works soso
[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 = 250
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 == 1000:
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 def calcAlternativeAlignments(self,location):
288 """
289 Given an alignment proposed by Vmatch this function calculates possible
290 alternative alignments taking into account for example matched
291 donor/acceptor positions.
292 """
293 def signum(a):
294 if a>0:
295 return 1
296 elif a<0:
297 return -1
298 else:
299 return 0
300
301 run = self.run
302
303 id = location['id']
304 chr = location['chr']
305 pos = location['pos']
306 strand = location['strand']
307 original_est = location['seq']
308 quality = location['prb']
309 cal_prb = location['cal_prb']
310
311 est = unbracket_est(original_est)
312 effective_len = len(est)
313
314 genomicSeq_start = pos
315 genomicSeq_stop = pos+1000#self.read_size+1#+self.intron_size*2+self.read_size*2
316
317 start = cpu()
318 currentDNASeq, currentAcc, currentDon = get_seq_and_scores(chr,strand,genomicSeq_start,genomicSeq_stop,run['dna_flat_files'])
319 stop = cpu()
320 self.get_time += stop-start
321 dna = currentDNASeq
322
323 alt_don,alt_acc = self.findHighestScoringSpliceSites(currentAcc,currentDon)
324
325 sort_acc=[ elem for elem in enumerate(currentAcc) ]
326 sort_acc.sort(lambda x,y: signum(x[1]-y[1]) )
327
328 (max_acc_pos,max_acc_score)=sort_acc[-1]
329
330 #print alt_don
331 #print alt_acc
332
333 alternativeScores = []
334
335 # inlined
336 h = self.h
337 d = self.d
338 a = self.a
339 mmatrix = self.mmatrix
340 qualityPlifs = self.qualityPlifs
341 # inlined
342
343 # compute dummy scores
344 #IntronScore = calculatePlif(h, [math.fabs(max_acc_pos-30)])[0]
345 #dummyAcceptorScore = calculatePlif(a, [max_acc_score])[0]
346 IntronScore = calculatePlif(h, [self.intron_size])[0] - 0.5
347 dummyAcceptorScore = calculatePlif(a, [0.25])[0]
348 dummyDonorScore = calculatePlif(d, [0.25])[0]
349
350 _start = cpu()
351 for (don_pos,don_score) in alt_don:
352 # remove mismatching positions in the second exon
353 original_est_cut=''
354
355 est_ptr=0
356 dna_ptr=0
357 ptr=0
358 while ptr<len(original_est):
359
360 if original_est[ptr]=='[':
361 dnaletter=original_est[ptr+1]
362 estletter=original_est[ptr+2]
363 if dna_ptr < don_pos:
364 original_est_cut+=original_est[ptr:ptr+4]
365 else:
366 original_est_cut+=estletter # EST letter
367 ptr+=4
368 else:
369 dnaletter=original_est[ptr]
370 estletter=dnaletter
371
372 original_est_cut+=estletter # EST letter
373 ptr+=1
374
375 if estletter=='-':
376 dna_ptr+=1
377 elif dnaletter=='-':
378 est_ptr+=1
379 else:
380 dna_ptr+=1
381 est_ptr+=1
382
383 assert(dna_ptr<=len(dna))
384 assert(est_ptr<=len(est))
385
386 #print "Donor"
387 DonorScore = calculatePlif(d, [don_score])[0]
388 #print DonorScore,don_score,don_pos
389
390 score = computeSpliceAlignScoreWithQuality(original_est_cut, quality, qualityPlifs, run, self.currentPhi)
391 score += dummyAcceptorScore + IntronScore + DonorScore
392
393 #print 'diff %f,%f,%f' % ((trueWeight.T * self.currentPhi)[0,0] - score,(trueWeight.T * self.currentPhi)[0,0], score)
394 alternativeScores.append(score)
395
396 _stop = cpu()
397 self.alternativeScoresTime += _stop-_start
398
399 _start = cpu()
400 for (acc_pos,acc_score) in alt_acc:
401 # remove mismatching positions in the second exon
402 original_est_cut=''
403
404 est_ptr=0
405 dna_ptr=0
406 ptr=0
407 while ptr<len(original_est):
408
409 if original_est[ptr]=='[':
410 dnaletter=original_est[ptr+1]
411 estletter=original_est[ptr+2]
412 if est_ptr>=acc_pos:
413 original_est_cut+=original_est[ptr:ptr+4]
414 else:
415 original_est_cut+=estletter # EST letter
416 ptr+=4
417 else:
418 dnaletter=original_est[ptr]
419 estletter=dnaletter
420
421 original_est_cut+=estletter # EST letter
422 ptr+=1
423
424 if estletter=='-':
425 dna_ptr+=1
426 elif dnaletter=='-':
427 est_ptr+=1
428 else:
429 dna_ptr+=1
430 est_ptr+=1
431
432 assert(dna_ptr<=len(dna))
433 assert(est_ptr<=len(est))
434
435 #print original_est,original_est_cut
436
437 AcceptorScore = calculatePlif(d, [acc_score])[0]
438 #print "Acceptor"
439 #print AcceptorScore,acc_score,acc_pos
440
441 #if acc_score<0.1:
442 # print currentAcc[0:50]
443 # print currentDon[0:50]
444
445 score = computeSpliceAlignScoreWithQuality(original_est_cut, quality, qualityPlifs, run, self.currentPhi)
446 score += AcceptorScore + IntronScore + dummyDonorScore
447
448 #print 'diff %f,%f,%f' % ((trueWeight.T * self.currentPhi)[0,0] - score,(trueWeight.T * self.currentPhi)[0,0], score)
449 alternativeScores.append(score)
450
451 _stop = cpu()
452 self.alternativeScoresTime += _stop-_start
453
454 return alternativeScores
455
456
457 def calcAlignmentScore(self,alignment):
458 """
459 Given an alignment (dna,exons,est) and the current parameter for QPalma
460 this function calculates the dot product of the feature representation of
461 the alignment and the parameter vector i.e the alignment score.
462 """
463
464 start = cpu()
465 run = self.run
466
467 # Lets start calculation
468 dna, exons, est, original_est, quality, acc_supp, don_supp = alignment
469
470 score = computeSpliceAlignScoreWithQuality(original_est, quality, self.qualityPlifs, run, self.currentPhi)
471
472 stop = cpu()
473 self.calcAlignmentScoreTime += stop-start
474
475 return score
476
477
478 def cpu():
479 return (resource.getrusage(resource.RUSAGE_SELF).ru_utime+\
480 resource.getrusage(resource.RUSAGE_SELF).ru_stime)
481
482
483 if __name__ == '__main__':
484 #run_fname = sys.argv[1]
485 #data_fname = sys.argv[2]
486 #param_filename = sys.argv[3]
487
488 dir = '/fml/ag-raetsch/home/fabio/tmp/QPalma_test/run_+_quality_+_splicesignals_+_intron_len'
489 jp = os.path.join
490
491 run_fname = jp(dir,'run_object.pickle')
492 #data_fname = '/fml/ag-raetsch/share/projects/qpalma/solexa/current_data/map.vm_unspliced_1k'
493
494 data_fname = '/fml/ag-raetsch/share/projects/qpalma/solexa/pipeline_data/map.vm_2k'
495 #data_fname = '/fml/ag-raetsch/share/projects/qpalma/solexa/pipeline_data/map.vm_100'
496
497 param_fname = jp(dir,'param_500.pickle')
498
499 ph1 = PipelineHeuristic(run_fname,data_fname,param_fname)
500
501 start = cpu()
502 ph1.filter()
503 stop = cpu()
504
505 print 'total time elapsed: %f' % (stop-start)
506 print 'time spend for get seq: %f' % ph1.get_time
507 print 'time spend for calcAlignmentScoreTime: %f' % ph1.calcAlignmentScoreTime
508 print 'time spend for alternativeScoresTime: %f' % ph1.alternativeScoresTime
509 print 'time spend for count time: %f' % ph1.count_time
510 print 'time spend for init time: %f' % ph1.init_time
511 print 'time spend for read_parsing time: %f' % ph1.read_parsing
512 print 'time spend for main_loop time: %f' % ph1.main_loop
513 print 'time spend for splice_site_time time: %f' % ph1.splice_site_time
514
515 print 'time spend for computeSpliceAlignWithQualityTime time: %f'% ph1.computeSpliceAlignWithQualityTime
516 print 'time spend for computeSpliceWeightsTime time: %f'% ph1.computeSpliceWeightsTime
517 print 'time spend for DotProdTime time: %f'% ph1.DotProdTime
518 print 'time spend forarray_stuff time: %f'% ph1.array_stuff
519 #import cProfile
520 #cProfile.run('ph1.filter()')
521
522 #import hotshot
523 #p = hotshot.Profile('profile.log')
524 #p.runcall(ph1.filter)