def augment(table: biom.Table, sampling_depth: int, augment_times: int, output_path_metadata: str,
raw_metadata: qiime2.Metadata, with_replacement: bool = False, rarefy_start: bool = True) -> biom.Table:
metadata = raw_metadata.to_dataframe()
metadata = metadata.sort_index()
all_df = table.to_dataframe().sort_index().sort_index(axis=1)
## change sorted table back to biom
table = biom.Table(all_df.values, all_df.index.to_list(), all_df.columns.to_list())
zero_df = all_df[all_df==0].fillna(0)
zero_table = biom.Table(zero_df.values, zero_df.index.to_list(), zero_df.columns.to_list())
sub_table = table.subsample(sampling_depth, axis='sample', by_id=False,
with_replacement=with_replacement)
if rarefy_start == True:
output_table = zero_table.merge(sub_table)
else:
output_table = table
output_metadata = metadata
for i in range(augment_times):
num = i+1
sub_table = table.subsample(sampling_depth, axis='sample', by_id=False,
with_replacement=with_replacement)
sub_df = sub_table.to_dataframe().sort_index().sort_index(axis=1)
## rename
sub_df_names = sub_df.columns.to_list()
sub_df_names_added = [x + '_' + str(num) for x in sub_df_names]
sub_df.columns = sub_df_names_added
sub_table = biom.Table(sub_df.values, sub_df.index.to_list(), sub_df.columns.to_list())
output_table = output_table.merge(sub_table)
metadata_names = metadata.index.to_list()
metadata_names_added = [x + '_' + str(num) for x in metadata_names]
tmp_metadata = metadata.copy()
tmp_metadata.index = metadata_names_added
print(output_metadata)
output_metadata = pd.concat((output_metadata, tmp_metadata))
output_metadata.index.name = 'sample-id'
output_metadata = qiime2.metadata.Metadata(output_metadata)
output_metadata.save(output_path_metadata)
if output_table.is_empty():
raise ValueError('The output table contains no features.')
return output_table
def subsample(table: biom.Table, subsampling_depth: int,
axis: str) -> biom.Table:
if axis == 'feature':
# we are transposing the table due to biocore/biom-format#759
table = table.transpose()
if len(table.ids()) < subsampling_depth:
raise ValueError('The subsampling depth exceeds the number of '
'elements on the desired axis. The maximum depth '
'is: %d.' % len(table.ids()))
# the axis is always 'sample' due to the above transpose
table = table.subsample(subsampling_depth, axis='sample', by_id=True)
# the inverted axis is always observation due to the above transpose
invaxis = 'observation'
table.filter(lambda v, i, m: v.sum() > 0, axis=invaxis)
if axis == 'feature':
# reverse the transpose necessary due to biocore/biom-format#759
table = table.transpose()
if table.is_empty():
raise ValueError('The subsampled table contains no samples or features'
' (samples/features that sum to zero after filtering'
' are automatically removed). It may be a good idea'
' to double check that your table is valid/nonempty.')
return table
def rarefy(table: biom.Table, sampling_depth: int) -> biom.Table:
table = table.subsample(sampling_depth, axis='sample', by_id=False)
if table.is_empty():
raise ValueError('The rarefied table contains no samples or features. '
'Verify your table is valid and that you provided a '
'shallow enough sampling depth.')
return table
def rarefy(table: biom.Table, sampling_depth: int) -> biom.Table:
return table.subsample(sampling_depth, axis='sample', by_id=False)
def rarefy(table: biom.Table, sampling_depth: int) -> biom.Table:
return table.subsample(sampling_depth, axis='sample', by_id=False)
