def BIOM_return_clipped_taxonomy(taxlevel, BIOM):
"""
Returns a BIOM table for which the taxonomy has been clipped at a certain level
"""
from biom.table import Table
import numpy as np
return_OTUs = {}
levels = ['kingdom', 'phylum', 'class', 'order', 'family', 'genus', 'species', 'unassigned']
clip_level=''
to_drop=[]
if not taxlevel in levels:
raise KeyError("The taxonomic level you are trying to search: '%s', is not valid" %level)
clip_level = int(levels.index(taxlevel))+1
#check if the first OTU has 'taxonomy' metadata attached, if yes assume all others have too and resume
if not 'taxonomy' in BIOM.metadata(axis='observation')[0]:
raise KeyError('The BIOM table you are trying to screen does not have taxonomy metadata attached to it')
else:
print "Found taxonomy metadata with OTUs - ok!"
sample_ids = BIOM.ids(axis='sample')
observation_ids = BIOM.ids(axis='observation')
data_to_biom = []
sample_metadata = BIOM.metadata(axis='sample')
observation_metadata = BIOM.metadata(axis='observation')
for OTU in observation_ids:
orig=BIOM.data(OTU, axis='observation')
data_to_biom.append(orig)
data = np.asarray(data_to_biom)
for i in range(len(observation_metadata)):
if len(observation_metadata[i]['taxonomy']) > clip_level:
observation_metadata[i]['taxonomy'] = observation_metadata[i]['taxonomy'][:clip_level]
if 'unknown' in observation_metadata[i]['taxonomy'][-1]:
print "fishy: %s" %observation_metadata[i]['taxonomy']
to_drop.append(observation_ids[i])
# print observation_metadata[i]['taxonomy']
#construct adjusted table
outtable = Table(data, observation_ids, sample_ids, table_id='OTU table', sample_metadata=sample_metadata, observation_metadata=observation_metadata)
if to_drop:
outtable.filter(to_drop, invert=True, axis='observation',inplace=True)
return outtable
def test_tree_filter_table_none(self):
rooted_nwk = io.StringIO("(O1:4.5,(O2:4,(a:1,b:1):2):0.5);")
tree = skbio.TreeNode.read(rooted_nwk)
table = Table(np.array([[0, 1, 3], [1, 1, 2]]), ['O1', 'O2'],
['S1', 'S2', 'S3'])
actual = filter_table(table, tree)
expected = table.filter(['O1', 'O2'], axis='observation')
self.assertEqual(actual, expected)
def filter_BIOM_by_per_sample_read_prop(BIOM, min_prop=0.01):
"""
Filter OTU table by mininimum reads per sample
"""
import numpy as np
from biom.table import Table
print "\nFiltering at level: %s %%\n" %(min_prop*100)
# print "input table:\n"
# print BIOM
# print "\n"
sample_ids = BIOM.ids(axis='sample')
observation_ids = BIOM.ids(axis='observation')
data_to_biom = []
sample_metadata = BIOM.metadata(axis='sample')
observation_metadata = BIOM.metadata(axis='observation')
sums = BIOM.sum(axis='sample')
for OTU in observation_ids:
orig=BIOM.data(OTU, axis='observation')
for i in range(len(orig)):
if not int(orig[i]) == 0:
if not int(orig[i]) >= sums[i]*min_prop:
orig[i] = '0.0'
data_to_biom.append(orig)
data = np.asarray(data_to_biom)
#construct adjusted table
table = Table(data, observation_ids, sample_ids, table_id='OTU table', sample_metadata=sample_metadata, observation_metadata=observation_metadata)
#Filter OTUs with sum = '0'
to_exclude = []
observation_sums = table.sum(axis='observation')
for i in range(len(observation_sums)):
if int(observation_sums[i]) == 0:
to_exclude.append(observation_ids[i])
print "Removing %i OTUs for lack of support\n" %len(to_exclude)
table.filter(to_exclude, invert=True, axis='observation',inplace=True)
# print table
return table
def test_tree_filter_table_none(self):
rooted_nwk = io.StringIO("(O1:4.5,(O2:4,(a:1,b:1):2):0.5);")
tree = skbio.TreeNode.read(rooted_nwk)
table = Table(np.array([[0, 1, 3], [1, 1, 2]]),
['O1', 'O2'],
['S1', 'S2', 'S3'])
actual = filter_table(table, tree)
expected = table.filter(['O1', 'O2'], axis='observation')
self.assertEqual(actual, expected)
