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PHEWAS.py
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PHEWAS.py
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import sys
import os
import matplotlib as mpl
#if os.environ.get('DISPLAY','') == '':
# print('no display found. Using non-interactive Agg backend')
mpl.use('Agg')
import numpy as np
import matplotlib.pyplot as plt
import math
import pandas as pd
## check string
def is_digit(str):
try:
tmp=float(str)
return True
except ValueError:
return False
Color = {}
## b g r c m y k
for i in range(400):
if i%4 ==0 :
Color[i] = 'b'
elif i%4 ==1 :
Color[i] = 'r'
elif i%4 ==2 :
Color[i] = 'y'
elif i%4 ==3 :
Color[i] = 'm'
## merged : merged and sorted bacterial Qassoc
merged=open('%s'%sys.argv[1],'r')
#w = open('%sDDD'%sys.argv[1],'w')
## mean of beta range
P_values_cutoff=str(sys.argv[2])
## P-value count Cutoff P< 0.05
P_count_cutoff=str(sys.argv[3])
DRECTORY=sys.argv[4]
## column ID ,SNP , BETA, P
ID_IDX= [] ## ID_IDX : DIR/BACTERIAL full ID
SNP_ID= []
BETA_IDX = {}
P_IDX = {}
cnt =0
PRE_SNP=''
Class = {}
Family= {}
Family_indx = []
for i in merged :
idx = i.strip('\n').split('\t')
BACTERIAL_ID=idx[0].split('.OTU.')[1].split(';')
Family_indx.append(BACTERIAL_ID[-2])
if 'linear' in BACTERIAL_ID[-1] :
Family_indx.append(idx[0].split('.OTU.')[1][:idx[0].split('.OTU.')[1].find('.assoc.linear')])
else :
Family_indx.append(idx[0].split('.OTU.')[1][:idx[0].split('.OTU.')[1].find('.qassoc')])
SNP= idx[1]
P_value= idx[3]
Beta=idx[2]
if P_value == 'NA' or Beta =='NA' :
continue
if SNP not in Family.keys() and is_digit(P_value) ==True and is_digit(Beta)==True:
#if SNP not in BETA_IDX.keys() and is_digit(P_value) ==True and is_digit(Beta)==True:
BETA_IDX[SNP]=[float(Beta)]
Family[SNP] =[Family_indx]
Family_indx =[]
Class[SNP]=[BACTERIAL_ID[-2]]
P_IDX[SNP]=[float(P_value)]
### mean of beta analysis
if len(PRE_SNP) !=0 :
MEAN_BETA= np.mean(BETA_IDX[PRE_SNP])
cnt_comfim_P = 0
#print Family[PRE_SNP]
for j in P_IDX[PRE_SNP] :
if float(j) < float(P_values_cutoff) : ## P is fixing
cnt_comfim_P = cnt_comfim_P +1
if cnt_comfim_P > int(P_count_cutoff):
print "P-vlause cut off counting:",cnt_comfim_P ,PRE_SNP,len(P_IDX[PRE_SNP])
fig = plt.figure()
fig.set_size_inches(10,10)
ax = plt.subplot(1, 1, 1)
plt.ylabel("-log(P-value)",size=15)
plt.title("Phe-WAS:%s"%PRE_SNP,size= 15)
# plt.ylim(0,15)
#P< 1e-4
plt.axhline(y=4, color='b', linewidth=1,linestyle=':')
plt.axhline(y=2, color='g', linewidth=1,linestyle=':')
plt.axhline(y=-math.log10(5e-8),
color='r', linewidth=1,linestyle=':')
X = 0
OO = -1
Cindx = {}
print PRE_SNP,Class[PRE_SNP][0], Family[PRE_SNP][0]
CLASS = []
HI = Class[PRE_SNP]
for DD in Class[PRE_SNP]:
if float(P_IDX[PRE_SNP][X]) == 0 :
Y = 0
else:
Y= -math.log10(float(P_IDX[PRE_SNP][X]))
#Name= Family[PRE_SNP][X][1][3:].split(';')[1]
Name =Family[PRE_SNP][X][1].split(';')[-1]
# print Name
if DD[3:] not in Cindx.keys() :
CLASS.append(DD[3:])
OO = OO + 1
Cindx[DD[3:]]= OO
#print Name
if Y > 2 :
plt.plot(OO,Y,ls='', marker='o',color= Color[OO%4])
plt.text(OO,Y+0.001,'%s'%Name,size= 12)
else :
plt.plot(OO,Y,ls='', marker='o',color= Color[OO%4])
else:
Cindx[DD[3:]]= Cindx[DD[3:]]+ 0.05
Col_idx = Color[int(Cindx[DD[3:]])%4]
if Y > 2 :
plt.plot(Cindx[DD[3:]],Y,ls='', marker='o',
color= Col_idx)
plt.text(Cindx[DD[3:]],Y+0.001,'%s'%Name)
else:
plt.plot(Cindx[DD[3:]],Y,ls='',
marker='o',color= Col_idx)
X = X +1
plt.xticks([GG for GG in range(len(CLASS))], CLASS)
for label in ax.xaxis.get_ticklabels() :
label.set_rotation(90)
plt.xlabel("Microbial Class",size=10)
plt.savefig('%s/%s_Pcount.png'%(DRECTORY,PRE_SNP),
dpi = 500,bbox_inches = 'tight')
plt.close()
##################################################################
######################## NetWork analysis ########################
##################################################################
#print len(Family)
#print len(P_IDX)
name_idx= []
for kd in Family[PRE_SNP] :
name_idx.append(kd[1])
mydict = pd.DataFrame({'Family':name_idx,'%s'%PRE_SNP:BETA_IDX[PRE_SNP]})
if 'Network' not in globals().keys():
Network=mydict
else :
Network = pd.merge(Network, mydict,on ='Family')#),inplace= True)
print Network.shape
# print Network
#print mydict['0']
# for k in P_IDX[PRE_SNP] :
# print k
# display(
##################################################################
######################## End of Analysis #########################
##################################################################
del BETA_IDX[PRE_SNP]
del Family[PRE_SNP]
del Class[PRE_SNP]
del P_IDX[PRE_SNP]
else:
del BETA_IDX[PRE_SNP]
del Family[PRE_SNP]
del Class[PRE_SNP]
del P_IDX[PRE_SNP]
elif SNP in Family.keys() and is_digit(P_value) ==True and is_digit(Beta)==True:
#elif SNP in BETA_IDX.keys() and is_digit(P_value) ==True and is_digit(Beta)==True:
## BETA
BETA_info= BETA_IDX[SNP]
BETA_info.append(float(Beta))
BETA_IDX[SNP]= BETA_info
FFF = Family[SNP]
FFF.append(Family_indx)
Family[SNP] = FFF
Family_indx=[]
Class_ii = Class[SNP]
Class_ii.append(BACTERIAL_ID[-2])
Class[SNP]=Class_ii
############P-value
P_info= P_IDX[SNP]
P_info.append(float(P_value))
P_IDX[SNP]= P_info
cnt = cnt +1
PRE_SNP=SNP
Network.to_csv("%sfor_network.csv"%sys.argv[1])
print BETA_IDX.keys()