git-svn-id: http://svn.tuebingen.mpg.de/ag-raetsch/projects/QPalma@8640 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 original_est = location['seq']
302 quality = location['prb']
303 cal_prb = location['cal_prb']
304
305 est = unbracket_est(original_est)
306 effective_len = len(est)
307
308 genomicSeq_start = pos
309 genomicSeq_stop = pos+self.intron_size*2+self.read_size*2
310
311 start = cpu()
312 currentDNASeq, currentAcc, currentDon = get_seq_and_scores(chr,strand,genomicSeq_start,genomicSeq_stop,run['dna_flat_files'])
313 stop = cpu()
314 self.get_time += stop-start
315 dna = currentDNASeq
316
317 alt_don,alt_acc = self.findHighestScoringSpliceSites(currentAcc,currentDon)
318 print alt_don
319 print alt_acc
320
321 alternativeScores = []
322
323 # inlined
324 h = self.h
325 d = self.d
326 a = self.a
327 mmatrix = self.mmatrix
328 qualityPlifs = self.qualityPlifs
329 # inlined
330
331 # compute dummy scores
332 IntronScore = calculatePlif(h, [self.intron_size+1])[0]
333 print IntronScore
334 dummyAcceptorScore = calculatePlif(a, [0.25])[0]
335 dummyDonorScore = calculatePlif(d, [0.25])[0]
336
337 _start = cpu()
338 for (don_pos,don_score) in alt_don:
339 # remove mismatching positions in the second exon
340 original_est_cut=''
341
342 est_ptr=0
343 dna_ptr=0
344 ptr=0
345 while ptr<len(original_est):
346
347 if original_est[ptr]=='[':
348 dnaletter=original_est[ptr+1]
349 estletter=original_est[ptr+2]
350 if dna_ptr < don_pos:
351 original_est_cut+=original_est[ptr:ptr+4]
352 else:
353 original_est_cut+=estletter # EST letter
354 ptr+=4
355 else:
356 dnaletter=original_est[ptr]
357 estletter=dnaletter
358
359 original_est_cut+=estletter # EST letter
360 ptr+=1
361
362 if estletter=='-':
363 dna_ptr+=1
364 elif dnaletter=='-':
365 est_ptr+=1
366 else:
367 dna_ptr+=1
368 est_ptr+=1
369
370 assert(dna_ptr<=len(dna))
371 assert(est_ptr<=len(est))
372
373 print "Donor"
374 DonorScore = calculatePlif(d, [don_score])[0]
375 print DonorScore,don_score,don_pos
376
377 score = computeSpliceAlignScoreWithQuality(original_est_cut, quality, qualityPlifs, run, self.currentPhi)
378 score += dummyAcceptorScore + IntronScore + DonorScore
379
380 #print 'diff %f,%f,%f' % ((trueWeight.T * self.currentPhi)[0,0] - score,(trueWeight.T * self.currentPhi)[0,0], score)
381 alternativeScores.append(score)
382
383 _stop = cpu()
384 self.alternativeScoresTime += _stop-_start
385
386 _start = cpu()
387 for (acc_pos,acc_score) in alt_acc:
388 # remove mismatching positions in the second exon
389 original_est_cut=''
390
391 est_ptr=0
392 dna_ptr=0
393 ptr=0
394 while ptr<len(original_est):
395
396 if original_est[ptr]=='[':
397 dnaletter=original_est[ptr+1]
398 estletter=original_est[ptr+2]
399 if est_ptr>=acc_pos:
400 original_est_cut+=original_est[ptr:ptr+4]
401 else:
402 original_est_cut+=estletter # EST letter
403 ptr+=4
404 else:
405 dnaletter=original_est[ptr]
406 estletter=dnaletter
407
408 original_est_cut+=estletter # EST letter
409 ptr+=1
410
411 if estletter=='-':
412 dna_ptr+=1
413 elif dnaletter=='-':
414 est_ptr+=1
415 else:
416 dna_ptr+=1
417 est_ptr+=1
418
419 assert(dna_ptr<=len(dna))
420 assert(est_ptr<=len(est))
421
422 #print original_est,original_est_cut
423
424 AcceptorScore = calculatePlif(d, [acc_score])[0]
425 print "Acceptor"
426 print AcceptorScore,acc_score,acc_pos
427
428 if acc_score<0.1:
429 print currentAcc[0:50]
430 print currentDon[0:50]
431
432 score = computeSpliceAlignScoreWithQuality(original_est_cut, quality, qualityPlifs, run, self.currentPhi)
433 score += AcceptorScore + IntronScore + dummyDonorScore
434
435 #print 'diff %f,%f,%f' % ((trueWeight.T * self.currentPhi)[0,0] - score,(trueWeight.T * self.currentPhi)[0,0], score)
436 alternativeScores.append(score)
437
438 _stop = cpu()
439 self.alternativeScoresTime += _stop-_start
440
441 return alternativeScores
442
443
444 def calcAlignmentScore(self,alignment):
445 """
446 Given an alignment (dna,exons,est) and the current parameter for QPalma
447 this function calculates the dot product of the feature representation of
448 the alignment and the parameter vector i.e the alignment score.
449 """
450
451 start = cpu()
452 run = self.run
453
454 # Lets start calculation
455 dna, exons, est, original_est, quality, acc_supp, don_supp = alignment
456
457 score = computeSpliceAlignScoreWithQuality(original_est, quality, self.qualityPlifs, run, self.currentPhi)
458
459 stop = cpu()
460 self.calcAlignmentScoreTime += stop-start
461
462 return score
463
464
465 def cpu():
466 return (resource.getrusage(resource.RUSAGE_SELF).ru_utime+\
467 resource.getrusage(resource.RUSAGE_SELF).ru_stime)
468
469
470 if __name__ == '__main__':
471 #run_fname = sys.argv[1]
472 #data_fname = sys.argv[2]
473 #param_filename = sys.argv[3]
474
475 dir = '/fml/ag-raetsch/home/fabio/tmp/QPalma_test/run_+_quality_+_splicesignals_+_intron_len'
476 jp = os.path.join
477
478 run_fname = jp(dir,'run_object.pickle')
479 #data_fname = '/fml/ag-raetsch/share/projects/qpalma/solexa/current_data/map.vm_unspliced_flag'
480
481 #data_fname = '/fml/ag-raetsch/share/projects/qpalma/solexa/pipeline_data/map.vm_2k'
482 data_fname = '/fml/ag-raetsch/share/projects/qpalma/solexa/pipeline_data/map.vm_100'
483
484 param_fname = jp(dir,'param_500.pickle')
485
486 ph1 = PipelineHeuristic(run_fname,data_fname,param_fname)
487
488 start = cpu()
489 ph1.filter()
490 stop = cpu()
491
492 print 'total time elapsed: %f' % (stop-start)
493 print 'time spend for get seq: %f' % ph1.get_time
494 print 'time spend for calcAlignmentScoreTime: %f' % ph1.calcAlignmentScoreTime
495 print 'time spend for alternativeScoresTime: %f' % ph1.alternativeScoresTime
496 print 'time spend for count time: %f' % ph1.count_time
497 print 'time spend for init time: %f' % ph1.init_time
498 print 'time spend for read_parsing time: %f' % ph1.read_parsing
499 print 'time spend for main_loop time: %f' % ph1.main_loop
500 print 'time spend for splice_site_time time: %f' % ph1.splice_site_time
501
502 print 'time spend for computeSpliceAlignWithQualityTime time: %f'% ph1.computeSpliceAlignWithQualityTime
503 print 'time spend for computeSpliceWeightsTime time: %f'% ph1.computeSpliceWeightsTime
504 print 'time spend for DotProdTime time: %f'% ph1.DotProdTime
505 print 'time spend forarray_stuff time: %f'% ph1.array_stuff
506 #import cProfile
507 #cProfile.run('ph1.filter()')
508
509 #import hotshot
510 #p = hotshot.Profile('profile.log')
511 #p.runcall(ph1.filter)