git-svn-id: http://svn.tuebingen.mpg.de/ag-raetsch/projects/QPalma@8614 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 max_don = -inf
265 don_pos = []
266 for idx,score in enumerate(currentDon):
267 if score > -inf and idx > 1 and idx < self.read_size:
268 don_pos.append(idx)
269
270 if len(don_pos) == 2:
271 break
272
273 max_acc = -inf
274 acc_pos = []
275 for idx,score in enumerate(currentAcc):
276 if score > -inf and idx >= self.intron_size:
277 acc_pos = idx
278 #acc_pos.append(idx)
279 break
280
281 return don_pos,acc_pos
282
283
284 def calcAlternativeAlignments(self,location):
285 """
286 Given an alignment proposed by Vmatch this function calculates possible
287 alternative alignments taking into account for example matched
288 donor/acceptor positions.
289 """
290
291 run = self.run
292
293 id = location['id']
294 chr = location['chr']
295 pos = location['pos']
296 strand = location['strand']
297 seq = location['seq']
298 #orig_seq = location['orig_seq']
299 prb = location['prb']
300 cal_prb = location['cal_prb']
301
302 orig_seq = self.original_reads[int(id)]
303
304 unb_seq = unbracket_est(seq)
305 effective_len = len(unb_seq)
306
307 genomicSeq_start = pos
308 genomicSeq_stop = pos+self.intron_size*2+self.read_size*2
309
310 start = cpu()
311 currentDNASeq, currentAcc, currentDon = get_seq_and_scores(chr,strand,genomicSeq_start,genomicSeq_stop,run['dna_flat_files'])
312 stop = cpu()
313 self.get_time += stop-start
314
315 dna = currentDNASeq
316
317 start = cpu()
318 alt_don_pos,acc_pos = self.findHighestScoringSpliceSites(currentAcc,currentDon)
319 stop = cpu()
320 self.splice_site_time = stop-start
321
322 alternativeScores = []
323
324 exons = zeros((2,2),dtype=numpy.int)
325 est = unb_seq
326 original_est = seq
327 quality = prb
328
329 # inlined
330 h = self.h
331 d = self.d
332 a = self.a
333 mmatrix = self.mmatrix
334 qualityPlifs = self.qualityPlifs
335 # inlined
336
337 _start = cpu()
338 for don_pos in alt_don_pos:
339 start = cpu()
340
341 exons[0,0] = 0
342 exons[0,1] = don_pos
343 exons[1,0] = acc_pos+1
344 exons[1,1] = acc_pos+1+(self.read_size-don_pos)
345
346 _dna = dna[:int(exons[1,1])]
347 _dna = _dna[:exons[1,0]] + orig_seq[don_pos:]
348
349 _currentAcc = currentAcc[:int(exons[1,1])]
350 _currentAcc = [0.25]*len(_currentAcc)
351
352 _currentDon = currentDon[:int(exons[1,1])]
353 _currentDon = [0.25]*len(_currentDon)
354
355 currentVMatchAlignment = _dna, exons, est, original_est, quality,\
356 _currentAcc, _currentDon
357
358 stop = cpu()
359 self.array_stuff += stop - start
360
361 #alternativeScore = self.calcAlignmentScore(currentVMatchAlignment)
362 #alternativeScores.append(self.calcAlignmentScore(currentVMatchAlignment))
363
364 # Lets start calculation
365 dna, exons, est, original_est, quality, acc_supp, don_supp =\
366 currentVMatchAlignment
367
368 # Berechne die Parameter des wirklichen Alignments (but with untrained d,a,h ...)
369 trueSpliceAlign, trueWeightMatch, trueWeightQuality ,dna_calc =\
370 computeSpliceAlignWithQuality(dna, exons, est, original_est,\
371 quality, qualityPlifs, run)
372
373 stop = cpu()
374 self.computeSpliceAlignWithQualityTime += stop-start
375 start = cpu()
376
377 # Calculate the weights
378 trueWeightDon, trueWeightAcc, trueWeightIntron =\
379 computeSpliceWeights(d, a, h, trueSpliceAlign, don_supp, acc_supp)
380
381 stop = cpu()
382 self.computeSpliceWeightsTime += stop-start
383
384 start = cpu()
385
386 trueWeight = numpy.vstack([trueWeightIntron, trueWeightDon, trueWeightAcc, trueWeightMatch, trueWeightQuality])
387
388 # Calculate w'phi(x,y) the total score of the alignment
389 alternativeScores.append((trueWeight.T * self.currentPhi)[0,0])
390
391 # remove mismatching positions in the second exon
392 original_est_cut=''
393
394 est_ptr=0
395 dna_ptr=0
396 ptr=0
397 while ptr<len(original_est):
398
399 if original_est[ptr]=='[':
400 dnaletter=original_est[ptr+1]
401 estletter=original_est[ptr+2]
402 if est_ptr<=exons[0,1]:
403 original_est_cut+=original_est[ptr:ptr+4]
404 else:
405 original_est_cut+=estletter # EST letter
406 ptr+=4
407 else:
408 dnaletter=original_est[ptr]
409 estletter=dnaletter
410
411 original_est_cut+=estletter # EST letter
412 ptr+=1
413
414 if estletter=='-':
415 dna_ptr+=1
416 elif dnaletter=='-':
417 est_ptr+=1
418 else:
419 dna_ptr+=1
420 est_ptr+=1
421
422 assert(dna_ptr<=len(dna))
423 assert(est_ptr<=len(est))
424
425 print original_est_cut,original_est
426 # new score
427 score = computeSpliceAlignScoreWithQuality(original_est_cut, quality, qualityPlifs, run, self.currentPhi)
428
429 print 'diff %f,%f,%f' % ((trueWeight.T * self.currentPhi)[0,0] - score,(trueWeight.T * self.currentPhi)[0,0], score)
430
431 stop = cpu()
432 self.DotProdTime += stop-start
433
434 _stop = cpu()
435 self.alternativeScoresTime += _stop-_start
436
437 return alternativeScores
438
439
440 def calcAlignmentScore(self,alignment):
441 """
442 Given an alignment (dna,exons,est) and the current parameter for QPalma
443 this function calculates the dot product of the feature representation of
444 the alignment and the parameter vector i.e the alignment score.
445 """
446
447 start = cpu()
448 run = self.run
449
450 # Lets start calculation
451 dna, exons, est, original_est, quality, acc_supp, don_supp = alignment
452
453 score = computeSpliceAlignScoreWithQuality(original_est, quality, self.qualityPlifs, run, self.currentPhi)
454
455 stop = cpu()
456 self.calcAlignmentScoreTime += stop-start
457
458 return score
459
460
461 def cpu():
462 return (resource.getrusage(resource.RUSAGE_SELF).ru_utime+\
463 resource.getrusage(resource.RUSAGE_SELF).ru_stime)
464
465
466 if __name__ == '__main__':
467 #run_fname = sys.argv[1]
468 #data_fname = sys.argv[2]
469 #param_filename = sys.argv[3]
470
471 dir = '/fml/ag-raetsch/home/fabio/tmp/QPalma_test/run_+_quality_+_splicesignals_+_intron_len'
472 jp = os.path.join
473
474 run_fname = jp(dir,'run_object.pickle')
475 #data_fname = '/fml/ag-raetsch/share/projects/qpalma/solexa/current_data/map.vm_unspliced_flag'
476
477 #data_fname = '/fml/ag-raetsch/share/projects/qpalma/solexa/pipeline_data/map.vm_2k'
478 data_fname = '/fml/ag-raetsch/share/projects/qpalma/solexa/pipeline_data/map.vm_100'
479
480 param_fname = jp(dir,'param_500.pickle')
481
482 ph1 = PipelineHeuristic(run_fname,data_fname,param_fname)
483
484 start = cpu()
485 ph1.filter()
486 stop = cpu()
487
488 print 'total time elapsed: %f' % (stop-start)
489 print 'time spend for get seq: %f' % ph1.get_time
490 print 'time spend for calcAlignmentScoreTime: %f' % ph1.calcAlignmentScoreTime
491 print 'time spend for alternativeScoresTime: %f' % ph1.alternativeScoresTime
492 print 'time spend for count time: %f' % ph1.count_time
493 print 'time spend for init time: %f' % ph1.init_time
494 print 'time spend for read_parsing time: %f' % ph1.read_parsing
495 print 'time spend for main_loop time: %f' % ph1.main_loop
496 print 'time spend for splice_site_time time: %f' % ph1.splice_site_time
497
498 print 'time spend for computeSpliceAlignWithQualityTime time: %f'% ph1.computeSpliceAlignWithQualityTime
499 print 'time spend for computeSpliceWeightsTime time: %f'% ph1.computeSpliceWeightsTime
500 print 'time spend for DotProdTime time: %f'% ph1.DotProdTime
501 print 'time spend forarray_stuff time: %f'% ph1.array_stuff
502 #import cProfile
503 #cProfile.run('ph1.filter()')
504
505 #import hotshot
506 #p = hotshot.Profile('profile.log')
507 #p.runcall(ph1.filter)