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cv_effect_sizes.py
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import os
#import util
import numpy as np
import pandas as pd
from argparse import ArgumentParser
import random
import patsy as pat
import statsmodels.api as sm
from sklearn.preprocessing import StandardScaler
from statsmodels.stats.multitest import multipletests
from sklearn.model_selection import StratifiedKFold
def get_year(s):
if isinstance(s,str):
return int(s.split('/')[-1])
else:
return np.nan
def standardize(mask,data):
scaler = StandardScaler(with_mean=False, with_std=True)
scaler.fit(data[mask])
standardized=scaler.transform(data)
return standardized
# redefined function from util to only generate standardized betamaps
def case_control(pheno,case,regressors,conn):
"""
pheno = dataframe:
-filtered to be only relevant subjects for case control (use mask_cc)
-case column is onehot encoded
case = column from pheno
regressors = list of strings, formatted for patsy
connectomes = n_subjects x n_edges array
Returns:
table = n_edges
- betas_std = including standardization on controls
- pvalues = pvalues
- qvalues = fdr corrected pvalues alpha = 0.05
"""
n_edges = conn.shape[1]
betas = np.zeros(n_edges)
betas_std = np.zeros(n_edges)
pvalues = np.zeros(n_edges)
formula = ' + '.join((regressors + [case]))
dmat = pat.dmatrix(formula, pheno, return_type='dataframe',NA_action='raise')
mask_std = ~pheno[case].to_numpy(dtype=bool)
conn_std = standardize(mask_std, conn)
for edge in range(n_edges):
model_std = sm.OLS(conn_std[:,edge],dmat)
results_std = model_std.fit()
betas_std[edge] = results_std.params[case]
pvalues[edge] = results_std.pvalues[case]
mt = multipletests(pvalues,method='fdr_bh')
reject = mt[0]
qvalues = mt[1]
table = pd.DataFrame(np.array([betas_std,pvalues,qvalues,reject]).transpose(),
columns=['betas_std','pvalues','qvalues','reject'])
return table
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')
parser.add_argument("--n_folds",help="Number of folds of cross_validation",dest='n_folds',type=int,default=5)
args = parser.parse_args()
#############
# LOAD DATA #
#############
path_pheno = args.path_pheno
path_connectomes = args.path_connectomes
path_out = args.path_out
n_folds = args.n_folds
print('Loading data...')
pheno = pd.read_csv(path_pheno,index_col=0)
connectomes = pd.read_csv(path_connectomes,index_col=0)
print('Done!')
regressors_mc = ['AGE','C(SEX)','FD_scrubbed', 'C(SITE)', 'mean_conn']
################
# CASE CONTROL #
################
cases =['SZ',
'BIP',
'ASD',
'ADHD',
'IBD',
'DEL1q21_1',
'DEL2q13',
'DEL13q12_12',
'DEL15q11_2',
'DEL16p11_2',
'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)
cc_rows = []
for case in cases:
print(f'{case} - estimating effect size...')
# SELECT SUBJECTS
if case in ipc:
mask_case = pheno[case].to_numpy(dtype=bool)
mask_con = pheno['CON_IPC'].to_numpy(dtype=bool)
mask = mask_case + mask_con
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)
idx = pheno[mask].index
strat_col = pheno[mask][case]
skf = StratifiedKFold(n_splits=n_folds)
es_train = []
es_test = []
split_train_idx = []
split_test_idx = []
k=0
for train_index, test_index in skf.split(idx, strat_col):
print(f'Fold {k+1}/{n_folds}...')
# Save split indexes for the bootstrap
split_train_idx.append(idx[train_index].to_list())
split_train_idx.append(idx[test_index].to_list())
betamap_train = case_control(pheno.loc[idx[train_index]],
case,
regressors_mc,
connectomes.loc[idx[train_index]].to_numpy())['betas_std']
rank = pd.qcut(betamap_train.abs(),10,labels=False)
decile_idx = rank[rank==9].index
# Get train ES
decile_train = betamap_train.abs()[decile_idx]
mtd_train = np.mean(decile_train)
es_train.append(mtd_train)
betamap_test = case_control(pheno.loc[idx[test_index]],
case,
regressors_mc,
connectomes.loc[idx[test_index]].to_numpy())['betas_std']
# Get test ES
decile_test = betamap_test.abs()[decile_idx]
mtd_test = np.mean(decile_test)
es_test.append(mtd_test)
k += 1
print(f'{case} - CV Effect size ',np.mean(es_test))
cc_rows.append([case,np.mean(es_train),np.mean(es_test)])
df_split_idx = pd.DataFrame(zip(split_train_idx,split_train_idx),columns=['train','test'])
df_split_idx.to_csv(os.path.join(path_out,f'{case}_fold_idx.csv'))
print('Saving case control results...')
df_cc = pd.DataFrame(cc_rows,columns=['case','train_ES','test_ES'])
df_cc.to_csv(os.path.join(path_out,'cc_CV_es.csv'))
print('Done!')