git-svn-id: http://svn.tuebingen.mpg.de/ag-raetsch/projects/QPalma@8636 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 alternativeScores = []
319
320 # inlined
321 h = self.h
322 d = self.d
323 a = self.a
324 mmatrix = self.mmatrix
325 qualityPlifs = self.qualityPlifs
326 # inlined
327
328 # compute dummy scores
329 IntronScore = calculatePlif(h, [self.intron_size+1])[0]
330 dummyAcceptorScore = calculatePlif(a, [0.25])[0]
331 dummyDonorScore = calculatePlif(d, [0.25])[0]
332
333 _start = cpu()
334 for (don_pos,don_score) in alt_don:
335 # remove mismatching positions in the second exon
336 original_est_cut=''
337
338 est_ptr=0
339 dna_ptr=0
340 ptr=0
341 while ptr<len(original_est):
342
343 if original_est[ptr]=='[':
344 dnaletter=original_est[ptr+1]
345 estletter=original_est[ptr+2]
346 if dna_ptr < don_pos:
347 original_est_cut+=original_est[ptr:ptr+4]
348 else:
349 original_est_cut+=estletter # EST letter
350 ptr+=4
351 else:
352 dnaletter=original_est[ptr]
353 estletter=dnaletter
354
355 original_est_cut+=estletter # EST letter
356 ptr+=1
357
358 if estletter=='-':
359 dna_ptr+=1
360 elif dnaletter=='-':
361 est_ptr+=1
362 else:
363 dna_ptr+=1
364 est_ptr+=1
365
366 assert(dna_ptr<=len(dna))
367 assert(est_ptr<=len(est))
368
369 DonorScore = calculatePlif(d, [don_score])[0]
370 #print DonorScore,don_score
371
372 score = computeSpliceAlignScoreWithQuality(original_est_cut, quality, qualityPlifs, run, self.currentPhi)
373 score += dummyAcceptorScore + IntronScore + DonorScore
374
375 #print 'diff %f,%f,%f' % ((trueWeight.T * self.currentPhi)[0,0] - score,(trueWeight.T * self.currentPhi)[0,0], score)
376 alternativeScores.append(score)
377
378 _stop = cpu()
379 self.alternativeScoresTime += _stop-_start
380
381 _start = cpu()
382 for (acc_pos,acc_score) in alt_acc:
383 # remove mismatching positions in the second exon
384 original_est_cut=''
385
386 est_ptr=0
387 dna_ptr=0
388 ptr=0
389 while ptr<len(original_est):
390
391 if original_est[ptr]=='[':
392 dnaletter=original_est[ptr+1]
393 estletter=original_est[ptr+2]
394 if est_ptr>=acc_pos:
395 original_est_cut+=original_est[ptr:ptr+4]
396 else:
397 original_est_cut+=estletter # EST letter
398 ptr+=4
399 else:
400 dnaletter=original_est[ptr]
401 estletter=dnaletter
402
403 original_est_cut+=estletter # EST letter
404 ptr+=1
405
406 if estletter=='-':
407 dna_ptr+=1
408 elif dnaletter=='-':
409 est_ptr+=1
410 else:
411 dna_ptr+=1
412 est_ptr+=1
413
414 assert(dna_ptr<=len(dna))
415 assert(est_ptr<=len(est))
416
417 print original_est,original_est_cut
418
419 AcceptorScore = calculatePlif(d, [acc_score])[0]
420 print AcceptorScore,acc_score
421 if acc_score<0.1:
422 print currentAcc[0:50]
423
424 score = computeSpliceAlignScoreWithQuality(original_est_cut, quality, qualityPlifs, run, self.currentPhi)
425 score += AcceptorScore + IntronScore + dummyDonorScore
426
427 #print 'diff %f,%f,%f' % ((trueWeight.T * self.currentPhi)[0,0] - score,(trueWeight.T * self.currentPhi)[0,0], score)
428 alternativeScores.append(score)
429
430 _stop = cpu()
431 self.alternativeScoresTime += _stop-_start
432
433 return alternativeScores
434
435
436 def calcAlignmentScore(self,alignment):
437 """
438 Given an alignment (dna,exons,est) and the current parameter for QPalma
439 this function calculates the dot product of the feature representation of
440 the alignment and the parameter vector i.e the alignment score.
441 """
442
443 start = cpu()
444 run = self.run
445
446 # Lets start calculation
447 dna, exons, est, original_est, quality, acc_supp, don_supp = alignment
448
449 score = computeSpliceAlignScoreWithQuality(original_est, quality, self.qualityPlifs, run, self.currentPhi)
450
451 stop = cpu()
452 self.calcAlignmentScoreTime += stop-start
453
454 return score
455
456
457 def cpu():
458 return (resource.getrusage(resource.RUSAGE_SELF).ru_utime+\
459 resource.getrusage(resource.RUSAGE_SELF).ru_stime)
460
461
462 if __name__ == '__main__':
463 #run_fname = sys.argv[1]
464 #data_fname = sys.argv[2]
465 #param_filename = sys.argv[3]
466
467 dir = '/fml/ag-raetsch/home/fabio/tmp/QPalma_test/run_+_quality_+_splicesignals_+_intron_len'
468 jp = os.path.join
469
470 run_fname = jp(dir,'run_object.pickle')
471 #data_fname = '/fml/ag-raetsch/share/projects/qpalma/solexa/current_data/map.vm_unspliced_flag'
472
473 data_fname = '/fml/ag-raetsch/share/projects/qpalma/solexa/pipeline_data/map.vm_2k'
474 #data_fname = '/fml/ag-raetsch/share/projects/qpalma/solexa/pipeline_data/map.vm_100'
475
476 param_fname = jp(dir,'param_500.pickle')
477
478 ph1 = PipelineHeuristic(run_fname,data_fname,param_fname)
479
480 start = cpu()
481 ph1.filter()
482 stop = cpu()
483
484 print 'total time elapsed: %f' % (stop-start)
485 print 'time spend for get seq: %f' % ph1.get_time
486 print 'time spend for calcAlignmentScoreTime: %f' % ph1.calcAlignmentScoreTime
487 print 'time spend for alternativeScoresTime: %f' % ph1.alternativeScoresTime
488 print 'time spend for count time: %f' % ph1.count_time
489 print 'time spend for init time: %f' % ph1.init_time
490 print 'time spend for read_parsing time: %f' % ph1.read_parsing
491 print 'time spend for main_loop time: %f' % ph1.main_loop
492 print 'time spend for splice_site_time time: %f' % ph1.splice_site_time
493
494 print 'time spend for computeSpliceAlignWithQualityTime time: %f'% ph1.computeSpliceAlignWithQualityTime
495 print 'time spend for computeSpliceWeightsTime time: %f'% ph1.computeSpliceWeightsTime
496 print 'time spend for DotProdTime time: %f'% ph1.DotProdTime
497 print 'time spend forarray_stuff time: %f'% ph1.array_stuff
498 #import cProfile
499 #cProfile.run('ph1.filter()')
500
501 #import hotshot
502 #p = hotshot.Profile('profile.log')
503 #p.runcall(ph1.filter)