def collapse_biom(table: biom.Table, mapping: dict, normalize=False):
"""Collapse a BIOM table in many-to-many mode.
Parameters
----------
table : biom.Table
Table to collapse.
mapping : dict of list of str
Source-to-target(s) mapping.
normalize : bool, optional
Whether normalize per-target counts by number of targets per source.
Returns
-------
biom.Table
Collapsed BIOM table.
Notes
-----
Metadata will not be retained in the collapsed table.
See Also
--------
.table.collapse_table
"""
# filter table features
table = table.filter(lambda data, id_, md: id_ in mapping,
axis='observation',
inplace=False)
# stop if no feature left
if table.is_empty():
return table
# add mapping to table metadata
table.add_metadata({k: dict(part=v)
for k, v in mapping.items()},
axis='observation')
# determine collapsing method
kwargs = dict(norm=False,
one_to_many=True,
axis='observation',
one_to_many_mode=('divide' if normalize else 'add'))
# collapse table in many-to-many mode
table = table.collapse(lambda id_, md: zip(md['part'], md['part']),
**kwargs)
# round to integers
if normalize:
round_biom(table)
# clean up
table.del_metadata(keys=['Path'])
return table
def collapse_biom(table: biom.Table, mapping: dict, divide=False, field=None):
"""Collapse a BIOM table in many-to-many mode.
Parameters
----------
table : biom.Table
Table to collapse.
mapping : dict of list of str
Source-to-target(s) mapping.
divide : bool, optional
Whether divide per-target counts by number of targets per source.
field : int, optional
Index of field to be collapsed in a stratified table.
Returns
-------
biom.Table
Collapsed BIOM table.
Raises
------
ValueError
Field index is not present in a feature ID.
Notes
-----
Metadata will not be retained in the collapsed table.
See Also
--------
.table.collapse_table
"""
# generate metadata
metadata = {}
for id_ in table.ids('observation'):
feature = id_
if field:
fields = feature.split('|')
try:
feature = fields[field]
except IndexError:
raise ValueError(
f'Feature "{feature}" has less than {field + 1} fields.')
if feature not in mapping:
continue
targets = []
for target in mapping[feature]:
if field:
fields[field] = target
target = '|'.join(fields)
targets.append(target)
metadata[id_] = dict(part=targets)
# filter table features
table = table.filter(lambda data, id_, md: id_ in metadata,
axis='observation',
inplace=False)
# stop if no feature left
if table.is_empty():
return table
# add mapping to table metadata
table.add_metadata(metadata, axis='observation')
# determine collapsing method
kwargs = dict(norm=False,
one_to_many=True,
axis='observation',
one_to_many_mode=('divide' if divide else 'add'))
# collapse table in many-to-many mode
table = table.collapse(lambda _, md: zip(md['part'], md['part']), **kwargs)
# round to integers
if divide:
round_biom(table)
# clean up
table.del_metadata(keys=['Path'])
return table