def newick_replace_otuids(tree, biom):
"""
Replace the OTU ids in the Newick phylogenetic tree format with truncated
OTU names
"""
for row in biom['rows']:
otu_loc = find_otu(row['id'], tree)
if otu_loc is not None:
tree = tree[:otu_loc] + oc.otu_name_biom(row) + tree[otu_loc+len(row['id']):]
else:
print 'ID not found:', row['id']
return tree
def test_otu_name_biom(self):
"""
Testing the otu_name_biom() function of otu_calc.py.
:return: Returns OK if the test goals were achieved, otherwise
raises error.
"""
self.result = oc.otu_name_biom(self.row[3])
hand_calc = 'Halanaerobium_Halanaerobiumsaccharolyticum'
# Testing the validity of otu_name_biom() function
self.assertEqual(
self.result, hand_calc,
msg='Error! otu_name_biom() output does not match manually identified name.'
)
def write_relative_abundance(rel_abd, biom, out_fn, sort_by=None):
"""
Given a BIOM table, calculate per-sample relative abundance for
each OTU and write out to a tab-separated file listing OTUs as
rows and Samples as columns.
:type biom: dict (translated json string)
:param biom: BIOM-formatted OTU/Sample abundance data
:type out_fn: str
:param out_fn: The full path to the desired output file.
:type sort_by: function
:param sort_by: A function acting as a sorting key that will determine
the order in which the Sample IDs appear as columns in
the output file.
"""
with open(out_fn, 'w') as out_f:
sids = sorted(set([col['id'] for col in biom['columns']]), key=sort_by)
out_f.write('#OTU ID\t{}\n'.format('\t'.join(sids)))
for row in biom['rows']:
otuName = oc.otu_name_biom(row)
otuid = row['id']
sabd = [str(rel_abd[sid][otuid]) if sid in rel_abd and otuid in rel_abd[sid] else '0' for sid in sids]
out_f.write('{}\t{}\n'.format(otuName, '\t'.join(sabd)))
def main():
args = handle_program_options()
try:
with open(args.otu_table):
pass
except IOError as ioe:
sys.exit(
'\nError with OTU_Sample abundance data file:{}\n'
.format(ioe)
)
try:
with open(args.mapping):
pass
except IOError as ioe:
sys.exit(
'\nError with mapping file:{}\n'
.format(ioe)
)
# input data
with open(args.otu_table) as bF:
biom = json.loads(bF.readline())
map_header, imap = util.parse_map_file(args.mapping)
# rewrite tree file with otu names
if args.input_tree:
with open(args.input_tree) as treF, open(args.output_tre, 'w') as outF:
tree = treF.readline()
if "'" in tree:
tree = tree.replace("'", '')
outF.write(newick_replace_otuids(tree, biom))
oid_rows = {row['id']: row for row in biom['rows']}
# calculate analysis results
categories = None
if args.map_categories is not None:
categories = args.map_categories.split(',')
# set transform if --stabilize_variance is specfied
tform = bc.arcsine_sqrt_transform if args.stabilize_variance else None
groups = util.gather_categories(imap, map_header, categories)
for group in groups.values():
if args.analysis_metric in ['MRA', 'NMRA']:
results = bc.MRA(biom, group.sids, transform=tform)
elif args.analysis_metric == 'raw':
results = bc.transform_raw_abundance(biom, sampleIDs=group.sids,
sample_abd=False)
group.results.update({oc.otu_name_biom(oid_rows[oid]): results[oid]
for oid in results})
# write iTol data set file
with open(args.output_itol_table, 'w') as itolF:
itolF.write('LABELS\t' + '\t'.join(groups.keys())+'\n')
itolF.write('COLORS\t{}\n'.format('\t'.join(['#ff0000'
for _ in range(len(groups))])))
all_otus = frozenset({oc.otu_name_biom(row) for row in biom['rows']})
for oname in all_otus:
row = ['{name}'] # \t{s:.2f}\t{ns:.2f}\n'
row_data = {'name': oname}
msum = 0
for name, group in groups.iteritems():
row.append('{{{}:.5f}}'.format(name))
if oname in group.results:
row_data[name] = group.results[oname]
else:
row_data[name] = 0.0
msum += row_data[name]
# normalize avg relative abundance data
if args.analysis_metric == 'NMRA' and msum > 0:
row_data.update({key: data/msum
for key, data in row_data.items()
if key != 'name'})
itolF.write('\t'.join(row).format(**row_data) + '\n')