def filter_strand_balance(c, pval):
'''
returns True if binomial test pvalue for strand balance is greater than
'pval', False otherwise
'''
p = binomial_cdf(0.5, c.encompassing_reads, min(c.strand_reads))
if p < pval:
logging.warning("Filtered chimera reads=%d '+'=%d '-'=%d pval=%f" %
(c.encompassing_reads, c.strand_reads[0],
c.strand_reads[1], p))
return p >= pval
def filter_read_balance(c, pval):
'''
returns True if binomial test pvalue for strand balance is greater than
'pval', False otherwise
'''
# count reads on either strand
mate_counts = [0, 0]
for r in c.spanning_reads:
mate_counts[r.mate] += 1
p = binomial_cdf(0.5, c.num_spanning_reads, min(mate_counts))
if p <= pval:
logging.warning("Filtered chimera spanning reads=%d read1=%d read2=%d pval=%f" %
(c.num_spanning_reads, mate_counts[0], mate_counts[1], p))
return p > pval
def filter_read_balance(c, pval):
'''
returns True if binomial test pvalue for strand balance is greater than
'pval', False otherwise
'''
# count reads on either strand
mate_counts = [0, 0]
for r in c.spanning_reads:
mate_counts[r.mate] += 1
p = binomial_cdf(0.5, c.num_spanning_reads, min(mate_counts))
if p <= pval:
logging.warning(
"Filtered chimera spanning reads=%d read1=%d read2=%d pval=%f" %
(c.num_spanning_reads, mate_counts[0], mate_counts[1], p))
return p > pval
