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
def interactions_above_threshold(posterior_t, posterior_f, threshold_up, threshold_low, chrom, domain = False):
enh_coordinates, pro_coordinates, indexes_p, indexes_e, total_p, total_e = negative_interactions.initialise_variables(chrom)
length_chr = len(indexes_p) + len(indexes_e)
interaction_matrix = np.zeros((length_chr, length_chr))
posterior_t, posterior_f = posterior_t[chrom], posterior_f[chrom]
i_s_f, j_s_f = positive_negative_interactions_for_MAP(chrom)
if domain:
import interacting_domain_clean as interacting_domain
if config_variables.TSS_or_intra_genic_for_domain_filter == "Intra_genic": coords_pro_domain = pro_coordinates[indexes_p]
elif config_variables.TSS_or_intra_genic_for_domain_filter == "TSS_only": coords_pro_domain = np.column_stack((TSS_coordinates[indexes_p]-1, TSS_coordinates[indexes_p]+1))
domain_matrix = interacting_domain.interacting_domains(coords_pro_domain, enh_coordinates[indexes_e], chrom, 'left', True)
domain_matrix = domain_matrix + interacting_domain.interacting_domains(coords_pro_domain, enh_coordinates[indexes_e], chrom, 'right', True)
else:
domain_matrix = True
if mode == "promoter_enhancer_interactions":
if config_variables.disentagled_features_validation:
chr_interactions_pro_enh = config_variables.chr_interactions_dict_pro_enh_TSS[chrom]
else:
chr_interactions_pro_enh = config_variables.chr_interactions_dict_pro_enh[chrom]
#chr_interactions_dict_pro_enh = config_variables.chr_interactions_dict_pro_enh
#true_inter_pro = un_string(chr_interactions_dict_pro_enh[chrom][:, :2]).astype(int)
true_inter_pro = un_string(chr_interactions_pro_enh[:, :2]).astype(int)
i_s_t, j_s_t = true_inter_pro[:,0], true_inter_pro[:,1]
#chr_interactions_dict_pro_enh = config_variables.chr_interactions_dict_pro_enh
#true_inter_pro = un_string(chr_interactions_dict_pro_enh[chrom][:, :2]).astype(int)
#i_s_t, j_s_t = true_inter_pro[:,0], true_inter_pro[:,1]
interaction_matrix[:,:] = np.min([np.min(posterior_t), np.min(posterior_f)])*0.999
interaction_matrix[i_s_t - total_p, j_s_t + len(indexes_p) - total_e] = posterior_t
interaction_matrix[i_s_f - total_p, j_s_f + len(indexes_p) - total_e] = posterior_f
#--------------------------------------------------------------------------the part to look at for Paolo !
#np.save("interaction_matrix_float", interaction_matrix) #I'm saving the interaction matrix for you
if normalised:
norm_factors = np.sum(interaction_matrix, axis = 0)
interaction_matrix = interaction_matrix/norm_factors
interaction_matrix[np.isnan(interaction_matrix)] = 0.
interacting_mask = np.zeros_like(interaction_matrix).astype(bool)
interacting_mask[i_s_t - total_p, j_s_t + len(indexes_p) - total_e] = True
true_pro_enh_inter_filtered = np.where(interacting_mask * (interaction_matrix >= threshold_low) * (interaction_matrix < threshold_up) * domain_matrix)
i_s_t_filt, j_s_t_filt = true_pro_enh_inter_filtered[0] + total_p, true_pro_enh_inter_filtered[1] - len(indexes_p) + total_e # that line tells you which of the positive (green) ChIA-PET-confirmed interactions lay within threshold_low, threshold_up interval
interacting_mask = np.zeros_like(interaction_matrix).astype(bool)
interacting_mask[i_s_f - total_p, j_s_f + len(indexes_p) - total_e] = True
false_pro_enh_inter_filtered = np.where(interacting_mask * (interaction_matrix >= threshold_low) * (interaction_matrix < threshold_up) * domain_matrix)
i_s_f_filt, j_s_f_filt = false_pro_enh_inter_filtered[0] + total_p, false_pro_enh_inter_filtered[1] - len(indexes_p) + total_e # that line tells you which of the negative (gray) interactions lay within threshold_low, threshold_up interval
#--------------------------------------------------------------------------the part to look at for Paolo !
return i_s_t_filt, j_s_t_filt, i_s_f_filt, j_s_f_filt # the function takes threshold_up, threshold_low as an argument and returns predicted interactions with probabilities within [threshold_low, threshold_up) interval
# i_s_t_filt, j_s_t_filt, i_s_f_filt, j_s_f_filt legend: i_s_t are promoters of ChIA-PET confirmed interactions, j_s_t are enhancers of the interactions, i_s_f, j_s_f are (promoters, enhancers) interactions which aren't ChIA-PET confirmed
#for paolo
if mode == "enhancer_enhancer_interactions":
chr_interactions_dict_enh_enh = config_variables.chr_interactions_dict_enh_enh
true_inter_enh = un_string(chr_interactions_dict_enh_enh[chrom][:, :2]).astype(int)
i_s_t, j_s_t = true_inter_enh[:,0], true_inter_enh[:,1]
interaction_matrix[:,:] = np.min([np.min(posterior_t), np.min(posterior_f)])*0.999
interaction_matrix[i_s_t + len(indexes_p) - total_e, j_s_t + len(indexes_p) - total_e] = posterior_t
interaction_matrix[i_s_f + len(indexes_p) - total_e, j_s_f + len(indexes_p) - total_e] = posterior_f
interaction_matrix[j_s_t + len(indexes_p) - total_e, i_s_t + len(indexes_p) - total_e] = posterior_t # transpose to create a full matrix
interaction_matrix[j_s_f + len(indexes_p) - total_e, i_s_f + len(indexes_p) - total_e] = posterior_f # transpose to create a full matrix
if normalised:
norm_factors = np.sum(interaction_matrix, axis = 0)
interaction_matrix = interaction_matrix/norm_factors
interaction_matrix[np.isnan(interaction_matrix)] = 0.
interacting_mask = np.zeros_like(interaction_matrix).astype(bool)
interacting_mask[i_s_t + len(indexes_p) - total_e, j_s_t + len(indexes_p) - total_e] = True
true_enh_enh_inter_filtered = np.where(interacting_mask * (interaction_matrix >= threshold_low) * (interaction_matrix < threshold_up))
i_s_t_filt, j_s_t_filt = true_enh_enh_inter_filtered[0] - len(indexes_p) + total_e, true_enh_enh_inter_filtered[1] - len(indexes_p) + total_e
interacting_mask = np.zeros_like(interaction_matrix).astype(bool)
interacting_mask[i_s_f + len(indexes_p) - total_e, j_s_f + len(indexes_p) - total_e] = True
false_enh_enh_inter_filtered = np.where(interacting_mask * (interaction_matrix >= threshold_low) * (interaction_matrix < threshold_up))
i_s_f_filt, j_s_f_filt = false_enh_enh_inter_filtered[0] - len(indexes_p) + total_e, false_enh_enh_inter_filtered[1] - len(indexes_p) + total_e
return i_s_t_filt, j_s_t_filt, i_s_f_filt, j_s_f_filt
def interactions_above_threshold(posterior_t,
posterior_f,
threshold_up,
threshold_low,
chrom,
domain=False):
enh_coordinates, pro_coordinates, indexes_p, indexes_e, total_p, total_e = negative_interactions.initialise_variables(
chrom)
length_chr = len(indexes_p) + len(indexes_e)
interaction_matrix = np.zeros((length_chr, length_chr))
posterior_t, posterior_f = posterior_t[chrom], posterior_f[chrom]
i_s_f, j_s_f = positive_negative_interactions_for_MAP(chrom)
if domain:
import interacting_domain_clean as interacting_domain
if config_variables.TSS_or_intra_genic_for_domain_filter == "Intra_genic":
coords_pro_domain = pro_coordinates[indexes_p]
elif config_variables.TSS_or_intra_genic_for_domain_filter == "TSS_only":
coords_pro_domain = np.column_stack(
(TSS_coordinates[indexes_p] - 1,
TSS_coordinates[indexes_p] + 1))
domain_matrix = interacting_domain.interacting_domains(
coords_pro_domain, enh_coordinates[indexes_e], chrom, 'left',
True)
domain_matrix = domain_matrix + interacting_domain.interacting_domains(
coords_pro_domain, enh_coordinates[indexes_e], chrom, 'right',
True)
else:
domain_matrix = True
if mode == "promoter_enhancer_interactions":
if config_variables.disentagled_features_validation:
chr_interactions_pro_enh = config_variables.chr_interactions_dict_pro_enh_TSS[
chrom]
else:
chr_interactions_pro_enh = config_variables.chr_interactions_dict_pro_enh[
chrom]
#chr_interactions_dict_pro_enh = config_variables.chr_interactions_dict_pro_enh
#true_inter_pro = un_string(chr_interactions_dict_pro_enh[chrom][:, :2]).astype(int)
true_inter_pro = un_string(
chr_interactions_pro_enh[:, :2]).astype(int)
i_s_t, j_s_t = true_inter_pro[:, 0], true_inter_pro[:, 1]
#chr_interactions_dict_pro_enh = config_variables.chr_interactions_dict_pro_enh
#true_inter_pro = un_string(chr_interactions_dict_pro_enh[chrom][:, :2]).astype(int)
#i_s_t, j_s_t = true_inter_pro[:,0], true_inter_pro[:,1]
interaction_matrix[:, :] = np.min(
[np.min(posterior_t), np.min(posterior_f)]) * 0.999
interaction_matrix[i_s_t - total_p,
j_s_t + len(indexes_p) - total_e] = posterior_t
interaction_matrix[i_s_f - total_p,
j_s_f + len(indexes_p) - total_e] = posterior_f
#--------------------------------------------------------------------------the part to look at for Paolo !
#np.save("interaction_matrix_float", interaction_matrix) #I'm saving the interaction matrix for you
if normalised:
norm_factors = np.sum(interaction_matrix, axis=0)
interaction_matrix = interaction_matrix / norm_factors
interaction_matrix[np.isnan(interaction_matrix)] = 0.
interacting_mask = np.zeros_like(interaction_matrix).astype(bool)
interacting_mask[i_s_t - total_p,
j_s_t + len(indexes_p) - total_e] = True
true_pro_enh_inter_filtered = np.where(
interacting_mask * (interaction_matrix >= threshold_low) *
(interaction_matrix < threshold_up) * domain_matrix)
i_s_t_filt, j_s_t_filt = true_pro_enh_inter_filtered[
0] + total_p, true_pro_enh_inter_filtered[1] - len(
indexes_p
) + total_e # that line tells you which of the positive (green) ChIA-PET-confirmed interactions lay within threshold_low, threshold_up interval
interacting_mask = np.zeros_like(interaction_matrix).astype(bool)
interacting_mask[i_s_f - total_p,
j_s_f + len(indexes_p) - total_e] = True
false_pro_enh_inter_filtered = np.where(
interacting_mask * (interaction_matrix >= threshold_low) *
(interaction_matrix < threshold_up) * domain_matrix)
i_s_f_filt, j_s_f_filt = false_pro_enh_inter_filtered[
0] + total_p, false_pro_enh_inter_filtered[1] - len(
indexes_p
) + total_e # that line tells you which of the negative (gray) interactions lay within threshold_low, threshold_up interval
#--------------------------------------------------------------------------the part to look at for Paolo !
return i_s_t_filt, j_s_t_filt, i_s_f_filt, j_s_f_filt # the function takes threshold_up, threshold_low as an argument and returns predicted interactions with probabilities within [threshold_low, threshold_up) interval
# i_s_t_filt, j_s_t_filt, i_s_f_filt, j_s_f_filt legend: i_s_t are promoters of ChIA-PET confirmed interactions, j_s_t are enhancers of the interactions, i_s_f, j_s_f are (promoters, enhancers) interactions which aren't ChIA-PET confirmed
#for paolo
if mode == "enhancer_enhancer_interactions":
chr_interactions_dict_enh_enh = config_variables.chr_interactions_dict_enh_enh
true_inter_enh = un_string(
chr_interactions_dict_enh_enh[chrom][:, :2]).astype(int)
i_s_t, j_s_t = true_inter_enh[:, 0], true_inter_enh[:, 1]
interaction_matrix[:, :] = np.min(
[np.min(posterior_t), np.min(posterior_f)]) * 0.999
interaction_matrix[i_s_t + len(indexes_p) - total_e,
j_s_t + len(indexes_p) - total_e] = posterior_t
interaction_matrix[i_s_f + len(indexes_p) - total_e,
j_s_f + len(indexes_p) - total_e] = posterior_f
interaction_matrix[
j_s_t + len(indexes_p) - total_e, i_s_t + len(indexes_p) -
total_e] = posterior_t # transpose to create a full matrix
interaction_matrix[
j_s_f + len(indexes_p) - total_e, i_s_f + len(indexes_p) -
total_e] = posterior_f # transpose to create a full matrix
if normalised:
norm_factors = np.sum(interaction_matrix, axis=0)
interaction_matrix = interaction_matrix / norm_factors
interaction_matrix[np.isnan(interaction_matrix)] = 0.
interacting_mask = np.zeros_like(interaction_matrix).astype(bool)
interacting_mask[i_s_t + len(indexes_p) - total_e,
j_s_t + len(indexes_p) - total_e] = True
true_enh_enh_inter_filtered = np.where(
interacting_mask * (interaction_matrix >= threshold_low) *
(interaction_matrix < threshold_up))
i_s_t_filt, j_s_t_filt = true_enh_enh_inter_filtered[0] - len(
indexes_p) + total_e, true_enh_enh_inter_filtered[1] - len(
indexes_p) + total_e
interacting_mask = np.zeros_like(interaction_matrix).astype(bool)
interacting_mask[i_s_f + len(indexes_p) - total_e,
j_s_f + len(indexes_p) - total_e] = True
false_enh_enh_inter_filtered = np.where(
interacting_mask * (interaction_matrix >= threshold_low) *
(interaction_matrix < threshold_up))
i_s_f_filt, j_s_f_filt = false_enh_enh_inter_filtered[0] - len(
indexes_p) + total_e, false_enh_enh_inter_filtered[1] - len(
indexes_p) + total_e
return i_s_t_filt, j_s_t_filt, i_s_f_filt, j_s_f_filt
