def hits_that_share_interactors(self, interactors):
"Return those hits that are associated with the given interactors."
filtered_hits = biopsy.HitVec()
for hit in self.analysis:
if hit.binder in self.hits_to_interactors:
if len(self.hits_to_interactors[hit.binder].intersection(
interactors)):
filtered_hits.append(hit)
return filtered_hits
def consolidate_hits(hits):
"""
Takes a sequence of BiFa hits and consolidates all of those for the same binder that overlap.
@arg hits: A sequence of BiFa hits
@return: A consolidated sequence of BiFa hits
"""
from itertools import chain
result = biopsy.HitVec()
for hits in hits_per_binder(hits).values():
result.extend(consolidate_overlapping_hits(hits))
return result
def map_hits(hits, binder_map):
"""
Takes a sequence of BiFa binding hits and returns a sequence of BiFa hits over the
binders that are mapped to binder_map.
@arg hits: BiFa hits
@arg binder_map: A dict like object that maps binder names. E.g. mapping BiFa pssm names to transcription factors
@return: A sequence of BiFa hits
"""
result = biopsy.HitVec()
for hit in hits:
for mapped_binder in binder_map[hit.binder]:
result.append(biopsy.Hit(mapped_binder, hit.location, hit.p_binding))
return result
def test_pssm_score():
# 'V$AP1_Q2'
pssm_acc = biopsy.get_transfac_pssm_accession('V$DEAF1_01')
pssm_info = biopsy.get_pssm(pssm_acc)
# print pssm_info.pssm
seq = 'tacatcatctgtctgcagtagtctaaccgaccccccccagttttagaagcagactgcatgcggacgggaccgcggatcgcgcggtgcgcctcagtgtacttccgaacgaatgagtcattaatagagcgctatatcgtaactgtctttgacgaagtataccgaaaccgtgcagccagacgtgatccgggcgttgtaaaggcgatcagcgccctaggagtaccatttttgccgtaggcttgcgtctcaaagaccagctggggcgtggtatcactcgtcagtacgatttctgccagatagatagcatagactgaaccttaggcccaatagggacacaattacccgagtgactgactggtctaaggggagtccccccttaaaacgttttacgtaatagcgggctccagaagcaaagcatcggtttgagccccagtactaaacgtttgagtgtttgctctcgtctgataggtaaaccgacaagagaaccaagctcaaggcgcggtaggtgcgccttgcgaactgttgatgccgtgagcgccaccatcccgtgcatcataggcagggagagaagaccacatggccttgcgaccgtatgagctgtttcagattaaatgccaacgggcatggtcggtgtccagcattttttgcagtcagctggtggtacacagtggggacaagaacgcctctggtagatgtcttctgaaggagtaactcatttcgttgaatcgaccttcccttgcgcttgaacgcggacctctagtctctctcgcagactggggtcgaaaatcaaggtagatatggaatgttccgcatgagggtagcgaccggatcgggcgtcaagtatatcctccctgctacgtccccctactagcctcagtccgcctcgaacctaggaagattggccacatcagcttggtggatgcctggtccatacttcagacccgagaatgttagacaggaccccatttggctcctttacgtacgatctatgtagacgcagtga'
for i in range(len(seq) - len(pssm_info.pssm) + 1):
s = biopsy.score_pssm(pssm_info.pssm, seq[i:])
p_binding = biopsy.get_p_binding(
biopsy.get_odds_ratio(s, pssm_info.get_dist(True, False),
pssm_info.get_dist(False, False)))
if p_binding > 0.05:
print i, s, p_binding
result = biopsy.HitVec()
p_binding = biopsy.score_pssm_on_sequence(pssm_acc, seq, 0.05, result)
print 'Got', len(result), 'hits from', len(seq), 'bases'
print p_binding
def write_svg(self,
filename,
hits,
max_threshold=0.0,
notes="",
max_chain=biopsy.HitVec()):
build_svg_args = biopsy.BuildSvgArgs(min_threshold=self.threshold,
max_threshold=max_threshold,
file=filename,
title=self.name,
notes=notes,
open_file=False)
if len(hits):
biopsy.build_analysis_svg(hits,
max_chain,
self.converted_seqs[0],
args=build_svg_args)
def test_pssm_pseudo_counts():
sascha_pssms = biopsy.SequenceVec()
sascha_acc = 'M00975'
# sascha_seq = 'gtaaaccaggctgcctGAgaacttgttgcgaatcc'
sascha_seq = 'ttgttgcga'
sascha_seq = 'ttgttgcaa'
# plot_likelihoods( biopsy.get_pssm( 'M00975' ), 'M00975' )
# plot_likelihoods( biopsy.get_pssm( 'R02146' ), 'R02146' )
print 'Binding,Background,odds,p(binding),cumulative p(binding),Sequence'
biopsy.PssmParameters.singleton().use_p_value = True
# biopsy.PssmParameters.singleton().binding_background_odds_prior = 1;
for pc in [0.0, 0.25, 0.5, 1.0, 2.0]:
# force cache load
biopsy.get_pssm(sascha_acc)
biopsy.clear_pssm_cache()
biopsy.PssmParameters.singleton().pseudo_counts = pc
p = biopsy.get_pssm(sascha_acc)
score = biopsy.score_pssm(p.pssm, sascha_seq)
(bind, back, cum_bind, cum_back, odds_ratio, cum_odds_ratio, p_bind,
cum_p_bind,
p_value_p_bind) = biopsy.get_pssm_likelihoods_for_score(p, score)
print pc,
print \
'%f,%f,%f,%f,%f,%f,%f' \
% \
( bind, back, cum_bind, cum_back, p_bind, cum_p_bind, p_value_p_bind )
biopsy.plot_likelihoods(p, sascha_acc + ': ' + str(pc), score)
# print 'Trying with standard distributions'
# biopsy.PssmParameters.singleton().use_cumulative_dists = False;
# hits = biopsy.HitVec()
# biopsy.score_pssm_on_sequence( sascha_acc, sascha_seq, 0.001, hits )
# print hits
print 'Trying with cumulative distributions'
biopsy.PssmParameters.singleton().use_cumulative_dists = True
hits = biopsy.HitVec()
biopsy.score_pssm_on_sequence(sascha_acc, sascha_seq, 0.001, hits)
print hits
print
def __missing__(self, k):
self[k] = biopsy.HitVec()
return self[k]
for species in remo.get_sequence_ids():
yield remo.get_sequence_for(species, True)
def histogram(acc, score_counts):
import pylab, numpy
pylab.clf()
pylab.bar(xrange(num_buckets), numpy.power(score_counts, 0.25))
pylab.savefig('graphs/%s.png' % acc)
try:
remome
except NameError:
remome_file = 'c:/data/remos/100/100.filtered'
print 'Loading remome: %s' % remome_file
remome = biopsy.Remome.load(remome_file)
score_counts = {}
for acc in itertools.islice(pssm_accs(), num_pssms):
score_counts[acc] = numpy.zeros(num_buckets, numpy.uint8)
for seq in itertools.islice(sequences_from_remome(remome), num_sequences):
hits = biopsy.HitVec()
p_bind = biopsy.score_pssm_on_sequence(pssm_name=acc,
threshold=0.0,
sequence=seq,
result=hits)
for h in hits:
score_counts[acc][int(h.p_binding * num_buckets)] += 1
histogram(acc, score_counts[acc])
def map_binders(hits, map):
result = biopsy.HitVec()
result.extend(imap(hit_mapper(map), hits))
return result