def update_best_p_value():
q = session.query(SNP, db.func.greatest(db.func.coalesce(db.func.max(TranscriptionFactorSNP.best_p_value), 0),
db.func.coalesce(db.func.max(CellLineSNP.best_p_value), 0))) \
.join(TranscriptionFactorSNP, SNP.tf_aggregated_snps, isouter=True) \
.join(CellLineSNP, SNP.cl_aggregated_snps, isouter=True) \
.group_by(SNP)
for i, (snp, best_p) in enumerate(q, 1):
if i % 50000 == 1:
print(i)
snp.best_p_value = best_p
snp.fdr_class = get_fdr_class(best_p)
session.commit()
max_es = max([
x for x in (row['es_mean_ref'], row['es_mean_alt'])
if x is not None
],
default=None)
mutation = CandidateSNP(
rs_id=row['ID'],
chromosome=row['#chr'],
position=row['pos'],
ref=row['ref'],
alt=row['alt'],
ag_level=param,
ag_id=ag_id,
best_p_value=min_pv,
fdr_class=get_fdr_class(min_pv),
best_es=max_es,
es_class=get_es_class(max_es),
)
snps.append(mutation)
session.add_all(snps)
session.commit()
session.close()
if SNP_RS:
q = session.query(CandidateSNP.rs_id,
db.func.max(CandidateSNP.best_p_value),
db.func.max(CandidateSNP.best_es)).group_by(
snps.append(mutation)
ag_data = {
'chromosome': row['#chr'],
'position': int(row['pos']),
'alt': row['alt'],
({
'TF': 'tf_id',
'CL': 'cl_id'
}[param]): ag_id,
'log_p_value_ref': -np.log10(row['fdrp_bh_ref']),
'log_p_value_alt': -np.log10(row['fdrp_bh_alt']),
'best_p_value': -np.log10(min_pv),
'best_es': max_es,
'fdr_class': get_fdr_class(-np.log10(min_pv)),
'es_class': get_es_class(max_es),
'es_ref': row['es_mean_ref'],
'es_alt': row['es_mean_alt'],
'is_asb': min_pv <= 0.1,
'mean_bad': row['mean_BAD'],
'peak_calls': row['n_peak_calls'],
'peak_callers': row['n_peak_callers'],
}
if param == 'TF':
ag_data.update({
'motif_log_p_ref': row['motif_log_pref'],
'motif_log_p_alt': row['motif_log_palt'],
'motif_log_2_fc': row['motif_fc'],
'motif_orientation': row['motif_orient'],
'motif_position': row['motif_pos'],