fragments = ['F' + str(i) for i in xrange(1, 7)]
if VERBOSE >= 3:
print 'fragments', fragments
# Iterate over samples and fragments
for adaID in adaIDs:
for fragment in fragments:
consensus = SeqIO.read(
get_consensus_filename(data_folder, adaID, fragment), 'fasta')
cmat = np.array(consensus)
counts = np.load(
get_allele_counts_filename(data_folder, adaID, fragment))
coverage = np.load(
get_coverage_filename(data_folder, adaID, fragment))
nu = filter_nus(counts, coverage, VERBOSE=VERBOSE)
# Note: not-covered positions are filtered, but argmax cannot work
# with masked arrays
cmat_af = alpha[nu.argmax(axis=0)]
if hasattr(nu, 'mask'):
cmat_af[nu.mask.all(axis=0)] = 'N'
# Check for consistency first
if len(cmat) != len(cmat_af):
print 'Consensus has a different length from allele frequency \
matrix... WTF?'
# Do not actually align, it makes a huge mess (we miss mistakes)
ali = [cmat, cmat_af]
if (ali[0] != ali[1]).any():
# If the script is called with no fragment, iterate over all
if not fragments:
fragments = ['F'+str(i) for i in xrange(1, 7)]
if VERBOSE >= 3:
print 'fragments', fragments
# Iterate over samples and fragments
for adaID in adaIDs:
for fragment in fragments:
consensus = SeqIO.read(get_consensus_filename(data_folder, adaID, fragment),
'fasta')
cmat = np.array(consensus)
counts = np.load(get_allele_counts_filename(data_folder, adaID, fragment))
coverage = np.load(get_coverage_filename(data_folder, adaID, fragment))
nu = filter_nus(counts, coverage, VERBOSE=VERBOSE)
# Note: not-covered positions are filtered, but argmax cannot work
# with masked arrays
cmat_af = alpha[nu.argmax(axis=0)]
if hasattr(nu, 'mask'):
cmat_af[nu.mask.all(axis=0)] = 'N'
# Check for consistency first
if len(cmat) != len(cmat_af):
print 'Consensus has a different length from allele frequency \
matrix... WTF?'
# Do not actually align, it makes a huge mess (we miss mistakes)
ali = [cmat, cmat_af]
if (ali[0] != ali[1]).any():
def check_overlap_allele_frequencies(data_folder, adaID, frag1, frag2, overlap,
VERBOSE=0, ax=None):
'''Check biases in allele frequencies in the overlap'''
(start_s2, end_s1, ali) = overlap
# Get allele counts and coverage
cou1 = np.load(get_allele_counts_filename(data_folder, adaID, frag1))
cov1 = np.load(get_coverage_filename(data_folder, adaID, frag1))
cou2 = np.load(get_allele_counts_filename(data_folder, adaID, frag2))
cov2 = np.load(get_coverage_filename(data_folder, adaID, frag2))
# Cut the counts and coverage to the overlap region
cou1 = cou1[:, :, start_s2:]
cov1 = cov1[:, start_s2:]
cou2 = cou2[:, :, :end_s1]
cov2 = cov2[:, :end_s1]
# Reduce the allele counts (fwd and rev have different status on most overlaps,
# because of the uneven coverage)
nu1 = filter_nus(cou1, cov1)
nu2 = filter_nus(cou2, cov2)
# FIXME
if nu1.shape != nu2.shape:
return
# Print table of called polymorphisms
print 'adaID', adaID, frag1, frag2, 'polymorphism matrix (NO | YES)'
print 3 * ' ', '|', '{:^10s}'.format(frag1)
print 15 * '-'
print 3 * ' ', '|', \
'{:3d}'.format(((nu1 < 1e-6) & (nu2 < 1e-6)).sum()), '|', \
'{:3d}'.format(((nu1 > 3e-3) & (nu2 < 1e-6)).sum())
print '{:3s}'.format(frag2), '+'+(5*'-')+'+'+(4*'-')
print 3 * ' ', '|', \
'{:3d}'.format(((nu1 < 1e-6) & (nu2 > 3e-3)).sum()), '|', \
'{:3d}'.format(((nu1 > 3e-6) & (nu2 > 3e-3)).sum())
# Plot scatter
import matplotlib.pyplot as plt
from matplotlib import cm
if ax is None:
show_plot = True
fig, ax = plt.subplots(1, 1, figsize=(6, 6))
ax.set_title('allele frequencies, adaID '+str(adaID)+', '+\
str(frag1)+' - '+str(frag2),
fontsize=18)
else:
show_plot = False
ax.set_title(str(frag1)+' - '+str(frag2), fontsize=18)
ax.scatter(np.abs(nu1 - 1e-5), np.abs(nu2 - 1e-5), s=30,
c=cm.jet([int(255.0 * i / len(nu1)) for i in xrange(len(nu1))]))
# Plot diagonal
ax.plot([1e-7, 2], [1e-7, 2], lw=1, c='k', ls='--')
ax.set_xlabel(r'$\nu_1$', fontsize=20)
ax.set_ylabel(r'$\nu_2$', fontsize=20)
ax.set_xscale('log')
ax.set_yscale('log')
ax.set_xlim(0.7e-5, 1.2)
ax.set_ylim(0.7e-5, 1.2)
if show_plot:
plt.tight_layout(w_pad=0.05)
plt.ion()
plt.show()
def check_overlap_allele_frequencies(data_folder, adaID, frag1, frag2, overlap, VERBOSE=0, ax=None):
"""Check biases in allele frequencies in the overlap"""
(start_s2, end_s1, ali) = overlap
# Get allele counts and coverage
cou1 = np.load(get_allele_counts_filename(data_folder, adaID, frag1))
cov1 = np.load(get_coverage_filename(data_folder, adaID, frag1))
cou2 = np.load(get_allele_counts_filename(data_folder, adaID, frag2))
cov2 = np.load(get_coverage_filename(data_folder, adaID, frag2))
# Cut the counts and coverage to the overlap region
cou1 = cou1[:, :, start_s2:]
cov1 = cov1[:, start_s2:]
cou2 = cou2[:, :, :end_s1]
cov2 = cov2[:, :end_s1]
# Reduce the allele counts (fwd and rev have different status on most overlaps,
# because of the uneven coverage)
nu1 = filter_nus(cou1, cov1)
nu2 = filter_nus(cou2, cov2)
# FIXME
if nu1.shape != nu2.shape:
return
# Print table of called polymorphisms
print "adaID", adaID, frag1, frag2, "polymorphism matrix (NO | YES)"
print 3 * " ", "|", "{:^10s}".format(frag1)
print 15 * "-"
print 3 * " ", "|", "{:3d}".format(((nu1 < 1e-6) & (nu2 < 1e-6)).sum()), "|", "{:3d}".format(
((nu1 > 3e-3) & (nu2 < 1e-6)).sum()
)
print "{:3s}".format(frag2), "+" + (5 * "-") + "+" + (4 * "-")
print 3 * " ", "|", "{:3d}".format(((nu1 < 1e-6) & (nu2 > 3e-3)).sum()), "|", "{:3d}".format(
((nu1 > 3e-6) & (nu2 > 3e-3)).sum()
)
# Plot scatter
import matplotlib.pyplot as plt
from matplotlib import cm
if ax is None:
show_plot = True
fig, ax = plt.subplots(1, 1, figsize=(6, 6))
ax.set_title("allele frequencies, adaID " + str(adaID) + ", " + str(frag1) + " - " + str(frag2), fontsize=18)
else:
show_plot = False
ax.set_title(str(frag1) + " - " + str(frag2), fontsize=18)
ax.scatter(
np.abs(nu1 - 1e-5), np.abs(nu2 - 1e-5), s=30, c=cm.jet([int(255.0 * i / len(nu1)) for i in xrange(len(nu1))])
)
# Plot diagonal
ax.plot([1e-7, 2], [1e-7, 2], lw=1, c="k", ls="--")
ax.set_xlabel(r"$\nu_1$", fontsize=20)
ax.set_ylabel(r"$\nu_2$", fontsize=20)
ax.set_xscale("log")
ax.set_yscale("log")
ax.set_xlim(0.7e-5, 1.2)
ax.set_ylim(0.7e-5, 1.2)
if show_plot:
plt.tight_layout(w_pad=0.05)
plt.ion()
plt.show()
