def initialize(self):
self.excluded = load_excluded(self)
self.annotations_dict = load_annotations_dict(self)
subset_annotations(self)
self.attributes_dict = load_attributes_dict(self)
self.attributes_indexes = get_indexes(self)
subset_attributes(self)
self.cells_dict = load_cells_dict(self)
subset_cells(self)
def initialize(self):
if self.is_init:
if self.annotations is not None:
self.excluded = load_excluded(self)
self.annotations_dict = load_annotations_dict(self)
subset_annotations(self)
self.observables_dict = load_observables_dict(self)
self.observables_categorical_dict = load_observables_categorical_dict(
self)
self.is_observables_categorical = get_is_observables_categorical(
self)
self.attributes_indexes = get_indexes(self)
subset_observables(self)
if self.attributes.cells is not None:
self.cells_dict = load_cells_dict(self)
subset_cells(self)
def test_subset_cells(self):
self.config.cells_dict = load_cells_dict(self.config)
self.config.attributes_indexes = list(range(5))
subset_cells(self.config)
self.assertEqual(self.config.cells_dict['CD8T'], [0, 0, 0.006011666, 0, 0])
def load_betas_adj(config):
fn_dict = get_data_base_path(config) + '/' + 'betas_adj_dict.pkl'
suffix = ''
if bool(config.experiment.data_params):
data_params = config.experiment.data_params
suffix += '_' + config.experiment.get_data_params_str()
else:
raise ValueError(f'Exog for residuals is empty.')
fn_data = get_data_base_path(config) + '/' + 'betas_adj' + suffix + '.npz'
if os.path.isfile(fn_dict) and os.path.isfile(fn_data):
f = open(fn_dict, 'rb')
config.betas_adj_dict = pickle.load(f)
f.close()
data = np.load(fn_data)
config.betas_adj_data = data['data']
else:
config.experiment.data_params = {}
load_betas(config)
config.betas_adj_dict = config.betas_dict
f = open(fn_dict, 'wb')
pickle.dump(config.betas_adj_dict, f, pickle.HIGHEST_PROTOCOL)
f.close()
exog_dict = {}
if 'cells' in data_params:
cells_dict = load_cells_dict(config)
if isinstance(data_params['cells'], list):
all_types = list(cells_dict.keys())
for key in all_types:
if key not in data_params['cells']:
cells_dict.pop(key)
if len(list(cells_dict.keys())) != len(data_params['cells']):
raise ValueError(f'Wrong number of cells types.')
exog_dict.update(cells_dict)
if 'observables' in data_params:
observables_dict = load_observables_dict(config)
if isinstance(data_params['observables'], list):
all_types = list(observables_dict.keys())
for key in all_types:
if key not in data_params['observables']:
observables_dict.pop(key)
if len(list(observables_dict.keys())) != len(
data_params['observables']):
raise ValueError(f'Wrong number of observables types.')
exog_dict.update(observables_dict)
exog_df = pd.DataFrame(exog_dict)
num_cpgs = config.betas_data.shape[0]
num_subjects = config.betas_data.shape[1]
config.betas_adj_data = np.zeros((num_cpgs, num_subjects),
dtype=np.float32)
for cpg, row in tqdm(config.betas_dict.items(),
mininterval=60.0,
desc='betas_adj_data creating'):
betas = config.betas_data[row, :]
mean = np.mean(betas)
endog_dict = {cpg: betas}
endog_df = pd.DataFrame(endog_dict)
reg_res = sm.OLS(endog=endog_df, exog=exog_df).fit()
residuals = list(map(np.float32, reg_res.resid))
betas_adj = residuals + mean
config.betas_adj_data[row] = betas_adj
np.savez_compressed(fn_data, data=config.betas_adj_data)
# Clear data
del config.betas_data
def load_resid_old(config):
suffix = ''
if bool(config.experiment.data_params):
data_params = config.experiment.data_params
suffix += '_' + config.experiment.get_data_params_str()
else:
raise ValueError('Exog for resid_old is empty.')
fn_dict = get_data_base_path(
config) + '/' + 'resid_old_dict' + suffix + '.pkl'
fn_missed_dict = get_data_base_path(
config) + '/' + 'resid_old_missed_dict' + suffix + '.pkl'
fn_data = get_data_base_path(config) + '/' + 'resid_old' + suffix + '.npz'
if os.path.isfile(fn_dict) and os.path.isfile(fn_data):
f = open(fn_dict, 'rb')
config.resid_old_dict = pickle.load(f)
f.close()
f = open(fn_missed_dict, 'rb')
config.resid_old_missed_dict = pickle.load(f)
f.close()
data = np.load(fn_data)
config.resid_old_data = data['data']
else:
data_params_copy = copy.deepcopy(config.experiment.data_params)
common_keys = ['norm']
config.experiment.data_params = {}
for key in common_keys:
if key in data_params_copy:
config.experiment.data_params[key] = data_params_copy[key]
load_betas(config)
config.resid_old_dict = config.betas_dict
f = open(fn_dict, 'wb')
pickle.dump(config.resid_old_dict, f, pickle.HIGHEST_PROTOCOL)
f.close()
config.resid_old_missed_dict = config.betas_missed_dict
f = open(fn_missed_dict, 'wb')
pickle.dump(config.resid_old_missed_dict, f, pickle.HIGHEST_PROTOCOL)
f.close()
exog_dict = {}
if 'cells' in data_params:
cells_dict = load_cells_dict(config)
if isinstance(data_params['cells'], list):
all_types = list(cells_dict.keys())
for key in all_types:
if key not in data_params['cells']:
cells_dict.pop(key)
if len(list(cells_dict.keys())) != len(data_params['cells']):
raise ValueError('Wrong number of cells types.')
exog_dict.update(cells_dict)
if 'observables' in data_params:
observables_categorical_dict = load_observables_categorical_dict(
config)
if isinstance(data_params['observables'], list):
all_types = list(observables_categorical_dict.keys())
for key in all_types:
if key not in data_params['observables']:
observables_categorical_dict.pop(key)
if len(list(observables_categorical_dict.keys())) != len(
data_params['observables']):
raise ValueError('Wrong number of observables types.')
exog_dict.update(observables_categorical_dict)
num_cpgs = config.betas_data.shape[0]
num_subjects = config.betas_data.shape[1]
config.resid_old_data = np.zeros((num_cpgs, num_subjects),
dtype=np.float32)
for cpg, row in tqdm(config.betas_dict.items(),
mininterval=60.0,
desc='resid_old_data creating'):
raw_betas = config.betas_data[row, :]
current_exog_dict = copy.deepcopy(exog_dict)
if len(config.betas_missed_dict[cpg]) > 0:
for key in current_exog_dict:
values = []
for value_id in range(0, len(current_exog_dict[key])):
if value_id not in config.betas_missed_dict[cpg]:
values.append(current_exog_dict[key][value_id])
current_exog_dict[key] = values
betas = []
passed_ids = []
for beta_id in range(0, len(raw_betas)):
if beta_id not in config.betas_missed_dict[cpg]:
betas.append(raw_betas[beta_id])
passed_ids.append(beta_id)
else:
betas = raw_betas
passed_ids = list(range(0, len(betas)))
endog_dict = {cpg: betas}
endog_df = pd.DataFrame(endog_dict)
exog_df = pd.DataFrame(current_exog_dict)
reg_res = sm.OLS(endog=endog_df, exog=exog_df).fit()
resid_old = list(map(np.float32, reg_res.resid))
resid_old_raw = np.zeros(num_subjects, dtype=np.float32)
for beta_id in range(0, len(passed_ids)):
resid_old_raw[passed_ids[beta_id]] = resid_old[beta_id]
for missed_id in config.resid_old_missed_dict[cpg]:
resid_old_raw[missed_id] = np.float32('nan')
config.resid_old_data[row] = resid_old_raw
np.savez_compressed(fn_data, data=config.resid_old_data)
# Clear data
del config.betas_data
