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compute_probs.py
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compute_probs.py
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#import math
import sys
from basic import *
from util import get_top_genes
#import matplotlib
#if make_png: matplotlib.use('Agg')
#import matplotlib.pyplot as plt
#import numpy as np
import tcr_sampler
from translation import get_translation
from amino_acids import amino_acids
new_probs = pipeline_params['new_probs']
if new_probs:
print 'compute_probs: new_probs'
import tcr_rearrangement_new as tcr_rearrangement ## all_rep_probs
else:
print 'compute_probs: old_probs'
import tcr_rearrangement
with Parser(locals()) as p:
#p.str('args').unspecified_default().multiple().required()
p.str('organism')
p.str('infile').required()
p.str('outfile').required()
#p.int('min_mice').default(2)
#p.float('float_arg') # --float_arg 9.6
#p.flag('plot') # --flag_arg (no argument passed)
p.flag('verbose').shorthand('v') # --flag_arg (no argument passed)
p.flag('allow_stop_codons') # --flag_arg (no argument passed)
p.flag('allow_X') # --flag_arg (no argument passed)
p.flag('clobber').shorthand('c') # --flag_arg (no argument passed)
p.flag('add_masked_seqs') # --flag_arg (no argument passed)
p.flag('filter') # --flag_arg (no argument passed)
p.int('max_cdr3_length_for_filtering').default(30) # --flag_arg (no argument passed)
p.int('min_cdr3_length_for_filtering').default(6) # otherwise compute_distances.py will fail...
p.flag('no_probabilities').described_as('Assign a probability of 1 to all TCRs.')
#p.flag('find_exact_matches') # --flag_arg (no argument passed)
#p.range('range_arg') # --range_arg 1:2
#p.multiword('multi_arg') # --multi_arg hello world
#p.file('file_arg') # --file_arg README.txt
#p.directory('dir_arg') # --dir_arg /tmp/
#p.str('floatlist').cast(lambda x: [float(val) for val in x.split(',')])
#p.multiword('intlist').cast(lambda x: [int(val) for val in x.split()])
assert add_masked_seqs
if exists(outfile):assert clobber
out = open(outfile,'w')
infields = []
outfields = []
for line in open(infile,'rU'):
if not infields:
if line[0] == '#':
infields = line[1:-1].split('\t')
else:
infields = line[:-1].split('\t')
outfields = infields[:]
outfields.extend( ['a_protseq_prob','cdr3a_protseq_prob','va_rep_prob','ja_rep_prob','a_nucseq_prob',
'b_protseq_prob','cdr3b_protseq_prob','vb_rep_prob','jb_rep_prob','b_nucseq_prob' ] )
if add_masked_seqs:
outfields.extend( ['cdr3a_protseq_masked','a_indels','cdr3a_new_nucseq',
'cdr3b_protseq_masked','b_indels','cdr3b_new_nucseq' ] )
out.write('\t'.join( outfields )+'\n' )
continue
assert infields
l = parse_tsv_line( line[:-1], infields )
if filter and 'status' in l and l['status'] != 'OK':continue
theid = line.split("\t")[0]
## ALPHA
va_gene = l['va_gene']
ja_gene = l['ja_gene']
vb_gene = l['vb_gene']
jb_gene = l['jb_gene']
cdr3a_protseq = l['cdr3a']
cdr3a_nucseq = l['cdr3a_nucseq']
cdr3b_protseq = l['cdr3b']
cdr3b_nucseq = l['cdr3b_nucseq']
if filter:
if 'UNK' in va_gene+ja_gene or 'TRa' in va_gene+ja_gene: continue
if 'UNK' in va_gene+ja_gene or 'TRa' in va_gene+ja_gene: continue
if ( len(cdr3a_protseq) > max_cdr3_length_for_filtering or
len(cdr3b_protseq) > max_cdr3_length_for_filtering or
len(cdr3a_protseq) < min_cdr3_length_for_filtering or
len(cdr3b_protseq) < min_cdr3_length_for_filtering ): continue
## check for stop codons
skip_me = False
for a in cdr3a_protseq + cdr3b_protseq:
if a not in amino_acids:
assert a in 'X*'
if ( a == '*' and not allow_stop_codons) or ( a == 'X' and not allow_X ):
Log('{} skipping: badseq: {} {}'.format(theid, cdr3a_protseq,cdr3b_protseq))
skip_me = True
break
if skip_me:
continue
## probs are computed by reps
va_reps = l['va_reps'].split(';')
ja_reps = l['ja_reps'].split(';')
va_countreps = l['va_countreps'].split(';')
ja_countreps = l['ja_countreps'].split(';')
va_cdr3_nucseq = tcr_sampler.get_v_cdr3_nucseq( organism, va_gene )
ja_cdr3_nucseq = tcr_sampler.get_j_cdr3_nucseq( organism, ja_gene )
va_cdr3_protseq,codons = get_translation( va_cdr3_nucseq, '+1' )
ja_cdr3_protseq,codons = get_translation( ja_cdr3_nucseq, '+{}'.format(1+len(ja_cdr3_nucseq)%3))
if no_probabilities or not tcr_rearrangement.probs_data_exist( organism,'A'):
##all probabilities will be set to 1 if this flag is set
aprob_nucseq = 1
aprob_protseq = 1
else:
aprob_nucseq,new_cdr3a_nucseq = tcr_sampler.alpha_cdr3_protseq_probability( theid, organism, va_gene, ja_gene,
cdr3_protseq='',
cdr3_nucseq=cdr3a_nucseq, verbose=verbose,
return_final_cdr3_nucseq=True )
if new_cdr3a_nucseq != cdr3a_nucseq: ## note note note
print 'new_cdr3a_nucseq:',len(new_cdr3a_nucseq),new_cdr3a_nucseq
print 'old_cdr3a_nucseq:',len(cdr3a_nucseq),cdr3a_nucseq
new_cdr3a_protseq = get_translation( new_cdr3a_nucseq, '+1' )[0]
else:
new_cdr3a_protseq = cdr3a_protseq[:]
assert new_cdr3a_protseq == get_translation( cdr3a_nucseq, '+1' )[0]
aprob_protseq = tcr_sampler.alpha_cdr3_protseq_probability( theid, organism, va_gene, ja_gene, new_cdr3a_protseq,
verbose=verbose )
## BETA
vb_reps = l['vb_reps'].split(';')
jb_reps = l['jb_reps'].split(';')
vb_countreps = l['vb_countreps'].split(';')
jb_countreps = l['jb_countreps'].split(';')
vb_cdr3_nucseq = tcr_sampler.get_v_cdr3_nucseq( organism, vb_gene )
jb_cdr3_nucseq = tcr_sampler.get_j_cdr3_nucseq( organism, jb_gene )
vb_cdr3_protseq,codons = get_translation( vb_cdr3_nucseq, '+1' )
jb_cdr3_protseq,codons = get_translation( jb_cdr3_nucseq, '+{}'.format(1+len(jb_cdr3_nucseq)%3))
if no_probabilities or not tcr_rearrangement.probs_data_exist( organism,'B'):
##all probabilities will be set to 1 if this flag is set
bprob_nucseq = 1
bprob_protseq = 1
else:
bprob_nucseq, new_cdr3b_nucseq \
= tcr_sampler.beta_cdr3_protseq_probability( theid, organism, vb_gene, jb_gene, cdr3_protseq='',
verbose=verbose, cdr3_nucseq=cdr3b_nucseq,
allow_early_nucseq_mismatches=True,
return_final_cdr3_nucseq=True )
if new_cdr3b_nucseq != cdr3b_nucseq: ## note note note
new_cdr3b_protseq = get_translation( new_cdr3b_nucseq, '+1' )[0]
else:
new_cdr3b_protseq = cdr3b_protseq[:]
assert new_cdr3b_protseq == get_translation( cdr3b_nucseq, '+1' )[0]
bprob_protseq = tcr_sampler.beta_cdr3_protseq_probability( theid, organism, vb_gene, jb_gene, new_cdr3b_protseq,
verbose=verbose )
vals = dict(l) #line.split('\t') + ['']*(len(outfields)-len(infields))
if add_masked_seqs:
## junction analysis
a_junction_results = tcr_sampler.analyze_junction( organism, va_gene, ja_gene, cdr3a_protseq, cdr3a_nucseq )
b_junction_results = tcr_sampler.analyze_junction( organism, vb_gene, jb_gene, cdr3b_protseq, cdr3b_nucseq )
cdr3a_new_nucseq, cdr3a_protseq_masked, cdr3a_protseq_new_nucleotide_countstring,a_trims,a_inserts \
= a_junction_results
cdr3b_new_nucseq, cdr3b_protseq_masked, cdr3b_protseq_new_nucleotide_countstring,b_trims,b_inserts \
= b_junction_results
# from tcr_sampler.py:
# trims = ( v_trim, d0_trim, d1_trim, j_trim )
# inserts = ( best_d_id, n_vd_insert, n_dj_insert, n_vj_insert )
assert a_trims[1] + a_trims[2] + a_inserts[0] + a_inserts[1] + a_inserts[2] + b_inserts[3] == 0
assert a_inserts[3] == len( cdr3a_new_nucseq )
ita = '+%d-%d'%(sum(a_inserts[1:]),sum(a_trims))
itb = '+%d-%d'%(sum(b_inserts[1:]),sum(b_trims))
vals[ 'cdr3a_protseq_masked'] = cdr3a_protseq_masked
vals[ 'a_indels'] = ita
vals[ 'cdr3a_new_nucseq' ] = cdr3a_new_nucseq
vals[ 'cdr3b_protseq_masked'] = cdr3b_protseq_masked
vals[ 'b_indels'] = itb
vals[ 'cdr3b_new_nucseq' ] = cdr3b_new_nucseq
## there's a little bit of a bias toward guys with more blast hits, ie shorted reads? since we take a max
if no_probabilities or not ( tcr_rearrangement.probs_data_exist(organism,'A') and
tcr_rearrangement.probs_data_exist(organism,'B') ):
##all probabilities will be set to 1 if this flag is set
va_rep_prob = 1
ja_rep_prob = 1
vb_rep_prob = 1
jb_rep_prob = 1
elif new_probs:
va_rep_prob = max( [ tcr_rearrangement.all_countrep_pseudoprobs[organism]['A']['V'][x] for x in va_countreps ] )
ja_rep_prob = max( [ tcr_rearrangement.all_countrep_pseudoprobs[organism]['A']['J'][x] for x in ja_countreps ] )
vb_rep_prob = max( [ tcr_rearrangement.all_countrep_pseudoprobs[organism]['B']['V'][x] for x in vb_countreps ] )
jb_rep_prob = max( [ tcr_rearrangement.all_countrep_pseudoprobs[organism]['B']['J'][x] for x in jb_countreps ] )
else:
va_rep_prob = max( [ tcr_rearrangement.all_rep_probs[organism][x] for x in va_reps ] )
ja_rep_prob = max( [ tcr_rearrangement.all_rep_probs[organism][x] for x in ja_reps ] )
vb_rep_prob = max( [ tcr_rearrangement.all_rep_probs[organism][x] for x in vb_reps ] )
jb_rep_prob = max( [ tcr_rearrangement.all_rep_probs[organism][x] for x in jb_reps ] )
vals['a_protseq_prob' ] = aprob_protseq * va_rep_prob * ja_rep_prob
vals['cdr3a_protseq_prob'] = aprob_protseq
vals['va_rep_prob' ] = va_rep_prob
vals['ja_rep_prob' ] = ja_rep_prob
vals['a_nucseq_prob' ] = aprob_nucseq * va_rep_prob * ja_rep_prob
vals['b_protseq_prob' ] = bprob_protseq * vb_rep_prob * jb_rep_prob
vals['cdr3b_protseq_prob'] = bprob_protseq
vals['vb_rep_prob' ] = vb_rep_prob
vals['jb_rep_prob' ] = jb_rep_prob
vals['b_nucseq_prob' ] = bprob_nucseq * vb_rep_prob * jb_rep_prob
assert len(vals.keys()) == len(outfields)
out.write( make_tsv_line( vals, outfields, '-' )+'\n' )
out.flush()
out.close()