def gen_pseudo_counts():
for pseudo_count in [
0.00,
0.01,
0.10,
0.25,
0.50,
0.75,
1.00,
]:
biopsy.PssmParameters.singleton().pseudo_counts = pseudo_count
biopsy.clear_pssm_cache()
yield "Pseudo-count: %.2f" % pseudo_count
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 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
