def plot_drug_resistance_mutation_trajectories(pcode):
'''
auxillary function to check for potential drug resistance evolution in RNA sequences
only p10 has drug resistance mutations in the last two samples
'''
plt.figure()
p = Patient.load(pcode)
RT = p.get_allele_frequency_trajectories('RT', type='aa')
for mt in ['NNRTI', 'NRTI']:
for aa1, pos, aa2 in drug_muts[mt]['mutations']:
traj = 1 - RT[:, alphaal.index(aa1), pos - 1]
if max(traj) > 0.1:
plt.plot(p.dsi, traj, '-o', label=mt + ' ' + str(pos))
PR = p.get_allele_frequency_trajectories('PR', type='aa')
for mt in ['PI']:
for aa1, pos, aa2 in drug_muts[mt]['mutations']:
traj = 1 - PR[:, alphaal.index(aa1), pos - 1]
if max(traj) > 0.1:
plt.plot(p.dsi, traj, '-o', label=mt + ' ' + str(pos))
plt.legend(loc=2, ncol=2)
def plot_drug_resistance_mutation_trajectories(pcode):
"""
auxillary function to check for potential drug resistance evolution in RNA sequences
only p10 has drug resistance mutations in the last two samples
"""
plt.figure()
p = Patient.load(pcode)
RT = p.get_allele_frequency_trajectories("RT", type="aa")
for mt in ["NNRTI", "NRTI"]:
for aa1, pos, aa2 in drug_muts[mt]["mutations"]:
traj = 1 - RT[:, alphaal.index(aa1), pos - 1]
if max(traj) > 0.1:
plt.plot(p.dsi, traj, "-o", label=mt + " " + str(pos))
PR = p.get_allele_frequency_trajectories("PR", type="aa")
for mt in ["PI"]:
for aa1, pos, aa2 in drug_muts[mt]["mutations"]:
traj = 1 - PR[:, alphaal.index(aa1), pos - 1]
if max(traj) > 0.1:
plt.plot(p.dsi, traj, "-o", label=mt + " " + str(pos))
plt.legend(loc=2, ncol=2)
def fitness_cost_mutation(region, data, aa_mutation_rates, pos, target_aa, nbootstraps=0):
'''
determine the fitness cost associated with a particular amino acid mutations such as K103N
this requires specification of the target amino acid and a specific calculation of
the mutation rate into the amino acid, which requires the ancestral codon present in
each individual patient
'''
def s(pats):
# calcute nu/mu for each patient with patient specific mutation rates excluding double hit mutations
nu_over_mu = [minor_af_by_pat[pat]/aa_mutation_rates[(codons[pat][pos],target_aa)] for pat in pats
if aa_mutation_rates[(codons[pat][pos],target_aa)]>0]
# return the inverse, i.e. essentially the harmonic mean
if len(nu_over_mu):
savg = 1.0/max(0.01, np.mean(nu_over_mu))
else:
savg=np.nan
return savg
target_ii = alphaal.index(target_aa)
codons = data['init_codon'][region]
minor_af_by_pat = {pat: x[target_ii,pos].sum(axis=0)/x[:20,pos].sum(axis=0)
for pat, x in data['af_by_pat'][region].iteritems() if pos in codons[pat]}
all_patients = minor_af_by_pat.keys()
if nbootstraps:
s_bs = []
for bi in xrange(nbootstraps):
tmp_s = s([all_patients[pi] for pi in np.random.randint(len(all_patients), size=len(all_patients))])
if not np.isnan(tmp_s):
s_bs.append(tmp_s)
if len(s_bs):
s_out = [np.percentile(s_bs, perc) for perc in [5, 25, 50, 75,95]]
else:
s_out = [np.nan for perc in [5, 25, 50, 75,95]]
else:
s_out = s(all_patients)
return s_out
def plot_drug_resistance_mutations(data, aa_mutation_rates, fname=None):
"""Plot the frequency of drug resistance mutations"""
import matplotlib.patches as patches
fs = 16
region = "pol"
pcodes = data["init_codon"][region].keys()
fig, axs = plt.subplots(2, 1, gridspec_kw={"height_ratios": [1, 6]})
ax = axs[1]
drug_afs_items = []
mut_types = []
drug_classes = ["PI", "NRTI", "NNRTI", "INI"]
for prot in drug_classes:
drug_afs = {}
drug_mut_rates = {}
offset = drug_muts[prot]["offset"]
for cons_aa, pos, target_aa in drug_muts[prot]["mutations"]:
codons = {pat: data["init_codon"][region][pat][pos + offset] for pat in pcodes}
mut_rates = {pat: np.sum([aa_mutation_rates[(codons[pat], aa)] for aa in target_aa]) for pat in pcodes}
freqs = {
pat: np.sum(
[
data["af_by_pat"][region][pat][alphaal.index(aa), pos + offset]
/ data["af_by_pat"][region][pat][:20, pos + offset].sum()
for aa in target_aa
]
)
for pat in pcodes
}
drug_afs[(cons_aa, pos, target_aa)] = freqs
drug_mut_rates[(cons_aa, pos, target_aa)] = mut_rates
drug_afs_items.extend(
filter(
lambda x: np.sum(filter(lambda y: ~np.isnan(y), x[1].values())) > 0,
sorted(drug_afs.items(), key=lambda x: x[0][1]),
)
)
mut_types.append(len(drug_afs_items))
# make list of all mutations whose non-nan frequencies sum to 0
mono_muts = [
"".join(map(str, x[0]))
for x in filter(
lambda x: np.sum(filter(lambda y: ~np.isnan(y), x[1].values())) == 0,
sorted(drug_afs.items(), key=lambda x: x[0][1]),
)
]
print("Monomorphic:", prot, mono_muts)
plt.ylim([1.1e-5, 1e-1])
for mi in mut_types[:-1]:
plt.plot([mi - 0.5, mi - 0.5], plt.ylim(), c=(0.3, 0.3, 0.3), lw=3, alpha=0.5)
ax.axhline(4e-5, c=(0.3, 0.3, 0.3), lw=3, alpha=0.5)
for ni, prot in enumerate(drug_classes):
plt.text(0.5 * (mut_types[ni] + (mut_types[ni - 1] if ni else 0)) - 0.5, 0.12, prot, fontsize=16, ha="center")
for mi in range(max(mut_types)):
c = 0.5 + 0.2 * (mi % 2)
ax.add_patch(
patches.Rectangle(
(mi - 0.5, plt.ylim()[0]), 1.0, plt.ylim()[1], color=(c, c, c), alpha=0.2 # (x,y), width, height
)
)
# plt.xticks(np.arange(len(all_muts)), ["".join(map(str, x)) for x in all_muts], rotation=60)
afdr = pd.DataFrame(
np.array([x[1].values() for x in drug_afs_items]).T, columns=["".join(map(str, x[0])) for x in drug_afs_items]
)
afdr[afdr < 0.8e-4] = 0
sns.stripplot(data=afdr, jitter=0.4, alpha=0.8, size=12, lw=1, edgecolor="white")
# Add the number of missing points at the bottom of the plot, and the cost
# at the top
dd = afdr.iloc[[0, 1, 2, 3, 4]].copy()
dd.index = ["x", "freq", "size", "cost", "mr"]
dd.loc["x"] = np.arange(dd.shape[1])
dd.loc["freq"] = 2e-5
dd.loc["n"] = afdr.shape[0] - (afdr > 1e-4).sum(axis=0)
dd.loc["size"] = dd.loc["n"] ** (1.4) + 13
dd.loc["cost"] = 1.0 / afdr.fillna(0).mean(axis=0)
dd.loc["mr"] = 0
# NOTE: the first 6 mutations are in PR, the rest in RT
import re
from Bio.Seq import translate
reference = HIVreference(refname="HXB2", load_alignment=False)
seq_PR = reference.annotation["PR"].extract(reference.seq)
seq_RT = reference.annotation["RT"].extract(reference.seq)
seq_IN = reference.annotation["IN"].extract(reference.seq)
murate = load_mutation_rates()["mu"]
for i, mut in enumerate(dd.T.index):
mr = 0
if i < 6:
seq_tmp = seq_PR
elif i < 6 + 5 + 4:
seq_tmp = seq_RT
else:
seq_tmp = seq_IN
aa_from, pos, aas_to = re.sub("([A-Z])(\d+)([A-Z]+)", r"\1_\2_\3", mut).split("_")
cod = str(seq_tmp.seq[(int(pos) - 1) * 3 : int(pos) * 3])
for pos_cod in xrange(3):
for nuc in ["A", "C", "G", "T"]:
codmut = list(cod)
codmut[pos_cod] = nuc
codmut = "".join(codmut)
if (codmut != cod) and (translate(cod) == aa_from) and (translate(codmut) in aas_to):
mr += murate[cod[pos_cod] + "->" + nuc]
dd.loc["cost", mut] *= mr
dd.loc["mr", mut] = mr
for im, (mutname, s) in enumerate(dd.T.iterrows()):
ax.scatter(
s["x"],
s["freq"],
s=s["size"] ** 2,
alpha=0.8,
edgecolor="white",
facecolor=sns.color_palette("husl", afdr.shape[1])[im],
lw=2,
)
ax.text(s["x"], s["freq"], str(int(s["n"])), fontsize=fs, ha="center", va="center")
plt.yscale("log")
plt.xticks(rotation=50)
plt.ylabel("minor variant frequency", fontsize=fs)
plt.tick_params(labelsize=fs * 0.8)
for tick in ax.xaxis.get_major_ticks():
tick.label.set_horizontalalignment("right")
# Fitness cost at the top
ax1 = axs[0]
ax1.set_xlim(*ax.get_xlim())
ax1.set_xticks(ax.get_xticks() + 0.5)
ax1.set_xticklabels([])
ax1.set_ylim(1e-3, 1)
ax1.set_yticks([1e-3, 1e-2, 1e-1, 1])
ax1.yaxis.set_tick_params(labelsize=fs * 0.8)
ax1.set_yscale("log")
ax1.set_ylabel("cost", fontsize=fs)
for im, (mut, y) in enumerate(dd.loc["cost"].iteritems()):
ax1.bar(im - 0.5, y, 1, color=sns.color_palette("husl", afdr.shape[1])[im])
plt.tight_layout()
if fname is not None:
for ext in ["svg", "pdf", "png"]:
plt.savefig(fname + "." + ext)
else:
plt.ion()
plt.show()
if region=='pol':
for prot in drug_muts:
drug_afs = {}
drug_mut_rates = {}
offset = drug_muts[prot]['offset']
all_muts = drug_muts[prot]['mutations']
for cons_aa, pos, muts in all_muts:
tmp = []
tmp_muts = []
for pcode in combined_af_by_pat[region]:
drug_af = 0
mut_rate = 0
af_vec = combined_af_by_pat[region][pcode][:,pos+offset]
tot=af_vec.sum()
init_codon = initial_codons_by_pat[region][pcode][pos+offset]
if af_vec.argmax()!=alphaal.index(cons_aa):
print('Doesn')
if tot:
for aa in muts:
print(prot, pcode, pos, cons_aa)
mut_rate += aminoacid_mutation_rate(init_codon, aa, nuc_muts, doublehit=True)
drug_af+=af_vec[alphaal.index(aa)]/tot
tmp.append(drug_af)
tmp_muts.append(mut_rate)
else:
tmp.append(np.nan)
tmp_muts.append(np.nan)
drug_afs[(cons_aa,pos,muts)] = np.array(tmp)
drug_mut_rates[(cons_aa,pos,muts)] = np.array(tmp_muts)
plt.figure()
def plot_drug_resistance_mutations(data, aa_mutation_rates, fname=None):
'''Plot the frequency of drug resistance mutations'''
import matplotlib.patches as patches
fs = 16
region = 'pol'
pcodes = data['init_codon'][region].keys()
fig, axs = plt.subplots(2, 1, gridspec_kw={'height_ratios': [1, 6]})
ax = axs[1]
drug_afs_items = []
mut_types = []
drug_classes = ['PI', 'NRTI', 'NNRTI', 'INI']
for prot in drug_classes:
drug_afs = {}
drug_mut_rates = {}
offset = drug_muts[prot]['offset']
for cons_aa, pos, target_aa in drug_muts[prot]['mutations']:
codons = {
pat: data['init_codon'][region][pat][pos + offset]
for pat in pcodes
}
mut_rates = {
pat: np.sum(
[aa_mutation_rates[(codons[pat], aa)] for aa in target_aa])
for pat in pcodes
}
freqs = {pat:np.sum([data['af_by_pat'][region][pat][alphaal.index(aa), pos+offset]\
/data['af_by_pat'][region][pat][:20,pos+offset].sum()
for aa in target_aa]) for pat in pcodes}
drug_afs[(cons_aa, pos, target_aa)] = freqs
drug_mut_rates[(cons_aa, pos, target_aa)] = mut_rates
drug_afs_items.extend(
filter(
lambda x: np.sum(filter(lambda y: ~np.isnan(y), x[1].values()))
> 0, sorted(drug_afs.items(), key=lambda x: x[0][1])))
mut_types.append(len(drug_afs_items))
#make list of all mutations whose non-nan frequencies sum to 0
mono_muts = [
''.join(map(str, x[0])) for x in filter(
lambda x: np.sum(filter(lambda y: ~np.isnan(y), x[1].values()))
== 0, sorted(drug_afs.items(), key=lambda x: x[0][1]))
]
print('Monomorphic:', prot, mono_muts)
plt.ylim([1.1e-5, 1e-1])
for mi in mut_types[:-1]:
plt.plot([mi - 0.5, mi - 0.5],
plt.ylim(),
c=(.3, .3, .3),
lw=3,
alpha=0.5)
ax.axhline(4e-5, c=(.3, .3, .3), lw=3, alpha=0.5)
for ni, prot in enumerate(drug_classes):
plt.text(0.5 * (mut_types[ni] + (mut_types[ni - 1] if ni else 0)) -
0.5,
0.12,
prot,
fontsize=16,
ha='center')
for mi in range(max(mut_types)):
c = 0.5 + 0.2 * (mi % 2)
ax.add_patch(
patches.Rectangle(
(mi - 0.5, plt.ylim()[0]),
1.0,
plt.ylim()[1], #(x,y), width, height
color=(c, c, c),
alpha=0.2))
#plt.xticks(np.arange(len(all_muts)), ["".join(map(str, x)) for x in all_muts], rotation=60)
afdr = pd.DataFrame(
np.array([x[1].values() for x in drug_afs_items]).T,
columns=["".join(map(str, x[0])) for x in drug_afs_items])
afdr[afdr < 0.8e-4] = 0
sns.stripplot(data=afdr,
jitter=0.4,
alpha=0.8,
size=12,
lw=1,
edgecolor='white')
# Add the number of missing points at the bottom of the plot, and the cost
# at the top
dd = afdr.iloc[[0, 1, 2, 3, 4]].copy()
dd.index = ['x', 'freq', 'size', 'cost', 'mr']
dd.loc['x'] = np.arange(dd.shape[1])
dd.loc['freq'] = 2e-5
dd.loc['n'] = afdr.shape[0] - (afdr > 1e-4).sum(axis=0)
dd.loc['size'] = dd.loc['n']**(1.4) + 13
dd.loc['cost'] = 1.0 / afdr.fillna(0).mean(axis=0)
dd.loc['mr'] = 0
# NOTE: the first 6 mutations are in PR, the rest in RT
import re
from Bio.Seq import translate
reference = HIVreference(refname='HXB2', load_alignment=False)
seq_PR = reference.annotation['PR'].extract(reference.seq)
seq_RT = reference.annotation['RT'].extract(reference.seq)
seq_IN = reference.annotation['IN'].extract(reference.seq)
murate = load_mutation_rates()['mu']
for i, mut in enumerate(dd.T.index):
mr = 0
if i < 6:
seq_tmp = seq_PR
elif i < 6 + 5 + 4:
seq_tmp = seq_RT
else:
seq_tmp = seq_IN
aa_from, pos, aas_to = re.sub('([A-Z])(\d+)([A-Z]+)', r'\1_\2_\3',
mut).split('_')
cod = str(seq_tmp.seq[(int(pos) - 1) * 3:int(pos) * 3])
for pos_cod in xrange(3):
for nuc in ['A', 'C', 'G', 'T']:
codmut = list(cod)
codmut[pos_cod] = nuc
codmut = ''.join(codmut)
if (codmut != cod) and (translate(cod)
== aa_from) and (translate(codmut)
in aas_to):
mr += murate[cod[pos_cod] + '->' + nuc]
dd.loc['cost', mut] *= mr
dd.loc['mr', mut] = mr
for im, (mutname, s) in enumerate(dd.T.iterrows()):
ax.scatter(
s['x'],
s['freq'],
s=s['size']**2,
alpha=0.8,
edgecolor='white',
facecolor=sns.color_palette('husl', afdr.shape[1])[im],
lw=2,
)
ax.text(s['x'],
s['freq'],
str(int(s['n'])),
fontsize=fs,
ha='center',
va='center')
plt.yscale('log')
plt.xticks(rotation=50)
plt.ylabel('minor variant frequency', fontsize=fs)
plt.tick_params(labelsize=fs * 0.8)
for tick in ax.xaxis.get_major_ticks():
tick.label.set_horizontalalignment('right')
# Fitness cost at the top
ax1 = axs[0]
ax1.set_xlim(*ax.get_xlim())
ax1.set_xticks(ax.get_xticks() + 0.5)
ax1.set_xticklabels([])
ax1.set_ylim(1e-3, 1)
ax1.set_yticks([1e-3, 1e-2, 1e-1, 1])
ax1.yaxis.set_tick_params(labelsize=fs * 0.8)
ax1.set_yscale('log')
ax1.set_ylabel('cost', fontsize=fs)
for im, (mut, y) in enumerate(dd.loc['cost'].iteritems()):
ax1.bar(im - 0.5,
y,
1,
color=sns.color_palette('husl', afdr.shape[1])[im])
plt.tight_layout()
if fname is not None:
for ext in ['svg', 'pdf', 'png']:
plt.savefig(fname + '.' + ext)
else:
plt.ion()
plt.show()
drug_afs = {}
drug_mut_rates = {}
offset = drug_muts[prot]['offset']
all_muts = drug_muts[prot]['mutations']
for cons_aa, pos, muts in all_muts:
tmp = []
tmp_muts = []
for pcode in combined_af_by_pat[region]:
drug_af = 0
mut_rate = 0
af_vec = combined_af_by_pat[region][pcode][:, pos +
offset]
tot = af_vec.sum()
init_codon = initial_codons_by_pat[region][pcode][
pos + offset]
if af_vec.argmax() != alphaal.index(cons_aa):
print('Doesn')
if tot:
for aa in muts:
print(prot, pcode, pos, cons_aa)
mut_rate += aminoacid_mutation_rate(
init_codon, aa, nuc_muts, doublehit=True)
drug_af += af_vec[alphaal.index(aa)] / tot
tmp.append(drug_af)
tmp_muts.append(mut_rate)
else:
tmp.append(np.nan)
tmp_muts.append(np.nan)
drug_afs[(cons_aa, pos, muts)] = np.array(tmp)
drug_mut_rates[(cons_aa, pos, muts)] = np.array(tmp_muts)