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generate_betamaps.py
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import os
import util
import numpy as np
import pandas as pd
from argparse import ArgumentParser
def get_year(s):
if isinstance(s,str):
return int(s.split('/')[-1])
else:
return np.nan
if __name__ == "__main__":
parser = ArgumentParser()
parser.add_argument("--path_pheno",help="path to phenotype .csv file",dest='path_pheno')
parser.add_argument("--path_connectomes",help="path to connectomes .csv file",dest='path_connectomes')
parser.add_argument("--path_out",help="path to output directory",dest='path_out')
args = parser.parse_args()
#############
# LOAD DATA #
#############
path_pheno = args.path_pheno
path_connectomes = args.path_connectomes
path_out = args.path_out
pheno = pd.read_csv(path_pheno,index_col=0)
connectomes = pd.read_csv(path_connectomes,index_col=0)
regressors_mc = ['AGE','C(SEX)','FD_scrubbed', 'C(SITE)', 'mean_conn']
regressors_nomc = ['AGE','C(SEX)','FD_scrubbed', 'C(SITE)']
################
# CASE CONTROL #
################
cases =['SZ','BIP','ASD','ADHD','IBD','DEL1q21_1','DEL2q13','DEL13q12_12','DEL15q11_2','DEL16p11_2',
'DEL17p12','DEL22q11_2','TAR_dup','DUP1q21_1','DUP2q13','DUP13q12_12','DUP15q11_2','DUP15q13_3_CHRNA7',
'DUP16p11_2','DUP16p13_11','DUP22q11_2']
ipc = ['SZ','BIP','ASD','ADHD']
df_pi = pheno.groupby('PI').sum()[cases]
mask_pi = (df_pi > 0)
# MEAN CORRECTED
summaries = []
for case in cases:
if case in ipc:
mask = util.mask_cc(pheno,case,'CON_IPC')
elif case == 'IBD':
mask_case = (pheno['IBD_str'] == 'IBD_K50_K51').to_numpy(dtype=bool)
mask_con = (pheno['IBD_str'] == 'no_IBD').to_numpy(dtype=bool)
mask = mask_case + mask_con
else:
mask_case = pheno[case].to_numpy(dtype=bool)
pi_list = df_pi[mask_pi[case]].index.to_list()
mask_con = np.array((pheno['PI'].isin(pi_list))&(pheno['non_carriers']==1))
mask = mask_case + mask_con
print(case,pi_list)
summary = util.case_control(pheno[mask],case,regressors_mc,connectomes.to_numpy()[mask],std=True)
summary.to_csv(path_out + '/cc_{}_results_mc.csv'.format(case))
np.savetxt(path_out + '/cc_{}_mc.tsv'.format(case),util.vec_to_connectome(summary['betas_std'].to_numpy()),delimiter='\t')
summaries.append(summary)
print('Completed {}.'.format(case))
# NOT MEAN CORRECTED
summaries_nomc = []
for case in cases:
if case in ipc:
mask = util.mask_cc(pheno,case,'CON_IPC')
elif case == 'IBD':
mask_case = (pheno['IBD_str'] == 'IBD_K50_K51').to_numpy(dtype=bool)
mask_con = (pheno['IBD_str'] == 'no_IBD').to_numpy(dtype=bool)
mask = mask_case + mask_con
else:
mask_case = pheno[case].to_numpy(dtype=bool)
pi_list = df_pi[mask_pi[case]].index.to_list()
mask_con = np.array((pheno['PI'].isin(pi_list))&(pheno['non_carriers']==1))
mask = mask_case + mask_con
print(case,pi_list)
summary = util.case_control(pheno[mask],case,regressors_nomc,connectomes.to_numpy()[mask],std=True)
summary.to_csv(path_out + '/cc_{}_results_nomc.csv'.format(case))
np.savetxt(path_out + '/cc_{}_nomc.tsv'.format(case),util.vec_to_connectome(summary['betas_std'].to_numpy()),delimiter='\t')
summaries_nomc.append(summary)
print('Completed {}.'.format(case))
#####################
# CONTINUOUS SCORES #
#####################
prs = ['Stand_PRS_height','Stand_PRS_BMI','Stand_PRS_BIP','Stand_PRS_newCDG2_ukbb','Stand_PRS_ASD','Stand_PRS_SCZ',
'Stand_PRS_LDL','Stand_PRS_CKD','Stand_PRS_SA','Stand_PRS_MDD','Stand_PRS_IQ','Stand_PRS_SCZwave3']
cont = prs + ['CT','SA','Vol','fluid_intelligence_score_all','Gfactor','Neuroticism']
# MEAN CORRECTED
summaries_cont_mc = []
for c in cont:
p = pheno.copy()
p['year'] = p['date_of_attending_assessment_centre_f53_2_0'].apply(get_year)
if ('Stand_PRS' in c):
p = p[p['PI']=='UKBB']
p = p[(p.PRS_eth == 'WB') | (p.PRS_eth == 'EUR')]
if (c in ['Stand_PRS_SA','Stand_PRS_thickness']):
p = p.dropna(subset=['date_of_attending_assessment_centre_f53_2_0'])
p = p[p['year'] > 2017]
if ('Stand_' not in c):
print('Dropping ',p[c].isna().sum(),' subjects w/ NaN for {}.'.format(c))
if (p[c].isna().sum() == p.shape[0]):
print('ERROR: No subjects with data for {}.'.format(c))
p = p.dropna(subset=[c])
print('Z-scoring contrast...')
p['{}_z'.format(c)] = (p[c] - p[c].mean())/p[c].std(ddof=0)
c = '{}_z'.format(c)
mask = util.mask_var(p,c)
match_conn_mask = pheno.index.isin(p.index)
conn = connectomes.to_numpy()[match_conn_mask]
summary = util.variable_effect(p[mask],c,regressors_mc,conn[mask],std=True)
summary.to_csv(path_out + '/cont_{}_results_mc.csv'.format(c))
np.savetxt(path_out + '/cont_{}_mc.tsv'.format(c),util.vec_to_connectome(summary['betas_std'].to_numpy()),delimiter='\t')
summaries_cont_mc.append(summary)
print('Completed {}.'.format(c))
# NOT MEAN CORRECTED
summaries_cont_nomc = []
for c in cont:
p = pheno.copy()
p['year'] = p['date_of_attending_assessment_centre_f53_2_0'].apply(get_year)
if ('Stand_PRS' in c):
p = p[p['PI']=='UKBB']
p = p[(p.PRS_eth == 'WB') | (p.PRS_eth == 'EUR')]
if (c in ['Stand_PRS_SA','Stand_PRS_thickness']):
p = p.dropna(subset=['date_of_attending_assessment_centre_f53_2_0'])
p = p[p['year'] > 2017]
if ('Stand_' not in c):
print('Dropping ',p[c].isna().sum(),' subjects w/ NaN for {}.'.format(c))
if (p[c].isna().sum() == p.shape[0]):
print('ERROR: No subjects with data for {}.'.format(c))
p = p.dropna(subset=[c])
print('Z-scoring contrast...')
p['{}_z'.format(c)] = (p[c] - p[c].mean())/p[c].std(ddof=0)
c = '{}_z'.format(c)
mask = util.mask_var(p,c)
match_conn_mask = pheno.index.isin(p.index)
conn = connectomes.to_numpy()[match_conn_mask]
summary = util.variable_effect(p[mask],c,regressors_nomc,conn[mask],std=True)
summary.to_csv(path_out + '/cont_{}_results_nomc.csv'.format(c))
np.savetxt(path_out + '/cont_{}_nomc.tsv'.format(c),util.vec_to_connectome(summary['betas_std'].to_numpy()),delimiter='\t')
summaries_cont_nomc.append(summary)
print('Completed {}.'.format(c))