def rank_chimeras(input_file, output_file, empirical_prob):
'''
rank the chimeras according to the empirical distribution
of encompassing read coverage, spanning read coverage,
and junction permiscuity
'''
# profile the chimeras
arr = []
for c in SpanningChimera.parse(open(input_file)):
arr.append(get_ranking_props(c))
arr = np.array(arr)
# choose bin sizes
maxbins = 500
bins = []
for d in xrange(arr.shape[1]):
bins.append(get_quantiles(arr[:,d], np.linspace(0, 1, maxbins)))
H, edges = np.histogramdd(arr, bins=bins)
#N = np.sum(H)
# now rank each chimera using the empirical distribution
chimera_scores = []
for c in SpanningChimera.parse(open(input_file)):
props = get_ranking_props(c)
p = hist_interp_prob(H, edges, props)
chimera_scores.append((1-p, c))
outfh = open(output_file, "w")
sorted_chimera_scores = sorted(chimera_scores, key=operator.itemgetter(0))
empirical_probs = np.array([x[0] for x in sorted_chimera_scores])
prob_cutoff = scoreatpercentile(empirical_probs, empirical_prob)
print >>outfh, '\t'.join(['#gene5p', 'start5p', 'end5p', 'gene3p',
'start3p', 'end3p', 'name', 'weighted_cov',
'strand5p', 'strand3p', 'type', 'distance',
'encompassing_reads', 'encompassing_reads_plus',
'encompassing_reads_minus', 'multimap_hist',
'isize5p', 'isize3p', 'exons5p', 'exons3p',
'junction_permiscuity5p',
'junction_permiscuity3p',
'encompassing_ids', 'encompassing_read1',
'encompassing_read2', 'junction_id',
'junction_pos', 'homology5p', 'homology3p',
'spanning_reads', 'encomp_and_spanning',
'total_reads', 'spanning_info',
'breakpoint_hist', 'empirical_prob'])
for p,c in sorted_chimera_scores:
if p > prob_cutoff:
break
arr = get_anchor_hist(c)
arrstring = ','.join([str(round(x,1)) for x in arr])
print >>outfh, '\t'.join(map(str, c.to_list() + [arrstring, p]))
outfh.close()
def get_quantiles(a, probs):
sorted_a = np.sort(a)
unique_a = np.unique(a)
maxbins = probs.shape[0]
if unique_a.shape[0] <= maxbins:
edges = list(unique_a)
else:
edges = []
for p in probs:
score = scoreatpercentile(sorted_a, p)
if len(edges) > 0 and (score == edges[-1]):
continue
edges.append(score)
if len(edges) == 1:
return 1
return edges
def get_quantiles(a, probs):
sorted_a = np.sort(a)
unique_a = np.unique(a)
maxbins = probs.shape[0]
if unique_a.shape[0] <= maxbins:
edges = list(unique_a)
else:
edges = []
for p in probs:
score = scoreatpercentile(sorted_a, p)
if len(edges) > 0 and (score == edges[-1]):
continue
edges.append(score)
if len(edges) == 1:
return 1
return edges
def rank_chimeras(input_file, output_file, empirical_prob):
'''
rank the chimeras according to the empirical distribution
of encompassing read coverage, spanning read coverage,
and junction permiscuity
'''
# profile the chimeras
arr = []
for c in SpanningChimera.parse(open(input_file)):
arr.append(get_ranking_props(c))
arr = np.array(arr)
# choose bin sizes
maxbins = 500
bins = []
for d in xrange(arr.shape[1]):
bins.append(get_quantiles(arr[:, d], np.linspace(0, 1, maxbins)))
H, edges = np.histogramdd(arr, bins=bins)
#N = np.sum(H)
# now rank each chimera using the empirical distribution
chimera_scores = []
for c in SpanningChimera.parse(open(input_file)):
props = get_ranking_props(c)
p = hist_interp_prob(H, edges, props)
chimera_scores.append((1 - p, c))
outfh = open(output_file, "w")
sorted_chimera_scores = sorted(chimera_scores, key=operator.itemgetter(0))
empirical_probs = np.array([x[0] for x in sorted_chimera_scores])
prob_cutoff = scoreatpercentile(empirical_probs, empirical_prob)
print >> outfh, '\t'.join([
'#gene5p', 'start5p', 'end5p', 'gene3p', 'start3p', 'end3p', 'name',
'weighted_cov', 'strand5p', 'strand3p', 'type', 'distance',
'encompassing_reads', 'encompassing_reads_plus',
'encompassing_reads_minus', 'multimap_hist', 'isize5p', 'isize3p',
'exons5p', 'exons3p', 'junction_permiscuity5p',
'junction_permiscuity3p', 'encompassing_ids', 'encompassing_read1',
'encompassing_read2', 'junction_id', 'junction_pos', 'homology5p',
'homology3p', 'spanning_reads', 'encomp_and_spanning', 'total_reads',
'spanning_info', 'breakpoint_hist', 'empirical_prob'
])
for p, c in sorted_chimera_scores:
if p > prob_cutoff:
break
arr = get_anchor_hist(c)
arrstring = ','.join([str(round(x, 1)) for x in arr])
print >> outfh, '\t'.join(map(str, c.to_list() + [arrstring, p]))
outfh.close()
