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
def test_auxiliary_datastructures():
# test postion map
readset, var_pos, _, _ = create_testinstance1()
index, rev_index = get_position_map(readset)
for i in range(len(var_pos)):
assert index[var_pos[i]] == i
assert rev_index == var_pos
# test relative coverage
clustering = [
[0, 4, 6],
[1, 2],
[7, 10, 13],
[9, 12, 14],
[3, 5, 8, 11],
[15, 16],
[17],
[18],
]
cov = get_coverage(readset, clustering, index)
assert cov[0] == {0: 0.5, 1: 0.5}
assert cov[1] == {0: 0.25, 1: 0.5, 4: 0.25}
assert cov[2] == {0: 1 / 3, 1: 1 / 3, 4: 1 / 3}
assert cov[3] == {0: 3 / 7, 1: 2 / 7, 4: 2 / 7}
assert cov[4] == {0: 3 / 8, 1: 2 / 8, 4: 3 / 8}
assert cov[5] == {0: 3 / 9, 1: 2 / 9, 4: 4 / 9}
assert cov[6] == {0: 3 / 9, 1: 2 / 9, 4: 4 / 9}
assert cov[7] == {0: 2 / 9, 1: 2 / 9, 2: 1 / 9, 4: 4 / 9}
assert cov[8] == {0: 2 / 10, 1: 1 / 10, 2: 2 / 10, 3: 1 / 10, 4: 4 / 10}
assert cov[9] == {0: 2 / 11, 1: 1 / 11, 2: 2 / 11, 3: 2 / 11, 4: 4 / 11}
assert cov[10] == {0: 1 / 11, 2: 3 / 11, 3: 3 / 11, 4: 4 / 11}
assert cov[11] == {0: 1 / 10, 2: 3 / 10, 3: 3 / 10, 4: 3 / 10}
assert cov[12] == {2: 3 / 8, 3: 3 / 8, 4: 2 / 8}
assert cov[13] == {2: 3 / 7, 3: 3 / 7, 4: 1 / 7}
assert cov[14] == {2: 3 / 8, 3: 3 / 8, 5: 2 / 8}
assert cov[15] == {2: 3 / 8, 3: 3 / 8, 5: 2 / 8}
assert cov[16] == {2: 3 / 10, 3: 3 / 10, 5: 2 / 10, 6: 1 / 10, 7: 1 / 10}
assert cov[17] == {2: 2 / 9, 3: 3 / 9, 5: 2 / 9, 6: 1 / 9, 7: 1 / 9}
assert cov[18] == {2: 1 / 7, 3: 2 / 7, 5: 2 / 7, 6: 1 / 7, 7: 1 / 7}
assert cov[19] == {2: 1 / 6, 3: 1 / 6, 5: 2 / 6, 6: 1 / 6, 7: 1 / 6}
assert cov[20] == {5: 2 / 4, 6: 1 / 4, 7: 1 / 4}
assert cov[21] == {5: 2 / 4, 6: 1 / 4, 7: 1 / 4}
# test absolute coverage
abs_cov = get_coverage_absolute(readset, clustering, index)
assert abs_cov[0] == {0: 1, 1: 1}
assert abs_cov[1] == {0: 1, 1: 2, 4: 1}
assert abs_cov[2] == {0: 2, 1: 2, 4: 2}
assert abs_cov[3] == {0: 3, 1: 2, 4: 2}
assert abs_cov[4] == {0: 3, 1: 2, 4: 3}
assert abs_cov[5] == {0: 3, 1: 2, 4: 4}
assert abs_cov[6] == {0: 3, 1: 2, 4: 4}
assert abs_cov[7] == {0: 2, 1: 2, 2: 1, 4: 4}
assert abs_cov[8] == {0: 2, 1: 1, 2: 2, 3: 1, 4: 4}
assert abs_cov[9] == {0: 2, 1: 1, 2: 2, 3: 2, 4: 4}
assert abs_cov[10] == {0: 1, 2: 3, 3: 3, 4: 4}
assert abs_cov[11] == {0: 1, 2: 3, 3: 3, 4: 3}
assert abs_cov[12] == {2: 3, 3: 3, 4: 2}
assert abs_cov[13] == {2: 3, 3: 3, 4: 1}
assert abs_cov[14] == {2: 3, 3: 3, 5: 2}
assert abs_cov[15] == {2: 3, 3: 3, 5: 2}
assert abs_cov[16] == {2: 3, 3: 3, 5: 2, 6: 1, 7: 1}
assert abs_cov[17] == {2: 2, 3: 3, 5: 2, 6: 1, 7: 1}
assert abs_cov[18] == {2: 1, 3: 2, 5: 2, 6: 1, 7: 1}
assert abs_cov[19] == {2: 1, 3: 1, 5: 2, 6: 1, 7: 1}
assert abs_cov[20] == {5: 2, 6: 1, 7: 1}
assert abs_cov[21] == {5: 2, 6: 1, 7: 1}