonPolarizedDSDSClusteringNorm += currProtein.clusteringHolder['DSDS'].norm
self.nonPolarizedDNDSClustering += currProtein.clusteringHolder['DNDS'].data
self.nonPolarizedDNDSClusteringNorm += currProtein.clusteringHolder['DNDS'].norm
self.speciesOneDNDNClustering += currProtein.polarizedClusteringHolder['DNDN1'].data
self.speciesOneDNDNClusteringNorm += currProtein.polarizedClusteringHolder['DNDN1'].norm
self.speciesTwoDNDNClustering += currProtein.polarizedClusteringHolder['DNDN2'].data
self.speciesTwoDNDNClusteringNorm += currProtein.polarizedClusteringHolder['DNDN2'].norm
self.btwnDNDNClustering += currProtein.polarizedClusteringHolder['DNDNcross'].data
self.btwnDNDNClusteringNorm += currProtein.polarizedClusteringHolder['DNDNcross'].norm
self.speciesOneChargeClusteringComp += currProtein.propertyClusteringHolder['Charge']['Species One Comp']
self.speciesOneChargeClusteringRein += currProtein.propertyClusteringHolder['Charge']['Species One Rein']
self.speciesOneSizeClusteringComp += currProtein.propertyClusteringHolder['Size']['Species One Comp']
self.speciesOneSizeClusteringRein += currProtein.propertyClusteringHolder['Size']['Species One Rein']
self.speciesOnePolarityClusteringComp += currProtein.propertyClusteringHolder['Polarity']['Species One Comp']
self.speciesOnePolarityClusteringRein += currProtein.propertyClusteringHolder['Polarity']['Species One Rein']
self.speciesTwoChargeClusteringComp += currProtein.propertyClusteringHolder['Charge']['Species Two Comp']
self.speciesTwoChargeClusteringRein += currProtein.propertyClusteringHolder['Charge']['Species Two Rein']
self.speciesTwoSizeClusteringComp += currProtein.propertyClusteringHolder['Size']['Species Two Comp']
self.speciesTwoSizeClusteringRein += currProtein.propertyClusteringHolder['Size']['Species Two Rein']
self.speciesTwoPolarityClusteringComp += currProtein.propertyClusteringHolder['Polarity']['Species Two Comp']
self.speciesTwoPolarityClusteringRein += currProtein.propertyClusteringHolder['Polarity']['Species Two Rein']
self.btwnChargeClusteringComp += currProtein.propertyClusteringHolder['Charge']['Cross Species Comp']
self.btwnChargeClusteringRein += currProtein.propertyClusteringHolder['Charge']['Cross Species Rein']
self.btwnSizeClusteringComp += currProtein.propertyClusteringHolder['Size']['Cross Species Comp']
self.btwnSizeClusteringRein += currProtein.propertyClusteringHolder['Size']['Cross Species Rein']
self.btwnPolarityClusteringComp += currProtein.propertyClusteringHolder['Polarity']['Cross Species Comp']
self.btwnPolarityClusteringRein += currProtein.propertyClusteringHolder['Polarity']['Cross Species Rein']
if parseIntronInfo:
self.nonPolarizedDNDNIntronClustering += currProtein.clusteringIntronHolder['DNDN'].data
self.nonPolarizedDNDNIntronSimulation.append(currProtein.clusteringIntronHolder['DNDN'].norm)
self.nonPolarizedDNDNIntronClusteringNorm += numpy.sum(currProtein.clusteringIntronHolder['DNDN'].norm,axis = 0)/currProtein.clusteringIntronHolder['DNDN'].norm.shape[0]
self.nonPolarizedDSDSIntronClustering += currProtein.clusteringIntronHolder['DSDS'].data
self.nonPolarizedDSDSIntronClusteringNorm += numpy.sum(currProtein.clusteringIntronHolder['DSDS'].norm,axis = 0)/currProtein.clusteringIntronHolder['DSDS'].norm.shape[0]
self.nonPolarizedDSDSIntronSimulation.append(currProtein.clusteringIntronHolder['DSDS'].norm)
#self.nonPolarizedDNDSIntronClustering += currProtein.clusteringIntronHolder['DNDS'].data
#self.nonPolarizedDNDSIntronClusteringNorm += currProtein.clusteringIntronHolder['DNDS'].norm
def calcNonPolarized(self, smooth = True , normalizeAsym = False):
DNDN = self.nonPolarizedDNDNClustering/self.nonPolarizedDNDNClusteringNorm
DNDS = self.nonPolarizedDNDSClustering/self.nonPolarizedDNDSClusteringNorm
DSDS = self.nonPolarizedDSDSClustering/self.nonPolarizedDSDSClusteringNorm
if normalizeAsym:
DNDN = DNDN/numpy.mean(DNDN[80:121])
DNDS = DNDS/numpy.mean(DNDS[80:121])
DSDS = DSDS/numpy.mean(DSDS[80:121])
if smooth:
DNDN = [0] + clust.windowSmoothing(DNDN[1:])
DNDS = [0] + clust.windowSmoothing(DNDS[1:])
DSDS = [0] + clust.windowSmoothing(DSDS[1:])
return {'DNDN':DNDN,'DNDS':DNDS,'DSDS':DSDS}
def plotNonPolarized(self,plotTitle = None, showLegend = True, normalizeAsym = False, minMaxX = (0,100), plotIntron = False, saveFigure = None, ax = None, plotDNDS = True):
if not plotIntron:
DNDN = self.nonPolarizedDNDNClustering/self.nonPolarizedDNDNClusteringNorm
DNDS = self.nonPolarizedDNDSClustering/self.nonPolarizedDNDSClusteringNorm
DSDS = self.nonPolarizedDSDSClustering/self.nonPolarizedDSDSClusteringNorm
if plotIntron:
DNDN = self.nonPolarizedDNDNIntronClustering/self.nonPolarizedDNDNIntronClusteringNorm
DNDS = self.nonPolarizedDNDSIntronClustering/self.nonPolarizedDNDSIntronClusteringNorm
DSDS = self.nonPolarizedDSDSIntronClustering/self.nonPolarizedDSDSIntronClusteringNorm
if normalizeAsym:
DNDN = DNDN/numpy.mean(DNDN[80:121])
DNDS = DNDS/numpy.mean(DNDS[80:121])
DSDS = DSDS/numpy.mean(DSDS[80:121])
if plotDNDS:
minY = min([min(clust.windowSmoothing(DNDN[1:])[minMaxX[0]:minMaxX[1]]),min(clust.windowSmoothing(DSDS[1:])[minMaxX[0]:minMaxX[1]])])
maxY = max([max(clust.windowSmoothing(DNDN[1:])[minMaxX[0]:minMaxX[1]]),max(clust.windowSmoothing(DSDS[1:])[minMaxX[0]:minMaxX[1]])])
else:
minY = min([min(clust.windowSmoothing(DNDN[1:])[minMaxX[0]:minMaxX[1]]),min(clust.windowSmoothing(DSDS[1:])[minMaxX[0]:minMaxX[1]])])
maxY = max([max(clust.windowSmoothing(DNDN[1:])[minMaxX[0]:minMaxX[1]]),max(clust.windowSmoothing(DSDS[1:])[minMaxX[0]:minMaxX[1]])])
if ax == None:
fig = plt.figure(figsize = (6,6))
ax = fig.add_subplot()
ax.plot(range(1,501),clust.windowSmoothing(DNDN[1:]),color='green',label='DNDN', linewidth = 1.5)
ax.plot(range(1,501),clust.windowSmoothing(DSDS[1:]),color='blue',label='DSDS', linewidth = 1.5)
ax.scatter(range(1,501),DNDN[1:],color = 'green', alpha = 0.5, marker = '+', s = 18, linewidth = 1)
ax.scatter(range(1,501),DSDS[1:],color = 'blue', alpha = 0.5, marker = '+', s = 18, linewidth = 1)
if plotDNDS:
ax.plot(range(1,501),clust.windowSmoothing(DNDS[1:]),color='orange',label='DNDS', linewidth = 1.5)
ax.scatter(range(1,501),DNDS[1:],color = 'orange', alpha = 0.5, marker = '+', s = 18, linewidth = 1)
ax.set(xlim = (minMaxX[0],minMaxX[1]),ylim = (minY*.9,maxY*1.1))
if showLegend:
ax.legend()
if plotTitle != None:
ax.title.set_text(plotTitle)
if saveFigure != None:
plt.savefig(saveFigure)
def calcPolarized(self, smooth = True, normalizeAsym = False):
DNDN1 = self.speciesOneDNDNClustering/self.speciesOneDNDNClusteringNorm
DNDN2 = self.speciesTwoDNDNClustering/self.speciesTwoDNDNClusteringNorm
DNDNcross = self.btwnDNDNClustering/self.btwnDNDNClusteringNorm
if normalizeAsym:
DNDN1 = DNDN1/numpy.mean(DNDN1[80:121])
DNDN2 = DNDN2/numpy.mean(DNDN2[80:121])
DNDNcross = DNDNcross/numpy.mean(DNDNcross[80:121])
if smooth:
DNDN1 = [0] + clust.windowSmoothing(DNDN1[1:])
DNDN2 = [0] + clust.windowSmoothing(DNDN2[1:])
DNDNcross = [0] + clust.windowSmoothing(DNDNcross[1:])
return {'DNDN1':DNDN1,'DNDN2':DNDN2,'DNDNcross':DNDNcross}
def plotPolarized(self,plotTitle = None, showLegend = True, normalizeAsym = False, customNameOne = None, customNameTwo = None, customNameOut = None, minMaxX = (0,100), saveFigure = None, ax = None):
if customNameOne == None:
customNameOne = 'DNDN ' + self.speciesOneName
if customNameTwo == None:
customNameTwo = 'DNDN ' + self.speciesTwoName
if customNameOut == None:
customNameOut = 'DNDN cross'
DNDN1 = self.speciesOneDNDNClustering/self.speciesOneDNDNClusteringNorm
DNDN2 = self.speciesTwoDNDNClustering/self.speciesTwoDNDNClusteringNorm
DNDNcross = self.btwnDNDNClustering/self.btwnDNDNClusteringNorm
if normalizeAsym:
DNDN1 = DNDN1/numpy.mean(DNDN1[80:121])
DNDN2 = DNDN2/numpy.mean(DNDN2[80:121])
DNDNcross = DNDNcross/numpy.mean(DNDNcross[80:121])
minY = min([min(clust.windowSmoothing(DNDN1[1:])[minMaxX[0]:minMaxX[1]]),min(clust.windowSmoothing(DNDN2[1:])[minMaxX[0]:minMaxX[1]]),min(clust.windowSmoothing(DNDNcross[1:])[minMaxX[0]:minMaxX[1]])])
maxY = max([max(clust.windowSmoothing(DNDN1[1:])[minMaxX[0]:minMaxX[1]]),max(clust.windowSmoothing(DNDN2[1:])[minMaxX[0]:minMaxX[1]]),max(clust.windowSmoothing(DNDNcross[1:])[minMaxX[0]:minMaxX[1]])])
if ax == None:
fig = plt.figure(figsize = (6,6))
ax = fig.add_subplot()
ax.plot(range(1,501),clust.windowSmoothing(DNDN1[1:]),color = 'green', label = customNameOne, linewidth = 1.5)
ax.scatter(range(1,501),DNDN1[1:],color = 'green', alpha = 0.5, marker = '+',s = 18,linewidth = 1)
ax.plot(range(1,501),clust.windowSmoothing(DNDN2[1:]),color = 'orange', label = customNameTwo, linewidth = 1.5)
ax.scatter(range(1,501),DNDN2[1:],color = 'orange', alpha = 0.5, marker = '+',s=18,linewidth = 1)
ax.plot(range(1,501),clust.windowSmoothing(DNDNcross[1:]),color = 'blue', label = customNameOut, linewidth = 1.5)
ax.scatter(range(1,501),DNDNcross[1:],color = 'blue', alpha = 0.5, marker = '+',s=18,linewidth = 1)
ax.set(xlim = (minMaxX[0],minMaxX[1]), ylim = (minY*0.9,maxY*1.1))
if showLegend:
ax.legend()
if plotTitle != None:
ax.title.set_text(plotTitle)
if saveFigure != None:
plt.savefig(saveFigure)
def plotProperty(self,propertyType,compOrRein,plotTitle = None, showLegend = True,customNameOne = None, customNameTwo = None, customNameOut = None, minMaxY = None, minMaxX = (0,100), saveFigure = None, ax = None):
if customNameOne == None:
customNameOne = self.speciesOneName + " lineage"
if customNameTwo == None:
customNameTwo = self.speciesTwoName + " lineage"
if customNameOut == None:
customNameOut = 'Between lineage'
if propertyType == "Charge":
comp1 = self.speciesOneChargeClusteringComp
comp2 = self.speciesTwoChargeClusteringComp
compCross = self.btwnChargeClusteringComp
rein1 = self.speciesOneChargeClusteringRein
rein2 = self.speciesTwoChargeClusteringRein
reinCross = self.btwnChargeClusteringRein
elif propertyType == "Size":
comp1 = self.speciesOneSizeClusteringComp
comp2 = self.speciesTwoSizeClusteringComp
compCross = self.btwnSizeClusteringComp
rein1 = self.speciesOneSizeClusteringRein
rein2 = self.speciesTwoSizeClusteringRein
reinCross = self.btwnSizeClusteringRein
elif propertyType == "Polarity":
comp1 = self.speciesOnePolarityClusteringComp
comp2 = self.speciesTwoPolarityClusteringComp
compCross = self.btwnPolarityClusteringComp
rein1 = self.speciesOnePolarityClusteringRein
rein2 = self.speciesTwoPolarityClusteringRein
reinCross = self.btwnPolarityClusteringRein
else:
print("Unknown type selected")
return
comp1 = comp1/self.speciesOneDNDNClustering
comp2 = comp2/self.speciesTwoDNDNClustering
compCross = compCross/self.btwnDNDNClustering
rein1 = rein1/self.speciesOneDNDNClustering
rein2 = rein2/self.speciesTwoDNDNClustering
reinCross = reinCross/self.btwnDNDNClustering
if minMaxY == None:
minY = min([min(clust.windowSmoothing(comp1[1:])[minMaxX[0]:minMaxX[1]]),min(clust.windowSmoothing(comp2[1:])[minMaxX[0]:minMaxX[1]]),min(clust.windowSmoothing(compCross[1:])[minMaxX[0]:minMaxX[1]]),min(clust.windowSmoothing(rein1[1:])[minMaxX[0]:minMaxX[1]]),min(clust.windowSmoothing(rein2[1:])[minMaxX[0]:minMaxX[1]]),min(clust.windowSmoothing(reinCross[1:])[minMaxX[0]:minMaxX[1]])])
maxY = max([max(clust.windowSmoothing(comp1[1:])[minMaxX[0]:minMaxX[1]]),max(clust.windowSmoothing(comp2[1:])[minMaxX[0]:minMaxX[1]]),max(clust.windowSmoothing(compCross[1:])[minMaxX[0]:minMaxX[1]]),max(clust.windowSmoothing(rein1[1:])[minMaxX[0]:minMaxX[1]]),max(clust.windowSmoothing(rein2[1:])[minMaxX[0]:minMaxX[1]]),max(clust.windowSmoothing(reinCross[1:])[minMaxX[0]:minMaxX[1]])])
yRange = maxY - minY
minY = minY - yRange * 0.4
maxY = maxY + yRange * 0.4
else:
minY = minMaxY[0]
maxY = minMaxY[1]
if ax == None:
fig = plt.figure(figsize = (6,6))
ax = fig.add_subplot()
if compOrRein == "Comp":
ax.plot(range(1,501),clust.windowSmoothing(comp1[1:]),color = 'green', label = customNameOne, linewidth = 1.5)
ax.scatter(range(1,501),comp1[1:],color = 'green', alpha = 0.5, marker = '+', s = 18, linewidth = 1)
ax.plot(range(1,501),clust.windowSmoothing(comp2[1:]),color = 'orange', label = customNameTwo, linewidth = 1.5)
ax.scatter(range(1,501),comp2[1:],color = 'orange', alpha = 0.5, marker = '+', s = 18, linewidth = 1)
ax.plot(range(1,501),clust.windowSmoothing(compCross[1:]),color = 'blue', label = customNameOut, linewidth = 1.5)
ax.scatter(range(1,501),compCross[1:],color = 'blue', alpha = 0.5, marker = '+', s = 18, linewidth = 1)
elif compOrRein == "Rein":
ax.plot(range(1,501),clust.windowSmoothing(rein1[1:]),color = 'green', label = customNameOne, linewidth = 1.5)
ax.scatter(range(1,501),rein1[1:],color = 'green', alpha = 0.5, marker = '+', s = 18, linewidth = 1)
ax.plot(range(1,501),clust.windowSmoothing(rein2[1:]),color = 'orange', label = customNameTwo, linewidth = 1.5)
ax.scatter(range(1,501),rein2[1:],color = 'orange', alpha = 0.5, marker = '+', s = 18, linewidth = 1)
ax.plot(range(1,501),clust.windowSmoothing(reinCross[1:]),color = 'blue', label = customNameOut, linewidth = 1.5)
ax.scatter(range(1,501),reinCross[1:],color = 'blue', alpha = 0.5, marker = '+', s = 18, linewidth = 1)
ax.set(xlim = (minMaxX[0],minMaxX[1]), ylim = (minY,maxY))
if showLegend:
ax.legend()
if plotTitle != None:
ax.title.set_text(plotTitle)
if saveFigure != None:
plt.savefig(saveFigure)
def helperGetPropertySpecified(self, whichSpecies = None, propertyType = None, compOrRein = None):
# whichSpecies == 3 indicates between
if whichSpecies == 1:
if propertyType == 'Charge' and compOrRein == 'Comp':
clustObs = self.speciesOneChargeClusteringComp
elif propertyType == 'Size' and compOrRein == 'Comp':
clustObs = self.speciesOneSizeClusteringComp
elif propertyType == 'Polarity' and compOrRein == 'Comp':
clustObs = self.speciesOnePolarityClusteringComp
elif propertyType == 'Charge' and compOrRein == 'Rein':
clustObs = self.speciesOneChargeClusteringRein
elif propertyType == 'Size' and compOrRein == 'Rein':
clustObs = self.speciesOneSizeClusteringRein
elif propertyType == 'Polarity' and compOrRein == 'Rein':
clustObs = self.speciesOnePolarityClusteringRein
elif whichSpecies == 2:
if propertyType == 'Charge' and compOrRein == 'Comp':
clustObs = self.speciesTwoChargeClusteringComp
elif propertyType == 'Size' and compOrRein == 'Comp':
clustObs = self.speciesTwoSizeClusteringComp
elif propertyType == 'Polarity' and compOrRein == 'Comp':
clustObs = self.speciesTwoPolarityClusteringComp
elif propertyType == 'Charge' and compOrRein == 'Rein':
clustObs = self.speciesTwoChargeClusteringRein
elif propertyType == 'Size' and compOrRein == 'Rein':
clustObs = self.speciesTwoSizeClusteringRein
elif propertyType == 'Polarity' and compOrRein == 'Rein':
clustObs = self.speciesTwoPolarityClusteringRein
elif whichSpecies == 3:
if propertyType == 'Charge' and compOrRein == 'Comp':
clustObs = self.btwnChargeClusteringComp
elif propertyType == 'Size' and compOrRein == 'Comp':
clustObs = self.btwnSizeClusteringComp
elif propertyType == 'Polarity' and compOrRein == 'Comp':
clustObs = self.btwnPolarityClusteringComp
elif propertyType == 'Charge' and compOrRein == 'Rein':
clustObs = self.btwnChargeClusteringRein
elif propertyType == 'Size' and compOrRein == 'Rein':
clustObs = self.btwnSizeClusteringRein
elif propertyType == 'Polarity' and compOrRein == 'Rein':
clustObs = self.btwnPolarityClusteringRein
return clustObs
def calculateSignificance(self,clustType = 'DNDN', normType = None, propertyType = None, compOrRein = None, significanceLength = 30):
# Norm type is only used for property clustering
# supposed to be either DNDN1, DNDN2, or DNDNbtwn, depending on what kind of property
if clustType == 'DNDN':
clustObs = self.nonPolarizedDNDNClustering
clustNorm = self.nonPolarizedDNDNClusteringNorm
elif clustType == 'DNDS':
clustObs = self.nonPolarizedDNDSClustering
clustNorm = self.nonPolarizedDNDSClusteringNorm
elif clustType == 'DSDS':
clustObs = self.nonPolarizedDSDSClustering
clustNorm = self.nonPolarizedDSDSClusteringNorm
elif clustType == "DNDNvsDSDS":
clustObs = self.nonPolarizedDNDNClustering
clustNorm = self.nonPolarizedDSDSClustering
elif clustType == 'DNDN1vsbtwn':
clustObs = self.speciesOneDNDNClustering
clustNorm = self.btwnDNDNClustering
elif clustType == 'DNDN2vsbtwn':
clustObs = self.speciesTwoDNDNClustering
clustNorm = self.btwnDNDNClustering
elif clustType == 'Property':
if normType == 'DNDN1':
clustNorm = self.speciesOneDNDNClustering
clustObs = self.helperGetPropertySpecified(1,propertyType,compOrRein)
elif normType == 'DNDN2':
clustNorm = self.speciesTwoDNDNClustering
clustObs = self.helperGetPropertySpecified(2,propertyType,compOrRein)
elif normType == 'DNDNbtwn':
clustNorm = self.btwnDNDNClustering
clustObs = self.helperGetPropertySpecified(3,propertyType,compOrRein)
elif normType == "DNDN1vsbtwn":
clustObs = self.helperGetPropertySpecified(1,propertyType,compOrRein)
clustNorm = self.helperGetPropertySpecified(3,propertyType,compOrRein)
elif normType == "DNDN2vsbtwn":
clustObs = self.helperGetPropertySpecified(2,propertyType,compOrRein)
clustNorm = self.helperGetPropertySpecified(3,propertyType,compOrRein)
observedPairs = numpy.sum(clustObs[1:significanceLength+1])
expectedPairs = numpy.sum(clustNorm[1:significanceLength+1])
normalizedExpectation = clustNorm[1:significanceLength+1]/expectedPairs*observedPairs
return stats.chisquare(clustObs[1:significanceLength+1],normalizedExpectation)[1]
def calculateSignificanceProperty(self,whichSpecies,propertyType = "Charge",compOrRein = "Comp",significanceLength = 20):
withinYes = sum(self.helperGetPropertySpecified(whichSpecies,propertyType,compOrRein)[1:significanceLength+1])
if whichSpecies == 1:
withinNo = sum(self.speciesOneDNDNClustering[1:significanceLength+1]) - withinYes
elif whichSpecies == 2:
withinNo = sum(self.speciesTwoDNDNClustering[1:significanceLength+1]) - withinYes
betweenYes = sum(self.helperGetPropertySpecified(3,propertyType,compOrRein)[1:significanceLength+1])
betweenNo = sum(self.btwnDNDNClustering[1:significanceLength+1]) - betweenYes
conTable = numpy.array([[withinYes,withinNo],[betweenYes,betweenNo]])
print(conTable)
return stats.chi2_contingency(conTable,lambda_="log-likelihood")
def clusteringExcess(self,NPorP,length=30):
if NPorP == "Nonpolarized":
DNDN = self.nonPolarizedDNDNClustering/self.nonPolarizedDNDNClusteringNorm
DNDS = self.nonPolarizedDNDSClustering/self.nonPolarizedDNDSClusteringNorm
DSDS = self.nonPolarizedDSDSClustering/self.nonPolarizedDSDSClusteringNorm
#DNDN = DNDN/numpy.mean(DNDN[80:121])
#DNDS = DNDS/numpy.mean(DNDS[80:121])
#DSDS = DSDS/numpy.mean(DSDS[80:121])
#DNDN_excess = sum(DNDN[1:length+1]-1)
#DNDS_excess = sum(DNDS[1:length+1]-1)
#DSDS_excess = sum(DSDS[1:length-1]-1)
DNDN_excess = sum(DNDN[1:length+1]-numpy.mean(DNDN[80:121]))
DNDS_excess = sum(DNDS[1:length+1]-numpy.mean(DNDS[80:121]))
DSDS_excess = sum(DSDS[1:length-1]-numpy.mean(DSDS[80:121]))
return {"DNDN":DNDN_excess,"DNDS":DNDS_excess,"DSDS":DSDS_excess}
elif NPorP == "Polarized":
DNDN1 = self.speciesOneDNDNClustering/self.speciesOneDNDNClusteringNorm
DNDN2 = self.speciesTwoDNDNClustering/self.speciesTwoDNDNClusteringNorm
DNDNcross = self.btwnDNDNClustering/self.btwnDNDNClusteringNorm
#DNDN1 = DNDN1/numpy.mean(DNDN1[80:121])
#DNDN2 = DNDN2/numpy.mean(DNDN2[80:121])
#DNDNcross = DNDNcross/numpy.mean(DNDNcross[80:121])
#DNDN1_excess = sum(DNDN1[1:length+1]-1)
#DNDN2_excess = sum(DNDN2[1:length+1]-1)
#DNDNcross_excess = sum(DNDNcross[1:length+1]-1)
DNDN1_excess = sum(DNDN1[1:length+1]-numpy.mean(DNDN1[80:121]))
DNDN2_excess = sum(DNDN2[1:length+1]-numpy.mean(DNDN2[80:121]))
DNDNcross_excess = sum(DNDNcross[1:length+1]-numpy.mean(DNDNcross[80:121]))
return {"DNDN1":DNDN1_excess,"DNDN2":DNDN2_excess,"DNDNcross":DNDNcross_excess}
else:
print("Unknown option selected")
return
def propertyClusteringExcess(self,propertyType,compOrRein,length=10):
if propertyType == "Charge":
comp1 = self.speciesOneChargeClusteringComp
comp2 = self.speciesTwoChargeClusteringComp
compCross = self.btwnChargeClusteringComp
rein1 = self.speciesOneChargeClusteringRein
rein2 = self.speciesTwoChargeClusteringRein
reinCross = self.btwnChargeClusteringRein
elif propertyType == "Size":
comp1 = self.speciesOneSizeClusteringComp
comp2 = self.speciesTwoSizeClusteringComp
compCross = self.btwnSizeClusteringComp
rein1 = self.speciesOneSizeClusteringRein
rein2 = self.speciesTwoSizeClusteringRein
reinCross = self.btwnSizeClusteringRein
elif propertyType == "Polarity":
comp1 = self.speciesOnePolarityClusteringComp
comp2 = self.speciesTwoPolarityClusteringComp
compCross = self.btwnPolarityClusteringComp
rein1 = self.speciesOnePolarityClusteringRein
rein2 = self.speciesTwoPolarityClusteringRein
reinCross = self.btwnPolarityClusteringRein
else:
print("Unknown type selected")
return
if compOrRein == "Comp":
comp1 = comp1/self.speciesOneDNDNClustering
comp2 = comp2/self.speciesTwoDNDNClustering
compCross = compCross/self.btwnDNDNClustering
comp1 = comp1/numpy.mean(comp1[80:121])
comp2 = comp2/numpy.mean(comp2[80:121])
compCross = compCross/numpy.mean(compCross[80:121])
comp1_excess = sum(comp1[1:length+1] - numpy.mean(comp1[80:121]))
comp2_excess = sum(comp2[1:length+1] - numpy.mean(comp2[80:121]))
compCross_excess = sum(compCross[1:length+1] - numpy.mean(compCross[80:121]))
return {"comp1":comp1_excess,"comp2":comp2_excess,"compCross":compCross_excess}
elif compOrRein == "Rein":
rein1 = rein1/self.speciesOneDNDNClustering
rein2 = rein2/self.speciesTwoDNDNClustering
reinCross = reinCross/self.btwnDNDNClustering
rein1 = rein1/numpy.mean(rein1[80:121])
rein2 = rein2/numpy.mean(rein2[80:121])
reinCross = reinCross/numpy.mean(reinCross[80:121])
rein1_excess = sum(rein1[1:length+1] - numpy.mean(rein1[80:121]))
rein2_excess = sum(rein2[1:length+1] - numpy.mean(rein2[80:121]))
reinCross_excess = sum(reinCross[1:length+1] - numpy.mean(reinCross[80:121]))
return {"rein1":rein1_excess,"rein2":rein2_excess,"reinCross":reinCross_excess}
else:
print("Unknown type selected")
return
class version():
def getVersion():
print("3")