def test_import_biom_obs(self):
"""
Tests if the import_biom function reads the correct database file,
and imports the biom file, by queriying the counts table.
:return:
"""
conn_object = BiomConnection()
conn_object.create_tables()
write_biom_table(testbiom, fmt='hdf5', filepath="test.biom")
import_biom("test.biom", mapping=None)
os.remove("test.biom")
conn = psycopg2.connect(
**{
"host": "localhost",
"database": "test",
"user": "******",
"password": "******"
})
cur = conn.cursor()
cur.execute("SELECT sampleid " "FROM counts " "LIMIT 5;")
result = cur.fetchall()
cur.close()
conn.close()
conn_object.delete_tables()
result = [x[0] for x in result]
self.assertCountEqual(
result, ['Sample1', 'Sample2', 'Sample3', 'Sample4', 'Sample5'])
def test_import_biom(self):
"""
Tests if the import_biom function reads the correct database file,
and imports the biom file, by querying the bioms table.
:return:
"""
conn_object = BiomConnection()
conn_object.create_tables()
write_biom_table(testbiom, fmt='hdf5', filepath="test.biom")
import_biom("test.biom", mapping=None)
os.remove("test.biom")
conn = psycopg2.connect(
**{
"host": "localhost",
"database": "test",
"user": "******",
"password": "******"
})
cur = conn.cursor()
cur.execute("SELECT studyID " "FROM bioms " "LIMIT 1;")
result = cur.fetchall()
cur.close()
conn.close()
conn_object.delete_tables()
self.assertEqual(result[0][0], 'test')
def add_metadata_to_biom_table(biom_input_fp, taxonomy_map_fp, biom_output_fp):
'''Load biom, add metadata, write to new table'''
newbiom = load_table(biom_input_fp)
if stat(taxonomy_map_fp).st_size == 0:
metadata = {}
else:
metadata = MetadataMap.from_file(taxonomy_map_fp,
header=['Sample ID', 'taxonomy', 'c'])
newbiom.add_metadata(metadata, 'observation')
write_biom_table(newbiom, 'json', biom_output_fp)
def add_metadata(input_fp, output_fp, sample_metadata_fp,
observation_metadata_fp, sc_separated, sc_pipe_separated,
int_fields, float_fields, sample_header, observation_header,
output_as_json):
"""Add metadata to a BIOM table.
Add sample and/or observation metadata to BIOM-formatted files. See
examples here: http://biom-format.org/documentation/adding_metadata.html
Example usage:
Add sample metadata to a BIOM table:
$ biom add-metadata -i otu_table.biom -o table_with_sample_metadata.biom
-m sample_metadata.txt
"""
table = load_table(input_fp)
if sample_metadata_fp is not None:
sample_metadata_f = open(sample_metadata_fp, 'U')
else:
sample_metadata_f = None
if observation_metadata_fp is not None:
observation_metadata_f = open(observation_metadata_fp, 'U')
else:
observation_metadata_f = None
if sc_separated is not None:
sc_separated = sc_separated.split(',')
if sc_pipe_separated is not None:
sc_pipe_separated = sc_pipe_separated.split(',')
if int_fields is not None:
int_fields = int_fields.split(',')
if float_fields is not None:
float_fields = float_fields.split(',')
if sample_header is not None:
sample_header = sample_header.split(',')
if observation_header is not None:
observation_header = observation_header.split(',')
result = _add_metadata(table, sample_metadata_f, observation_metadata_f,
sc_separated, sc_pipe_separated, int_fields,
float_fields, sample_header, observation_header)
if output_as_json:
fmt = 'json'
else:
fmt = 'hdf5'
write_biom_table(result, fmt, output_fp)
def write_bioms(self, fmt='hdf5'):
"""
Utility function that writes BIOM files
in a Batch object to HDF5 files.
OTU files are always written to disk,
the rest only if required.
:param fmt: Format for writing; 'hdf5' or 'json'.
:return:
"""
for x in self.inputs['name']:
for level in self.inputs['levels']:
filename = self.inputs['fp'] + '/' + x + '_' + level + '.hdf5'
try:
write_biom_table(self.levels[level][x], fmt, filename)
except Exception:
logger.error("Cannot write " + str(x) + " to disk", exc_info=True)
def denoise_to_feature_table(demux_seqs,
trim_left,
trunc_len,
community_dir,
rep_seqs_fn='rep_seqs',
feature_table_fn='feature_table.qza',
biom_table_fn='feature_table.biom',
summary_fn='feature_table_summary.qzv'):
'''SampleData[SequencesWithQuality] -> FeatureData[Sequence] +
FeatureTable[Frequency]
denoise fastqs with dada2, create feature table, rep_seqs,
and view stats.
demux_seqs = SampleData[SequencesWithQuality]
demultiplexed seqs output from qiime2.demux.methods.emp()
trim_left = int
trim X bases from 5' end
trunc_len = int
length to truncate all sequences
community_dir: path
destination directory to print results
rep_seqs_fn = str
filename of representative sequences output Artifact
feature_table_fn = str
filename of feature table output Artifact
summary_fn = str
filename of feature table summary output visualization
'''
biom_table, rep_seqs = dada2.methods.denoise_single(demux_seqs,
trim_left=trim_left,
trunc_len=trunc_len)
# save Artifact
rep_seqs.save(join(community_dir, rep_seqs_fn))
# save biom Artifact
biom_table.save(join(community_dir, feature_table_fn))
biom_table_fp = join(community_dir, biom_table_fn)
write_biom_table(biom_table.view(Table), 'hdf5', biom_table_fp)
# summarize feature table
feature_table_summary = feature_table.visualizers.summarize(biom_table)
feature_table_summary.visualization.save(join(community_dir, summary_fn))
return biom_table, rep_seqs
def test_run_network(self):
"""
Checks whether combine_data returns
a batch object if inputs are supplied.
"""
inputs = {
'biom_file': [(testloc[0] + '/data/test.biom')],
'cluster': None,
'otu_meta': None,
'prefix': None,
'sample_data': None,
'split': None,
'tax_table': None,
'fp': testloc[0],
'otu_table': None,
'tools': ['spiec-easi', 'conet'],
'spiec': None,
'spar': None,
'conet': (os.path.dirname(massoc.__file__)[:-6] + 'tests\\CoNet3'),
'conet_bash': None,
'spar_pval': None,
'spar_boot': None,
'levels': ['family'],
'prev': 20,
'min': None,
'rar': None,
'name': ['test'],
'cores': None
}
write_biom_table(testbiom['test'],
fmt='hdf5',
filepath=(inputs['biom_file'][0]))
get_input(inputs)
inputs['settings'] = inputs['fp'] + '/settings.json'
run_network(inputs)
test = Path(inputs['fp'] + "/conet_family_test.txt")
self.assertTrue(test.is_file())
call(("rm " + inputs['biom_file'][0]))
call(("rm " + inputs['fp'] + "/settings.json"))
call(("rm " + inputs['fp'] + "/test_family.hdf5"))
call(("rm " + inputs['fp'] + "/test_otu.hdf5"))
call(("rm " + inputs['fp'] + "/conet_test_family.txt"))
call(("rm " + inputs['fp'] + "/spiec-easi_test_family.txt"))
def from_uc(input_fp, output_fp, rep_set_fp):
"""Create a BIOM table from a vsearch/uclust/usearch BIOM file.
Example usage:
Simple BIOM creation:
$ biom from-uc -i in.uc -o out.biom
BIOM creation with OTU re-naming:
$ biom from-uc -i in.uc -o out.biom --rep-set-fp rep-set.fna
"""
input_f = open(input_fp, 'U')
if rep_set_fp is not None:
rep_set_f = open(rep_set_fp, 'U')
else:
rep_set_f = None
table = _from_uc(input_f, rep_set_f)
write_biom_table(table, 'hdf5', output_fp)
def from_uc(input_fp, output_fp, rep_set_fp):
"""Create a BIOM table from a vsearch/uclust/usearch BIOM file.
Example usage:
Simple BIOM creation:
$ biom from-uc -i in.uc -o out.biom
BIOM creation with OTU re-naming:
$ biom from-uc -i in.uc -o out.biom --rep-set-fp rep-set.fna
"""
input_f = open(input_fp, "U")
if rep_set_fp is not None:
rep_set_f = open(rep_set_fp, "U")
else:
rep_set_f = None
table = _from_uc(input_f, rep_set_f)
write_biom_table(table, "hdf5", output_fp)
def normalize_table(input_fp, output_fp, relative_abund, presence_absence, axis):
"""Normalize a BIOM table.
Normalize the values of a BIOM table through various methods. Relative
abundance will take the relative abundance of each observation in terms of
samples or observations. Presence absensece will convert observations to
1's and 0's based on presence of the observation.
Example usage:
Normalizing a BIOM table to relative abundnace:
$ biom normalize-table -i table.biom -r -o normalized_table.biom
Converting a BIOM table to a presence/absence table:
$ biom normalize-table -i table.biom -p -o converted_table.biom
"""
table = load_table(input_fp)
result = _normalize_table(table, relative_abund, presence_absence, axis)
write_biom_table(result, "hdf5" if HAVE_H5PY else "json", output_fp)
def normalize_table(input_fp, output_fp, relative_abund, presence_absence,
axis):
"""Normalize a BIOM table.
Normalize the values of a BIOM table through various methods. Relative
abundance will take the relative abundance of each observation in terms of
samples or observations. Presence absensece will convert observations to
1's and 0's based on presence of the observation.
Example usage:
Normalizing a BIOM table to relative abundnace:
$ biom normalize-table -i table.biom -r -o normalized_table.biom
Converting a BIOM table to a presence/absence table:
$ biom normalize-table -i table.biom -p -o converted_table.biom
"""
table = load_table(input_fp)
result = _normalize_table(table, relative_abund, presence_absence, axis)
write_biom_table(result, 'hdf5' if HAVE_H5PY else 'json', output_fp)
def test_get_input(self):
"""
Checks whether get_input writes a settings file
if inputs are supplied.
"""
inputs = {
'biom_file': [(testloc[0] + '/data/test.biom')],
'cluster': None,
'otu_meta': None,
'prefix': None,
'sample_data': None,
'split': None,
'tax_table': None,
'fp': testloc[0],
'otu_table': None,
'tools': ['spiec-easi', 'conet'],
'spiec': None,
'conet': None,
'spar_pval': None,
'spar_boot': None,
'levels': ['family'],
'prev': 20,
'min': None,
'rar': None,
'name': ['test'],
'cores': None
}
write_biom_table(testbiom['test'],
fmt='hdf5',
filepath=(inputs['biom_file'][0]))
get_input(inputs)
test = Path(inputs['fp'] + "/settings.json")
self.assertTrue(test.is_file())
call(("rm " + inputs['biom_file'][0]))
call(("rm " + inputs['fp'] + "/settings.json"))
call(("rm " + inputs['fp'] + "/test_family.hdf5"))
call(("rm " + inputs['fp'] + "/test_otu.hdf5"))
def test_import_biom_mapping(self):
"""
Tests if the BiomConnection uses the mapping dict.
:return:
"""
conn_object = BiomConnection()
conn_object.create_tables()
write_biom_table(testbiom, fmt='hdf5', filepath="test.biom")
import_biom("test.biom", mapping={'test': 'banana'})
os.remove("test.biom")
conn = psycopg2.connect(
**{
"host": "localhost",
"database": "test",
"user": "******",
"password": "******"
})
cur = conn.cursor()
cur.execute("SELECT studyID " "FROM bioms " "LIMIT 1;")
result = cur.fetchall()
cur.close()
conn.close()
conn_object.delete_tables()
self.assertEqual(result[0][0], 'banana')
def test_run_netstats(self):
"""
Checks whether combine_data returns
a batch object if inputs are supplied.
"""
inputs = {
'biom_file': [(testloc[0] + '/data/test.biom')],
'cluster': None,
'otu_meta': None,
'prefix': None,
'sample_data': None,
'split': None,
'tax_table': None,
'fp': testloc[0],
'otu_table': None,
'tools': ['spiec-easi', 'conet'],
'spiec': None,
'spar': None,
'conet': (os.path.dirname(massoc.__file__)[:-6] + 'tests\\CoNet3'),
'conet_bash': None,
'spar_pval': None,
'spar_boot': None,
'levels': ['family'],
'prev': 20,
'min': None,
'rar': None,
'name': ['test'],
'cores': None,
'address': 'bolt://localhost:7687',
'username': '******',
'password': '******',
'quit': False,
'clear': False,
'write': False,
'add': False,
'output': 'network',
'logic': ['union', 'difference', 'intersection'],
'neo4j': (os.path.dirname(massoc.__file__)[:-6] + 'tests\\neo4j')
}
write_biom_table(testbiom['test'],
fmt='hdf5',
filepath=(inputs['biom_file'][0]))
get_input(inputs)
inputs['settings'] = inputs['fp'] + '/settings.json'
run_network(inputs)
inputs['job'] = 'start'
run_neo4j(inputs)
inputs['job'] = 'upload'
run_neo4j(inputs)
run_netstats(inputs)
inputs['job'] = 'clear'
run_neo4j(inputs)
inputs['job'] = 'quit'
run_neo4j(inputs)
test = Path(inputs['fp'] + "/difference_network.graphml")
self.assertTrue(test.is_file())
call(("rm " + inputs['biom_file'][0]))
call(("rm " + inputs['fp'] + "/settings.json"))
call(("rm " + inputs['fp'] + "/test_family.hdf5"))
call(("rm " + inputs['fp'] + "/test_otu.hdf5"))
call(("rm " + inputs['fp'] + "/conet_test_family.txt"))
call(("rm " + inputs['fp'] + "/spiec-easi_test_family.txt"))
call(("rm " + inputs['fp'] + "/network.graphml"))
call(("rm " + inputs['fp'] + "/difference_network.graphml"))
call(("rm " + inputs['fp'] + "/union_network.graphml"))
call(("rm " + inputs['fp'] + "/intersection_network.graphml"))
def _convert(table,
output_filepath,
sample_metadata=None,
observation_metadata=None,
to_json=False,
to_hdf5=False,
to_tsv=False,
collapsed_samples=False,
collapsed_observations=False,
header_key=None,
output_metadata_id=None,
table_type=None,
process_obs_metadata=None,
tsv_metadata_formatter='sc_separated'):
if sum([to_tsv, to_hdf5, to_json]) == 0:
raise ValueError("Must specify an output format")
elif sum([to_tsv, to_hdf5, to_json]) > 1:
raise ValueError("Can only specify a single output format")
if table_type is None:
if table.type in [None, "None"]:
table.type = "Table"
else:
pass
else:
table.type = table_type
if tsv_metadata_formatter is not None:
obs_md_fmt_f = observation_metadata_formatters[tsv_metadata_formatter]
if sample_metadata is not None:
table.add_metadata(sample_metadata)
# if the user does not specify a name for the output metadata column,
# set it to the same as the header key
output_metadata_id = output_metadata_id or header_key
if process_obs_metadata is not None and not to_tsv:
if table.metadata(axis='observation') is None:
raise ValueError("Observation metadata processing requested "
"but it doesn't appear that there is any "
"metadata to operate on!")
# and if this came in as TSV, then we expect only a single type of
# metadata
md_key = list(table.metadata(axis='observation')[0].keys())[0]
process_f = observation_metadata_types[process_obs_metadata]
it = zip(table.ids(axis='observation'),
table.metadata(axis='observation'))
new_md = {id_: {md_key: process_f(md[md_key])} for id_, md in it}
if observation_metadata:
for k, v in observation_metadata.items():
new_md[k].update(v)
table.add_metadata(new_md, 'observation')
if to_tsv:
result = table.to_tsv(header_key=header_key,
header_value=output_metadata_id,
metadata_formatter=obs_md_fmt_f)
with open(output_filepath, 'w') as f:
f.write(result)
return
elif to_json:
fmt = 'json'
result = table
elif to_hdf5:
fmt = 'hdf5'
result = table
if collapsed_observations:
metadata = [{
'collapsed_ids': sorted(md.keys())
} for md in result.metadata(axis='observation')]
result._observation_metadata = metadata
if collapsed_samples:
metadata = [{
'collapsed_ids': sorted(md.keys())
} for md in result.metadata()]
result._sample_metadata = metadata
if collapsed_observations or collapsed_samples:
# We have changed the metadata, it is safer to make sure that
# it is correct
result._cast_metadata()
write_biom_table(result, fmt, output_filepath)
return
def setUpClass(cls):
_table1 = [
'a\ta\t1\t0.0\t0.5\t0.1', 'a\ta\t1\t1.0\t1.0\t0.2',
'a\ta\t1\t2.0\t1.5\t0.2', 'a\tb\t1\t3.0\t2.0\t8.',
'a\tb\t1\t4.0\t2.5\t9.', 'a\tb\t1\t5.0\t3.0\t10.',
'b\ta\t1\t0.0\t2.0\t0.1', 'b\ta\t1\t1.0\t3.0\t0.3',
'b\ta\t1\t2.0\t4.0\t0.1', 'b\tb\t1\t3.0\t5.0\t9.',
'b\tb\t1\t4.0\t6.0\t11.', 'b\tb\t1\t5.0\t7.0\t10.'
]
cls.table1 = pd.DataFrame(
[(n.split('\t')) for n in _table1],
columns=['group', 'dataset', 'level', 'x', 'y', 'c'],
dtype=float)
cls.table2 = """{"id": "None",
"format": "Biological Observation Matrix 1.0.0",
"format_url": "http:\/\/biom-format.org",
"type": "OTU table",
"generated_by": "greg",
"date": "2013-08-22T13:10:23.907145",
"matrix_type": "sparse",
"matrix_element_type": "float",
"shape": [
3,
4
],
"data": [
[
0,
0,
1
],
[
0,
1,
2
],
[
0,
2,
3
],
[
0,
3,
4
],
[
1,
0,
2
],
[
1,
1,
0
],
[
1,
2,
7
],
[
1,
3,
8
],
[
2,
0,
9
],
[
2,
1,
10
],
[
2,
2,
11
],
[
2,
3,
12
]
],
"rows": [
{
"id": "o1",
"metadata": {
"domain": "Archaea"
}
},
{
"id": "o2",
"metadata": {
"domain": "Bacteria"
}
},
{
"id": "o3",
"metadata": {
"domain": "Bacteria"
}
}
],
"columns": [
{
"id": "s1",
"metadata": {
"method": "A",
"Sample": "A",
"parameters": "A"
}
},
{
"id": "s2",
"metadata": {
"method": "A",
"Sample": "A",
"parameters": "B"
}
},
{
"id": "s3",
"metadata": {
"method": "A",
"Sample": "A",
"parameters": "C"
}
},
{
"id": "s4",
"metadata": {
"method": "B",
"Sample": "A",
"parameters": "D"
}
}
]
}"""
# table 2
# OTU ID s1 s2 s3 s4
# o1 1.0 2.0 3.0 4.0
# o2 2.0 0.0 7.0 8.0
# o3 9.0 10.0 11.0 12.0
cls.tmpdir = mkdtemp()
cls.table2 = Table.from_json(json.loads(cls.table2))
write_biom_table(cls.table2, 'hdf5', join(cls.tmpdir, 'table2.biom'))
cls.dm, cls.s_md = make_distance_matrix(join(cls.tmpdir,
'table2.biom'),
method="braycurtis")
cls.dist = per_method_distance(cls.dm,
cls.s_md,
group_by='method',
standard='B',
metric='distance',
sample='Sample')
def merge_expected_and_observed_tables(expected_results_dir,
results_dirs,
md_key='taxonomy',
min_count=0,
taxonomy_level=6,
taxa_to_keep=None,
biom_fp='merged_table.biom',
filename_pattern='table.L{0}-taxa.biom',
dataset_ids=None,
reference_ids=None,
method_ids=None,
parameter_ids=None,
force=False):
'''For each dataset in expected_results_dir, merge expected and observed
taxonomy compositions.
dataset_ids: list
dataset ids (mock community study ID) to process. Defaults to None
(process all).
reference_ids: list
reference database data to process. Defaults to None (process all).
method_ids: list
methods to process. Defaults to None (process all).
parameter_ids: list
parameters to process. Defaults to None (process all).
'''
# Quick and dirty way to keep merge from running automatically in notebooks
# when users "run all" cells. This is really just a convenience function
# that is meant to be called from the tax-credit notebooks and causing
# force=False to kill the function is the best simple control. The
# alternative is to work out a way to weed out expected_tables that have a
# merged biom, and just load that biom instead of overwriting if
# force=False. Then do the same for result_tables. If any new result_tables
# exist, perform merge if force=True. The only time force=False should
# result in a new table is when a new mock community/reference dataset
# combo is added — so just let users set force=True if that's the case.
if force is False:
exit('Skipping merge. Set force=True if you intend to generate new '
'merged tables.')
# Find expected tables, add sample metadata
expected_table_lookup = get_expected_tables_lookup(
expected_results_dir, filename_pattern=filename_pattern)
expected_tables = {}
for dataset_id, expected_dict in expected_table_lookup.items():
expected_tables[dataset_id] = {}
for reference_id, expected_table_fp in expected_dict.items():
if not exists(
join(expected_results_dir, dataset_id, reference_id,
biom_fp)) or force is True:
expected_tables[dataset_id][reference_id] = \
add_sample_metadata_to_table(expected_table_fp,
dataset_id=dataset_id,
reference_id=reference_id,
min_count=min_count,
taxonomy_level=taxonomy_level,
taxa_to_keep=taxa_to_keep,
md_key='taxonomy',
method='expected',
params='expected')
# Find observed results tables, add sample metadata
result_tables = seek_results(results_dirs, dataset_ids, reference_ids,
method_ids, parameter_ids)
for dataset_id, ref_id, method, params, actual_table_fp in result_tables:
biom_destination = join(expected_results_dir, dataset_id, ref_id,
biom_fp)
if not exists(biom_destination) or force is True:
try:
expected_table_fp = \
expected_table_lookup[dataset_id][ref_id]
except KeyError:
raise KeyError("Can't find expected table for \
({0}, {1}).".format(dataset_id, ref_id))
# import expected table, amend sample ids
actual_table = \
add_sample_metadata_to_table(actual_table_fp,
dataset_id=dataset_id,
reference_id=ref_id,
min_count=min_count,
taxonomy_level=taxonomy_level,
taxa_to_keep=taxa_to_keep,
md_key='taxonomy',
method=method,
params=params)
# merge expected and resutls tables
expected_tables[dataset_id][ref_id] = \
expected_tables[dataset_id][ref_id].merge(actual_table)
# write biom table to destination
write_biom_table(expected_tables[dataset_id][ref_id], 'hdf5',
biom_destination)
def _convert(table, output_filepath, sample_metadata=None,
observation_metadata=None, to_json=False, to_hdf5=False,
to_tsv=False, collapsed_samples=False,
collapsed_observations=False, header_key=None,
output_metadata_id=None, table_type=None,
process_obs_metadata=None, tsv_metadata_formatter='sc_separated'):
if sum([to_tsv, to_hdf5, to_json]) == 0:
raise ValueError("Must specify an output format")
elif sum([to_tsv, to_hdf5, to_json]) > 1:
raise ValueError("Can only specify a single output format")
if table_type is None:
if table.type in [None, "None"]:
table.type = "Table"
else:
pass
else:
table.type = table_type
if tsv_metadata_formatter is not None:
obs_md_fmt_f = observation_metadata_formatters[tsv_metadata_formatter]
if sample_metadata is not None:
table.add_metadata(sample_metadata)
# if the user does not specify a name for the output metadata column,
# set it to the same as the header key
output_metadata_id = output_metadata_id or header_key
if process_obs_metadata is not None and not to_tsv:
if table.metadata(axis='observation') is None:
raise ValueError("Observation metadata processing requested "
"but it doesn't appear that there is any "
"metadata to operate on!")
# and if this came in as TSV, then we expect only a single type of
# metadata
md_key = list(table.metadata(axis='observation')[0].keys())[0]
process_f = observation_metadata_types[process_obs_metadata]
it = zip(table.ids(axis='observation'),
table.metadata(axis='observation'))
new_md = {id_: {md_key: process_f(md[md_key])} for id_, md in it}
if observation_metadata:
for k, v in observation_metadata.items():
new_md[k].update(v)
table.add_metadata(new_md, 'observation')
if to_tsv:
result = table.to_tsv(header_key=header_key,
header_value=output_metadata_id,
metadata_formatter=obs_md_fmt_f)
with open(output_filepath, 'w') as f:
f.write(result)
return
elif to_json:
fmt = 'json'
result = table
elif to_hdf5:
fmt = 'hdf5'
result = table
if collapsed_observations:
metadata = [{'collapsed_ids': sorted(md.keys())}
for md in result.metadata(axis='observation')]
result._observation_metadata = metadata
if collapsed_samples:
metadata = [{'collapsed_ids': sorted(md.keys())}
for md in result.metadata()]
result._sample_metadata = metadata
if collapsed_observations or collapsed_samples:
# We have changed the metadata, it is safer to make sure that
# it is correct
result._cast_metadata()
write_biom_table(result, fmt, output_filepath)
return
def extract_mockrobiota_data(communities,
community_md,
ref_dbs,
mockrobiota_dir,
mock_data_dir,
expected_data_dir,
biom_fn='table.L6-taxa.biom'):
'''Extract sample metadata, raw data files, and expected taxonomy
from mockrobiota, copy to new destination
communities: LIST of mock communities to extract
community_md: DICT of metadata for mock community.
see extract_mockrobiota_dataset_metadata()
ref_dbs = DICT mapping marker_gene to reference set names
mockrobiota_dir = PATH to mockrobiota repo directory
mock_data_dir = PATH to destination directory
expected_data_dir = PATH to destination for expected taxonomy files
'''
for community in communities:
# extract dataset metadata/params
forward_read_url, index_read_url, marker_gene = community_md[community]
ref_outdir, ref_indir, ref_version, otu_id = ref_dbs[marker_gene][0:4]
# mockrobiota source directory
mockrobiota_community_dir = join(mockrobiota_dir, "data", community)
# new mock community directory
community_dir = join(mock_data_dir, community)
seqs_dir = join(community_dir, 'raw_seqs')
if not exists(seqs_dir):
makedirs(seqs_dir)
# copy sample-metadata.tsv
copyfile(join(mockrobiota_community_dir, 'sample-metadata.tsv'),
join(community_dir, 'sample-metadata.tsv'))
# download raw data files
for file_url_dest in [(forward_read_url, 'sequences.fastq.gz'),
(index_read_url, 'barcodes.fastq.gz')]:
destination = join(seqs_dir, file_url_dest[1])
if not exists(destination) and file_url_dest[0] != 'NA':
try:
urlretrieve(file_url_dest[0], destination)
except ValueError:
print('Error retrieving {0}'.format(file_url_dest[0]))
# new directory containing expected taxonomy assignments at each level
expected_taxa_dir = join(expected_data_dir, community, ref_outdir,
"expected")
if not exists(expected_taxa_dir):
makedirs(expected_taxa_dir)
# copy expected taxonomy.tsv and convert to biom
exp_taxa_fp = join(expected_taxa_dir, 'expected-taxonomy.tsv')
exp_biom_fp = join(expected_taxa_dir, biom_fn)
copyfile(
join(mockrobiota_community_dir, ref_indir, ref_version, otu_id,
'expected-taxonomy.tsv'), exp_taxa_fp)
newbiom = amend_biom_taxonomy_ids(load_table(exp_taxa_fp))
# add taxonomy ids (names) as observation metadata
metadata = {
sid: {
'taxonomy': sid.split(';')
}
for sid in newbiom.ids(axis='observation')
}
newbiom.add_metadata(metadata, 'observation')
write_biom_table(newbiom, 'hdf5', exp_biom_fp)