git-svn-id: http://svn.tuebingen.mpg.de/ag-raetsch/projects/QPalma@8632 e1793c9e...
[qpalma.git] / scripts / PipelineHeuristic.py
1 #!/usr/bin/env python
2 # -*- coding: utf-8 -*-
3
4 import cPickle
5 import sys
6 import pdb
7 import os
8 import os.path
9 import math
10 import resource
11
12 from qpalma.DataProc import *
13 from qpalma.computeSpliceWeights import *
14 from qpalma.set_param_palma import *
15 from qpalma.computeSpliceAlignWithQuality import *
16 from qpalma.penalty_lookup_new import *
17 from qpalma.compute_donacc import *
18 from qpalma.TrainingParam import Param
19 from qpalma.Plif import Plf
20
21 from qpalma.tools.splicesites import getDonAccScores
22 from qpalma.Configuration import *
23
24 from compile_dataset import getSpliceScores, get_seq_and_scores
25
26 from numpy.matlib import mat,zeros,ones,inf
27 from numpy import inf,mean
28
29 from qpalma.parsers import PipelineReadParser
30
31
32 def unbracket_est(est):
33 new_est = ''
34 e = 0
35
36 while True:
37 if e >= len(est):
38 break
39
40 if est[e] == '[':
41 new_est += est[e+2]
42 e += 4
43 else:
44 new_est += est[e]
45 e += 1
46
47 return "".join(new_est).lower()
48
49
50 class PipelineHeuristic:
51 """
52 This class wraps the filter which decides whether an alignment found by
53 vmatch is spliced an should be then newly aligned using QPalma or not.
54 """
55
56 def __init__(self,run_fname,data_fname,param_fname):
57 """
58 We need a run object holding information about the nr. of support points
59 etc.
60 """
61
62 run = cPickle.load(open(run_fname))
63 self.run = run
64
65 start = cpu()
66
67 self.data_fname = data_fname
68
69 self.param = cPickle.load(open(param_fname))
70
71 # Set the parameters such as limits penalties for the Plifs
72 [h,d,a,mmatrix,qualityPlifs] = set_param_palma(self.param,True,run)
73
74 self.h = h
75 self.d = d
76 self.a = a
77 self.mmatrix = mmatrix
78 self.qualityPlifs = qualityPlifs
79
80 # when we look for alternative alignments with introns this value is the
81 # mean of intron size
82 self.intron_size = 90
83
84 self.read_size = 36
85
86 self.original_reads = {}
87
88 for line in open('/fml/ag-raetsch/share/projects/qpalma/solexa/allReads.pipeline'):
89 line = line.strip()
90 id,seq,q1,q2,q3 = line.split()
91 id = int(id)
92 self.original_reads[id] = seq
93
94 lengthSP = run['numLengthSuppPoints']
95 donSP = run['numDonSuppPoints']
96 accSP = run['numAccSuppPoints']
97 mmatrixSP = run['matchmatrixRows']*run['matchmatrixCols']
98 numq = run['numQualSuppPoints']
99 totalQualSP = run['totalQualSuppPoints']
100
101 currentPhi = zeros((run['numFeatures'],1))
102 currentPhi[0:lengthSP] = mat(h.penalties[:]).reshape(lengthSP,1)
103 currentPhi[lengthSP:lengthSP+donSP] = mat(d.penalties[:]).reshape(donSP,1)
104 currentPhi[lengthSP+donSP:lengthSP+donSP+accSP] = mat(a.penalties[:]).reshape(accSP,1)
105 currentPhi[lengthSP+donSP+accSP:lengthSP+donSP+accSP+mmatrixSP] = mmatrix[:]
106
107 totalQualityPenalties = self.param[-totalQualSP:]
108 currentPhi[lengthSP+donSP+accSP+mmatrixSP:] = totalQualityPenalties[:]
109 self.currentPhi = currentPhi
110
111 # we want to identify spliced reads
112 # so true pos are spliced reads that are predicted "spliced"
113 self.true_pos = 0
114
115 # as false positives we count all reads that are not spliced but predicted
116 # as "spliced"
117 self.false_pos = 0
118
119 self.true_neg = 0
120 self.false_neg = 0
121
122 # total time spend for get seq and scores
123 self.get_time = 0.0
124 self.calcAlignmentScoreTime = 0.0
125 self.alternativeScoresTime = 0.0
126
127 self.count_time = 0.0
128 self.read_parsing = 0.0
129 self.main_loop = 0.0
130 self.splice_site_time = 0.0
131 self.computeSpliceAlignWithQualityTime = 0.0
132 self.computeSpliceWeightsTime = 0.0
133 self.DotProdTime = 0.0
134 self.array_stuff = 0.0
135 stop = cpu()
136
137 self.init_time = stop-start
138
139 def filter(self):
140 """
141 This method...
142 """
143 run = self.run
144
145 start = cpu()
146
147 rrp = PipelineReadParser(self.data_fname)
148 all_remapped_reads = rrp.parse()
149
150 stop = cpu()
151
152 self.read_parsing = stop-start
153
154 ctr = 0
155 unspliced_ctr = 0
156 spliced_ctr = 0
157
158 print 'Starting filtering...'
159 _start = cpu()
160
161 for readId,currentReadLocations in all_remapped_reads.items():
162 for location in currentReadLocations[:1]:
163
164 id = location['id']
165 chr = location['chr']
166 pos = location['pos']
167 strand = location['strand']
168 mismatch = location['mismatches']
169 length = location['length']
170 off = location['offset']
171 seq = location['seq']
172 prb = location['prb']
173 cal_prb = location['cal_prb']
174 chastity = location['chastity']
175
176 id = int(id)
177
178 if strand == '-':
179 continue
180
181 if ctr == 100:
182 break
183
184 #if pos > 10000000:
185 # continue
186
187 unb_seq = unbracket_est(seq)
188 effective_len = len(unb_seq)
189
190 genomicSeq_start = pos
191 genomicSeq_stop = pos+effective_len-1
192
193 start = cpu()
194 #print genomicSeq_start,genomicSeq_stop
195 currentDNASeq, currentAcc, currentDon = get_seq_and_scores(chr,strand,genomicSeq_start,genomicSeq_stop,run['dna_flat_files'])
196 stop = cpu()
197 self.get_time += stop-start
198
199 dna = currentDNASeq
200 exons = zeros((2,1))
201 exons[0,0] = 0
202 exons[1,0] = effective_len
203 est = unb_seq
204 original_est = seq
205 quality = prb
206
207 #pdb.set_trace()
208
209 currentVMatchAlignment = dna, exons, est, original_est, quality,\
210 currentAcc, currentDon
211 vMatchScore = self.calcAlignmentScore(currentVMatchAlignment)
212
213 alternativeAlignmentScores = self.calcAlternativeAlignments(location)
214
215 start = cpu()
216 # found no alternatives
217 if alternativeAlignmentScores == []:
218 continue
219
220 maxAlternativeAlignmentScore = max(alternativeAlignmentScores)
221 #print 'vMatchScore/alternativeScore: %f %f ' % (vMatchScore,maxAlternativeAlignmentScore)
222 #print 'all candidates %s' % str(alternativeAlignmentScores)
223
224 new_id = id - 1000000300000
225
226 unspliced = False
227 # unspliced
228 if new_id > 0:
229 unspliced = True
230
231 # Seems that according to our learned parameters VMatch found a good
232 # alignment of the current read
233 if maxAlternativeAlignmentScore < vMatchScore:
234 unspliced_ctr += 1
235
236 if unspliced:
237 self.true_neg += 1
238 else:
239 self.false_neg += 1
240
241 # We found an alternative alignment considering splice sites that scores
242 # higher than the VMatch alignment
243 else:
244 spliced_ctr += 1
245
246 if unspliced:
247 self.false_pos += 1
248 else:
249 self.true_pos += 1
250
251 ctr += 1
252 stop = cpu()
253 self.count_time = stop-start
254
255 _stop = cpu()
256 self.main_loop = _stop-_start
257
258 print 'Unspliced/Splice: %d %d'%(unspliced_ctr,spliced_ctr)
259 print 'True pos / false pos : %d %d'%(self.true_pos,self.false_pos)
260 print 'True neg / false neg : %d %d'%(self.true_neg,self.false_neg)
261
262
263 def findHighestScoringSpliceSites(self,currentAcc,currentDon):
264
265 acc = []
266 for idx,score in enumerate(currentAcc):
267 if score > -inf:
268 acc.append((idx,score))
269 if idx>self.read_size:
270 break
271
272 acc.sort(lambda x,y: x[0]-y[0])
273 acc=acc[-2:]
274
275 don = []
276 for idx,score in enumerate(currentDon):
277 if score > -inf:
278 don.append((idx,score))
279 if idx>self.read_size:
280 break
281
282 don.sort(lambda x,y: x[0]-y[0])
283 don=don[-2:]
284
285 return don,acc
286
287
288 def calcAlternativeAlignments(self,location):
289 """
290 Given an alignment proposed by Vmatch this function calculates possible
291 alternative alignments taking into account for example matched
292 donor/acceptor positions.
293 """
294
295 run = self.run
296
297 id = location['id']
298 chr = location['chr']
299 pos = location['pos']
300 strand = location['strand']
301 seq = location['seq']
302 #orig_seq = location['orig_seq']
303 prb = location['prb']
304 cal_prb = location['cal_prb']
305
306 orig_seq = self.original_reads[int(id)]
307
308 unb_seq = unbracket_est(seq)
309 effective_len = len(unb_seq)
310
311 genomicSeq_start = pos
312 genomicSeq_stop = pos+self.intron_size*2+self.read_size*2
313
314 start = cpu()
315 currentDNASeq, currentAcc, currentDon = get_seq_and_scores(chr,strand,genomicSeq_start,genomicSeq_stop,run['dna_flat_files'])
316 stop = cpu()
317 self.get_time += stop-start
318
319 dna = currentDNASeq
320
321 start = cpu()
322 alt_don,alt_acc = self.findHighestScoringSpliceSites(currentAcc,currentDon)
323 stop = cpu()
324 self.splice_site_time = stop-start
325
326 alternativeScores = []
327
328 exons = zeros((2,2),dtype=numpy.int)
329 est = unb_seq
330 original_est = seq
331 quality = prb
332
333 # inlined
334 h = self.h
335 d = self.d
336 a = self.a
337 mmatrix = self.mmatrix
338 qualityPlifs = self.qualityPlifs
339 # inlined
340
341 IntronScore = calculatePlif(h, [self.intron_size+1])[0]
342 dummyAcceptorScore = calculatePlif(a, [0.25])[0]
343 dummyDonorScore = calculatePlif(d, [0.25])[0]
344
345 _start = cpu()
346 for (don_pos,don_score) in alt_don:
347 """
348 start = cpu()
349
350 acc_pos = don_pos + self.intron_size
351
352 exons[0,0] = 0
353 exons[0,1] = don_pos
354 exons[1,0] = acc_pos+1
355 exons[1,1] = acc_pos+1+(self.read_size-don_pos)
356
357 _dna = dna[:int(exons[1,1])]
358 _dna = _dna[:exons[1,0]] + est[don_pos:]# only correct if there are no indels!!!
359
360 _currentAcc = currentAcc[:int(exons[1,1])]
361 _currentAcc = [0.25]*len(_currentAcc)
362
363 _currentDon = currentDon[:int(exons[1,1])]
364 #_currentDon = [0.25]*len(_currentDon)
365
366 currentVMatchAlignment = _dna, exons, est, original_est, quality,\
367 _currentAcc, _currentDon
368
369 stop = cpu()
370 self.array_stuff += stop - start
371
372 #alternativeScore = self.calcAlignmentScore(currentVMatchAlignment)
373 #alternativeScores.append(self.calcAlignmentScore(currentVMatchAlignment))
374
375 # Lets start calculation
376 dna, exons, est, original_est, quality, acc_supp, don_supp =\
377 currentVMatchAlignment
378
379 # Berechne die Parameter des wirklichen Alignments (but with untrained d,a,h ...)
380 trueSpliceAlign, trueWeightMatch, trueWeightQuality ,dna_calc =\
381 computeSpliceAlignWithQuality(dna, exons, est, original_est,\
382 quality, qualityPlifs, run)
383
384 stop = cpu()
385 self.computeSpliceAlignWithQualityTime += stop-start
386 start = cpu()
387
388 # Calculate the weights
389 trueWeightDon, trueWeightAcc, trueWeightIntron =\
390 computeSpliceWeights(d, a, h, trueSpliceAlign, don_supp, acc_supp)
391
392 #for i in xrange(0,len(trueWeightDon)):
393 # trueWeightDon[i]=0.0
394 #for i in xrange(0,len(trueWeightAcc)):
395 # trueWeightAcc[i]=0.0
396 #for i in xrange(0,len(trueWeightIntron)):
397 # trueWeightIntron[i]=0.0
398
399 stop = cpu()
400 self.computeSpliceWeightsTime += stop-start
401
402 start = cpu()
403
404 trueWeight = numpy.vstack([trueWeightIntron, trueWeightDon, trueWeightAcc, trueWeightMatch, trueWeightQuality])
405 stop = cpu()
406 self.DotProdTime += stop-start
407
408 # Calculate w'phi(x,y) the total score of the alignment
409 alternativeScores.append((trueWeight.T * self.currentPhi)[0,0])
410 """
411
412 # new score computation
413
414 # remove mismatching positions in the second exon
415 original_est_cut=''
416
417 est_ptr=0
418 dna_ptr=0
419 ptr=0
420 while ptr<len(original_est):
421
422 if original_est[ptr]=='[':
423 dnaletter=original_est[ptr+1]
424 estletter=original_est[ptr+2]
425 if est_ptr<=exons[0,1]:
426 original_est_cut+=original_est[ptr:ptr+4]
427 else:
428 original_est_cut+=estletter # EST letter
429 ptr+=4
430 else:
431 dnaletter=original_est[ptr]
432 estletter=dnaletter
433
434 original_est_cut+=estletter # EST letter
435 ptr+=1
436
437 if estletter=='-':
438 dna_ptr+=1
439 elif dnaletter=='-':
440 est_ptr+=1
441 else:
442 dna_ptr+=1
443 est_ptr+=1
444
445 assert(dna_ptr<=len(dna))
446 assert(est_ptr<=len(est))
447
448 DonorScore = calculatePlif(d, [don_score])[0]
449 print DonorScore,don_score
450
451 score = computeSpliceAlignScoreWithQuality(original_est_cut, quality, qualityPlifs, run, self.currentPhi)
452 score += dummyAcceptorScore + IntronScore + DonorScore
453
454 #print 'diff %f,%f,%f' % ((trueWeight.T * self.currentPhi)[0,0] - score,(trueWeight.T * self.currentPhi)[0,0], score)
455 alternativeScores.append(score)
456
457 _stop = cpu()
458 self.alternativeScoresTime += _stop-_start
459
460 return alternativeScores
461
462
463 def calcAlignmentScore(self,alignment):
464 """
465 Given an alignment (dna,exons,est) and the current parameter for QPalma
466 this function calculates the dot product of the feature representation of
467 the alignment and the parameter vector i.e the alignment score.
468 """
469
470 start = cpu()
471 run = self.run
472
473 # Lets start calculation
474 dna, exons, est, original_est, quality, acc_supp, don_supp = alignment
475
476 score = computeSpliceAlignScoreWithQuality(original_est, quality, self.qualityPlifs, run, self.currentPhi)
477
478 stop = cpu()
479 self.calcAlignmentScoreTime += stop-start
480
481 return score
482
483
484 def cpu():
485 return (resource.getrusage(resource.RUSAGE_SELF).ru_utime+\
486 resource.getrusage(resource.RUSAGE_SELF).ru_stime)
487
488
489 if __name__ == '__main__':
490 #run_fname = sys.argv[1]
491 #data_fname = sys.argv[2]
492 #param_filename = sys.argv[3]
493
494 dir = '/fml/ag-raetsch/home/fabio/tmp/QPalma_test/run_+_quality_+_splicesignals_+_intron_len'
495 jp = os.path.join
496
497 run_fname = jp(dir,'run_object.pickle')
498 #data_fname = '/fml/ag-raetsch/share/projects/qpalma/solexa/current_data/map.vm_unspliced_flag'
499
500 #data_fname = '/fml/ag-raetsch/share/projects/qpalma/solexa/pipeline_data/map.vm_2k'
501 data_fname = '/fml/ag-raetsch/share/projects/qpalma/solexa/pipeline_data/map.vm_100'
502
503 param_fname = jp(dir,'param_500.pickle')
504
505 ph1 = PipelineHeuristic(run_fname,data_fname,param_fname)
506
507 start = cpu()
508 ph1.filter()
509 stop = cpu()
510
511 print 'total time elapsed: %f' % (stop-start)
512 print 'time spend for get seq: %f' % ph1.get_time
513 print 'time spend for calcAlignmentScoreTime: %f' % ph1.calcAlignmentScoreTime
514 print 'time spend for alternativeScoresTime: %f' % ph1.alternativeScoresTime
515 print 'time spend for count time: %f' % ph1.count_time
516 print 'time spend for init time: %f' % ph1.init_time
517 print 'time spend for read_parsing time: %f' % ph1.read_parsing
518 print 'time spend for main_loop time: %f' % ph1.main_loop
519 print 'time spend for splice_site_time time: %f' % ph1.splice_site_time
520
521 print 'time spend for computeSpliceAlignWithQualityTime time: %f'% ph1.computeSpliceAlignWithQualityTime
522 print 'time spend for computeSpliceWeightsTime time: %f'% ph1.computeSpliceWeightsTime
523 print 'time spend for DotProdTime time: %f'% ph1.DotProdTime
524 print 'time spend forarray_stuff time: %f'% ph1.array_stuff
525 #import cProfile
526 #cProfile.run('ph1.filter()')
527
528 #import hotshot
529 #p = hotshot.Profile('profile.log')
530 #p.runcall(ph1.filter)