8b5e6404b98d33af24fc531878697337f86bfcf3
[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 = 150
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+1000#self.read_size+1#+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
319 sort_acc=enumerate(currentAcc).sort(lambda x,y: x[1]-y[1])
320 (max_acc_pos,max_acc_score)=sort_acc[-1]
321
322 #print alt_don
323 #print alt_acc
324
325 alternativeScores = []
326
327 # inlined
328 h = self.h
329 d = self.d
330 a = self.a
331 mmatrix = self.mmatrix
332 qualityPlifs = self.qualityPlifs
333 # inlined
334
335 # compute dummy scores
336 IntronScore = calculatePlif(h, [max_acc_pos-30])[0]
337 #print IntronScore
338 dummyAcceptorScore = calculatePlif(a, [max_acc_score])[0]
339 dummyDonorScore = calculatePlif(d, [0.25])[0]
340
341 _start = cpu()
342 for (don_pos,don_score) in alt_don:
343 # remove mismatching positions in the second exon
344 original_est_cut=''
345
346 est_ptr=0
347 dna_ptr=0
348 ptr=0
349 while ptr<len(original_est):
350
351 if original_est[ptr]=='[':
352 dnaletter=original_est[ptr+1]
353 estletter=original_est[ptr+2]
354 if dna_ptr < don_pos:
355 original_est_cut+=original_est[ptr:ptr+4]
356 else:
357 original_est_cut+=estletter # EST letter
358 ptr+=4
359 else:
360 dnaletter=original_est[ptr]
361 estletter=dnaletter
362
363 original_est_cut+=estletter # EST letter
364 ptr+=1
365
366 if estletter=='-':
367 dna_ptr+=1
368 elif dnaletter=='-':
369 est_ptr+=1
370 else:
371 dna_ptr+=1
372 est_ptr+=1
373
374 assert(dna_ptr<=len(dna))
375 assert(est_ptr<=len(est))
376
377 #print "Donor"
378 DonorScore = calculatePlif(d, [don_score])[0]
379 #print DonorScore,don_score,don_pos
380
381 score = computeSpliceAlignScoreWithQuality(original_est_cut, quality, qualityPlifs, run, self.currentPhi)
382 score += dummyAcceptorScore + IntronScore + DonorScore
383
384 #print 'diff %f,%f,%f' % ((trueWeight.T * self.currentPhi)[0,0] - score,(trueWeight.T * self.currentPhi)[0,0], score)
385 alternativeScores.append(score)
386
387 _stop = cpu()
388 self.alternativeScoresTime += _stop-_start
389
390 _start = cpu()
391 for (acc_pos,acc_score) in alt_acc:
392 # remove mismatching positions in the second exon
393 original_est_cut=''
394
395 est_ptr=0
396 dna_ptr=0
397 ptr=0
398 while ptr<len(original_est):
399
400 if original_est[ptr]=='[':
401 dnaletter=original_est[ptr+1]
402 estletter=original_est[ptr+2]
403 if est_ptr>=acc_pos:
404 original_est_cut+=original_est[ptr:ptr+4]
405 else:
406 original_est_cut+=estletter # EST letter
407 ptr+=4
408 else:
409 dnaletter=original_est[ptr]
410 estletter=dnaletter
411
412 original_est_cut+=estletter # EST letter
413 ptr+=1
414
415 if estletter=='-':
416 dna_ptr+=1
417 elif dnaletter=='-':
418 est_ptr+=1
419 else:
420 dna_ptr+=1
421 est_ptr+=1
422
423 assert(dna_ptr<=len(dna))
424 assert(est_ptr<=len(est))
425
426 #print original_est,original_est_cut
427
428 AcceptorScore = calculatePlif(d, [acc_score])[0]
429 #print "Acceptor"
430 #print AcceptorScore,acc_score,acc_pos
431
432 #if acc_score<0.1:
433 # print currentAcc[0:50]
434 # print currentDon[0:50]
435
436 score = computeSpliceAlignScoreWithQuality(original_est_cut, quality, qualityPlifs, run, self.currentPhi)
437 score += AcceptorScore + IntronScore + dummyDonorScore
438
439 #print 'diff %f,%f,%f' % ((trueWeight.T * self.currentPhi)[0,0] - score,(trueWeight.T * self.currentPhi)[0,0], score)
440 alternativeScores.append(score)
441
442 _stop = cpu()
443 self.alternativeScoresTime += _stop-_start
444
445 return alternativeScores
446
447
448 def calcAlignmentScore(self,alignment):
449 """
450 Given an alignment (dna,exons,est) and the current parameter for QPalma
451 this function calculates the dot product of the feature representation of
452 the alignment and the parameter vector i.e the alignment score.
453 """
454
455 start = cpu()
456 run = self.run
457
458 # Lets start calculation
459 dna, exons, est, original_est, quality, acc_supp, don_supp = alignment
460
461 score = computeSpliceAlignScoreWithQuality(original_est, quality, self.qualityPlifs, run, self.currentPhi)
462
463 stop = cpu()
464 self.calcAlignmentScoreTime += stop-start
465
466 return score
467
468
469 def cpu():
470 return (resource.getrusage(resource.RUSAGE_SELF).ru_utime+\
471 resource.getrusage(resource.RUSAGE_SELF).ru_stime)
472
473
474 if __name__ == '__main__':
475 #run_fname = sys.argv[1]
476 #data_fname = sys.argv[2]
477 #param_filename = sys.argv[3]
478
479 dir = '/fml/ag-raetsch/home/fabio/tmp/QPalma_test/run_+_quality_+_splicesignals_+_intron_len'
480 jp = os.path.join
481
482 run_fname = jp(dir,'run_object.pickle')
483 #data_fname = '/fml/ag-raetsch/share/projects/qpalma/solexa/current_data/map.vm_unspliced_flag'
484
485 #data_fname = '/fml/ag-raetsch/share/projects/qpalma/solexa/pipeline_data/map.vm_2k'
486 data_fname = '/fml/ag-raetsch/share/projects/qpalma/solexa/pipeline_data/map.vm_100'
487
488 param_fname = jp(dir,'param_500.pickle')
489
490 ph1 = PipelineHeuristic(run_fname,data_fname,param_fname)
491
492 start = cpu()
493 ph1.filter()
494 stop = cpu()
495
496 print 'total time elapsed: %f' % (stop-start)
497 print 'time spend for get seq: %f' % ph1.get_time
498 print 'time spend for calcAlignmentScoreTime: %f' % ph1.calcAlignmentScoreTime
499 print 'time spend for alternativeScoresTime: %f' % ph1.alternativeScoresTime
500 print 'time spend for count time: %f' % ph1.count_time
501 print 'time spend for init time: %f' % ph1.init_time
502 print 'time spend for read_parsing time: %f' % ph1.read_parsing
503 print 'time spend for main_loop time: %f' % ph1.main_loop
504 print 'time spend for splice_site_time time: %f' % ph1.splice_site_time
505
506 print 'time spend for computeSpliceAlignWithQualityTime time: %f'% ph1.computeSpliceAlignWithQualityTime
507 print 'time spend for computeSpliceWeightsTime time: %f'% ph1.computeSpliceWeightsTime
508 print 'time spend for DotProdTime time: %f'% ph1.DotProdTime
509 print 'time spend forarray_stuff time: %f'% ph1.array_stuff
510 #import cProfile
511 #cProfile.run('ph1.filter()')
512
513 #import hotshot
514 #p = hotshot.Profile('profile.log')
515 #p.runcall(ph1.filter)