axis.set_ylabel('Fixed mutations')
axis.set_xlim([-1, 22])
if population_idx == 5:
axis.set_xlabel('Clones')
########################################
#
# Now do the plotting (focal first, then rest)
#
########################################
theory_times = numpy.arange(0, 121) * 500
gene_names, start_positions, end_positions, promoter_start_positions, promoter_end_positions, gene_sequences, strands = parse_file.parse_gene_list(
)
gene_name_position_map = {
gene_names[i]: (start_positions[i], end_positions[i])
for i in xrange(0, len(gene_names))
}
state_color_map = {
parse_file.clade_hmm_states['FB']: '0.7',
parse_file.clade_hmm_states['FM']: '#7a0177',
parse_file.clade_hmm_states['Fm']: '#f768a1'
}
for metapopulation_idx in xrange(0, 2):
for population_idx in xrange(0, 6):
mutator_axis.text(0.6,
1.5e05,
figure_utils.get_panel_label('b'),
fontsize=6,
fontweight='bold')
####
#
# Do calculation
#
####
excluded_types = set(['sv', 'indel'])
reference_sequence = parse_file.parse_reference_genome()
gene_data = parse_file.parse_gene_list()
repeat_data = parse_file.parse_repeat_list()
mask_data = parse_file.parse_mask_list()
position_gene_map, effective_gene_lengths, substitution_specific_synonymous_fraction = parse_file.create_annotation_map(
gene_data, repeat_data, mask_data)
#Ltot = 4.4e06
Ltot = len(reference_sequence) - effective_gene_lengths['masked']
sys.stderr.write("Ltot = %d\n" % Ltot)
for population_group in ['nonmutators', 'mutators']:
if population_group == 'nonmutators':
populations = parse_file.complete_nonmutator_lines
color = figure_utils.nonmutator_group_color
def plot_within_taxon_paralleliism(taxon, slope_null=1):
fig = plt.figure(figsize=(12, 8))
gene_data = parse_file.parse_gene_list(taxon)
gene_names, gene_start_positions, gene_end_positions, promoter_start_positions, promoter_end_positions, gene_sequences, strands, genes, features, protein_ids = gene_data
# to get the common gene names for each ID
ax_multiplicity = plt.subplot2grid((2, 3), (0, 0), colspan=1)
ax_mult_freq = plt.subplot2grid((2, 3), (0, 1), colspan=1)
ax_venn = plt.subplot2grid((2, 3), (0, 2), colspan=1)
ax_multiplicity.set_xscale('log', base=10)
ax_multiplicity.set_yscale('log', base=10)
ax_multiplicity.set_xlabel('Gene multiplicity, ' + r'$m$', fontsize=14)
ax_multiplicity.set_ylabel('Fraction mutations ' + r'$\geq m$',
fontsize=14)
ax_multiplicity.text(-0.1,
1.07,
pt.sub_plot_labels[0],
fontsize=18,
fontweight='bold',
ha='center',
va='center',
transform=ax_multiplicity.transAxes)
ax_multiplicity.set_ylim([0.001, 1.1])
ax_multiplicity.set_xlim([0.07, 130])
ax_mult_freq.set_xscale('log', base=10)
ax_mult_freq.set_yscale('log', base=10)
ax_mult_freq.set_xlabel('Gene multiplicity, ' + r'$m$', fontsize=14)
ax_mult_freq.set_ylabel('Mean maximum allele frequency, ' +
r'$\overline{f}_{max}$',
fontsize=11)
ax_mult_freq.text(-0.1,
1.07,
pt.sub_plot_labels[1],
fontsize=18,
fontweight='bold',
ha='center',
va='center',
transform=ax_mult_freq.transAxes)
ax_venn.axis('off')
ax_venn.text(-0.1,
1.07,
pt.sub_plot_labels[2],
fontsize=18,
fontweight='bold',
ha='center',
va='center',
transform=ax_venn.transAxes)
alpha_treatment_dict = {'0': 0.5, '1': 0.5, '2': 0.8}
significant_multiplicity_dict = {}
significant_multiplicity_values_dict = {}
multiplicity_dict = {}
g_score_p_label_dict = {}
all_mults = []
all_freqs = []
treatments_in_taxon = []
label_y_axes = [0.3, 0.2, 0.1]
for treatment_idx, treatment in enumerate(pt.treatments):
significan_multiplicity_taxon_path = pt.get_path(
) + '/data/timecourse_final/parallel_genes_%s.txt' % (treatment +
taxon)
if os.path.exists(significan_multiplicity_taxon_path) == False:
continue
treatments_in_taxon.append(treatment)
significan_multiplicity_taxon = open(
significan_multiplicity_taxon_path, "r")
significan_multiplicity_list = []
for i, line in enumerate(significan_multiplicity_taxon):
if i == 0:
continue
line = line.strip()
items = line.split(",")
significan_multiplicity_list.append(items[0])
if items[0] not in significant_multiplicity_values_dict:
significant_multiplicity_values_dict[items[0]] = {}
significant_multiplicity_values_dict[
items[0]][treatment] = float(items[-2])
else:
significant_multiplicity_values_dict[
items[0]][treatment] = float(items[-2])
significant_multiplicity_dict[treatment] = significan_multiplicity_list
populations = [
treatment + taxon + replicate for replicate in pt.replicates
]
# Load convergence matrix
convergence_matrix = parse_file.parse_convergence_matrix(
pt.get_path() + '/data/timecourse_final/' +
("%s_convergence_matrix.txt" % (treatment + taxon)))
gene_parallelism_statistics = mutation_spectrum_utils.calculate_parallelism_statistics(
convergence_matrix, populations, Lmin=100)
#print(gene_parallelism_statistics)
G, pvalue = mutation_spectrum_utils.calculate_total_parallelism(
gene_parallelism_statistics)
sys.stdout.write("Total parallelism for %s = %g (p=%g)\n" %
(treatment + taxon, G, pvalue))
predictors = []
responses = []
gene_hits = []
gene_predictors = []
mean_gene_freqs = []
Ls = []
ax_mult_freqs_x = []
ax_mult_freqs_y = []
for gene_name in convergence_matrix.keys():
convergence_matrix[gene_name][
'length'] < 50 and convergence_matrix[gene_name]['length']
Ls.append(convergence_matrix[gene_name]['length'])
m = gene_parallelism_statistics[gene_name]['multiplicity']
if gene_name not in multiplicity_dict:
multiplicity_dict[gene_name] = {}
multiplicity_dict[gene_name][treatment] = m
else:
multiplicity_dict[gene_name][treatment] = m
n = 0
nfixed = 0
freqs = []
nf_max = 0
for population in populations:
for t, L, f, f_max in convergence_matrix[gene_name][
'mutations'][population]:
fixed_weight = timecourse_utils.calculate_fixed_weight(
L, f)
predictors.append(m)
responses.append(fixed_weight)
n += 1
nfixed += fixed_weight
# get freqs for regression
#if L == parse_file.POLYMORPHIC:
#freqs.append(f_max)
nf_max += timecourse_utils.calculate_fixed_weight(L, f_max)
if n > 0.5:
gene_hits.append(n)
gene_predictors.append(m)
#mean_gene_freqs.append(np.mean(freqs))
if nf_max > 0:
ax_mult_freqs_x.append(m)
ax_mult_freqs_y.append(nf_max / n)
Ls = np.asarray(Ls)
ntot = len(predictors)
mavg = ntot * 1.0 / len(Ls)
predictors, responses = (np.array(x) for x in zip(
*sorted(zip(predictors, responses), key=lambda pair: (pair[0]))))
gene_hits, gene_predictors = (np.array(x) for x in zip(*sorted(
zip(gene_hits, gene_predictors), key=lambda pair: (pair[0]))))
rescaled_predictors = np.exp(np.fabs(np.log(predictors / mavg)))
null_survival_function = mutation_spectrum_utils.NullMultiplicitySurvivalFunction.from_parallelism_statistics(
gene_parallelism_statistics)
# default base is 10
theory_ms = np.logspace(-2, 2, 100)
theory_survivals = null_survival_function(theory_ms)
theory_survivals /= theory_survivals[0]
sys.stderr.write("Done!\n")
ax_multiplicity.plot(theory_ms,
theory_survivals,
lw=3,
color=pt.get_colors(treatment),
alpha=0.8,
ls=':',
zorder=1)
ax_multiplicity.plot(
predictors, (len(predictors) - np.arange(0, len(predictors))) *
1.0 / len(predictors),
lw=3,
color=pt.get_colors(treatment),
alpha=0.8,
ls='--',
label=str(int(10**int(treatment))) + '-day',
drawstyle='steps',
zorder=2)
#ax_multiplicity.text(0.2, 0.3, g_score_p_label_dict['0'], fontsize=25, fontweight='bold', ha='center', va='center', transform=ax_multiplicity.transAxes)
#ax_multiplicity.text(0.2, 0.2, g_score_p_label_dict['1'], fontsize=25, fontweight='bold', ha='center', va='center', transform=ax_multiplicity.transAxes)
#ax_multiplicity.text(0.2, 0.1, g_score_p_label_dict['2'], fontsize=25, fontweight='bold', ha='center', va='center', transform=ax_multiplicity.transAxes)
if pvalue < 0.001:
pretty_pvalue = r'$\ll 0.001$'
else:
pretty_pvalue = '=' + str(round(pvalue, 4))
g_score_p_label = r'$\Delta \ell_{{{}}}=$'.format(
str(10**int(treatment))) + str(round(
G, 3)) + ', ' + r'$P$' + pretty_pvalue
text_color = pt.lighten_color(pt.get_colors(treatment), amount=1.3)
ax_multiplicity.text(0.26,
label_y_axes[treatment_idx],
g_score_p_label,
fontsize=7,
ha='center',
va='center',
color='k',
transform=ax_multiplicity.transAxes)
ax_mult_freq.scatter(ax_mult_freqs_x,
ax_mult_freqs_y,
color=pt.get_colors(treatment),
edgecolors=pt.get_colors(treatment),
marker=pt.plot_species_marker(taxon),
alpha=alpha_treatment_dict[treatment])
all_mults.extend(ax_mult_freqs_x)
all_freqs.extend(ax_mult_freqs_y)
#slope, intercept, r_value, p_value, std_err = stats.linregress(np.log10(ax_mult_freqs_x), np.log10(ax_mult_freqs_y))
#print(slope, p_value)
# make treatment pairs
treatments_in_taxon.sort(key=float)
for i in range(0, len(treatments_in_taxon)):
for j in range(i + 1, len(treatments_in_taxon)):
ax_mult_i_j = plt.subplot2grid((2, 3), (1, i + j - 1), colspan=1)
ax_mult_i_j.set_xscale('log', base=10)
ax_mult_i_j.set_yscale('log', base=10)
ax_mult_i_j.set_xlabel(str(10**int(treatments_in_taxon[i])) +
'-day gene multiplicity, ' + r'$m$',
fontsize=14)
ax_mult_i_j.set_ylabel(str(10**int(treatments_in_taxon[j])) +
'-day gene multiplicity, ' + r'$m$',
fontsize=14)
ax_mult_i_j.plot([0.05, 200], [0.05, 200],
lw=3,
c='grey',
ls='--',
zorder=1)
ax_mult_i_j.set_xlim([0.05, 200])
ax_mult_i_j.set_ylim([0.05, 200])
ax_mult_i_j.text(-0.1,
1.07,
pt.sub_plot_labels[2 + i + j],
fontsize=18,
fontweight='bold',
ha='center',
va='center',
transform=ax_mult_i_j.transAxes)
multiplicity_pair = [
(multiplicity_dict[gene_name][treatments_in_taxon[i]],
multiplicity_dict[gene_name][treatments_in_taxon[j]])
for gene_name in sorted(multiplicity_dict)
if (multiplicity_dict[gene_name][treatments_in_taxon[i]] > 0)
and (multiplicity_dict[gene_name][treatments_in_taxon[j]] > 0)
]
significant_multiplicity_pair = [
(significant_multiplicity_values_dict[gene_name][
treatments_in_taxon[i]],
significant_multiplicity_values_dict[gene_name][
treatments_in_taxon[j]])
for gene_name in sorted(significant_multiplicity_values_dict)
if (treatments_in_taxon[i] in
significant_multiplicity_values_dict[gene_name]) and (
treatments_in_taxon[j] in
significant_multiplicity_values_dict[gene_name])
]
# get mean colors
ccv = ColorConverter()
color_1 = np.array(
ccv.to_rgb(pt.get_colors(treatments_in_taxon[i])))
color_2 = np.array(
ccv.to_rgb(pt.get_colors(treatments_in_taxon[j])))
mix_color = 0.7 * (color_1 + color_2)
mix_color = np.min([mix_color, [1.0, 1.0, 1.0]], 0)
if (treatments_in_taxon[i] == '0') and (treatments_in_taxon[j]
== '1'):
#mix_color = pt.lighten_color(mix_color, amount=2.8)
mix_color = 'gold'
mult_i = [x[0] for x in multiplicity_pair]
mult_j = [x[1] for x in multiplicity_pair]
ax_mult_i_j.scatter(mult_i,
mult_j,
marker=pt.plot_species_marker(taxon),
facecolors=mix_color,
edgecolors='none',
alpha=0.8,
s=90,
zorder=2)
mult_significant_i = [x[0] for x in significant_multiplicity_pair]
mult_significant_j = [x[1] for x in significant_multiplicity_pair]
ax_mult_i_j.scatter(mult_significant_i,
mult_significant_j,
marker=pt.plot_species_marker(taxon),
facecolors=mix_color,
edgecolors='k',
lw=1.5,
alpha=0.7,
s=90,
zorder=3)
#slope_mult, intercept_mult, r_value_mult, p_value_mult, std_err_mult = stats.linregress(np.log10(mult_significant_i), np.log10(mult_significant_j))
mult_ij = mult_significant_i + mult_significant_j + mult_i + mult_j
ax_mult_i_j.set_xlim([min(mult_ij) * 0.5, max(mult_ij) * 1.5])
ax_mult_i_j.set_ylim([min(mult_ij) * 0.5, max(mult_ij) * 1.5])
# null slope of 1
#ratio = (slope_mult - slope_null) / std_err_mult
#p_value_mult_new_null = stats.t.sf(np.abs(ratio), len(mult_significant_j)-2)*2
#if p_value_mult_new_null < 0.05:
# x_log10_fit_range = np.linspace(np.log10(min(mult_i) * 0.5), np.log10(max(mult_i) * 1.5), 10000)
# y_fit_range = 10 ** (slope_mult*x_log10_fit_range + intercept_mult)
# ax_mult_i_j.plot(10**x_log10_fit_range, y_fit_range, c='k', lw=3, linestyle='--', zorder=4)
#ax_mult_i_j.text(0.05, 0.9, r'$\beta_{1}=$'+str(round(slope_mult,3)), fontsize=12, transform=ax_mult_i_j.transAxes)
#ax_mult_i_j.text(0.05, 0.82, r'$r^{2}=$'+str(round(r_value_mult**2,3)), fontsize=12, transform=ax_mult_i_j.transAxes)
#ax_mult_i_j.text(0.05, 0.74, pt.get_p_value_latex(p_value_mult_new_null), fontsize=12, transform=ax_mult_i_j.transAxes)
#if taxon == 'F':
# subset_tuple = (len( significant_multiplicity_dict['0']), \
# len( significant_multiplicity_dict['1']), \
# len(set(significant_multiplicity_dict['0']) & set(significant_multiplicity_dict['1'])))
# venn = venn2(subsets = subset_tuple, ax=ax_venn, set_labels=('', '', ''), set_colors=(pt.get_colors('0'), pt.get_colors('1')))
# c = venn2_circles(subsets=subset_tuple, ax=ax_venn, linestyle='dashed')
subset_tuple = (len( significant_multiplicity_dict['0']), \
len( significant_multiplicity_dict['1']), \
len(set(significant_multiplicity_dict['0']) & set(significant_multiplicity_dict['1'])), \
len(significant_multiplicity_dict['2']), \
len(set(significant_multiplicity_dict['0']) & set(significant_multiplicity_dict['2'])), \
len(set(significant_multiplicity_dict['1']) & set(significant_multiplicity_dict['2'])), \
len(set(significant_multiplicity_dict['1']) & set(significant_multiplicity_dict['1']) & set(significant_multiplicity_dict['2'])))
venn = venn3(subsets=subset_tuple,
ax=ax_venn,
set_labels=('', '', ''),
set_colors=(pt.get_colors('0'), pt.get_colors('1'),
pt.get_colors('2')))
c = venn3_circles(subsets=subset_tuple, ax=ax_venn, linestyle='dashed')
ax_mult_freq.set_xlim([min(all_mults) * 0.5, max(all_mults) * 1.5])
ax_mult_freq.set_ylim([min(all_freqs) * 0.5, max(all_freqs) * 1.5])
fig.suptitle(pt.latex_dict[taxon], fontsize=30)
fig.subplots_adjust(wspace=0.3) #hspace=0.3, wspace=0.5
fig_name = pt.get_path() + "/figs/multiplicity_%s.jpg" % taxon
fig.savefig(fig_name,
format='jpg',
bbox_inches="tight",
pad_inches=0.4,
dpi=600)
plt.close()
# mutual information / joing entropy
joint_entropy = stats.entropy(array_1,array_2)
standardized_gene_overlap = {}
for taxon in pt.taxa:
#if taxon == 'J':
# continue
gene_dict = {}
N_significant_genes_dict = {}
gene_data = parse_file.parse_gene_list(taxon)
gene_names, gene_start_positions, gene_end_positions, promoter_start_positions, promoter_end_positions, gene_sequences, strands, genes, features, protein_ids = gene_data
#locus_tag_to_gene_dict = {}
#for gene_name_idx, gene_name in enumerate(gene_names):
# gene = genes[gene_name_idx]
# if gene == '':
# continue
# locus_tag_to_gene_dict[gene_name] = genes[gene_name_idx]
if taxon == 'J':
treatments_convergence = ['0', '1']
else:
treatments_convergence = ['0', '1', '2']
import phik
np.random.seed(123456789)
# to-do: re-do analysis for enriched genes in *either* treatment you're comparing
# read in nonsignificant genes and add those conts in..
permutations_divergence = 10000
treatment_pairs = [['0','1'],['0','2'],['1','2']]
gene_data_B = parse_file.parse_gene_list('B')
gene_names_B, gene_start_positions_B, gene_end_positions_B, promoter_start_positions_B, promoter_end_positions_B, gene_sequences_B, strands_B, genes_B, features_B, protein_ids_B = gene_data_B
gene_name_dict = dict(zip(gene_names_B, genes_B ))
protein_id_dict = dict(zip(gene_names_B, protein_ids_B ))
significant_multiplicity_dict = {}
significant_n_mut_dict = {}
gene_size_dict = {}
gene_mean_size_dict = {}
for taxon in pt.taxa:
significant_multiplicity_dict[taxon] = {}
significant_n_mut_dict[taxon] = {}
gene_size_dict[taxon] = {}
gene_data = parse_file.parse_gene_list(taxon)
def process_output():
for strain in strains:
parse_gene_list = pf.parse_gene_list(taxon=strain)
for treatment in treatments:
for rep in reps:
print('%s%s%s' % (treatment, strain, rep))
sample = '%s%s%s' % (treatment, strain, rep)
snp_timecourse_filename = '%s%s%s_snp_timecourse.bz' % (
treatment, strain, rep)
snp_timecourse_path = pt.get_path(
) + '/data/timecourse_snp/' + snp_timecourse_filename
snp_file = bz2.open(snp_timecourse_path, "rt")
depth_timecourse_filename = '%s%s%s_depth_timecourse.bz' % (
treatment, strain, rep)
depth_timecourse_path = pt.get_path(
) + '/data/timecourse_depth/' + depth_timecourse_filename
depth_file = bz2.open(depth_timecourse_path, "rt")
for depth in depth_file:
depth_split = [x.strip() for x in depth.split(',')]
D_pt_median = depth_split[-1].split(' ')
D_pt_median = np.asarray([float(x) for x in D_pt_median])
file_mutations = []
for snp in snp_file:
snp_split = [x.strip() for x in snp.split(',')]
t_pm = snp_split[3].split(' ')
t_pm = np.asarray([int(x) for x in t_pm])
A_pm = snp_split[4].split(' ')
A_pm = np.asarray([int(x) for x in A_pm])
D_pm = snp_split[5].split(' ')
D_pm = np.asarray([int(x) for x in D_pm])
if len(t_pm) == 1:
continue
# remove D_pmt < 5
remove_D_pmt = np.asarray(
[x for x, y in enumerate(D_pm) if y < 5])
D_pt_median_copy = np.empty_like(D_pt_median)
D_pt_median_copy[:] = D_pt_median
if len(remove_D_pmt) > 0:
t_pm = np.delete(t_pm, remove_D_pmt)
A_pm = np.delete(A_pm, remove_D_pmt)
D_pm = np.delete(D_pm, remove_D_pmt)
D_pt_median_copy = np.delete(D_pt_median_copy,
remove_D_pmt)
# remove low coverage timepoints
d_pm = D_pm / D_pt_median_copy
# don't look at trajectories with fewer than four
if len(t_pm[1:]) < 4:
continue
l_list = [
get_likelihood(t_, t=t_pm, d_pm=d_pm)
for t_ in t_pm[1:-2]
]
l_list = sorted(l_list, key=lambda x: x[-1])
max_l = l_list[-1]
# r threshold of 0.5 too conservative
#if max_l[-2] >= 0.5:
# continue
n = len(t_pm)
sigma = np.sqrt((1 / n) * sum(d_pm**2) -
(((1 / n) * sum(d_pm))**2))
if (max_l[4] == 0) or (max_l[2] == 0):
continue
delta_l = max_l[3] * np.log(
sigma / max_l[4]) + max_l[1] * np.log(sigma / max_l[2])
# try permutation test for log likelihood?
# need upper threshold for delta_l, chose 20 for now
#if delta_l > 20:
# continue
C_star_mut, I_mut, T_mut = get_test_statistics(
t_pm, A_pm, D_pm)
if np.isnan(np.sum([C_star_mut, I_mut, T_mut])):
continue
f_pm = A_pm / D_pm
if (f_pm[0] > 0.9) and (f_pm[-1] > 0.9):
continue
#if f_pm[-1] > 0.05:
if (sample == '0C1') or (sample == '0C4'):
last_timepoint = -2
else:
last_timepoint = -1
if f_pm[last_timepoint] > 0.05:
allele_split = snp_split[2].split('->')
anc = allele_split[0]
der = allele_split[1]
site = int(snp_split[1])
genes_names_site = []
for k in list(range(len(parse_gene_list[0]))):
gene_name = parse_gene_list[0][k]
start = int(parse_gene_list[1][k])
stop = int(parse_gene_list[2][k])
if (site >= start) and (site <= stop):
genes_names_site.append(gene_name)
genes_names_site_merged = ",".join(genes_names_site)
file_mutations.append([
snp_split[0], genes_names_site_merged,
snp_split[1], anc, der,
str(f_pm[-1])
])
# out file
header = [
'Contig', 'Locus_tag', 'Site', 'Ancestral', 'Derived',
'Final_freq'
]
mutation_filename = '%s%s%s_snp_final.txt' % (treatment,
strain, rep)
mutation_file = open(
pt.get_path() + '/data/timecourse_prelim_poly/' +
mutation_filename, 'w')
mutation_file.write('\t'.join(header) + '\n')
for mutation in file_mutations:
mutation_file.write("\t".join(mutation))
mutation_file.write("\n")
mutation_file.close()
