def read_pvalues(bedfilename, log_pvalues, verbose):
''' read in p-values from a bed file score field.
returns: list sorted by signifance (most significant first)'''
pvals = []
if verbose:
print >>sys.stderr, ">> reading p-values from %s .." % bedfilename
with maybe_gzip_open(bedfilename) as bedfile:
for datum in read_bed(bedfile):
if log_pvalues:
pval = datum.score
else:
pval = -1 * log10(pvalue)
pvals.append(pval)
if verbose:
print >>sys.stderr, ">> read %d p-values" % len(pvals)
# sort the pvalues from most to least signif (smallest to largest) and
# reverse so largest are first
pvals.sort()
# if pvals are log transformed, biggest (i.e. most significant) are
# first
if log_pvalues: pvals.reverse()
return pvals
def read_qvalues(qvalue_filename):
''' read in q-values from file.
returns: sorted list of q-values'''
qvalues = []
with maybe_gzip_open(qvalue_filename) as qvalue_file:
for datum in DictReader(qvalue_file, QVAL_FIELDNAMES):
qvalues.append(float(datum['qvalue']))
qvalues.sort()
return qvalues
def get_region_counts(bedfilenames, verbose):
# counts the number of times a base is covered by a peak call
region_counts = defaultdict(Counter)
for bedfilename in bedfilenames:
if verbose:
print >>sys.stderr, ">> loading regions from %s" % \
bedfilename
with maybe_gzip_open(bedfilename) as bedfile:
for datum in read_bed(bedfile):
for pos in range(datum.chromStart, datum.chromEnd):
region_counts[datum.chrom][pos] += 1
return region_counts
def calc_qvalues(real_bedfilename, null_bedfilename, log_pvalues, verbose):
# read in real p-values.
real_pvals = read_pvalues(real_bedfilename, log_pvalues, verbose)
# read in null p-values
null_pvals = read_pvalues(null_bedfilename, log_pvalues, verbose)
num_real = float(len(real_pvals))
num_null = float(len(null_pvals))
# make sure both are defined
assert num_real and num_null
# normalization factor to account for different numbers of real and
# null p-values
frac_real = num_real / num_null
if verbose:
print >>sys.stderr, ">> normalization factor: %.5f" % frac_real
# compute pvalue thresholds
pval_thresh = compute_pval_thresh(real_pvals, null_pvals, verbose)
# go back over real pvalues and assign qvalues
with maybe_gzip_open(real_bedfilename) as bedfile:
for datum in read_bed(bedfile):
if log_pvalues:
pval = float(datum.score)
else:
pval = -1 * log10(pvalue)
qval = pval_thresh[pval]
norm_qval = qval * frac_real
# print in table format
fields = (datum.chrom, datum.chromStart, datum.chromEnd,
datum.name, pval, datum.strand, norm_qval)
print '\t'.join(map(str, fields))
def interpolate_peaks(gdfilename, bedfilename, trackname, spec_chrom,
verbose):
warnings.simplefilter('ignore')
with Genome(gdfilename) as genome, \
maybe_gzip_open(bedfilename) as bedfile:
for datum in read_native(bedfile):
chrom = datum.chrom
peak_start = datum.chromStart
peak_end = datum.chromEnd
# can parallelize by chrom
if spec_chrom and spec_chrom != chrom: continue
xs = range(peak_start, peak_end)
ys = genome[chrom][peak_start:peak_end, trackname]
interp_start, interp_end = fit_spline(xs, ys)
fields = (chrom, interp_start, interp_end,
datum.name, datum.score, datum.strand)
print '\t'.join(map(str, fields))