def roi_pair_ttest():
"""
compare rsfc difference between ROIs
scheme: hemi-separately network-wise
"""
import numpy as np
import pickle as pkl
import pandas as pd
from scipy.stats.stats import ttest_rel
from cxy_hcp_ffa.lib.predefine import net2label_cole
from commontool.stats import EffectSize
# inputs
hemis = ('lh', 'rh')
roi_pair = ('pFus-face', 'mFus-face')
data_file = pjoin(work_dir, 'rsfc_individual2Cole_{}.pkl')
compare_name = f"{roi_pair[0].split('-')[0]}_vs_" \
f"{roi_pair[1].split('-')[0]}"
# outputs
out_file = pjoin(work_dir,
f"rsfc_individual2Cole_{compare_name}_ttest_paired.csv")
# start
trg_names = list(net2label_cole.keys())
trg_labels = list(net2label_cole.values())
out_data = {'network': trg_names}
es = EffectSize()
for hemi in hemis:
data = pkl.load(open(data_file.format(hemi), 'rb'))
assert data['trg_label'] == trg_labels
out_data[f'CohenD_{hemi}'] = []
out_data[f't_{hemi}'] = []
out_data[f'P_{hemi}'] = []
for trg_idx, trg_name in enumerate(trg_names):
sample1 = data[roi_pair[0]][:, trg_idx]
sample2 = data[roi_pair[1]][:, trg_idx]
nan_vec1 = np.isnan(sample1)
nan_vec2 = np.isnan(sample2)
nan_vec = np.logical_or(nan_vec1, nan_vec2)
print(f'#NAN in sample1 or sample2:', np.sum(nan_vec))
sample1 = sample1[~nan_vec]
sample2 = sample2[~nan_vec]
d = es.cohen_d(sample1, sample2)
t, p = ttest_rel(sample1, sample2)
out_data[f'CohenD_{hemi}'].append(d)
out_data[f't_{hemi}'].append(t)
out_data[f'P_{hemi}'].append(p)
# save out
out_data = pd.DataFrame(out_data)
out_data.to_csv(out_file, index=False)
def prepare_plot(gid=1, hemi='lh'):
import numpy as np
import pickle as pkl
from scipy.stats import sem
from cxy_hcp_ffa.lib.predefine import net2label_cole
# inputs
data_file = pjoin(work_dir, f'rsfc_individual2Cole_G{gid}_{hemi}.pkl')
# outputs
out_file = pjoin(work_dir, f'plot_rsfc_individual2Cole_G{gid}_{hemi}.pkl')
# load data
data = pkl.load(open(data_file, 'rb'))
trg_names = list(net2label_cole.keys())
trg_labels = list(net2label_cole.values())
assert data['trg_label'] == trg_labels
# prepare seed_names and trg_names
seed_names = ['IOG-face', 'pFus-face', 'mFus-face']
n_seed = len(seed_names)
n_trg = len(trg_names)
# calculate mean and sem
means = np.ones((n_seed, n_trg)) * np.nan
sems = np.ones((n_seed, n_trg)) * np.nan
stds = np.ones((n_seed, n_trg)) * np.nan
for seed_idx, seed_name in enumerate(seed_names):
for trg_idx in range(n_trg):
samples = data[seed_name][:, trg_idx]
samples = samples[~np.isnan(samples)]
means[seed_idx, trg_idx] = np.mean(samples)
sems[seed_idx, trg_idx] = sem(samples)
stds[seed_idx, trg_idx] = np.std(samples, ddof=1)
out_dict = {
'shape': 'n_seed x n_target',
'seed': seed_names,
'target': trg_names,
'trg_label': trg_labels,
'mean': means,
'sem': sems,
'std': stds
}
pkl.dump(out_dict, open(out_file, 'wb'))
