def main(key, remade=True):
table_annotated = key + get_ending("annotation")
output = get_results_file(key, 'BAD')
with open(get_merged_badmaps_dict_path(remade=remade), "r") as read_file:
d = json.load(read_file)
rev_d = make_reverse_dict(d)
badmap_file_name = rev_d[key]
print('Now doing {} \n with BAD map file {}'.format(table_annotated, badmap_file_name))
badmap_file_path = create_badmaps_path_function(badmap_file_name, valid=remade)
with open(badmap_file_path, 'r') as badmap_file, open(output, 'w') as out, open(table_annotated, 'r') as table_file:
out.write(pack(['#chr', 'pos', 'ID', 'ref', 'alt', 'ref_read_counts', 'alt_read_counts',
'repeat_type'] + callers_names + ['BAD'] + ["Q{:.2f}".format(x) for x in segmentation_states] +
['SNP_count', 'sum_cover']))
u = UnpackBadSegments(None)
for chr, pos, ID, ref, alt, ref_c, alt_c, repeat_type, in_callers, \
in_intersection, segment_BAD, segment_snps, segment_snp_ids,\
segment_sumcov, Qual in \
Intersection(table_file, badmap_file, write_segment_args=True, write_intersect=True,
unpack_snp_function=lambda x: unpack(x, use_in='Pcounter'),
unpack_segments_function=lambda x: u.unpack_bad_segments(x, segmentation_states)):
if in_intersection and ID.startswith('rs'):
out.write(pack([chr, pos, ID, ref, alt, ref_c, alt_c, repeat_type] +
[in_callers[name] for name in callers_names] +
[segment_BAD] + [Qual[x] for x in Qual] + [segment_snp_ids, segment_sumcov]))
def collect_fixed_alt_statistics(master_df,
key_name=None,
BAD=None,
suffix='',
remade=True):
out_t = None
with open(get_merged_badmaps_dict_path(remade=remade), "r") as read_file:
d = json.load(read_file)
rev_d = make_reverse_dict(d)
for index, row in master_df.iterrows():
if key_name is not None:
if row['CELLS'] not in key_name: # <------
continue
base_path = create_path_from_master_list_df(row)
if not is_valid(base_path, rev_d, remade=remade):
continue
bad_table_path = create_path_from_master_list_df(row, 'BAD')
if not os.path.isfile(bad_table_path):
continue
df = pd.read_table(bad_table_path)
if df.empty:
continue
if BAD is not None:
sum_df = df[df['BAD'] == BAD][[
'ref_read_counts', 'alt_read_counts'
]] # <------
else:
sum_df = df[['ref_read_counts', 'alt_read_counts']]
if out_t is None:
out_t = pd.DataFrame()
out_t['alt_counts'] = sum_df['alt_read_counts']
out_t['ref_counts'] = sum_df['ref_read_counts']
out_t = out_t.groupby(['alt_counts', 'ref_counts'
]).size().reset_index(name='counts')
out_t.fillna(0, inplace=True)
else:
tmp_df = pd.DataFrame()
tmp_df['alt_counts'] = sum_df['alt_read_counts']
tmp_df['ref_counts'] = sum_df['ref_read_counts']
tmp_df = tmp_df.groupby(['alt_counts', 'ref_counts'
]).size().reset_index(name='counts')
tmp_df.fillna(0, inplace=True)
out_t = out_t.append(tmp_df).groupby(['alt_counts', 'ref_counts'],
as_index=False).sum()
if out_t is None:
return
out_t.to_csv(create_neg_bin_stats_path_function(BAD, suffix),
sep="\t",
index=False)
def main(what_for, remade=True):
check_if_in_expected_args(what_for)
aggregation_dict_path = get_aggregation_dict_path(what_for)
with open(get_merged_badmaps_dict_path(remade=remade), "r") as read_file:
old_rev_d = make_reverse_dict(json.load(read_file))
rev_d = {
get_results_file(k, 'p-value', False): v
for k, v in old_rev_d.items()
}
with open(aggregation_dict_path, 'r') as read_file:
d = json.loads(read_file.readline())
with open(out_path, 'w') as file:
for key in sorted(d.keys()):
is_empty = True
for value in d[key]:
if os.path.isfile(value) and is_valid(
split_ext_recursive(value), rev_d, remade=remade):
is_empty = False
if is_empty:
continue
file.write(key + '\n')
def main(for_what, remade=True):
master_df = pd.read_table(master_list_path, dtype=dtype_dict)
master_df = master_df[~master_df['EXP_TYPE'].
isin(['chip_control', 'chipexo_control'])]
master_df['path'] = master_df.apply(create_path_from_master_list_df,
axis=1)
master_df = master_df[master_df['path'].apply(
lambda x: os.path.isfile(x + get_ending('vcf')))]
if for_what == 'badmaps':
with open(get_merged_badmaps_dict_path(remade=remade),
"r") as read_file:
d = json.load(read_file)
rev_d = make_reverse_dict(d)
master_df = master_df[master_df.apply(
lambda row: os.path.isfile(row['path'] + get_ending(
'annotation')) and is_valid(row['path'], rev_d, remade=remade),
axis=1)]
master_df['path'].to_csv(out_path, sep='\t', index=False, header=False)
elif for_what == 'annotation':
master_df[['path', 'PEAKS']].to_csv(out_path,
sep='\t',
index=False,
header=False)
def main(what_for, key_name, remade=True):
check_if_in_expected_args(what_for)
table_path = get_result_table_path(what_for, key_name)
with open(get_merged_badmaps_dict_path(remade=remade), "r") as read_file:
old_rev_d = make_reverse_dict(json.load(read_file))
rev_d = {
get_results_file(k, 'p-value', False): v
for k, v in old_rev_d.items()
}
tables = []
if what_for == "CL":
tables = cell_lines_dict[key_name]
if what_for == "TF":
tables = tf_dict[key_name]
print('Reading datasets for {} {}'.format(what_for, key_name))
common_snps = dict()
for table in tables:
if os.path.isfile(table) and is_valid(
split_ext_recursive(table), rev_d, remade=remade):
table_name = get_name(table)
another_agr = get_another_agr(table, what_for)
with open(table, 'r') as file:
for line in file:
try:
(chromosome, pos, ID, ref, alt, ref_c, alt_c, repeat,
in_callers, BAD, Quals, seg_c, sum_cov, p_ref, p_alt,
es_ref, es_alt) = unpack(line, use_in="Aggregation")
except ValueError:
if line.startswith('#'):
continue
else:
raise
if np.isnan(p_ref) or ID == '.':
continue
cov = ref_c + alt_c
try:
common_snps[(chromosome, pos, ID, ref, alt,
repeat)].append(
(cov, ref_c, alt_c, in_callers, BAD,
Quals, seg_c, sum_cov, p_ref, p_alt,
es_ref, es_alt, table_name,
another_agr))
except KeyError:
common_snps[(chromosome, pos, ID, ref, alt,
repeat)] = [
(cov, ref_c, alt_c, in_callers, BAD,
Quals, seg_c, sum_cov, p_ref, p_alt,
es_ref, es_alt, table_name,
another_agr)
]
else:
print("There is no {}".format(table))
print('Writing {}'.format(key_name))
with open(table_path, 'w') as out:
out.write(
pack([
'#chr', 'pos', 'ID', 'ref', 'alt', 'repeat_type',
'n_peak_calls', 'n_peak_callers', 'mean_BAD',
'mean_SNP_per_segment', 'n_aggregated', 'refc_mostsig_ref',
'altc_mostsig_ref', 'BAD_mostsig_ref', 'es_mostsig_ref',
'p_mostsig_ref', 'refc_mostsig_alt', 'altc_mostsig_alt',
'BAD_mostsig_alt', 'es_mostsig_alt', 'p_mostsig_alt',
'min_cover', 'max_cover', 'median_cover', 'total_cover',
'es_mean_ref', 'es_mean_alt', 'logitp_ref', 'logitp_alt'
]))
SNP_counter = 0
print('{} snps'.format(len(common_snps)))
if len(common_snps) == 0:
os.remove(table_path)
sys.exit(0)
origin_of_snp_dict = OrderedDict()
keys = list(common_snps.keys())
keys = sorted(keys, key=lambda chr_pos: chr_pos[1])
keys = sorted(keys, key=lambda chr_pos: chr_pos[0])
for key in keys:
chromosome, pos, ID, ref, alt, repeat = key
value = common_snps[key]
SNP_counter += 1
if SNP_counter % 10000 == 0:
print('done {}'.format(SNP_counter))
unique_callers_counter = dict(
zip(callers_names, [False] * len(callers_names)))
total_callers_counter = 0
BAD_array = []
SNPs_per_segment_array = []
p_ref_array = []
p_alt_array = []
cover_array = []
ref_effect_size_array = []
alt_effect_size_array = []
table_names_array = []
another_agr_name = []
ref_counts_array = []
alt_counts_array = []
for v in value:
cov, ref_c, alt_c, in_callers, BAD, Quals, seg_c, sum_cov, p_ref, p_alt, es_ref, es_alt, table_name, \
another_agr = v
table_names_array.append(table_name)
another_agr_name.append(another_agr)
for caller in callers_names:
unique_callers_counter[caller] = unique_callers_counter[
caller] or in_callers[caller]
total_callers_counter += in_callers[caller]
BAD_array.append(BAD)
SNPs_per_segment_array.append(seg_c)
p_ref_array.append(p_ref)
p_alt_array.append(p_alt)
if not np.isnan(es_ref):
ref_effect_size_array.append(es_ref / np.log(2))
if not np.isnan(es_alt):
alt_effect_size_array.append(es_alt / np.log(2))
cover_array.append(cov)
ref_counts_array.append(ref_c)
alt_counts_array.append(alt_c)
p = 1 / (BAD + 1)
min_cover = min(cover_array)
max_cover = max(cover_array)
med_cover = median_grouped(cover_array)
total_cover = sum(cover_array)
unique_callers = sum(unique_callers_counter[caller]
for caller in callers_names)
mean_BAD = np.round(np.mean(BAD_array), 2)
mean_SNPs_per_segment = np.round(np.mean(SNPs_per_segment_array),
1)
n_aggregated = len(value)
logitp_ref = logit_combine_p_values(p_ref_array)
logitp_palt = logit_combine_p_values(p_alt_array)
if ref_effect_size_array:
weights = [-1 * np.log10(x) for x in p_ref_array if x != 1]
es_mean_ref = np.round(
np.average(ref_effect_size_array, weights=weights), 3)
es_mostsig_ref = ref_effect_size_array[int(np.argmax(weights))]
idx = int(np.argmax([-x for x in p_ref_array]))
p_mostsig_ref = p_ref_array[idx]
ref_c_mostsig_ref = ref_counts_array[idx]
alt_c_mostsig_ref = alt_counts_array[idx]
BAD_mostsig_ref = BAD_array[idx]
else:
es_mean_ref = 'NaN'
es_mostsig_ref = 'NaN'
ref_c_mostsig_ref = 'NaN'
p_mostsig_ref = 'NaN'
alt_c_mostsig_ref = 'NaN'
BAD_mostsig_ref = 'NaN'
if alt_effect_size_array:
weights = [-1 * np.log10(x) for x in p_alt_array if x != 1]
es_mean_alt = np.round(
np.average(alt_effect_size_array, weights=weights), 3)
es_mostsig_alt = alt_effect_size_array[int(np.argmax(weights))]
idx = int(np.argmax([-x for x in p_alt_array]))
p_mostsig_alt = p_alt_array[idx]
ref_c_mostsig_alt = ref_counts_array[idx]
alt_c_mostsig_alt = alt_counts_array[idx]
BAD_mostsig_alt = BAD_array[idx]
else:
es_mean_alt = 'NaN'
es_mostsig_alt = 'NaN'
ref_c_mostsig_alt = 'NaN'
p_mostsig_alt = 'NaN'
alt_c_mostsig_alt = 'NaN'
BAD_mostsig_alt = 'NaN'
out.write(
pack([
chromosome, pos, ID, ref, alt, repeat,
total_callers_counter, unique_callers, mean_BAD,
mean_SNPs_per_segment, n_aggregated, ref_c_mostsig_ref,
alt_c_mostsig_ref, BAD_mostsig_ref, es_mostsig_ref,
p_mostsig_ref, ref_c_mostsig_alt, alt_c_mostsig_alt,
BAD_mostsig_alt, es_mostsig_alt, p_mostsig_alt, min_cover,
max_cover, med_cover, total_cover, es_mean_ref,
es_mean_alt, logitp_ref, logitp_palt
]))
origin_of_snp_dict["\t".join(map(str, key))] = {
'aligns': table_names_array,
expected_args[what_for]: another_agr_name,
'ref_counts': ref_counts_array,
'alt_counts': alt_counts_array,
'ref_ef': ref_effect_size_array,
'alt_ef': alt_effect_size_array,
'BAD': BAD_array,
'ref_pvalues': p_ref_array,
'alt_pvalues': p_alt_array,
}
print("Counting FDR")
table = pd.read_table(table_path)
if table.empty:
os.remove(table_path)
sys.exit(0)
mc_filter_array = np.array(table['max_cover'] >= 20)
if sum(mc_filter_array) != 0:
bool_ar_ref, p_val_ref, _, _ = statsmodels.stats.multitest.multipletests(
table[mc_filter_array]["logitp_ref"], alpha=0.05, method='fdr_bh')
bool_ar_alt, p_val_alt, _, _ = statsmodels.stats.multitest.multipletests(
table[mc_filter_array]["logitp_alt"], alpha=0.05, method='fdr_bh')
else:
p_val_ref = []
p_val_alt = []
bool_ar_ref = []
bool_ar_alt = []
fdr_by_ref = np.array(['NaN'] * len(table.index), dtype=np.float128)
fdr_by_ref[mc_filter_array] = p_val_ref
table["fdrp_bh_ref"] = fdr_by_ref
fdr_by_alt = np.array(['NaN'] * len(table.index), dtype=np.float128)
fdr_by_alt[mc_filter_array] = p_val_alt
table["fdrp_bh_alt"] = fdr_by_alt
table.to_csv(table_path, sep="\t", index=False)
bool_ar = np.array([False] * len(table.index), dtype=np.bool)
bool_ar[mc_filter_array] = bool_ar_alt + bool_ar_ref
with open(
os.path.join(results_path,
what_for + '_DICTS/{}.json'.format(key_name)),
'w') as out:
json.dump(origin_of_snp_dict, out)