def test_path_with_affine():
readset, var_pos, clustering, genotypes = create_testinstance1()
ploidy = 3
index, rev_index = get_position_map(readset)
num_vars = len(rev_index)
positions = get_cluster_start_end_positions(readset, clustering, index)
coverage = get_coverage(readset, clustering, index)
cov_map = get_pos_to_clusters_map(coverage, ploidy)
consensus = get_local_cluster_consensus(readset, clustering, cov_map, positions)
path = compute_threading_path(
readset, clustering, num_vars, coverage, cov_map, consensus, ploidy, genotypes
)
cluster_paths = ["".join([str(path[i][j]) for i in range(len(path))]) for j in range(3)]
first_block = set([cluster_paths[0][:9], cluster_paths[1][:9], cluster_paths[2][:9]])
first_truth = set(["000000000", "111111111", "044444444"])
second_block = set([cluster_paths[0][9:20], cluster_paths[1][9:20], cluster_paths[2][9:20]])
second_truth = set(["33333333333", "22222222222", "44444555555"])
third_block = set([cluster_paths[0][20:], cluster_paths[1][20:], cluster_paths[2][20:]])
third_truth = set(["66", "77", "55"])
print(cluster_paths)
assert first_block == first_truth
assert second_block == second_truth
assert third_block == third_truth
def find_inconsistencies(readset, clustering, ploidy):
# Returns the number of cluster positions with inconsistencies
# (counts position multiple times, if multiple clusters are inconsistent there)
# Also returns a list of read pairs, which need to be seperated
num_inconsistent_positions = 0
separated_pairs = []
exp_error = 0.05
p_val_threshold = 0.02
# Compute consensus and coverage
index, rev_index = get_position_map(readset)
num_vars = len(rev_index)
coverage = get_coverage(readset, clustering, index)
cov_map = get_pos_to_clusters_map(coverage, ploidy)
positions = get_cluster_start_end_positions(readset, clustering, index)
abs_coverage = get_coverage_absolute(readset, clustering, index)
consensus = get_local_cluster_consensus_withfrac(readset, clustering,
cov_map, positions)
# Search for positions in clusters with ambivalent consensus
for pos in range(num_vars):
# print(str(pos)+" -> "+str(len(coverage[pos]))+" , "+str(len(consensus[pos])))
for c_id in coverage[pos]:
if c_id not in consensus[pos]:
continue
# do binomial hypothesis test, whether the deviations from majority allele is significant enough for splitting
abs_count = abs_coverage[pos][c_id]
abs_deviations = int(abs_count * (1 - consensus[pos][c_id][1]))
p_val = binom_test(abs_deviations,
abs_count,
exp_error,
alternative="greater")
if p_val < p_val_threshold:
# print(" inconsistency in cluster "+str(c_id)+" at position"+str(pos)+" with coverage "+str(coverage[pos][c_id])+" and consensus "+str(consensus[pos][c_id]))
num_inconsistent_positions += 1
zero_reads = []
one_reads = []
for read in clustering[c_id]:
for var in readset[read]:
if index[var.position] == pos:
if var.allele == 0:
zero_reads.append(read)
else:
one_reads.append(read)
for r0 in zero_reads:
for r1 in one_reads:
separated_pairs.append((r0, r1))
return num_inconsistent_positions, separated_pairs