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# ----------------------------------------------------------------------------
# Copyright (c) 2016-2022, QIIME 2 development team.
#
# Distributed under the terms of the Modified BSD License.
#
# The full license is in the file LICENSE, distributed with this software.
# ----------------------------------------------------------------------------
import biom
import qiime2
import numpy as np
import pandas as pd
def _get_biom_filter_function(ids_to_keep, min_frequency, max_frequency,
min_nonzero, max_nonzero):
ids_to_keep = set(ids_to_keep)
if max_frequency is None:
max_frequency = np.inf
if max_nonzero is None:
max_nonzero = np.inf
def f(data_vector, id_, metadata):
return (id_ in ids_to_keep) and \
(min_frequency <= data_vector.sum() <= max_frequency) and \
(min_nonzero <= (data_vector > 0).sum() <= max_nonzero)
return f
_other_axis_map = {'sample': 'observation', 'observation': 'sample'}
def _filter_table(table, min_frequency, max_frequency, min_nonzero,
max_nonzero, metadata, where, axis, exclude_ids=False,
filter_opposite_axis=True):
if min_frequency == 0 and max_frequency is None and min_nonzero == 0 and\
max_nonzero is None and metadata is None and where is None and\
exclude_ids is False:
raise ValueError("No filtering was requested.")
if metadata is None and where is not None:
raise ValueError("Metadata must be provided if 'where' is "
"specified.")
if metadata is None and exclude_ids is True:
raise ValueError("Metadata must be provided if 'exclude_ids' "
"is True.")
if metadata is not None:
ids_to_keep = metadata.get_ids(where=where)
else:
ids_to_keep = table.ids(axis=axis)
if exclude_ids is True:
ids_to_keep = set(table.ids(axis=axis)) - set(ids_to_keep)
filter_fn1 = _get_biom_filter_function(
ids_to_keep, min_frequency, max_frequency, min_nonzero, max_nonzero)
table.filter(filter_fn1, axis=axis, inplace=True)
# filter on the opposite axis to remove any entities that now have a
# frequency of zero
if filter_opposite_axis:
filter_fn2 = _get_biom_filter_function(
ids_to_keep=table.ids(axis=_other_axis_map[axis]), min_frequency=0,
max_frequency=None, min_nonzero=1, max_nonzero=None)
table.filter(filter_fn2, axis=_other_axis_map[axis], inplace=True)
def filter_samples(table: biom.Table, min_frequency: int = 0,
max_frequency: int = None, min_features: int = 0,
max_features: int = None,
metadata: qiime2.Metadata = None, where: str = None,
exclude_ids: bool = False,
filter_empty_features: bool = True)\
-> biom.Table:
_filter_table(table=table, min_frequency=min_frequency,
max_frequency=max_frequency, min_nonzero=min_features,
max_nonzero=max_features, metadata=metadata,
where=where, axis='sample', exclude_ids=exclude_ids,
filter_opposite_axis=filter_empty_features)
return table
def filter_features(table: biom.Table, min_frequency: int = 0,
max_frequency: int = None, min_samples: int = 0,
max_samples: int = None,
metadata: qiime2.Metadata = None, where: str = None,
exclude_ids: bool = False,
filter_empty_samples: bool = True)\
-> biom.Table:
_filter_table(table=table, min_frequency=min_frequency,
max_frequency=max_frequency, min_nonzero=min_samples,
max_nonzero=max_samples, metadata=metadata,
where=where, axis='observation', exclude_ids=exclude_ids,
filter_opposite_axis=filter_empty_samples)
return table
def filter_seqs(data: pd.Series, table: biom.Table = None,
metadata: qiime2.Metadata = None, where: str = None,
exclude_ids: bool = False) -> pd.Series:
if table is not None and metadata is not None:
raise ValueError('Filtering with metadata and filtering with a table '
'are mutually exclusive.')
elif table is None and metadata is None:
raise ValueError('No filtering requested. Must provide either table '
'or metadata.')
elif table is not None:
ids_to_keep = table.ids(axis='observation')
else:
# Note, no need to check for missing feature IDs in the metadata,
# because that is basically the point of this method.
ids_to_keep = metadata.get_ids(where=where)
if exclude_ids is True:
ids_to_keep = set(data.index) - set(ids_to_keep)
filtered = data[data.index.isin(ids_to_keep)]
if filtered.empty is True:
raise ValueError('All features were filtered out of the data.')
return filtered
def filter_features_conditionally(table: biom.Table,
abundance: float,
prevalence: float,
) -> biom.Table:
"""
A function to perform joint filtering because it makes life better
"""
num_observations, num_samples = table.shape
prevalence = prevalence * num_samples
# Calculates the filtering parameters on the original table
def _filter_f(values, id_, metadata):
return (values >= abundance).sum() >= prevalence
# Normalized the table to get the prevalance
# Copy is because biom really wants to normalize the original table. By
# copying and not using inplace, the original table is preserved.
# Redundant, but better safe that sorry.
table_norm = table.copy().norm(axis='sample', inplace=False)
table_norm.filter(_filter_f, axis='observation', inplace=True)
filter_ids = table_norm.ids(axis='observation')
new_table = table.filter(filter_ids, axis='observation', inplace=False)
return new_table
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