def main(table_in, table_out, pathways, to_classic):
# setup
table = load_table(table_in)
pathway_dict = get_pathway2kos()
# get set of kos from pathways
pathways_kos = set()
for pathway in pathways:
pathways_kos = pathways_kos | pathway_dict[pathway.strip()[-5:]]
# get selected kos
kos_to_keep = set(table.ids('observation')) & \
pathways_kos
if len(kos_to_keep) == 0:
raise EmptySetERROR('Intersection created empty set')
obs_ids = np.array(list(kos_to_keep))
data = np.empty([len(obs_ids), len(table.ids('sample'))])
for i, obs in enumerate(obs_ids):
data[i] = table.data(obs, 'observation')
# output
new_table = Table(data, obs_ids, table.ids('sample'), type="OTU table")
if to_classic:
# print to tab delimited biom table
f = open(table_out, 'w')
f.write(new_table.to_tsv())
else:
# print json biom table
new_table.to_json("filter_KOs_by_pathway.py", open(table_out, 'w'))
def main():
args = parser.parse_args()
input_fp = args.input_biom
output_fp = args.output_biom
threshold = args.abundance_threshold
as_fraction = args.abundance_as_fraction
if as_fraction:
if not 0 <= threshold <= 1:
raise ValueError("The value passed for -n "
"(--abundance_as_fraction) must be in the "
"interval [0, 1]")
if not as_fraction:
if not str(threshold).replace('.', '', 1).isdigit():
raise ValueError("If you want to express the minimum threshold as "
"a fraction of the total sequences in a sample, "
"use -n in combination with -f. Otherwise, if "
"you want to express the minimum threshold as an "
"absolute sequence count minimum, the value "
"passed for -n must be an integer.")
threshold = int(threshold)
input_table = load_table(input_fp)
new_data = []
append_new_data = new_data.append
for abundances in input_table.iter_data():
if as_fraction:
abundance_fractions = abundances.astype(float) / sum(abundances)
indices = [
i for (i, j) in enumerate(abundance_fractions > threshold)
if not j
]
else:
indices = [
i for (i, j) in enumerate(abundances > threshold) if not j
]
item_set = abundances.itemset
for index in indices:
item_set(index, 0)
append_new_data(abundances)
new_data = array(new_data).transpose()
new_table = Table(new_data, input_table.ids('observation'),
input_table.ids(),
input_table.metadata(axis='observation'),
input_table.metadata())
with open(output_fp, 'w') as output_fd:
new_table.to_json('one-time generation', output_fd)
def _1(data: biom.Table) -> BIOMV100Format:
data = _drop_axis_metadata(data)
ff = BIOMV100Format()
with ff.open() as fh:
fh.write(data.to_json(generated_by=_get_generated_by()))
return ff
def _1(data: biom.Table) -> BIOMV100Format:
data = _drop_axis_metadata(data)
ff = BIOMV100Format()
with ff.open() as fh:
fh.write(data.to_json(generated_by=_get_generated_by()))
return ff
def generate_per_sample_biom(biom_file, limit):
"""Generate per-sample BIOM files
Parameters
----------
biom_file : str
A filepath to a BIOM table
limit : int or None
Limit the number of tables to load
Returns
-------
str
The sample ID
str
The table in BIOM Format v1.0
str
The table in the classic OTU table format
"""
table = load_table(biom_file)
obs_ids = table.ids(axis='observation')
obs_md = table.metadata(axis='observation')
if limit is None:
limit = np.inf
count = 0
for v, sample, _ in table.iter():
if count >= limit:
break
single_sample = Table(v[:, np.newaxis], obs_ids, [sample], obs_md)
single_sample.filter(lambda v_, i, md: v_ > 0, axis='observation')
biomv1 = single_sample.to_json('AG')
biomtxt = single_sample.to_tsv(
header_key='taxonomy',
header_value='taxonomy',
metadata_formatter=lambda x: '; '.join(x))
yield (sample, biomv1, biomtxt)
count += 1
