def tfam(args):
"""
Report the information about the samples in the DB in TFAM format:
http://pngu.mgh.harvard.edu/~purcell/plink/data.shtml
"""
query = ("select family_id, name, paternal_id, maternal_id, "
"sex, phenotype from samples")
gq = GeminiQuery(args.db)
gq.run(query)
for row in gq:
print " ".join(map(str, [row['family_id'], row['name'], row['paternal_id'],
row['maternal_id'], row['sex'], row['phenotype']]))
def tfam(args):
"""
Report the information about the samples in the DB in TFAM format:
http://pngu.mgh.harvard.edu/~purcell/plink/data.shtml
"""
query = ("select family_id, name, paternal_id, maternal_id, "
"sex, phenotype from samples")
gq = GeminiQuery(args.db)
gq.run(query)
for row in gq:
print " ".join(map(str, [row['family_id'], row['name'], row['paternal_id'],
row['maternal_id'], row['sex'], row['phenotype']]))
def genewise(db, gt_filters, filter=None, columns=None, min_filters=None,
min_variants=1,
grouper="gene"):
assert os.path.exists(db)
orig_columns = [x.strip() for x in (columns or "").split(",")]
added_cols = add_cols(orig_columns, "||".join(gt_filters))
if grouper not in orig_columns:
added_cols.append(grouper)
columns = orig_columns + added_cols
assert not any(';' in c for c in columns)
# NOTE: we could make the WHERE part customizable.
query = "SELECT {columns} FROM variants WHERE (is_exonic = 1 AND impact_severity != 'LOW')"
if filter:
query += " AND " + filter
query += " ORDER BY CHROM, gene"
gq = GeminiQuery(db, include_gt_cols=True)
# use the GeminiQuery machinery to transform to something that's eval'able
# then compile once for speed.
cleaned_filters = []
for gt_filter in gt_filters:
assert gq._is_gt_filter_safe(gt_filter)
gt_filter = gq._correct_genotype_filter(gt_filter)
cleaned_filters.append(compile(gt_filter, gt_filter, 'eval'))
gq.run(query.format(columns=", ".join(columns)))
if isinstance(grouper, basestring):
grouper = operator.itemgetter(grouper)
user_dict = dict(sample_info=gq.sample_info)
header_printed = False
for groupkey, grp in it.groupby(gq, grouper):
grp = list(grp)
for x in gen_results(list(grp), cleaned_filters, min_filters or 0,
min_variants, columns, user_dict=user_dict):
for c in added_cols:
if c != 'gene':
del x.print_fields[c]
if not header_printed:
print "\t".join(x.print_fields.keys())
header_printed = True
print x
def genewise(db,
gt_filters,
filter=None,
columns=None,
min_filters=None,
min_variants=1,
grouper="gene"):
assert os.path.exists(db)
orig_columns = [x.strip() for x in (columns or "").split(",")]
added_cols = add_cols(orig_columns, "||".join(gt_filters))
if grouper not in orig_columns:
added_cols.append(grouper)
columns = orig_columns + added_cols
assert not any(';' in c for c in columns)
# NOTE: we could make the WHERE part customizable.
query = "SELECT {columns} FROM variants WHERE (is_exonic = 1 AND impact_severity != 'LOW')"
if filter:
query += " AND " + filter
query += " ORDER BY CHROM, gene"
gq = GeminiQuery(db, include_gt_cols=True)
# use the GeminiQuery machinery to transform to something that's eval'able
# then compile once for speed.
cleaned_filters = []
for gt_filter in gt_filters:
assert gq._is_gt_filter_safe(gt_filter)
gt_filter = gq._correct_genotype_filter(gt_filter)
cleaned_filters.append(compile(gt_filter, gt_filter, 'eval'))
gq.run(query.format(columns=", ".join(columns)))
if isinstance(grouper, basestring):
grouper = operator.itemgetter(grouper)
user_dict = dict(sample_info=gq.sample_info)
header_printed = False
for groupkey, grp in it.groupby(gq, grouper):
grp = list(grp)
for x in gen_results(list(grp),
cleaned_filters,
min_filters or 0,
min_variants,
columns,
user_dict=user_dict):
for c in added_cols:
if c != 'gene':
del x.print_fields[c]
if not header_printed:
print "\t".join(x.print_fields.keys())
header_printed = True
print x