def write_frequency_files(data_folder,
adaID,
fragment,
nu_filtered,
VERBOSE=0):
'''Write the corrected allele frequencies to file'''
if VERBOSE:
print 'Storing allele frequencies to file:', adaID, fragment
nu_filtered.dump(
get_allele_frequencies_filename(data_folder, adaID, fragment))
def merge_allele_frequencies(data_folder, adaID, fragments, VERBOSE=0):
'''Merge allele frequencies at overlapping pairs'''
import warnings
import numpy as np
consensi = {frag: SeqIO.read(get_consensus_filename(data_folder, adaID, frag,
trim_primers=True), 'fasta')
for frag in fragments}
nus = {frag: np.load(get_allele_frequencies_filename(data_folder, adaID, frag))
for frag in fragments}
pairs = get_overlapping_fragments(fragments)
overlaps = {}
for (frag1, frag2) in pairs:
overlap = get_overlap(data_folder, adaID, frag1, frag2, VERBOSE=VERBOSE)
is_diff = check_overlap_consensus(data_folder, adaID, frag1, frag2,
overlap, VERBOSE=VERBOSE)
if is_diff:
warnings.warn(frag1+' and '+frag2+' have different consensi.', RuntimeWarning)
overlaps[(frag1, frag2)] = overlap
nu = []
fragments = sorted(fragments)
for i, frag in enumerate(fragments):
# If the start is not an overlap, start a new chunk and copy all
if (i == 0) or (fragments[i-1], frag) not in overlaps:
nuf = [[frag], nus[frag]]
nu.append(nuf)
# else, copy from the end of the overlap on
# FIXME: we could average the consensus zone out of indels...
else:
nuf = nu[-1]
nuf[0].append(frag)
tmp = overlaps[(fragments[i-1], frag)]
if tmp is not None:
(_, start, _) = tmp
#(recursion is not the most efficient but -- oh, well)
nuf[1] = np.concatenate([nuf[1], nus[frag][:, start:]], axis=1)
else:
tmp = np.zeros((nuf[1].shape[0], 10), float)
tmp[-1] = 1
nuf[1] = np.concatenate([nuf[1], tmp, nus[frag][:, start:]], axis=1)
return nu
def plot_minor_allele_frequency(data_folder, adaID, fragments, VERBOSE=0,
savefig=False):
'''Plot minor allele frequency along the genome'''
from hivwholeseq.sequencing.filenames import get_minor_allele_frequency_figure_filename as gff
import matplotlib
params = {'axes.labelsize': 20,
'text.fontsize': 20,
'legend.fontsize': 8,
'xtick.labelsize': 16,
'ytick.labelsize': 16,
'text.usetex': False}
matplotlib.rcParams.update(params)
from matplotlib import cm
import matplotlib.pyplot as plt
plot_grid = [(1, 1), (1, 2), (1, 3), (2, 2), (1, 5), (2, 3)]
# Store in globals structures
covs = {}
nus_minor = {}
alls_minor = {}
nus_filtered = {}
nus_minor_filtered = {}
for fragment in fragments:
coverage = np.load(get_coverage_filename(data_folder, adaID, fragment))
covs[fragment] = coverage
counts = np.load(get_allele_counts_filename(data_folder, adaID, fragment))
(counts_major,
counts_minor,
counts_minor2) = get_minor_allele_counts(counts, n_minor=2)
# Get minor allele frequencies and identities
nu_minor = 1.0 * counts_minor[:, :, 1] / (coverage + 1e-6)
nus_minor[fragment] = nu_minor
all_minor = counts_minor[:, :, 0]
alls_minor[fragment] = all_minor
# Filter the minor frequencies by comparing the read types
try:
nu_filtered = np.load(get_allele_frequencies_filename(data_folder, adaID, fragment))
except IOError:
nu_filtered = filter_nus(counts, coverage)
nut = np.zeros(nu_filtered.shape[-1])
for pos, nupos in enumerate(nu_filtered.T):
nut[pos] = np.sort(nupos)[-2]
nus_filtered[fragment] = nu_filtered
nus_minor_filtered[fragment] = nut
# Plot them
(n_plots_y, n_plots_x) = plot_grid[len(fragments) - 1]
fig, axs = plt.subplots(n_plots_y, n_plots_x, figsize=(13, 8))
if len(fragments) > 1:
axs = axs.ravel()
else:
axs = [axs]
fig.suptitle('adapterID '+adaID, fontsize=20)
labss = {'read1 f': 'read1 fwd', 'read1 r': 'read1 rev',
'read2 f': 'read2 fwd', 'read2 r': 'read2 rev'}
for i, fragment in enumerate(fragments):
ax = axs[i]
ax.set_yscale('log')
ax.set_title(fragment)
if i in [0, 3]:
ax.set_ylabel(r'$\nu$')
if i > 2:
ax.set_xlabel('Position')
# Plot divided by readtype
for js, nu_minorjs in enumerate(nus_minor[fragment]):
color = cm.jet(int(255.0 * js / len(read_types)))
ax.plot(nu_minorjs, lw=1.5, c=color, label=labss[read_types[js]])
ax.scatter(np.arange(len(nu_minorjs)), nu_minorjs, lw=1.5,
color=color)
# Plot filtered
ax.plot(nus_minor_filtered[fragment], lw=1.5, c='k',
alpha=0.5, label='Filtered')
ax.scatter(np.arange(len(nus_minor_filtered[fragment])),
nus_minor_filtered[fragment], lw=1.5, c='k',
alpha=0.5)
# Plot 1/max(coverage)
coverage = covs[fragment]
cov_tot = coverage.sum(axis=0)
ax.plot(1.0 / cov_tot, lw=1.2, c='r', label='Detection limit')
ax.set_xlim(-100, len(nu_minorjs) + 100)
plt.grid()
plt.legend(loc='upper right')
plt.tight_layout(rect=(0, 0, 1, 0.95))
if savefig:
outputfile = gff(data_folder, adaID, fragment)
fig.savefig(outputfile)
plt.close(fig)
else:
plt.ion()
plt.show()
def write_frequency_files(data_folder, adaID, fragment, nu_filtered, VERBOSE=0):
'''Write the corrected allele frequencies to file'''
if VERBOSE:
print 'Storing allele frequencies to file:', adaID, fragment
nu_filtered.dump(get_allele_frequencies_filename(data_folder, adaID, fragment))
def plot_minor_allele_frequency_filtered(data_folder, adaID, fragments, VERBOSE=0,
savefig=False):
'''Plot minor allele frequency along the genome'''
from hivwholeseq.sequencing.filenames import get_minor_allele_frequency_figure_filename as gff
import matplotlib
params = {'axes.labelsize': 20,
'text.fontsize': 20,
'legend.fontsize': 8,
'xtick.labelsize': 16,
'ytick.labelsize': 16,
'text.usetex': False}
matplotlib.rcParams.update(params)
from matplotlib import cm
import matplotlib.pyplot as plt
# Store in globals structures
covs = {}
nus_minor_filtered = {}
for fragment in fragments:
coverage = np.load(get_coverage_filename(data_folder, adaID, fragment))
covs[fragment] = coverage
try:
nu_filtered = np.load(get_allele_frequencies_filename(data_folder,
adaID, fragment))
except IOError:
counts = np.load(get_allele_counts_filename(data_folder, adaID, fragment))
nu_filtered = filter_nus(counts)
nut = np.zeros(nu_filtered.shape[-1])
for pos, nupos in enumerate(nu_filtered.T):
nut[pos] = np.sort(nupos)[-2]
nus_minor_filtered[fragment] = nut
# Plot them
plot_grid = [(1, 1), (1, 2), (1, 3), (2, 2), (1, 5), (2, 3)]
(n_plots_y, n_plots_x) = plot_grid[len(fragments) - 1]
fig, axs = plt.subplots(n_plots_y, n_plots_x, figsize=(13, 8))
if len(fragments) > 1:
axs = axs.ravel()
else:
axs = [axs]
fig.suptitle('adapterID '+adaID, fontsize=20)
for i, fragment in enumerate(fragments):
ax = axs[i]
ax.set_yscale('log')
ax.set_title(fragment)
if i in [0, 3]:
ax.set_ylabel(r'$\nu$')
if i > 2:
ax.set_xlabel('Position')
# Plot filtered
ax.plot(nus_minor_filtered[fragment], lw=1.5, c='k',
alpha=0.5, label='Filtered')
ax.scatter(np.arange(len(nus_minor_filtered[fragment])),
nus_minor_filtered[fragment], lw=1.5, c='k',
alpha=0.5)
ax.set_xlim(-100, len(nus_minor_filtered[fragment]) + 100)
#plt.legend(loc='upper right')
plt.tight_layout(rect=(0, 0, 1, 0.95))
if savefig:
outputfile = gff(data_folder, adaID, fragment, only_filtered=True)
fig.savefig(outputfile)
plt.close(fig)
else:
plt.ion()
plt.show()
