###################################################################################################### # get the intersection of gene_samples and snp_samples satisfying piS being low and coverage being high both both. desired_samples = numpy.array(list(set(snp_samples) & set(gene_samples[marker_coverages>min_coverage]))) # figure out what the indexes are for the desired samples in the snp_samples and gene_samples snp_sample_idx_map = parse_midas_data.calculate_sample_idx_map(desired_samples, snp_samples) gene_sample_idx_map = parse_midas_data.calculate_sample_idx_map(desired_samples, gene_samples) ### time pair idxs where patients may have up to 3 time points: # Calculate which pairs of idxs belong to different time points for both high cov and low piS # time_pair_idxs is comprised of 2 arrays. The first array corresponds to indecies for the 1st visno. The second array corresponds to indecies for the second or 3d visno time_pair_idxs, visno_snps_genes, day_snps_genes = parse_midas_data.calculate_time_pairs(subject_sample_time_map, desired_samples) # since the time_pair_idx are in terms of desired samples ordering,I need to convert the desired_samples idxs to the snp_sample and gene_sample orders. time_pair_snp_idxs= parse_midas_data.apply_sample_index_map_to_indices(snp_sample_idx_map, time_pair_idxs) #use these idxs to get the relevant fields from total_fixation_matrix time_pair_gene_idxs= parse_midas_data.apply_sample_index_map_to_indices(gene_sample_idx_map, time_pair_idxs) # use these idxs to get the relevant fields from gene_hamming_matrix #### time pair idxs where patients can have exactly 1 time point (so that points plotted are iid) time_pair_idxs_unique, visno_snps_genes_unique, day_snps_genes_unique = parse_midas_data.calculate_unique_time_pairs(subject_sample_time_map, desired_samples) time_pair_snp_idxs_unique= parse_midas_data.apply_sample_index_map_to_indices(snp_sample_idx_map, time_pair_idxs_unique) time_pair_gene_idxs_unique= parse_midas_data.apply_sample_index_map_to_indices(gene_sample_idx_map, time_pair_idxs_unique) ### different patient idx: # to compare results to time_pair idxs, we want different patient pair idxs. This helps us to contextualize if we are seeing events within patients that resemble replacements or modifications. # Calculate which pairs of idxs belong to the same sample, which to the same subject # and which to different subjects
# Calculate which pairs of idxs belong to the same sample, which to the same subject
# and which to different subjects
#desired_same_sample_idxs, desired_same_subject_idxs, desired_diff_subject_idxs = parse_midas_data.calculate_subject_pairs(subject_sample_map, desired_samples)
# Calculate which pairs of idxs belong to the same sample, which to the same subject
# and which to different subjects
desired_same_sample_idxs, desired_same_subject_idxs, desired_diff_subject_idxs = parse_midas_data.calculate_ordered_subject_pairs(
sample_order_map, desired_samples)
snp_sample_idx_map = parse_midas_data.calculate_sample_idx_map(
desired_samples, snp_samples)
gene_sample_idx_map = parse_midas_data.calculate_sample_idx_map(
desired_samples, gene_samples)
same_sample_snp_idxs = parse_midas_data.apply_sample_index_map_to_indices(
snp_sample_idx_map, desired_same_sample_idxs)
same_sample_gene_idxs = parse_midas_data.apply_sample_index_map_to_indices(
gene_sample_idx_map, desired_same_sample_idxs)
same_subject_snp_idxs = parse_midas_data.apply_sample_index_map_to_indices(
snp_sample_idx_map, desired_same_subject_idxs)
same_subject_gene_idxs = parse_midas_data.apply_sample_index_map_to_indices(
gene_sample_idx_map, desired_same_subject_idxs)
diff_subject_snp_idxs = parse_midas_data.apply_sample_index_map_to_indices(
snp_sample_idx_map, desired_diff_subject_idxs)
diff_subject_gene_idxs = parse_midas_data.apply_sample_index_map_to_indices(
gene_sample_idx_map, desired_diff_subject_idxs)
same_subject_ploidy_changes = []
# Only plot samples above a certain depth threshold
high_coverage_samples = samples[median_coverages >= min_coverage]
high_coverage_low_pi_samples = samples[(median_coverages >= min_coverage) *
(pis <= 1e-03)]
# Calculate which pairs of idxs belong to the same sample, which to the same subject
# and which to different subjects
high_coverage_same_sample_idxs, high_coverage_same_subject_idxs, high_coverage_diff_subject_idxs = parse_midas_data.calculate_subject_pairs(
subject_sample_map, high_coverage_samples)
sample_idx_map = parse_midas_data.calculate_sample_idx_map(
high_coverage_samples, samples)
same_sample_idxs = parse_midas_data.apply_sample_index_map_to_indices(
sample_idx_map, high_coverage_same_sample_idxs)
#
same_subject_idxs = parse_midas_data.apply_sample_index_map_to_indices(
sample_idx_map, high_coverage_same_subject_idxs)
#
diff_subject_idxs = parse_midas_data.apply_sample_index_map_to_indices(
sample_idx_map, high_coverage_diff_subject_idxs)
# Calculate which pairs of idxs belong to the same sample, which to the same subject
# and which to different subjects
high_coverage_low_pi_same_sample_idxs, high_coverage_low_pi_same_subject_idxs, high_coverage_low_pi_diff_subject_idxs = parse_midas_data.calculate_subject_pairs(
subject_sample_map, high_coverage_low_pi_samples)
low_pi_sample_idx_map = parse_midas_data.calculate_sample_idx_map(
high_coverage_low_pi_samples, samples)