def homogeneity_plot(sample_set_names_list, plot_file_prefix):
rcParams.update({'font.size': 6})
letter_list_part1 = ["A", "B", "C", "D", "E", "F", "G", "H", "I", "J"]
feature_type_list = ["all", "pre_5'_UTR", "5'_UTR", "CDS", "3'_UTR"]
figure = plt.figure(1,
dpi=600,
figsize=(5 * 1.47, 2 * len(sample_set_names_list)))
index = 1
for sample, letter in zip(sample_set_names_list, letter_list_part1):
findex = 1
for feature_type in feature_type_list:
vcf_file = "%s_good.vcf" % sample if feature_type == "all" \
else "%s_pre_UTR_variants_only_intergenic_l_300.vcf" % sample \
if feature_type == "pre_5'_UTR" \
else "%s_%s_variants.vcf" % (sample, feature_type)
collection = CollectionVCF(from_file=True, vcf_file=vcf_file)
sample_data = collection.count_strandness("%s_%s_variants" %
(sample, feature_type))
ax = plt.subplot(len(sample_set_names_list),
len(feature_type_list), index)
n_groups = 4
points = np.arange(n_groups)
bar_width = 0.35
C_values = sample_data["all"][0]
G_values = sample_data["all"][1]
rects1 = plt.bar(points,
C_values,
bar_width,
color='b',
label='C->T')
rects2 = plt.bar(points + bar_width,
G_values,
bar_width,
color='g',
label='G->A')
table = [C_values[1:-1], G_values[1:-1]]
g, p_value, dof, expctd = chi2_contingency(table)
phi = phi_coefficient_correlation(table)
if index > len(feature_type_list) * (len(sample_set_names_list) -
1):
plt.xlabel('Strand')
if findex == 1:
plt.ylabel('N of SNV')
plt.text(-0.50,
0.5,
sample[:-3],
rotation=90,
fontweight="bold",
transform=ax.transAxes,
fontsize=10,
horizontalalignment='center',
verticalalignment='center')
#plt.title("%s%i. %s (%i SNV)\np=%.3f, phi=%.3f" % (letter, findex, sample, len(collection), p_value, phi),
# fontweight='bold')
#plt.title("%s%i. %s (%s)\np=%.3f, phi=%.3f" % (letter, findex, sample, feature_type, p_value, phi),
# fontweight='bold', fontsize=6)
title = "%s%i" % (letter, findex)
title_text = r"$p=%.2f, \varphi=%.2f$" % (p_value, phi) if p_value >= 0.01 \
else r"$p=%.1e, \varphi=%.2f$" % (p_value, phi)
plt.text(
0.23,
1.1,
title_text,
rotation=0,
transform=ax.transAxes,
fontsize=8,
#horizontalalignment='center',
verticalalignment='center')
plt.title(title, fontweight='bold', fontsize=11, loc="left")
plt.xticks(points + bar_width, ('None', '+', '-', 'Both'))
if findex == len(feature_type_list):
plt.legend(prop={'size': 8})
if index <= len(feature_type_list):
plt.text(0.5,
1.25,
feature_type,
rotation=0,
fontweight="bold",
transform=ax.transAxes,
fontsize=10,
horizontalalignment='center',
verticalalignment='center')
#plt.suptitle("Strandness histograms", fontweight="bold", fontsize=20)
findex += 1
index += 1
#plt.tight_layout()
plt.subplots_adjust(hspace=0.5,
wspace=0.25,
top=0.88,
left=0.09,
right=0.99)
for extension in [".pdf", ".svg", ".eps", ".png"]:
plt.savefig("%s%s" % (plot_file_prefix, extension))
plt.close()
if __name__ == "__main__":
workdir = "/media/mahajrod/d9e6e5ee-1bf7-4dba-934e-3f898d9611c8/Data/LAN2xx/combined_vcf/clusters/all/all/"
letter_list_part1 = ["A", "B", "C", "D", "E", "F", "G", "H", "I", "J"]
os.chdir(workdir)
sample_set_names_list = ["PmCDA1_3d",
"PmCDA1_sub1_3d",
"PmCDA1_6d",
"PmCDA1_sub1_6d"
]
rcParams.update({'font.size': 7})
plt.figure(1, dpi=300, figsize=(6, 6))
index = 1
for sample, letter in zip(sample_set_names_list, letter_list_part1):
collection = CollectionVCF(from_file=True, vcf_file=sample + "_good.vcf")
sample_data = collection.count_strandness(sample + "_good_strandness")
plt.subplot(2, 2, index)
n_groups = 4
points = np.arange(n_groups)
bar_width = 0.35
C_values = sample_data["all"][0]
G_values = sample_data["all"][1]
rects1 = plt.bar(points, C_values, bar_width,
color='b',
label='C->T')
rects2 = plt.bar(points + bar_width, G_values, bar_width,
color='g',
label='G->A')
if __name__ == "__main__":
workdir = "/media/mahajrod/d9e6e5ee-1bf7-4dba-934e-3f898d9611c8/Data/LAN2xx/combined_vcf/clusters/all/pre_UTR_strandness/"
letter_list_part1 = ["A", "B", "C", "D", "E", "F", "G", "H", "I", "J"]
os.chdir(workdir)
sample_set_names_list = [
"PmCDA1_3d", "PmCDA1_sub1_3d", "PmCDA1_6d", "PmCDA1_sub1_6d"
]
rcParams.update({'font.size': 7})
plt.figure(1, dpi=300, figsize=(6, 6))
index = 1
for sample, letter in zip(sample_set_names_list, letter_list_part1):
collection = CollectionVCF(
from_file=True,
vcf_file=sample + "_pre_UTR_variants_only_intergenic_l_300.vcf")
sample_data = collection.count_strandness(
sample + "_pre_UTR_variants_only_intergenic_l_300_strandness")
plt.subplot(2, 2, index)
n_groups = 4
points = np.arange(n_groups)
bar_width = 0.35
C_values = sample_data["all"][0]
G_values = sample_data["all"][1]
rects1 = plt.bar(points, C_values, bar_width, color='b', label='C->T')
rects2 = plt.bar(points + bar_width,
G_values,
bar_width,
color='g',
label='G->A')
os.chdir(workdir)
sample_set_names_list = [
"PmCDA1_3d", "PmCDA1_sub1_3d", "PmCDA1_6d", "PmCDA1_sub1_6d"
]
rcParams.update({'font.size': 7})
feature_type_list = ["5'_UTR", "CDS", "3'_UTR"]
for feature_type in feature_type_list:
plt.figure(1, dpi=300, figsize=(6, 6))
index = 1
for sample, letter in zip(sample_set_names_list, letter_list_part1):
collection = CollectionVCF(from_file=True,
vcf_file="%s_%s_variants.vcf" %
(sample, feature_type))
sample_data = collection.count_strandness("%s_%s_variants" %
(sample, feature_type))
plt.subplot(2, 2, index)
n_groups = 4
points = np.arange(n_groups)
bar_width = 0.35
C_values = sample_data["all"][0]
G_values = sample_data["all"][1]
rects1 = plt.bar(points,
C_values,
bar_width,
color='b',
label='C->T')
rects2 = plt.bar(points + bar_width,