git-svn-id: http://svn.tuebingen.mpg.de/ag-raetsch/projects/QPalma@8634 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
316 #dna = currentDNASeq
317
318 alt_don,alt_acc = self.findHighestScoringSpliceSites(currentAcc,currentDon)
319 alternativeScores = []
320
321 # inlined
322 h = self.h
323 d = self.d
324 a = self.a
325 mmatrix = self.mmatrix
326 qualityPlifs = self.qualityPlifs
327 # inlined
328
329 # compute dummy scores
330 IntronScore = calculatePlif(h, [self.intron_size+1])[0]
331 dummyAcceptorScore = calculatePlif(a, [0.25])[0]
332 dummyDonorScore = calculatePlif(d, [0.25])[0]
333
334 _start = cpu()
335 for (don_pos,don_score) in alt_don:
336 # remove mismatching positions in the second exon
337 original_est_cut=''
338
339 est_ptr=0
340 dna_ptr=0
341 ptr=0
342 while ptr<len(original_est):
343
344 if original_est[ptr]=='[':
345 dnaletter=original_est[ptr+1]
346 estletter=original_est[ptr+2]
347 if dna_ptr < don_pos:
348 original_est_cut+=original_est[ptr:ptr+4]
349 else:
350 original_est_cut+=estletter # EST letter
351 ptr+=4
352 else:
353 dnaletter=original_est[ptr]
354 estletter=dnaletter
355
356 original_est_cut+=estletter # EST letter
357 ptr+=1
358
359 if estletter=='-':
360 dna_ptr+=1
361 elif dnaletter=='-':
362 est_ptr+=1
363 else:
364 dna_ptr+=1
365 est_ptr+=1
366
367 assert(dna_ptr<=len(dna))
368 assert(est_ptr<=len(est))
369
370 DonorScore = calculatePlif(d, [don_score])[0]
371 #print DonorScore,don_score
372
373 score = computeSpliceAlignScoreWithQuality(original_est_cut, quality, qualityPlifs, run, self.currentPhi)
374 score += dummyAcceptorScore + IntronScore + DonorScore
375
376 #print 'diff %f,%f,%f' % ((trueWeight.T * self.currentPhi)[0,0] - score,(trueWeight.T * self.currentPhi)[0,0], score)
377 alternativeScores.append(score)
378
379 _stop = cpu()
380 self.alternativeScoresTime += _stop-_start
381
382 _start = cpu()
383 for (acc_pos,acc_score) in alt_acc:
384 # remove mismatching positions in the second exon
385 original_est_cut=''
386
387 est_ptr=0
388 dna_ptr=0
389 ptr=0
390 while ptr<len(original_est):
391
392 if original_est[ptr]=='[':
393 dnaletter=original_est[ptr+1]
394 estletter=original_est[ptr+2]
395 if est_ptr>=acc_pos:
396 original_est_cut+=original_est[ptr:ptr+4]
397 else:
398 original_est_cut+=estletter # EST letter
399 ptr+=4
400 else:
401 dnaletter=original_est[ptr]
402 estletter=dnaletter
403
404 original_est_cut+=estletter # EST letter
405 ptr+=1
406
407 if estletter=='-':
408 dna_ptr+=1
409 elif dnaletter=='-':
410 est_ptr+=1
411 else:
412 dna_ptr+=1
413 est_ptr+=1
414
415 assert(dna_ptr<=len(dna))
416 assert(est_ptr<=len(est))
417
418 print original_est,original_est_cut
419
420 AcceptorScore = calculatePlif(d, [acc_score])[0]
421 print AcceptorScore,acc_score
422
423 score = computeSpliceAlignScoreWithQuality(original_est_cut, quality, qualityPlifs, run, self.currentPhi)
424 score += AcceptorScore + IntronScore + dummyDonorScore
425
426 #print 'diff %f,%f,%f' % ((trueWeight.T * self.currentPhi)[0,0] - score,(trueWeight.T * self.currentPhi)[0,0], score)
427 alternativeScores.append(score)
428
429 _stop = cpu()
430 self.alternativeScoresTime += _stop-_start
431
432 return alternativeScores
433
434
435 def calcAlignmentScore(self,alignment):
436 """
437 Given an alignment (dna,exons,est) and the current parameter for QPalma
438 this function calculates the dot product of the feature representation of
439 the alignment and the parameter vector i.e the alignment score.
440 """
441
442 start = cpu()
443 run = self.run
444
445 # Lets start calculation
446 dna, exons, est, original_est, quality, acc_supp, don_supp = alignment
447
448 score = computeSpliceAlignScoreWithQuality(original_est, quality, self.qualityPlifs, run, self.currentPhi)
449
450 stop = cpu()
451 self.calcAlignmentScoreTime += stop-start
452
453 return score
454
455
456 def cpu():
457 return (resource.getrusage(resource.RUSAGE_SELF).ru_utime+\
458 resource.getrusage(resource.RUSAGE_SELF).ru_stime)
459
460
461 if __name__ == '__main__':
462 #run_fname = sys.argv[1]
463 #data_fname = sys.argv[2]
464 #param_filename = sys.argv[3]
465
466 dir = '/fml/ag-raetsch/home/fabio/tmp/QPalma_test/run_+_quality_+_splicesignals_+_intron_len'
467 jp = os.path.join
468
469 run_fname = jp(dir,'run_object.pickle')
470 #data_fname = '/fml/ag-raetsch/share/projects/qpalma/solexa/current_data/map.vm_unspliced_flag'
471
472 data_fname = '/fml/ag-raetsch/share/projects/qpalma/solexa/pipeline_data/map.vm_2k'
473 #data_fname = '/fml/ag-raetsch/share/projects/qpalma/solexa/pipeline_data/map.vm_100'
474
475 param_fname = jp(dir,'param_500.pickle')
476
477 ph1 = PipelineHeuristic(run_fname,data_fname,param_fname)
478
479 start = cpu()
480 ph1.filter()
481 stop = cpu()
482
483 print 'total time elapsed: %f' % (stop-start)
484 print 'time spend for get seq: %f' % ph1.get_time
485 print 'time spend for calcAlignmentScoreTime: %f' % ph1.calcAlignmentScoreTime
486 print 'time spend for alternativeScoresTime: %f' % ph1.alternativeScoresTime
487 print 'time spend for count time: %f' % ph1.count_time
488 print 'time spend for init time: %f' % ph1.init_time
489 print 'time spend for read_parsing time: %f' % ph1.read_parsing
490 print 'time spend for main_loop time: %f' % ph1.main_loop
491 print 'time spend for splice_site_time time: %f' % ph1.splice_site_time
492
493 print 'time spend for computeSpliceAlignWithQualityTime time: %f'% ph1.computeSpliceAlignWithQualityTime
494 print 'time spend for computeSpliceWeightsTime time: %f'% ph1.computeSpliceWeightsTime
495 print 'time spend for DotProdTime time: %f'% ph1.DotProdTime
496 print 'time spend forarray_stuff time: %f'% ph1.array_stuff
497 #import cProfile
498 #cProfile.run('ph1.filter()')
499
500 #import hotshot
501 #p = hotshot.Profile('profile.log')
502 #p.runcall(ph1.filter)