def survival_calculator(input_data, session_key):
plot_path = "/home/ubuntu/django_proj/pcta_updated/main/static/images/" + session_key + "/"
template_plot_path = "images/" + session_key + "/"
test_name = "bcr_analysis"
if not os.path.exists(plot_path):
os.mkdir(plot_path)
else:
files = glob.glob(plot_path + "*")
for f in files:
os.remove(f)
filecount = 0
file_list = []
pt = MY_PLOT()
surv_dataset = ['GSE40272', 'GSE70769']
def table_split(cph):
cph_d = cph.summary
cph_d_index = cph_d.index.tolist()
if len(cph_d_index) > 1:
t_arr = []
for x in cph_d_index:
testing = x
coef = format(cph_d['coef'].loc[x], ".2f")
haz_c = format(cph_d['exp(coef)'].loc[x], ".2f")
haz_c_int = str(
format(np.exp(
cph_d['lower 0.95'].loc[x]), ".2f")) + "-" + str(
format(np.exp(cph_d['upper 0.95'].loc[x]), ".2f"))
pval = format(cph_d['p'].loc[x], ".3f")
concor = format(
concordance_index(
cph.durations,
-cph.predict_partial_hazard(cph.data).values.ravel(),
cph.event_observed), ".2f")
t_arr.append([
testing + "(Multivariate)", coef,
haz_c + "(" + haz_c_int + ")", pval, concor
])
return t_arr
else:
for x in cph_d_index:
testing = x
coef = format(cph_d['coef'].loc[x], ".2f")
haz_c = format(cph_d['exp(coef)'].loc[x], ".2f")
haz_c_int = str(
format(np.exp(
cph_d['lower 0.95'].loc[x]), ".2f")) + "-" + str(
format(np.exp(cph_d['upper 0.95'].loc[x]), ".2f"))
pval = format(cph_d['p'].loc[x], ".3f")
concor = format(
concordance_index(
cph.durations,
-cph.predict_partial_hazard(cph.data).values.ravel(),
cph.event_observed), ".2f")
return [[
testing, coef, haz_c + "(" + haz_c_int + ")", pval, concor
]]
expr_set_arr = []
clinical_set_arr = []
for surv in surv_dataset:
expr_set = pd.read_csv('user_data/clinical_data/' + surv + '.csv',
index_col=0)
expr_set.index = expr_set.index.astype(int)
expr_set.index = expr_set.index.astype(str)
clinical_set = pd.read_excel('user_data/clinical_data/' + surv +
'_clinical.xlsx',
index_col=0)
expr_set_arr.append(expr_set)
clinical_set_arr.append(clinical_set)
test_set_arr = []
for expr in expr_set_arr:
if len(input_data) > 1:
df_data = gene_set_zscore(expr,
input_data,
sample_status="multiple")
test_set = pd.Series(data=df_data,
index=expr.columns.tolist(),
name='Input_Zscore')
test_set_arr.append(test_set)
surv_input = 'Input_Zscore'
elif len(input_data) == 1:
test_set = expr.loc[input_data[0]]
test_set_arr.append(test_set)
surv_input = input_data[0]
dv_arr = []
for i, test in enumerate(test_set_arr):
test_set_up = test[test > test.median()]
test_set_down = test[test < test.median()]
clinical_up = clinical_set_arr[i].loc[test_set_up.index.tolist()]
clinical_down = clinical_set_arr[i].loc[test_set_down.index.tolist()]
clinical_arr = [clinical_up, clinical_down]
dv_arr.append(clinical_arr)
for dv in dv_arr:
pt.survival_plot_and_cox(
dv,
label=[surv_input + '_up', surv_input + '_down'],
filename=plot_path + test_name + str(filecount))
file_list.append(template_plot_path + test_name + str(filecount) +
".png")
filecount += 1
table_arr = []
cph = CoxPHFitter()
for i, test in enumerate(test_set_arr):
cph_data = test
cph_data.loc[cph_data > test_set.median()] = 1
cph_data.loc[cph_data < test_set.median()] = 0
cph_data = pd.concat([cph_data, clinical_set_arr[i]], axis=1)
table_data = []
cph.fit(cph_data[[surv_input, 'bcrstatus', 'bcrmonth']].dropna(),
'bcrmonth',
event_col='bcrstatus')
table_data = table_split(cph)
cph.fit(cph_data[['gleason', 'bcrstatus', 'bcrmonth']].dropna(),
'bcrmonth',
event_col='bcrstatus')
table_data = table_data + table_split(cph)
cph.fit(cph_data[[surv_input, 'gleason', 'bcrstatus',
'bcrmonth']].dropna(),
'bcrmonth',
event_col='bcrstatus')
table_data = table_data + table_split(cph)
#table_data = [['Variable','Coefficient','Hazard Ratio','P-value','C-index']]+table_data
table_arr.append(table_data)
rw = open(plot_path + surv_dataset[i] + '.cox.tsv', "w")
rw.write("Variable\tCoefficient\tHR(95%Conf)\tP-value\tC-index\n")
for x in table_data:
rw.write('\t'.join(x) + '\n')
rw.close()
return file_list, table_arr
def set_calculator(input_data,
group_info,
group_samples,
session_key,
set_name='USER_GENE_SET'):
def fet_f(a1, b1, total_gene_assum):
a1_inter_b1 = list(set(a1).intersection(b1))
a1_unique_fromb1 = list(set(a1) - set(a1_inter_b1))
b1_unique_froma1 = list(set(b1) - set(a1_inter_b1))
oddsratio, pvalue = stats.fisher_exact(
[[len(a1_inter_b1), len(b1_unique_froma1)],
[
len(a1_unique_fromb1),
total_gene_assum - (len(a1_inter_b1) + len(b1_unique_froma1) +
len(a1_unique_fromb1))
]])
return len(a1), len(a1_inter_b1), pvalue
plot_path = "/home/ubuntu/django_proj/pcta_updated/main/static/images/" + session_key + "/"
nginx_plot_path = "/home/ubuntu/django_proj/pcta_updated/main/staticimages/" + session_key + "/"
template_plot_path = "images/" + session_key + "/"
test_name = "USER_SET"
if not os.path.exists(plot_path):
os.mkdir(plot_path)
os.mkdir(nginx_plot_path)
else:
files = glob.glob(plot_path + "*")
for f in files:
os.remove(f)
files = glob.glob(nginx_plot_path + "*")
for f in files:
os.remove(f)
filecount = 0
file_list = []
pt = MY_PLOT()
df_all = all_expr_df
gsea_mapping_rate = len(
list(set(input_data).intersection(df_all.index.tolist())))
list_numb = len(input_data)
gsea_mapping_rate = float(gsea_mapping_rate) / float(list_numb) * 100
gsea_mapping_rate = "%.2f" % gsea_mapping_rate
#############GSEA#############
gmt_temp = set_name + '\tNA\t' + '\t'.join(input_data)
#fixed_path_gmt = 'user_data/'+userID+'/user.gmt'
fixed_path_gmt = plot_path + 'user.gmt'
rw = open(fixed_path_gmt, 'w')
rw.write(gmt_temp)
rw.close()
sample_list = group_samples
class_vector = [[group_info[i]] * len(item)
for i, item in enumerate(sample_list)]
class_vector = [y for x in class_vector for y in x]
class_vector = map(str, class_vector)
df_user_s = [df_all[s] for s in sample_list]
df_user_s = pd.concat(df_user_s, axis=1)
df_user_s.columns = range(len(df_user_s.columns.tolist()))
df_user_s = df_user_s.reset_index()
gsea_result = gseapy.gsea(data=df_user_s,
gene_sets=fixed_path_gmt,
cls=class_vector,
outdir=plot_path,
min_size=2,
max_size=1000,
weighted_score_type=1,
permutation_type='phenotype',
method='signal_to_noise',
ascending=False,
figsize=(6.5, 6),
format='png')
#ledge_genes = gsea_result.results[gsea_result.results.keys()[0]]['ledge_genes'].split(";")## leading edge subset
pval_es = gsea_result.results[gsea_result.results.keys()[0]][
'pval'] ###GSEA p-value
#with Image(filename=plot_path+set_name+".gsea.pdf", resolution=300) as img:
# with Image(width=img.width, height=img.height, background=Color("white")) as bg:
# bg.composite(img,0,0)
# bg.save(filename=plot_path+set_name+".gsea.png")
if pval_es <= 0.05:
file_list.append(template_plot_path + set_name + ".gsea.png")
filecount += 1
else:
file_list.append(" ")
gsea_mapping_rate = 'Not Applicable'
filecount += 1
#############GSEA#############
fold_change = all_expr_df[sample_list[0]].median(
axis=1) - all_expr_df[sample_list[1]].median(axis=1)
if pval_es <= 0.05:
#############MRA#############
mra_set_t = mra_set.T
mra_list = list(set(mra_set.index.tolist()))
mra_targets = [mra_set_t[x].values.tolist() for x in mra_list]
mra_targets = [
map(str, x[0]) if type(x[0]) == list else [str(x[0])]
for x in mra_targets
]
total_genes = len(list(set(mra_set.values.flatten())))
pvals = [
fet_f(mra_targets[a], input_data, total_genes)
for a in range(len(mra_list))
]
#pvals = [fet_f(mra_targets[a], ledge_genes, total_genes) for a in range(len(mra_list))] ## leading edge subset
pvals_list = [[
mra_list[i],
int(item[0]),
int(item[1]),
float("{0:.4f}".format(item[2])),
float("{0:.4f}".format(fold_change.loc[mra_list[i]]))
] for i, item in enumerate(pvals) if item[2] < 0.01
and fold_change.loc[mra_list[i]] >= 0.1 and item[0] > 10]
#table_arr = pvals_list #####Table data
index_change = lambda x, y: [y] + x[1:]
#table_arr = [index_change(x,pcta_id.loc[x[0]]['Symbol']) for x in table_arr]
network_data = pd.DataFrame(
data=pvals_list, columns=['TF', 'targets', 'mapped', 'pval', 'fc'])
network_data = network_data.set_index('TF')
network_data['Symbol'] = pcta_id.loc[map(
str, network_data.index.tolist())]['Symbol']
network_data[
'prob_mapped'] = network_data['mapped'] / network_data['targets']
network_data = network_data.sort_values('prob_mapped', ascending=False)
network_data = network_data.loc[network_data.index.tolist()[:10]]
network_data = network_data.round(4)
network_data['targets'].astype(int)
network_data['mapped'].astype(int)
selected_network_expr = df_all[group_samples[0]].loc[
network_data.index.tolist()[:10]]
selected_network_expr['Symbol'] = pcta_id.loc[map(
str, selected_network_expr.index.tolist())]['Symbol']
network_data = network_data.set_index('Symbol')
selected_network_expr = selected_network_expr.set_index('Symbol')
pt.network_plot(network_data,
selected_network_expr,
tit=group_info[0],
filename=plot_path + test_name + str(filecount))
file_list.append(template_plot_path + test_name + str(filecount) +
".png")
network_data = network_data[[
'targets', 'mapped', 'prob_mapped', 'fc', 'pval'
]]
int_ = lambda x: [int(x[0]), int(x[1])] + x[2:]
table_arr = [[i] + int_(network_data.loc[i].tolist())
for i in network_data.index.tolist()]
network_data.to_csv(plot_path + 'mra_candidates.csv')
os.system("cp -rf %s/* %s" % (plot_path, nginx_plot_path))
#############MRA#############
else:
table_arr = []
file_list.append(" ")
return file_list, table_arr, gsea_mapping_rate
def association_calculator(input_data, group_info, group_samples, session_key):
plot_path = "/home/ubuntu/django_proj/pcta_updated/main/static/images/" + session_key + "/"
template_plot_path = "images/" + session_key + "/"
test_name = "association_test"
if not os.path.exists(plot_path):
os.mkdir(plot_path)
else:
files = glob.glob(plot_path + "*")
print files
for f in files:
os.remove(f)
filecount = 0
file_list = []
#####Lollipop
pt = MY_PLOT()
df = all_expr_df
df = df.drop('Symbol', axis=1)
if len(input_data) > 1:
df_data = [
gene_set_zscore(df[samples], input_data, sample_status="multiple")
for samples in group_samples
]
ylab = 'Z score'
elif len(input_data) == 1:
df_data = [
df[samples].loc[input_data[0]].values.tolist()
for samples in group_samples
]
ylab = 'Expression'
pt.lollipop([sorted(d) for d in df_data],
ylab=ylab,
label_data=group_info,
filename=plot_path + test_name + str(filecount))
file_list.append(template_plot_path + test_name + str(filecount) + ".png")
filecount += 1
#####Lollipop
#####Violin
df_data_arr = [pd.DataFrame(data=d) for d in df_data]
df_data_arr = pd.concat(df_data_arr, axis=1)
df_data_arr.columns = group_info
pt.violin_plt(df_data_arr,
ylab=ylab,
tit='',
filename=plot_path + test_name + str(filecount))
file_list.append(template_plot_path + test_name + str(filecount) + ".png")
filecount += 1
#####Violin
#####Histogram
pt.histogram_group(df_data_arr,
legend=True,
filename=plot_path + test_name + str(filecount))
file_list.append(template_plot_path + test_name + str(filecount) + ".png")
filecount += 1
#####Histogram
return file_list
def correlation_calculator(input_data1, input_data2, group_info, group_samples,
session_key):
####ALL SAMPLES INSERTION####
group_info = ['ALL'] + group_info
all_samples = [sample for samples in group_samples for sample in samples]
group_samples.insert(0, all_samples)
####ALL SAMPLES INSERTION####
def input_data_process(df, input_d):
if len(input_d) > 1:
df_data = [
gene_set_zscore(df[samples], input_d, sample_status="multiple")
for samples in group_samples
]
elif len(input_d) == 1:
df_data = [
df[samples].loc[input_d[0]].values.tolist()
for samples in group_samples
]
return df_data
plot_path = "/home/ubuntu/django_proj/pcta_updated/main/static/images/" + session_key + "/"
nginx_plot_path = "/home/ubuntu/django_proj/pcta_updated/main/staticimages/" + session_key + "/"
template_plot_path = "images/" + session_key + "/"
test_name = "correlation_test"
if not os.path.exists(plot_path):
os.mkdir(plot_path)
os.mkdir(nginx_plot_path)
else:
files = glob.glob(plot_path + "*")
for f in files:
os.remove(f)
filecount = 0
file_list = []
#####Scatter
pt = MY_PLOT()
df = all_expr_df
df = df.drop('Symbol', axis=1)
df_data1 = input_data_process(df, input_data1)
df_data2 = input_data_process(df, input_data2)
group_df = []
for a in range(len(group_info)):
d = {'Input1': df_data1[a], 'Input2': df_data2[a]}
group_df.append(pd.DataFrame(data=d))
pt.shared_scatter(group_df,
x_dat='Input1',
y_dat='Input2',
title=group_info,
filename=plot_path + test_name + str(filecount))
file_list.append(template_plot_path + test_name + str(filecount) + ".png")
filecount += 1
os.system("cp %s/* %s" % (plot_path, nginx_plot_path))
return file_list
def set_c(input_data, group_info, group_samples, session_key):
def fet_f(a1, b1, total_gene_assum):
a1_inter_b1 = list(set(a1).intersection(b1))
a1_unique_fromb1 = list(set(a1) - set(a1_inter_b1))
b1_unique_froma1 = list(set(b1) - set(a1_inter_b1))
oddsratio, pvalue = stats.fisher_exact(
[[len(a1_inter_b1), len(b1_unique_froma1)],
[
len(a1_unique_fromb1),
total_gene_assum - (len(a1_inter_b1) + len(b1_unique_froma1) +
len(a1_unique_fromb1))
]])
return len(a1), len(a1_inter_b1), pvalue
plot_path = "/home/ubuntu/django_proj/pcta_updated/main/static/images/" + session_key + "/"
template_plot_path = "images/" + session_key + "/"
test_name = "USER_SET"
if not os.path.exists(plot_path):
os.mkdir(plot_path)
else:
files = glob.glob(plot_path + "*")
for f in files:
os.remove(f)
filecount = 0
file_list = []
pt = MY_PLOT()
df_all = all_expr_df
#############GSEA#############
gmt_temp = 'USER_SET\tNA\t' + '\t'.join(input_data)
#fixed_path_gmt = 'user_data/'+userID+'/user.gmt'
fixed_path_gmt = plot_path + 'user.gmt'
rw = open(fixed_path_gmt, 'w')
rw.write(gmt_temp)
rw.close()
sample_list = group_samples
class_vector = [[group_info[i]] * len(item)
for i, item in enumerate(sample_list)]
class_vector = [y for x in class_vector for y in x]
class_vector = map(str, class_vector)
current_task.update_state(state='PROGRESS', meta={'process_percent3': 20})
df_user_s = [df_all[s] for s in sample_list]
df_user_s = pd.concat(df_user_s, axis=1)
df_user_s.columns = range(len(df_user_s.columns.tolist()))
df_user_s = df_user_s.reset_index()
gseapy.gsea(data=df_user_s,
gene_sets=fixed_path_gmt,
cls=class_vector,
outdir=plot_path,
min_size=2,
max_size=1000,
weighted_score_type=1,
permutation_type='gene_set',
method='signal_to_noise',
ascending=False,
figsize=(6.5, 6),
format='png')
file_list.append(template_plot_path + "USER_SET.gsea.png")
filecount += 1
#############GSEA#############
current_task.update_state(state='PROGRESS', meta={'process_percent3': 40})
#############MRA#############
mra_set_t = mra_set.T
mra_list = list(set(mra_set.index.tolist()))
mra_targets = [mra_set_t[x].values.tolist() for x in mra_list]
mra_targets = [
map(str, x[0]) if type(x[0]) == list else [str(x[0])]
for x in mra_targets
]
total_genes = len(list(set(mra_set.values.flatten())))
pvals = [
fet_f(mra_targets[a], input_data, total_genes)
for a in range(len(mra_list))
]
pvals_list = [[mra_list[i], item[0], item[1], item[2]]
for i, item in enumerate(pvals)
if item[2] < 0.01 and item[0] > 10]
table_arr = pvals_list #####Table data
current_task.update_state(state='PROGRESS', meta={'process_percent3': 60})
rw = open(plot_path + 'mra_candidates.tsv', "w")
rw.write("Gene(EntrezID)\tTF_targets\tMapped_genes\tP-value\n")
for x in table_arr:
x = [str(y) for y in x]
rw.write('\t'.join(x) + '\n')
rw.close()
network_data = pd.DataFrame(data=pvals_list,
columns=['TF', 'targets', 'mapped', 'pval'])
network_data = network_data.set_index('TF')
network_data[
'prob_mapped'] = network_data['mapped'] / network_data['targets']
network_data = network_data.sort_values('prob_mapped', ascending=False)
network_data = network_data.loc[network_data.index.tolist()[:10]]
current_task.update_state(state='PROGRESS', meta={'process_percent': 80})
selected_network_expr = df_all[group_samples[0]].loc[
network_data.index.tolist()[:10]]
pt.network_plot(network_data,
selected_network_expr,
tit=group_info[0],
filename=plot_path + test_name + str(filecount))
file_list.append(template_plot_path + test_name + str(filecount) + ".png")
current_task.update_state(state='PROGRESS', meta={'process_percent3': 100})
#############MRA#############
return random.random()
def correlation_c(input_data1,
input_data2,
dataset_input_data,
group_info,
group_samples,
plot_color,
session_key,
name1='Input 1',
name2='Input 2'):
current_task.update_state(state='PROGRESS', meta={'process_percent2': 0})
####ALL SAMPLES INSERTION####
group_info = ['ALL'] + group_info
all_samples = [sample for samples in group_samples for sample in samples]
group_samples.insert(0, all_samples)
####ALL SAMPLES INSERTION####
def input_data_process(df, input_d):
if len(input_d) > 1:
df_data = [
gene_set_zscore_single_thr(df[samples],
input_d,
sample_status="multiple")
for samples in group_samples
]
elif len(input_d) == 1:
df_data = [
df[samples].loc[input_d[0]].values.tolist()
for samples in group_samples
]
return df_data
current_task.update_state(state='PROGRESS', meta={'process_percent2': 20})
plot_path = "/home/ubuntu/django_proj/pcta_updated/main/static/images/" + session_key + "/"
nginx_plot_path = "/home/ubuntu/django_proj/pcta_updated/main/staticimages/" + session_key + "/"
template_plot_path = "images/" + session_key + "/"
test_name = "correlation_test"
if not os.path.exists(plot_path):
os.mkdir(plot_path)
os.mkdir(nginx_plot_path)
else:
files = glob.glob(plot_path + "*")
for f in files:
os.remove(f)
files = glob.glob(nginx_plot_path + "*")
for f in files:
os.remove(f)
filecount = 0
file_list = []
#####Scatter
pt = MY_PLOT()
if dataset_input_data == 'PCTA':
df = all_expr_df
df = df.drop('Symbol', axis=1)
else:
df = tcga_expr_df
df_data1 = input_data_process(df, input_data1)
current_task.update_state(state='PROGRESS', meta={'process_percent2': 40})
df_data2 = input_data_process(df, input_data2)
current_task.update_state(state='PROGRESS', meta={'process_percent2': 60})
group_df = []
for a in range(len(group_info)):
d = {name1: df_data1[a], name2: df_data2[a]}
group_df.append(pd.DataFrame(data=d))
current_task.update_state(state='PROGRESS',
meta={'process_percent2': 80})
pt.shared_scatter(group_df,
x_dat=name1,
y_dat=name2,
title=group_info,
color_arr=plot_color,
filename=plot_path + test_name + str(filecount))
file_list.append(template_plot_path + test_name + str(filecount) + ".png")
filecount += 1
os.system("cp -rf %s/* %s" % (plot_path, nginx_plot_path))
current_task.update_state(state='PROGRESS', meta={'process_percent2': 100})
return random.random()
def survival_c(input_data, session_key):
current_task.update_state(state='PROGRESS', meta={'process_percent1': 0})
plot_path = "/home/ubuntu/django_proj/pcta_updated/main/static/images/" + session_key + "/"
nginx_plot_path = "/home/ubuntu/django_proj/pcta_updated/main/staticimages/" + session_key + "/"
template_plot_path = "images/" + session_key + "/"
test_name = "bcr_analysis"
if not os.path.exists(plot_path):
os.mkdir(plot_path)
os.mkdir(nginx_plot_path)
else:
files = glob.glob(plot_path + "*")
for f in files:
os.remove(f)
files = glob.glob(nginx_plot_path + "*")
for f in files:
os.remove(f)
filecount = 0
file_list = []
pt = MY_PLOT()
surv_dataset = ['TCGA_PRAD', 'GSE40272', 'GSE70769']
expr_set_arr = []
clinical_set_arr = []
for surv in surv_dataset:
if surv != 'TCGA_PRAD':
expr_set = pd.read_csv('user_data/clinical_data/' + surv + '.csv',
index_col=0)
expr_set.index = expr_set.index.astype(int)
expr_set.index = expr_set.index.astype(str)
clinical_set = pd.read_excel('user_data/clinical_data/' + surv +
'_clinical.xlsx',
index_col=0)
else:
expr_set = tcga_expr_df
clinical_set = pd.read_excel('user_data/clinical_data/' + surv +
'_clinical.xlsx',
index_col=0)
expr_set_arr.append(expr_set)
clinical_set_arr.append(clinical_set)
current_task.update_state(state='PROGRESS', meta={'process_percent1': 20})
test_set_arr = []
for expr in expr_set_arr:
if len(input_data) > 1:
df_data = gene_set_zscore_single_thr(expr,
input_data,
sample_status="multiple")
test_set = pd.Series(data=df_data,
index=expr.columns.tolist(),
name='Input_Zscore')
test_set_arr.append(test_set)
surv_input = 'Input_Zscore'
elif len(input_data) == 1:
test_set = expr.loc[input_data[0]]
symbol = pcta_id.loc[input_data[0]]['Symbol']
test_set = test_set.rename(symbol)
test_set_arr.append(test_set)
#surv_input = input_data[0]
surv_input = symbol
current_task.update_state(state='PROGRESS', meta={'process_percent1': 40})
dv_arr = []
for i, test in enumerate(test_set_arr):
test_set_up = test[test > test.median()]
test_set_down = test[test < test.median()]
clinical_up = clinical_set_arr[i].loc[test_set_up.index.tolist()]
clinical_down = clinical_set_arr[i].loc[test_set_down.index.tolist()]
clinical_arr = [clinical_up, clinical_down]
dv_arr.append(clinical_arr)
current_task.update_state(state='PROGRESS', meta={'process_percent1': 60})
for dv in dv_arr:
pt.survival_plot_and_cox(
dv,
label=[surv_input + '_up', surv_input + '_down'],
filename=plot_path + test_name + str(filecount))
file_list.append(template_plot_path + test_name + str(filecount) +
".png")
filecount += 1
current_task.update_state(state='PROGRESS', meta={'process_percent1': 80})
table_arr = []
cph = CoxPHFitter()
for i, test in enumerate(test_set_arr):
cph_data = test
cph_data.loc[cph_data > test_set.median()] = 1
cph_data.loc[cph_data < test_set.median()] = 0
cph_data = pd.concat([cph_data, clinical_set_arr[i]], axis=1)
table_data = []
cph.fit(cph_data[[surv_input, 'bcrstatus', 'bcrmonth']].dropna(),
'bcrmonth',
event_col='bcrstatus')
table_data = table_split(cph)
cph.fit(cph_data[['gleason', 'bcrstatus', 'bcrmonth']].dropna(),
'bcrmonth',
event_col='bcrstatus')
table_data = table_data + table_split(cph)
cph.fit(cph_data[[surv_input, 'gleason', 'bcrstatus',
'bcrmonth']].dropna(),
'bcrmonth',
event_col='bcrstatus')
table_data = table_data + table_split(cph)
#table_data = [['Variable','Coefficient','Hazard Ratio','P-value','C-index']]+table_data
table_arr.append(table_data)
rw = open(plot_path + surv_dataset[i] + '.cox.tsv', "w")
rw.write("Variable\tCoefficient\tHR(95%Conf)\tP-value\tC-index\n")
for x in table_data:
rw.write('\t'.join(x) + '\n')
rw.close()
os.system("cp -rf %s/* %s" % (plot_path, nginx_plot_path))
current_task.update_state(state='PROGRESS', meta={'process_percent1': 100})
return random.random()
def association_c(input_data, dataset_input_data, group_info, group_samples,
plot_color, session_key):
current_task.update_state(state='PROGRESS', meta={'process_percent': 0})
plot_path = "/home/ubuntu/django_proj/pcta_updated/main/static/images/" + session_key + "/"
nginx_plot_path = "/home/ubuntu/django_proj/pcta_updated/main/staticimages/" + session_key + "/"
template_plot_path = "images/" + session_key + "/"
test_name = "association_test"
if not os.path.exists(plot_path):
os.mkdir(plot_path)
os.mkdir(nginx_plot_path)
else:
files = glob.glob(plot_path + "*")
for f in files:
os.remove(f)
files = glob.glob(nginx_plot_path + "*")
for f in files:
os.remove(f)
filecount = 0
file_list = []
#####Lollipop
pt = MY_PLOT()
if dataset_input_data == 'PCTA':
df = all_expr_df
df = df.drop('Symbol', axis=1)
else:
df = tcga_expr_df
current_task.update_state(state='PROGRESS', meta={'process_percent': 10})
if len(input_data) > 1:
df_data = [
gene_set_zscore_single_thr(df[samples],
input_data,
sample_status="multiple")
for samples in group_samples
]
ylab = 'Z score'
elif len(input_data) == 1:
df_data = [
df[samples].loc[input_data[0]].values.tolist()
for samples in group_samples
]
ylab = 'Expression'
current_task.update_state(state='PROGRESS', meta={'process_percent': 20})
pt.lollipop([sorted(d) for d in df_data],
label_data=group_info,
ylab=ylab,
color_arr=plot_color,
filename=plot_path + test_name + str(filecount))
###########Oneway ANNOVA with group data
if len(df_data) == 3:
fv, pv = stats.f_oneway(df_data[0], df_data[1], df_data[2])
else:
fv, pv = stats.f_oneway(df_data[0], df_data[1], df_data[2], df_data[3])
rw = open(plot_path + 'onewayANOVA_result.tsv', "w")
rw.write("F-value\tP-value\n")
rw.write(str('{:.3f}'.format(fv)) + '\t' + float_format_change(pv) + '\n')
rw.close()
###########Oneway ANNOVA with group data
file_list.append(template_plot_path + test_name + str(filecount) + ".png")
filecount += 1
#####Lollipop
current_task.update_state(state='PROGRESS', meta={'process_percent': 30})
#####Violin or boxplot
df_data_arr = [pd.DataFrame(data=d) for d in df_data]
df_data_arr = pd.concat(df_data_arr, axis=1)
df_data_arr.columns = group_info
pt.violin_plt(df_data_arr,
ylab=ylab,
tit='',
color_arr=plot_color,
filename=plot_path + test_name + str(filecount))
###########Ranksum with group data
rw = open(plot_path + 'ranksum_result.tsv', "w")
rw.write("Test group\tFoldChange\tP-value\n")
if 'GS=7' in group_info:
#others_index = [ i for i,item in enumerate(group_info) if item!='mCRPC' ]
#others_values = [ df_data[i] for i in others_index ]
#others_values = list(sum(others_values, []))
#test_values = df_data[group_info.index('mCRPC')]
if dataset_input_data == 'PCTA':
test_values, others_values = test_and_others_selection(
'mCRPC',
group_info,
df_data,
others_name=['GS=7', 'GS<7', 'GS>7'])
rv, pv = stats.ranksums(test_values, others_values)
fc = float(np.mean(test_values)) - float(np.mean(others_values))
rw.write("mCRPC VS Primary" + '\t' + str('{:.3f}'.format(fc)) +
'\t' + float_format_change(pv) + '\n')
test_values, others_values = test_and_others_selection(
'Benign',
group_info,
df_data,
others_name=['GS=7', 'GS<7', 'GS>7'])
rv, pv = stats.ranksums(test_values, others_values)
fc = float(np.mean(others_values)) - float(np.mean(test_values))
rw.write("Primary VS Benign" + '\t' + str('{:.3f}'.format(fc)) +
'\t' + float_format_change(pv) + '\n')
rw.close()
else:
test_values, others_values = test_and_others_selection(
'GS>7', group_info, df_data, others_name=['GS=7', 'GS<7'])
rv, pv = stats.ranksums(test_values, others_values)
fc = float(np.mean(test_values)) - float(np.mean(others_values))
rw.write("GS>7 VS Others" + '\t' + str('{:.3f}'.format(fc)) +
'\t' + float_format_change(pv) + '\n')
test_values, others_values = test_and_others_selection(
'GS<7', group_info, df_data, others_name=['GS=7', 'GS>7'])
rv, pv = stats.ranksums(test_values, others_values)
fc = float(np.mean(others_values)) - float(np.mean(test_values))
rw.write("GS<7 VS Others" + '\t' + str('{:.3f}'.format(fc)) +
'\t' + float_format_change(pv) + '\n')
rw.close()
#ranksum_test_case = [df_data[group_info.index('mCRPC')], ]
elif 'PCS1' in group_info:
test_values, others_values = test_and_others_selection(
'PCS1', group_info, df_data)
rv, pv = stats.ranksums(test_values, others_values)
fc = float(np.mean(test_values)) - float(np.mean(others_values))
rw.write("PCS1 VS Others" + '\t' + str('{:.3f}'.format(fc)) + '\t' +
float_format_change(pv) + '\n')
rw.close()
elif 'LumB' in group_info:
test_values, others_values = test_and_others_selection(
'LumB', group_info, df_data)
rv, pv = stats.ranksums(test_values, others_values)
fc = float(np.mean(test_values)) - float(np.mean(others_values))
rw.write("LumB VS Others" + '\t' + str('{:.3f}'.format(fc)) + '\t' +
float_format_change(pv) + '\n')
rw.close()
current_task.update_state(state='PROGRESS', meta={'process_percent': 40})
###########Ranksum with group data
file_list.append(template_plot_path + test_name + str(filecount) + ".png")
filecount += 1
#####Violin or boxplot
current_task.update_state(state='PROGRESS', meta={'process_percent': 50})
#####Histogram
#pt.histogram_group(df_data_arr,legend=True,colo=plot_color,filename=plot_path+test_name+str(filecount))
pt.line_trend(df_data_arr,
ylab='Mean of ' + ylab,
legend=True,
colo=plot_color,
filename=plot_path + test_name + str(filecount))
file_list.append(template_plot_path + test_name + str(filecount) + ".png")
filecount += 1
os.system("cp -rf %s/* %s" % (plot_path, nginx_plot_path))
current_task.update_state(state='PROGRESS', meta={'process_percent': 100})
#####Histogram
#return file_list
return random.random()