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MAFLD_tag.py
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MAFLD_tag.py
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import os,math
import pandas as pd
import numpy as np
from icd_data_process import process_relative_data
def get_MAFLD(path):
if os.path.exists('data_target/MAFLD1.csv'):
pass
else:
if os.path.exists('data_target/FLI_MAFLD.csv'):
FLI_data=pd.read_csv('data_target/FLI_MAFLD.csv',index_col='Participant ID')
else:
def FLI(triglycerides,ggt,waist_circumference,BMI):
triglycerides=88.6*triglycerides
e_index=math.exp(0.935*math.log(triglycerides)+0.139*BMI+0.718*math.log(ggt)+0.053*waist_circumference-15.745)
return 100*e_index/(1+e_index)
FLI_data=pd.read_csv(path+'/MAFLD+糖尿病基线.csv',index_col='Participant ID')
FLI_data['FLI再次']=FLI_data.apply(lambda x: FLI(triglycerides=x['甘油三酯'],
ggt=x['GGT'],waist_circumference=x['腰围'],BMI=x['BMI'],
),axis=1)
FLI_data.loc[(FLI_data['FLI再次']>60) & (FLI_data['代谢异常']==1),['MAFLD']]=1
FLI_data=FLI_data.loc[:,['FLI再次','MAFLD']]
FLI_data['MAFLD']=FLI_data['MAFLD'].fillna(0).astype(int)
FLI_data.to_csv('data_target/FLI_MAFLD.csv')
print('FLI_MAFLD数据维度\t:'+str(FLI_data.shape))
print('MAFLD数量:\t'+str(sum(FLI_data['MAFLD']==1)))
baseline_data=pd.read_csv(path+'/基线资料.csv',index_col=0)
print('基线数据维度\t:'+str(baseline_data.shape))
print('Townsend empty\t:'+str(
sum(baseline_data['Townsend deprivation index at recruitment'].isna())
))
print('FLI empty\t:'+str(
sum(FLI_data['FLI再次'].isna())
))
baseline_data=baseline_data.join(FLI_data)
baseline_data=baseline_data.loc[
(baseline_data['Townsend deprivation index at recruitment'].notna())
&(baseline_data['FLI再次'].notna())
,
['Sex','Age','Townsend deprivation index at recruitment','MAFLD','Drink','BMI25',]]
print(f'shape:{baseline_data.shape[0]}')
print('w/ MAFLD:'+str(sum(baseline_data['MAFLD']==1)))
baseline_data.to_csv('data_target/MAFLD.csv')
def get_prs(path):
if os.path.exists('data_target/MAFLD1.csv'):
pass
else:
if os.path.exists('data_target/prs.csv'):
prs=pd.read_csv('data_target/prs.csv',index_col=0)
else:
prs=pd.read_csv('data_original/PRS2.csv',index_col=0)
tertile_low=prs['PRS'].quantile(1/3)
tertile_high=prs['PRS'].quantile(2/3)
prs['PRS']=prs['PRS'].apply(lambda x:0 if x<tertile_low else (1 if x>tertile_high else np.nan ) )
prs=prs[['PRS']]
prs[['PRS']].to_csv('data_target/prs.csv')
print(f'prs shape:{prs.shape[0]}')
print(f'prs null:')
print(sum(prs['PRS'].isna()))
prs=prs.rename(columns={'PRS': 'MAFLD'})
baseline_data=pd.read_csv(path+'/基线资料.csv',index_col='Participant ID')
print('基线数据维度\t:'+str(baseline_data.shape))
print('Townsend empty\t:'+str(
sum(baseline_data['Townsend deprivation index at recruitment'].isna())
))
baseline_data=baseline_data.join(prs)
baseline_data=baseline_data.loc[
(baseline_data['Townsend deprivation index at recruitment'].notna())
&(baseline_data['MAFLD'].notna())
,
['Sex','Age','Townsend deprivation index at recruitment','MAFLD','Drink','BMI25',]]
print(f'final shape:{baseline_data.shape[0]}')
print('w/ MAFLD:'+str(sum(baseline_data['MAFLD']==1)))
baseline_data.to_csv('data_target/MAFLD.csv')
def psm_result():
data=pd.read_csv('r_result/PSM匹配.csv',index_col=0)
print(data.shape)
death_info=process_relative_data()
data['death']=death_info['40000-0.0'].notna().astype(int)
data['fillin']=death_info['fillin'].dt.days
print(data.shape)
data.to_csv('r_result/psm_tags.csv')
data=data.drop(columns=['distance','subclass','weights'])
data=data[data['MAFLD']==1]
data.to_csv('data_target/MAFLD_death.csv')
print(data.shape)
return
def NFS_tag():
data=pd.read_excel('data_original/NFS 纤维化数据.xlsx',index_col=1)
baseline_data=pd.read_csv('r_result/psm_tags.csv',index_col=0)
baseline_data=baseline_data.join(data[['NFS']])
baseline_data['NFS_above']=(baseline_data['NFS']>=-1.455).astype(float)
baseline_data['NFS_above']=baseline_data['NFS_above'].where(baseline_data['NFS_above']==1,np.nan)
baseline_data['NFS_above']=baseline_data['NFS_above'].mask((baseline_data['NFS']<-1.455)&(baseline_data['MAFLD']==0),0)
baseline_data['NFS_above']=baseline_data['NFS_above'].mask(baseline_data['NFS'].isna(),np.nan)
baseline_data['NFS_below']=(baseline_data['MAFLD']&(baseline_data['NFS']<-1.455)).astype(float)
baseline_data['NFS_below']=baseline_data['NFS_below'].where(baseline_data['NFS_below']==1,np.nan)
baseline_data['NFS_below']=baseline_data['NFS_below'].mask((baseline_data['NFS']<-1.455)&(baseline_data['MAFLD']==0),0)
baseline_data['NFS_below']=baseline_data['NFS_below'].mask(baseline_data['NFS'].isna(),np.nan)
baseline_data.to_csv('r_result/psm_tags.csv')