def prior_producer():
import config_variables
import correl_distance_extractor_clean
np = config_variables.np
log_distances = config_variables.log_distances
chroms_in_prior = config_variables.chroms_in_prior
dataset_names_option = config_variables.dataset_names_option
mode = config_variables.mode
domain = config_variables.domain
negative_interactions = config_variables.negative_interactions
one_sided_or_two_sided = config_variables.one_sided_or_two_sided
domain_like_chromosome_correction = config_variables.domain_like_chromosome_correction
prior_elements = {}
if config_variables.MoG_classificator:
prior_mode = False
else: prior_mode = True
for type_of_interaction in ["promoter_enhancer_interactions", "enhancer_enhancer_interactions"]:
prior_elements[type_of_interaction] = {}
for classification_of_interactions in ["positive_interactions", "negative_interactions"]:
prior_elements[type_of_interaction][classification_of_interactions] = {}
for attribute_of_interaction in ["distance", "correlation"]:
prior_elements[type_of_interaction][classification_of_interactions][attribute_of_interaction] = {}
if attribute_of_interaction == "correlation":
for data_set_name in dataset_names_option:
prior_elements[type_of_interaction][classification_of_interactions][attribute_of_interaction][data_set_name] = {}
prior_elements[type_of_interaction][classification_of_interactions][attribute_of_interaction][data_set_name]["attribute_values"] = {}
prior_elements[type_of_interaction][classification_of_interactions][attribute_of_interaction][data_set_name]["prior_bins"] = np.array([])
prior_elements[type_of_interaction][classification_of_interactions][attribute_of_interaction][data_set_name]["prior_frequencies"] = np.array([])
for chrom_ in chroms_in_prior:
prior_elements[type_of_interaction][classification_of_interactions][attribute_of_interaction][data_set_name]["attribute_values"][chrom_] = np.array([])
else:
prior_elements[type_of_interaction][classification_of_interactions][attribute_of_interaction]["attribute_values"] = {}
prior_elements[type_of_interaction][classification_of_interactions][attribute_of_interaction]["prior_bins"] = np.array([])
prior_elements[type_of_interaction][classification_of_interactions][attribute_of_interaction]["prior_frequencies"] = np.array([])
for chrom_ in chroms_in_prior:
prior_elements[type_of_interaction][classification_of_interactions][attribute_of_interaction]["attribute_values"][chrom_] = np.array([])
if not(domain) and not(domain_like_chromosome_correction):
if one_sided_or_two_sided == "double_sided":
if log_distances:
prior_elements = correl_distance_extractor_clean.correl_distance_extractor(mode, prior_elements, chroms_in_prior, absolute = False, logged = True, prior_mode = prior_mode)
else:
prior_elements = correl_distance_extractor_clean.correl_distance_extractor(mode, prior_elements, chroms_in_prior, absolute = False, logged = False, prior_mode = prior_mode)
elif one_sided_or_two_sided == "single_sided":
if log_distances:
prior_elements = correl_distance_extractor_clean.correl_distance_extractor(mode, prior_elements, chroms_in_prior, absolute = True, logged = True, prior_mode = prior_mode)
else:
prior_elements = correl_distance_extractor_clean.correl_distance_extractor(mode, prior_elements, chroms_in_prior, absolute = True, logged = False, prior_mode = prior_mode)
else:
if one_sided_or_two_sided == "double_sided":
prior_elements = correl_distance_extractor_clean.correl_distance_extractor(mode, prior_elements, chroms_in_prior, absolute = False, logged = False, prior_mode = prior_mode)
elif one_sided_or_two_sided == "single_sided":
prior_elements = correl_distance_extractor_clean.correl_distance_extractor(mode, prior_elements, chroms_in_prior, absolute = True, logged = False, prior_mode = prior_mode)
import interacting_domain_clean as interacting_domain
from prepare_interactions_clean import un_string
dataset_time_series_dict = config_variables.dataset_time_series_dict
link_data_set_name_to_file_name = config_variables.link_data_set_name_to_file_name
chr_interactions_dict_pro_enh = config_variables.chr_interactions_dict_pro_enh
chr_interactions_dict_enh_enh = config_variables.chr_interactions_dict_enh_enh
def interacting_enhancers_coordinates_function():
interacting_enhancers_coord = {}
enh_coordinates = dataset_time_series_dict[link_data_set_name_to_file_name["enhancers"]['ER']][1]
for chrom in chroms_in_prior:
if mode == "promoter_enhancer_interactions":
chrom_interacting_enhancers_pro = np.unique(un_string(chr_interactions_dict_pro_enh[chrom])[:,1])
interacting_enhancers_coord[chrom] = enh_coordinates[chrom_interacting_enhancers_pro]
#if config_variables.alternative_classificator_outside_enhancers:
#interacting_enhancers_coord[chrom] = enh_coordinates[config_variables.chrom_interacting_enhancers_pro[chrom]]
elif mode == "enhancer_enhancer_interactions":
chrom_interacting_enhancers_enh = np.unique(un_string(chr_interactions_dict_enh_enh[chrom])[:,0])
chrom_interacting_enhancers_enh = np.unique(np.r_[chrom_interacting_enhancers_enh, np.unique(un_string(chr_interactions_dict_enh_enh[chrom])[:,1])])
interacting_enhancers_coord[chrom] = enh_coordinates[chrom_interacting_enhancers_enh]
return interacting_enhancers_coord
prior_elements[mode]["interacting_enhancers_coordinates"] = interacting_enhancers_coordinates_function()
#chrom, interacting_enhancer_coordinates, distances, possible_distances_counts, length = chrom, interacting_enhancer_coordinates_chrom, total_array, possible_distances_counts, 200
def adaptive_domain_dummy_inter_matrix_version(chrom, interacting_enhancer_coordinates, distances, possible_distances_counts, length):
possible_interaction_centres = interacting_enhancer_coordinates.mean(1)[:, None] + distances #should be if dist - then one end if dist + then other end
reshape_size = possible_interaction_centres.size
possible_interaction_coordinates = np.c_[(possible_interaction_centres.reshape(reshape_size) - length)[:, None], (possible_interaction_centres.reshape(reshape_size) + length)[:, None]]
def state_dependent_allocation(state, chrom):
allocation_of_coordinates, size_domains = interacting_domain.interacting_domains(interacting_enhancer_coordinates, possible_interaction_coordinates, chrom, state, matrix_version = False, chromosome_version = domain_like_chromosome_correction) # matrix_mode = False
allocations_of_interacting_enhancers = allocation_of_coordinates[:len(interacting_enhancer_coordinates)]
allocations_of_interacting_enhancer_possible_interactions = allocation_of_coordinates[len(interacting_enhancer_coordinates):]
allocations_of_interacting_enhancer_possible_interactions = allocations_of_interacting_enhancer_possible_interactions.reshape(len(interacting_enhancer_coordinates), len(distances))
dummy_interactions_in_the_same_domain_as_enhancer = allocations_of_interacting_enhancers[:,None] == allocations_of_interacting_enhancer_possible_interactions
return dummy_interactions_in_the_same_domain_as_enhancer, size_domains
dummy_interactions_in_the_same_domain_as_enhancer_left, size_domains = state_dependent_allocation("left", chrom)
dummy_interactions_in_the_same_domain_as_enhancer_right, size_domains = state_dependent_allocation("right", chrom)
possible_distances_counts_chrom = ((dummy_interactions_in_the_same_domain_as_enhancer_left + dummy_interactions_in_the_same_domain_as_enhancer_right).astype(int)).sum(0)
possible_distances_counts = possible_distances_counts + possible_distances_counts_chrom
return possible_distances_counts, size_domains
chrom_domain_sizes = {}
import itertools
for classification_of_interactions in ["positive_interactions", "negative_interactions"]:
total_array = [prior_elements[mode][classification_of_interactions]["distance"]["attribute_values"][chrom_] for chrom_ in chroms_in_prior]
total_array = list(itertools.chain.from_iterable(total_array))
possible_distances_counts = np.zeros_like(total_array)
for chrom in chroms_in_prior:
interacting_enhancer_coordinates_chrom = prior_elements[mode]["interacting_enhancers_coordinates"][chrom]
possible_distances_counts, size_domains_chrom = adaptive_domain_dummy_inter_matrix_version(chrom, interacting_enhancer_coordinates_chrom, total_array, possible_distances_counts, 200)
chrom_domain_sizes[chrom] = size_domains_chrom
prior_elements[mode][classification_of_interactions]["distance"]["possible_distances_counts"] = possible_distances_counts
if any(possible_distances_counts == 0):
#interacting_enhancer_coordinates = prior_elements[mode]["interacting_enhancers_coordinates"]
#possible_distances_counts, total_array = corrector(interacting_enhancer_coordinates, total_array, possible_distances_counts, 200)
if mode == "promoter_enhancer_interactions" and classification_of_interactions == "positive_interactions":
print "possible_distances_p_e_counts_true should not have 0 count distances which could be corrected by corrector() i.e something wrong"
if one_sided_or_two_sided == "double_sided":
if log_distances:
prior_elements = correl_distance_extractor_clean.correl_distance_extractor(mode, prior_elements, chroms_in_prior, absolute = False, logged = True, prior_mode = prior_mode)
elif one_sided_or_two_sided == "single_sided":
if log_distances:
prior_elements = correl_distance_extractor_clean.correl_distance_extractor(mode, prior_elements, chroms_in_prior, absolute = True, logged = True, prior_mode = prior_mode)
return prior_elements
def infered_elements_filler():
import config_variables
import correl_distance_extractor_clean
mode = config_variables.mode
domain = config_variables.domain
chroms_to_infer = config_variables.chroms_to_infer
np = config_variables.np
dataset_names_option = config_variables.dataset_names_option
one_sided_or_two_sided = config_variables.one_sided_or_two_sided
log_distances = config_variables.log_distances
infered_elements = {}
for type_of_interaction in ["promoter_enhancer_interactions", "enhancer_enhancer_interactions"]:
infered_elements[type_of_interaction] = {}
for classification_of_interactions in ["positive_interactions", "negative_interactions"]:
infered_elements[type_of_interaction][classification_of_interactions] = {}
for attribute_of_interaction in ["distance", "correlation"]:
infered_elements[type_of_interaction][classification_of_interactions][attribute_of_interaction] = {}
if attribute_of_interaction == "correlation":
for data_set_name in dataset_names_option:
infered_elements[type_of_interaction][classification_of_interactions][attribute_of_interaction][data_set_name] = {}
infered_elements[type_of_interaction][classification_of_interactions][attribute_of_interaction][data_set_name]["attribute_values"] = {}
for probability_of_being_positive_or_negative in ["probabilities_of_being_positive_interactions", "probabilities_of_being_negative_interactions"]:
infered_elements[type_of_interaction][classification_of_interactions][attribute_of_interaction][data_set_name][probability_of_being_positive_or_negative] = {}
for chrom_ in chroms_to_infer:
infered_elements[type_of_interaction][classification_of_interactions][attribute_of_interaction][data_set_name]["attribute_values"][chrom_] = np.array([])
for probability_of_being_positive_or_negative in ["probabilities_of_being_positive_interactions", "probabilities_of_being_negative_interactions"]:
infered_elements[type_of_interaction][classification_of_interactions][attribute_of_interaction][data_set_name][probability_of_being_positive_or_negative][chrom_] = np.array([])
else:
infered_elements[type_of_interaction][classification_of_interactions][attribute_of_interaction]["attribute_values"] = {}
for probability_of_being_positive_or_negative in ["probabilities_of_being_positive_interactions", "probabilities_of_being_negative_interactions"]:
infered_elements[type_of_interaction][classification_of_interactions][attribute_of_interaction][probability_of_being_positive_or_negative] = {}
for chrom_ in chroms_to_infer:
infered_elements[type_of_interaction][classification_of_interactions][attribute_of_interaction]["attribute_values"][chrom_] = np.array([])
for probability_of_being_positive_or_negative in ["probabilities_of_being_positive_interactions", "probabilities_of_being_negative_interactions"]:
infered_elements[type_of_interaction][classification_of_interactions][attribute_of_interaction][probability_of_being_positive_or_negative][chrom_] = np.array([])
if one_sided_or_two_sided == "double_sided":
if log_distances:
infered_elements = correl_distance_extractor_clean.correl_distance_extractor(mode, infered_elements, chroms_to_infer, absolute = False, logged = True)
else:
infered_elements = correl_distance_extractor_clean.correl_distance_extractor(mode, infered_elements, chroms_to_infer, absolute = False, logged = False)
elif one_sided_or_two_sided == "single_sided":
if log_distances:
infered_elements = correl_distance_extractor_clean.correl_distance_extractor(mode, infered_elements, chroms_to_infer, absolute = True, logged = True)
else:
infered_elements = correl_distance_extractor_clean.correl_distance_extractor(mode, infered_elements, chroms_to_infer, absolute = True, logged = False)
return infered_elements
