import os
import nibabel as nb
import numpy as np
import pandas as pd
from CPAC.cwas.mdmr import mdmr
from CPAC.utils import correlation
from CPAC.pipeline.cpac_ga_model_generator import (create_merge_mask,
create_merged_copefile)
[docs]def joint_mask(subjects, mask_file=None):
"""
Creates a joint mask (intersection) common to all the subjects in a provided list
and a provided mask
Parameters
----------
subjects : dict of strings
A length `N` list of file paths of the nifti files of subjects
mask_file : string
Path to a mask file in nifti format
Returns
-------
joint_mask : string
Path to joint mask file in nifti format
"""
if not mask_file:
files = list(subjects.values())
cope_file = os.path.join(os.getcwd(), 'joint_cope.nii.gz')
mask_file = os.path.join(os.getcwd(), 'joint_mask.nii.gz')
create_merged_copefile(files, cope_file)
create_merge_mask(cope_file, mask_file)
return mask_file
def calc_mdmrs(D, regressor, cols, permutations):
cols = np.array(cols, dtype=np.int32)
F_set, p_set = mdmr(D, regressor, cols, permutations)
return F_set, p_set
def calc_subdists(subjects_data, voxel_range):
subjects, voxels, _ = subjects_data.shape
D = np.zeros((len(voxel_range), subjects, subjects))
for i, v in enumerate(voxel_range):
profiles = np.zeros((subjects, voxels))
for si in range(subjects):
profiles[si] = correlation(subjects_data[si, v], subjects_data[si])
profiles = np.clip(np.nan_to_num(profiles), -0.9999, 0.9999)
profiles = np.arctanh(np.delete(profiles, v, 1))
D[i] = correlation(profiles, profiles)
D = np.sqrt(2.0 * (1.0 - D))
return D
def calc_cwas(subjects_data, regressor, regressor_selected_cols, permutations, voxel_range):
D = calc_subdists(subjects_data, voxel_range)
F_set, p_set = calc_mdmrs(
D, regressor, regressor_selected_cols, permutations)
return F_set, p_set
[docs]def nifti_cwas(subjects, mask_file, regressor_file, participant_column,
columns_string, permutations, voxel_range):
"""
Performs CWAS for a group of subjects
Parameters
----------
subjects : dict of strings:strings
A length `N` dict of id and file paths of the nifti files of subjects
mask_file : string
Path to a mask file in nifti format
regressor_file : string
file path to regressor CSV or TSV file (phenotypic info)
columns_string : string
comma-separated string of regressor labels
permutations : integer
Number of pseudo f values to sample using a random permutation test
voxel_range : ndarray
Indexes from range of voxels (inside the mask) to perform cwas on.
Index ordering is based on the np.where(mask) command
Returns
-------
F_file : string
.npy file of pseudo-F statistic calculated for every voxel
p_file : string
.npy file of significance probabilities of pseudo-F values
voxel_range : tuple
Passed on by the voxel_range provided in parameters, used to make parallelization
easier
"""
try:
regressor_data = pd.read_table(regressor_file,
sep=None, engine="python",
dtype={ participant_column: str })
except:
regressor_data = pd.read_table(regressor_file,
sep=None, engine="python")
regressor_data = regressor_data.astype({ participant_column: str })
# drop duplicates
regressor_data = regressor_data.drop_duplicates()
regressor_cols = list(regressor_data.columns)
if not participant_column in regressor_cols:
raise ValueError('Participant column was not found in regressor file.')
if participant_column in columns_string:
raise ValueError('Participant column can not be a regressor.')
subject_ids = list(subjects.keys())
subject_files = list(subjects.values())
# check for inconsistency with leading zeroes
# (sometimes, the sub_ids from individual will be something like
# '0002601' and the phenotype will have '2601')
for index, row in regressor_data.iterrows():
pheno_sub_id = str(row[participant_column])
for sub_id in subject_ids:
if str(sub_id).lstrip('0') == str(pheno_sub_id):
regressor_data.at[index, participant_column] = str(sub_id)
regressor_data.index = regressor_data[participant_column]
# Keep only data from specific subjects
ordered_regressor_data = regressor_data.loc[subject_ids]
columns = columns_string.split(',')
regressor_selected_cols = [
i for i, c in enumerate(regressor_cols) if c in columns
]
if len(regressor_selected_cols) == 0:
regressor_selected_cols = [i for i, c in enumerate(regressor_cols)]
regressor_selected_cols = np.array(regressor_selected_cols)
# Remove participant id column from the dataframe and convert it to a numpy matrix
regressor = ordered_regressor_data \
.drop(columns=[participant_column]) \
.reset_index(drop=True) \
.values \
.astype(np.float64)
if len(regressor.shape) == 1:
regressor = regressor[:, np.newaxis]
elif len(regressor.shape) != 2:
raise ValueError('Bad regressor shape: %s' % str(regressor.shape))
if len(subject_files) != regressor.shape[0]:
raise ValueError('Number of subjects does not match regressor size')
mask = nb.load(mask_file).get_data().astype('bool')
mask_indices = np.where(mask)
subjects_data = np.array([
nb.load(subject_file).get_data().astype('float64')[mask_indices].T
for subject_file in subject_files
])
F_set, p_set = calc_cwas(subjects_data, regressor, regressor_selected_cols,
permutations, voxel_range)
cwd = os.getcwd()
F_file = os.path.join(cwd, 'pseudo_F.npy')
p_file = os.path.join(cwd, 'significance_p.npy')
np.save(F_file, F_set)
np.save(p_file, p_set)
return F_file, p_file, voxel_range
def create_cwas_batches(mask_file, batches):
mask = nb.load(mask_file).get_data().astype('bool')
voxels = mask.sum(dtype=int)
return np.array_split(np.arange(voxels), batches)
def volumize(mask_image, data):
mask_data = mask_image.get_fdata().astype('bool')
volume = np.zeros_like(mask_data, dtype=data.dtype)
volume[np.where(mask_data == True)] = data
return nb.Nifti1Image(
volume,
header=mask_image.get_header(),
affine=mask_image.get_affine()
)
def merge_cwas_batches(cwas_batches, mask_file):
_, _, voxel_range = zip(*cwas_batches)
voxels = np.array(np.concatenate(voxel_range))
mask_image = nb.load(mask_file)
F_set = np.zeros_like(voxels, dtype=np.float64)
p_set = np.zeros_like(voxels, dtype=np.float64)
for F_file, p_file, voxel_range in cwas_batches:
F_set[voxel_range] = np.load(F_file)
p_set[voxel_range] = np.load(p_file)
log_p_set = -np.log10(p_set)
F_vol = volumize(mask_image, F_set)
p_vol = volumize(mask_image, p_set)
log_p_vol = volumize(mask_image, log_p_set)
cwd = os.getcwd()
F_file = os.path.join(cwd, 'pseudo_F_volume.nii.gz')
p_file = os.path.join(cwd, 'p_significance_volume.nii.gz')
log_p_file = os.path.join(cwd, 'neglog_p_significance_volume.nii.gz')
F_vol.to_filename(F_file)
p_vol.to_filename(p_file)
log_p_vol.to_filename(log_p_file)
return F_file, p_file, log_p_file
