def equalize_tables_at_rarefaction_point(otu_table_p,name_otu_table,reference_table_p,name_reference_table,seq_number,output_p):
sample_de_novo = subset_samples_by_seq_number(biom_stats(reference_table_p),seq_number)
sample_ref = subset_samples_by_seq_number(biom_stats(otu_table_p),seq_number)
common_sample_ids = sample_de_novo.intersection(sample_ref)
#filtering from otu table
new_otu_table = filter_samples(otu_table_p,common_sample_ids,0,np.inf)
new_reference_table = filter_samples(reference_table_p,common_sample_ids,0,np.inf)
doc1 = open(output_p+"/"+name_otu_table.replace("/","_")+"_equalized_"+str(seq_number)+'.biom',"w")
doc1.write(format_biom_table(new_otu_table))
doc1.close()
doc2 = open(output_p+"/"+name_reference_table.replace("/","_")+"_equalized_"+str(seq_number)+'.biom',"w")
doc2.write(format_biom_table(new_reference_table))
doc2.close()
print " \nPercentage of Samples : {}/{} , {}\n".format(
len(new_otu_table.SampleIds) ,
len(otu_table_p.SampleIds) ,
(len(new_otu_table.SampleIds)/len(otu_table_p.SampleIds)) * 100
)
return
def equalize_tables_at_rarefaction_point(otu_table_p, name_otu_table,
reference_table_p,
name_reference_table, seq_number,
output_p):
sample_de_novo = subset_samples_by_seq_number(
biom_stats(reference_table_p), seq_number)
sample_ref = subset_samples_by_seq_number(biom_stats(otu_table_p),
seq_number)
common_sample_ids = sample_de_novo.intersection(sample_ref)
#filtering from otu table
new_otu_table = filter_samples(otu_table_p, common_sample_ids, 0, np.inf)
new_reference_table = filter_samples(reference_table_p, common_sample_ids,
0, np.inf)
doc1 = open(
output_p + "/" + name_otu_table.replace("/", "_") + "_equalized_" +
str(seq_number) + '.biom', "w")
doc1.write(format_biom_table(new_otu_table))
doc1.close()
doc2 = open(
output_p + "/" + name_reference_table.replace("/", "_") +
"_equalized_" + str(seq_number) + '.biom', "w")
doc2.write(format_biom_table(new_reference_table))
doc2.close()
print " \nPercentage of Samples : {}/{} , {}\n".format(
len(new_otu_table.SampleIds), len(otu_table_p.SampleIds),
(len(new_otu_table.SampleIds) / len(otu_table_p.SampleIds)) * 100)
return
def setUp(self):
"""Define some test data."""
self.qiime_config = load_qiime_config()
self.dirs_to_remove = []
self.tmp_dir = self.qiime_config['temp_dir'] or '/tmp/'
if not exists(self.tmp_dir):
makedirs(self.tmp_dir)
# if test creates the temp dir, also remove it
self.dirs_to_remove.append(self.tmp_dir)
self.otu_table1 = table_factory(data=array([[2, 0, 0, 1],
[1, 1, 1, 1],
[0, 0, 0, 0]]).T,
sample_ids=list('XYZ'),
observation_ids=list('abcd'),
constructor=DenseOTUTable)
fd, self.otu_table1_fp = mkstemp(dir=self.tmp_dir,
prefix='alpha_diversity_tests',
suffix='.biom')
close(fd)
open(self.otu_table1_fp, 'w').write(
format_biom_table(self.otu_table1))
self.otu_table2 = table_factory(data=array([[2, 0, 0, 1],
[1, 1, 1, 1],
[0, 0, 0, 0]]).T,
sample_ids=list('XYZ'),
observation_ids=['a', 'b', 'c', 'd_'],
constructor=DenseOTUTable)
fd, self.otu_table2_fp = mkstemp(dir=self.tmp_dir,
prefix='alpha_diversity_tests',
suffix='.biom')
close(fd)
open(self.otu_table2_fp, 'w').write(
format_biom_table(self.otu_table2))
self.single_sample_otu_table = table_factory(
data=array([[2, 0, 0, 1]]).T,
sample_ids=list('X'),
observation_ids=list(
'abcd'),
constructor=DenseOTUTable)
fd, self.single_sample_otu_table_fp = mkstemp(
dir=self.tmp_dir,
prefix='alpha_diversity_tests',
suffix='.biom')
close(fd)
open(self.single_sample_otu_table_fp, 'w').write(
format_biom_table(self.single_sample_otu_table))
self.tree1 = parse_newick('((a:2,b:3):2,(c:1,d:2):7);')
self.tree2 = parse_newick("((a:2,'b':3):2,(c:1,'d_':2):7);")
self.files_to_remove = [self.otu_table1_fp, self.otu_table2_fp,
self.single_sample_otu_table_fp]
def setUp(self):
self.qiime_config = load_qiime_config()
self.tmp_dir = self.qiime_config['temp_dir'] or '/tmp/'
self.l19_data = numpy.array([
[7, 1, 0, 0, 0, 0, 0, 0, 0],
[4, 2, 0, 0, 0, 1, 0, 0, 0],
[2, 4, 0, 0, 0, 1, 0, 0, 0],
[1, 7, 0, 0, 0, 0, 0, 0, 0],
[0, 8, 0, 0, 0, 0, 0, 0, 0],
[0, 7, 1, 0, 0, 0, 0, 0, 0],
[0, 4, 2, 0, 0, 0, 2, 0, 0],
[0, 2, 4, 0, 0, 0, 1, 0, 0],
[0, 1, 7, 0, 0, 0, 0, 0, 0],
[0, 0, 8, 0, 0, 0, 0, 0, 0],
[0, 0, 7, 1, 0, 0, 0, 0, 0],
[0, 0, 4, 2, 0, 0, 0, 3, 0],
[0, 0, 2, 4, 0, 0, 0, 1, 0],
[0, 0, 1, 7, 0, 0, 0, 0, 0],
[0, 0, 0, 8, 0, 0, 0, 0, 0],
[0, 0, 0, 7, 1, 0, 0, 0, 0],
[0, 0, 0, 4, 2, 0, 0, 0, 4],
[0, 0, 0, 2, 4, 0, 0, 0, 1],
[0, 0, 0, 1, 7, 0, 0, 0, 0]
])
self.l19_sample_names = [
'sam1', 'sam2', 'sam3', 'sam4', 'sam5', 'sam6',
'sam7', 'sam8', 'sam9', 'sam_middle', 'sam11', 'sam12', 'sam13',
'sam14', 'sam15', 'sam16', 'sam17', 'sam18', 'sam19']
self.l19_taxon_names = ['tax1', 'tax2', 'tax3', 'tax4', 'endbigtaxon',
'tax6', 'tax7', 'tax8', 'tax9']
self.l19_taxon_names_w_underscore = ['ta_x1', 'tax2', 'tax3', 'tax4',
'endbigtaxon', 'tax6', 'tax7', 'tax8', 'tax9']
l19_str = format_biom_table(DenseOTUTable(self.l19_data.T,
self.l19_sample_names,
self.l19_taxon_names))
fd, self.l19_fp = mkstemp(dir=self.tmp_dir,
prefix='test_bdiv_otu_table', suffix='.blom')
close(fd)
open(self.l19_fp, 'w').write(l19_str)
l19_str_w_underscore = format_biom_table(DenseOTUTable(self.l19_data.T,
self.l19_sample_names,
self.l19_taxon_names_w_underscore))
fd, self.l19_str_w_underscore_fp = mkstemp(dir=self.tmp_dir,
prefix='test_bdiv_otu_table', suffix='.blom')
close(fd)
open(self.l19_str_w_underscore_fp, 'w').write(l19_str_w_underscore)
self.l19_tree_str = '((((tax7:0.1,tax3:0.2):.98,tax8:.3, tax4:.3):.4,\
((tax1:0.3, tax6:.09):0.43,tax2:0.4):0.5):.2, (tax9:0.3, endbigtaxon:.08));'
self.l19_tree = parse_newick(self.l19_tree_str, PhyloNode)
self.files_to_remove = [self.l19_fp, self.l19_str_w_underscore_fp]
self.folders_to_remove = []
def setUp(self):
self.qiime_config = load_qiime_config()
self.tmp_dir = self.qiime_config['temp_dir'] or '/tmp/'
self.l19_data = numpy.array([[7, 1, 0, 0, 0, 0, 0, 0, 0],
[4, 2, 0, 0, 0, 1, 0, 0, 0],
[2, 4, 0, 0, 0, 1, 0, 0, 0],
[1, 7, 0, 0, 0, 0, 0, 0, 0],
[0, 8, 0, 0, 0, 0, 0, 0, 0],
[0, 7, 1, 0, 0, 0, 0, 0, 0],
[0, 4, 2, 0, 0, 0, 2, 0, 0],
[0, 2, 4, 0, 0, 0, 1, 0, 0],
[0, 1, 7, 0, 0, 0, 0, 0, 0],
[0, 0, 8, 0, 0, 0, 0, 0, 0],
[0, 0, 7, 1, 0, 0, 0, 0, 0],
[0, 0, 4, 2, 0, 0, 0, 3, 0],
[0, 0, 2, 4, 0, 0, 0, 1, 0],
[0, 0, 1, 7, 0, 0, 0, 0, 0],
[0, 0, 0, 8, 0, 0, 0, 0, 0],
[0, 0, 0, 7, 1, 0, 0, 0, 0],
[0, 0, 0, 4, 2, 0, 0, 0, 4],
[0, 0, 0, 2, 4, 0, 0, 0, 1],
[0, 0, 0, 1, 7, 0, 0, 0, 0]])
self.l19_sample_names = ['sam1', 'sam2', 'sam3', 'sam4', 'sam5','sam6',\
'sam7', 'sam8', 'sam9', 'sam_middle', 'sam11', 'sam12', 'sam13', \
'sam14', 'sam15', 'sam16', 'sam17', 'sam18', 'sam19']
self.l19_taxon_names = ['tax1', 'tax2', 'tax3', 'tax4', 'endbigtaxon',\
'tax6', 'tax7', 'tax8', 'tax9']
self.l19_taxon_names_w_underscore = [
'ta_x1', 'tax2', 'tax3', 'tax4', 'endbigtaxon', 'tax6', 'tax7',
'tax8', 'tax9'
]
l19_str = format_biom_table(
DenseOTUTable(self.l19_data.T, self.l19_sample_names,
self.l19_taxon_names))
self.l19_fp = get_tmp_filename(tmp_dir=self.tmp_dir,
prefix='test_bdiv_otu_table',
suffix='.blom')
open(self.l19_fp, 'w').write(l19_str)
l19_str_w_underscore = format_biom_table(
DenseOTUTable(self.l19_data.T, self.l19_sample_names,
self.l19_taxon_names_w_underscore))
self.l19_str_w_underscore_fp = get_tmp_filename(
tmp_dir=self.tmp_dir, prefix='test_bdiv_otu_table', suffix='.blom')
open(self.l19_str_w_underscore_fp, 'w').write(l19_str_w_underscore)
self.l19_tree_str = '((((tax7:0.1,tax3:0.2):.98,tax8:.3, tax4:.3):.4,\
((tax1:0.3, tax6:.09):0.43,tax2:0.4):0.5):.2, (tax9:0.3, endbigtaxon:.08));'
self.l19_tree = parse_newick(self.l19_tree_str, PhyloNode)
self.files_to_remove = [self.l19_fp, self.l19_str_w_underscore_fp]
self.folders_to_remove = []
def setUp(self):
"""Define some test data."""
self.qiime_config = load_qiime_config()
self.dirs_to_remove = []
self.tmp_dir = self.qiime_config['temp_dir'] or '/tmp/'
if not exists(self.tmp_dir):
makedirs(self.tmp_dir)
# if test creates the temp dir, also remove it
self.dirs_to_remove.append(self.tmp_dir)
self.otu_table1 = table_factory(data=array([[2, 0, 0, 1], [1, 1, 1, 1],
[0, 0, 0, 0]]).T,
sample_ids=list('XYZ'),
observation_ids=list('abcd'),
constructor=DenseOTUTable)
self.otu_table1_fp = get_tmp_filename(tmp_dir=self.tmp_dir,
prefix='alpha_diversity_tests',
suffix='.biom',
result_constructor=str)
open(self.otu_table1_fp,'w').write(\
format_biom_table(self.otu_table1))
self.otu_table2 = table_factory(data=array([[2, 0, 0, 1], [1, 1, 1, 1],
[0, 0, 0, 0]]).T,
sample_ids=list('XYZ'),
observation_ids=['a', 'b', 'c', 'd_'],
constructor=DenseOTUTable)
self.otu_table2_fp = get_tmp_filename(tmp_dir=self.tmp_dir,
prefix='alpha_diversity_tests',
suffix='.biom',
result_constructor=str)
open(self.otu_table2_fp,'w').write(\
format_biom_table(self.otu_table2))
self.single_sample_otu_table = table_factory(
data=array([[2, 0, 0, 1]]).T,
sample_ids=list('X'),
observation_ids=list('abcd'),
constructor=DenseOTUTable)
self.single_sample_otu_table_fp = get_tmp_filename(
tmp_dir=self.tmp_dir,
prefix='alpha_diversity_tests',
suffix='.biom',
result_constructor=str)
open(self.single_sample_otu_table_fp,'w').write(\
format_biom_table(self.single_sample_otu_table))
self.tree1 = parse_newick('((a:2,b:3):2,(c:1,d:2):7);')
self.tree2 = parse_newick("((a:2,'b':3):2,(c:1,'d_':2):7);")
self.files_to_remove = [
self.otu_table1_fp, self.otu_table2_fp,
self.single_sample_otu_table_fp
]
def test_split_otu_table_on_sample_metadata_extra_mapping_entries(self):
""" split_otu_table_on_sample_metadata functions as expected with extra mapping data """
actual = list(split_otu_table_on_sample_metadata(self.otu_table_f1,
self.mapping_f2,
"Treatment"))
actual = [(id_, parse_biom_table(e)) for id_, e in actual]
exp = [(id_, parse_biom_table(e)) for id_, e in otu_table_exp1]
actual.sort()
exp.sort()
for a, e in zip(actual, exp):
self.assertEqual(a, e, "OTU tables are not equal:\n%s\n%s" %
(format_biom_table(a[1]), format_biom_table(e[1])))
def test_split_otu_table_on_sample_metadata_extra_mapping_entries(self):
""" split_otu_table_on_sample_metadata functions as expected with extra mapping data """
actual = list(split_otu_table_on_sample_metadata(self.otu_table_f1,
self.mapping_f2,
"Treatment"))
actual = [(id_,parse_biom_table(e)) for id_, e in actual]
exp = [(id_,parse_biom_table(e)) for id_, e in otu_table_exp1]
actual.sort()
exp.sort()
for a,e in zip(actual,exp):
self.assertEqual(a,e,"OTU tables are not equal:\n%s\n%s" % \
(format_biom_table(a[1]),format_biom_table(e[1])))
def setUp(self):
self.qiime_config = load_qiime_config()
self.tmp_dir = self.qiime_config['temp_dir'] or '/tmp/'
self.otu_table_data = numpy.array([[2, 1, 0], [0, 5, 0], [0, 3, 0],
[1, 2, 0]])
self.sample_names = list('YXZ')
self.taxon_names = list('bacd')
self.otu_metadata = [{
'domain': 'Archaea'
}, {
'domain': 'Bacteria'
}, {
'domain': 'Bacteria'
}, {
'domain': 'Bacteria'
}]
self.otu_table = table_factory(self.otu_table_data, self.sample_names,
self.taxon_names)
self.otu_table_meta = table_factory(
self.otu_table_data,
self.sample_names,
self.taxon_names,
observation_metadata=self.otu_metadata)
self.otu_table_str = format_biom_table(self.otu_table)
self.otu_table_meta_str = format_biom_table(self.otu_table_meta)
_, self.otu_table_fp = mkstemp(dir=self.tmp_dir,
prefix='test_rarefaction',
suffix='.biom')
close(_)
_, self.otu_table_meta_fp = mkstemp(dir=self.tmp_dir,
prefix='test_rarefaction',
suffix='.biom')
close(_)
self.rare_dir = mkdtemp(dir=self.tmp_dir,
prefix='test_rarefaction_dir',
suffix='')
open(self.otu_table_fp, 'w').write(self.otu_table_str)
open(self.otu_table_meta_fp, 'w').write(self.otu_table_meta_str)
self._paths_to_clean_up = [self.otu_table_fp, self.otu_table_meta_fp]
self._dirs_to_clean_up = [self.rare_dir]
def generate_full_otu_table(study, study_input_dir, zip_fname, files_to_remove,
biom_files,output_dir):
""" Merge OTU tables """
master = parse_biom_table(open(biom_files[0],'U'))
# only merge if there is more than 1 biom file
if len(biom_files) > 1:
for input_fp in biom_files[1:]:
master = master.merge(parse_biom_table(open(input_fp,'U')))
# write full biom-table
full_biom_table_fname='study_%s_closed_reference_otu_table.biom' % \
(str(study))
full_biom_table_fp=join(output_dir,full_biom_table_fname)
# add to list of files to remove
files_to_remove.append(full_biom_table_fp)
biom_f = open(join(full_biom_table_fp),'w')
biom_f.write(format_biom_table(master))
biom_f.close()
# zip the full biom-table file
#cmd_call='cd %s; tar rzvf %s %s' % (study_input_dir,zip_fname,
# full_biom_table_fname)
#system(cmd_call)
return files_to_remove
def main():
option_parser, opts, args = parse_command_line_parameters(**script_info)
labels = opts.labels.split(',')
if opts.all_strings:
process_fs = [str] * len(labels)
observation_metadata = parse_taxonomy_to_otu_metadata(\
open(opts.taxonomy_fp,'U'),labels=labels,process_fs=process_fs)
else:
observation_metadata = parse_taxonomy_to_otu_metadata(\
open(opts.taxonomy_fp,'U'),labels=labels)
otu_table = parse_biom_table(open(opts.input_fp, 'U'))
if otu_table.ObservationMetadata != None:
# if there is already metadata associated with the
# observations, confirm that none of the metadata names
# are already present
existing_keys = otu_table.ObservationMetadata[0].keys()
for label in labels:
if label in existing_keys:
option_parser.error(\
"%s is already an observation metadata field."
" Can't add it, so nothing is being added." % label)
otu_table.addObservationMetadata(observation_metadata)
output_f = open(opts.output_fp, 'w')
output_f.write(format_biom_table(otu_table))
output_f.close()
def split_otu_table_on_taxonomy_to_files(otu_table_fp,
level,
output_dir,
md_identifier='taxonomy',
md_processor=process_md_as_list):
""" Split OTU table by taxonomic level, writing otu tables to output dir
"""
results = []
otu_table = parse_biom_table(open(otu_table_fp, 'U'))
create_dir(output_dir)
def split_f(obs_md):
try:
result = md_processor(obs_md, md_identifier, level)
except KeyError:
raise KeyError,\
"Metadata identifier (%s) is not associated with all (or any) observerations. You can modify the key with the md_identifier parameter." % md_identifier
except TypeError:
raise TypeError,\
"Can't correctly process the metadata string. If your input file was generated from QIIME 1.4.0 or earlier you may need to pass --md_as_string."
except AttributeError:
raise AttributeError,\
"Metadata category not found. If your input file was generated from QIIME 1.4.0 or earlier you may need to pass --md_identifier \"Consensus Lineage\"."
return result
for bin, sub_otu_table in otu_table.binObservationsByMetadata(split_f):
output_fp = '%s/otu_table_%s.biom' % (output_dir, bin)
output_f = open(output_fp, 'w')
output_f.write(format_biom_table(sub_otu_table))
output_f.close()
results.append(output_fp)
return results
def main():
option_parser, opts, args = parse_command_line_parameters(**script_info)
labels = opts.labels.split(',')
if opts.all_strings:
process_fs = [str] * len(labels)
observation_metadata = parse_taxonomy_to_otu_metadata(\
open(opts.taxonomy_fp,'U'),labels=labels,process_fs=process_fs)
else:
observation_metadata = parse_taxonomy_to_otu_metadata(\
open(opts.taxonomy_fp,'U'),labels=labels)
otu_table = parse_biom_table(open(opts.input_fp,'U'))
if otu_table.ObservationMetadata != None:
# if there is already metadata associated with the
# observations, confirm that none of the metadata names
# are already present
existing_keys = otu_table.ObservationMetadata[0].keys()
for label in labels:
if label in existing_keys:
option_parser.error(\
"%s is already an observation metadata field."
" Can't add it, so nothing is being added." % label)
otu_table.addObservationMetadata(observation_metadata)
output_f = open(opts.output_fp,'w')
output_f.write(format_biom_table(otu_table))
output_f.close()
def main():
option_parser, opts, args =\
parse_command_line_parameters(**script_info)
otu_table_data = parse_biom_table(open(opts.input_otu_table,'U'))
sort_field = opts.sort_field
mapping_fp = opts.mapping_fp
sorted_sample_ids_fp = opts.sorted_sample_ids_fp
if sort_field and mapping_fp:
mapping_data = parse_mapping_file(open(mapping_fp,'U'))
result = sort_otu_table_by_mapping_field(otu_table_data,
mapping_data,
sort_field)
elif sorted_sample_ids_fp:
sorted_sample_ids = sample_ids_from_f(open(sorted_sample_ids_fp,'U'))
result = sort_otu_table(otu_table_data,
sorted_sample_ids)
else:
result = sort_otu_table(otu_table_data,
natsort_case_insensitive(otu_table_data.SampleIds))
# format and write the otu table
result_str = format_biom_table(result)
of = open(opts.output_fp,'w')
of.write(result_str)
of.close()
def main():
option_parser, opts, args =\
parse_command_line_parameters(**script_info)
otu_table_data = parse_biom_table(open(opts.input_otu_table, 'U'))
sort_field = opts.sort_field
mapping_fp = opts.mapping_fp
sorted_sample_ids_fp = opts.sorted_sample_ids_fp
if sort_field and mapping_fp:
mapping_data = parse_mapping_file(open(mapping_fp, 'U'))
result = sort_otu_table_by_mapping_field(otu_table_data, mapping_data,
sort_field)
elif sorted_sample_ids_fp:
sorted_sample_ids = sample_ids_from_f(open(sorted_sample_ids_fp, 'U'))
result = sort_otu_table(otu_table_data, sorted_sample_ids)
else:
result = sort_otu_table(
otu_table_data, natsort_case_insensitive(otu_table_data.SampleIds))
# format and write the otu table
result_str = format_biom_table(result)
of = open(opts.output_fp, 'w')
of.write(result_str)
of.close()
def split_otu_table_on_taxonomy_to_files(otu_table_fp,
level,
output_dir,
md_identifier='taxonomy',
md_processor=process_md_as_list):
""" Split OTU table by taxonomic level, writing otu tables to output dir
"""
results = []
otu_table = parse_biom_table(open(otu_table_fp,'U'))
create_dir(output_dir)
def split_f(obs_md):
try:
result = md_processor(obs_md,md_identifier,level)
except KeyError:
raise KeyError,\
"Metadata identifier (%s) is not associated with all (or any) observerations. You can modify the key with the md_identifier parameter." % md_identifier
except TypeError:
raise TypeError,\
"Can't correctly process the metadata string. If your input file was generated from QIIME 1.4.0 or earlier you may need to pass --md_as_string."
except AttributeError:
raise AttributeError,\
"Metadata category not found. If your input file was generated from QIIME 1.4.0 or earlier you may need to pass --md_identifier \"Consensus Lineage\"."
return result
for bin, sub_otu_table in otu_table.binObservationsByMetadata(split_f):
output_fp = '%s/otu_table_%s.biom' % (output_dir,bin)
output_f = open(output_fp,'w')
output_f.write(format_biom_table(sub_otu_table))
output_f.close()
results.append(output_fp)
return results
def make_otu_table(otu_map_f,
otu_to_taxonomy=None,
delim='_',
table_id=None,
sample_metadata=None,
constructor=SparseOTUTable):
data, sample_ids, otu_ids = parse_otu_map(otu_map_f, delim)
if otu_to_taxonomy is not None:
otu_metadata = []
for o in otu_ids:
try:
otu_metadata.append({'taxonomy': otu_to_taxonomy[o]})
except KeyError:
otu_metadata.append({'taxonomy': ["None"]})
else:
otu_metadata = None
if sample_metadata is not None:
raise NotImplementedError(
"Passing of sample metadata to make_otu_table is not currently supported.")
try:
otu_table = table_factory(data, sample_ids, otu_ids,
sample_metadata=sample_metadata,
observation_metadata=otu_metadata,
table_id=table_id,
constructor=constructor,
dtype=int)
except ValueError as e:
raise ValueError("Couldn't create OTU table. Is your OTU map empty?"
" Original error message: %s" % (str(e)))
return format_biom_table(otu_table)
def main():
option_parser, opts, args =\
parse_command_line_parameters(**script_info)
input_table = parse_biom_table(open(opts.input_otu_table_fp, 'U'))
output_table_f = open(opts.output_otu_table_fp, 'w')
metadata_field = opts.metadata_field
positive_taxa = opts.positive_taxa
negative_taxa = opts.negative_taxa
if positive_taxa is not None:
positive_taxa = positive_taxa.split(',')
else:
positive_taxa = None
if negative_taxa is not None:
negative_taxa = negative_taxa.split(',')
else:
negative_taxa = None
filter_fn = get_otu_ids_from_taxonomy_f(
positive_taxa,
negative_taxa,
metadata_field)
output_table = input_table.filterObservations(filter_fn)
output_table_f.write(format_biom_table(output_table))
output_table_f.close()
def make_otu_table(otu_map_f,
otu_to_taxonomy=None,
delim='_',
table_id=None,
sample_metadata=None,
constructor=SparseOTUTable):
data, sample_ids, otu_ids = parse_otu_map(otu_map_f, delim)
if otu_to_taxonomy is not None:
otu_metadata = []
for o in otu_ids:
try:
otu_metadata.append({'taxonomy': otu_to_taxonomy[o]})
except KeyError:
otu_metadata.append({'taxonomy': ["None"]})
else:
otu_metadata = None
if sample_metadata is not None:
raise NotImplementedError(
"Passing of sample metadata to make_otu_table is not currently supported.")
try:
otu_table = table_factory(data, sample_ids, otu_ids,
sample_metadata=sample_metadata,
observation_metadata=otu_metadata,
table_id=table_id,
constructor=constructor,
dtype=int)
except ValueError as e:
raise ValueError("Couldn't create OTU table. Is your OTU map empty?"
" Original error message: %s" % (str(e)))
return format_biom_table(otu_table)
def _write_rarefaction(self, fname, sub_otu_table):
""" depth and rep can be numbers or strings
"""
if sub_otu_table.isEmpty():
return
f = open(fname, 'w')
f.write(format_biom_table(sub_otu_table))
f.close()
def _write_rarefaction(self, fname, sub_otu_table):
""" depth and rep can be numbers or strings
"""
if sub_otu_table.isEmpty():
return
f = open(fname, 'w')
f.write(format_biom_table(sub_otu_table))
f.close()
def setUp(self):
"""Define some test data."""
self.qiime_config = load_qiime_config()
self.dirs_to_remove = []
self.tmp_dir = self.qiime_config["temp_dir"] or "/tmp/"
if not exists(self.tmp_dir):
makedirs(self.tmp_dir)
# if test creates the temp dir, also remove it
self.dirs_to_remove.append(self.tmp_dir)
self.otu_table1 = table_factory(
data=array([[2, 0, 0, 1], [1, 1, 1, 1], [0, 0, 0, 0]]).T,
sample_ids=list("XYZ"),
observation_ids=list("abcd"),
constructor=DenseOTUTable,
)
self.otu_table1_fp = get_tmp_filename(
tmp_dir=self.tmp_dir, prefix="alpha_diversity_tests", suffix=".biom", result_constructor=str
)
open(self.otu_table1_fp, "w").write(format_biom_table(self.otu_table1))
self.otu_table2 = table_factory(
data=array([[2, 0, 0, 1], [1, 1, 1, 1], [0, 0, 0, 0]]).T,
sample_ids=list("XYZ"),
observation_ids=["a", "b", "c", "d_"],
constructor=DenseOTUTable,
)
self.otu_table2_fp = get_tmp_filename(
tmp_dir=self.tmp_dir, prefix="alpha_diversity_tests", suffix=".biom", result_constructor=str
)
open(self.otu_table2_fp, "w").write(format_biom_table(self.otu_table2))
self.single_sample_otu_table = table_factory(
data=array([[2, 0, 0, 1]]).T, sample_ids=list("X"), observation_ids=list("abcd"), constructor=DenseOTUTable
)
self.single_sample_otu_table_fp = get_tmp_filename(
tmp_dir=self.tmp_dir, prefix="alpha_diversity_tests", suffix=".biom", result_constructor=str
)
open(self.single_sample_otu_table_fp, "w").write(format_biom_table(self.single_sample_otu_table))
self.tree1 = parse_newick("((a:2,b:3):2,(c:1,d:2):7);")
self.tree2 = parse_newick("((a:2,'b':3):2,(c:1,'d_':2):7);")
self.files_to_remove = [self.otu_table1_fp, self.otu_table2_fp, self.single_sample_otu_table_fp]
def main():
option_parser, opts, args =\
parse_command_line_parameters(**script_info)
input_fp = opts.input_fp
output_fp = opts.output_fp
mapping_fp = opts.mapping_fp
output_mapping_fp = opts.output_mapping_fp
valid_states = opts.valid_states
min_count = opts.min_count
max_count = opts.max_count
sample_id_fp = opts.sample_id_fp
if not ((mapping_fp and valid_states) or min_count != 0
or not isinf(max_count) or sample_id_fp is not None):
option_parser.error(
"No filtering requested. Must provide either "
"mapping_fp and valid states, min counts, "
"max counts, or sample_id_fp (or some combination of those).")
if output_mapping_fp and not mapping_fp:
option_parser.error("Must provide input mapping file to generate"
" output mapping file.")
otu_table = parse_biom_table(open(opts.input_fp, 'U'))
output_f = open(opts.output_fp, 'w')
if (mapping_fp and valid_states):
sample_ids_to_keep = sample_ids_from_metadata_description(
open(mapping_fp, 'U'), valid_states)
else:
sample_ids_to_keep = otu_table.SampleIds
if (sample_id_fp is not None):
sample_id_f_ids = set([
l.strip().split()[0] for l in open(sample_id_fp, 'U')
if not l.startswith('#')
])
sample_ids_to_keep = set(sample_ids_to_keep) & sample_id_f_ids
filtered_otu_table = filter_samples_from_otu_table(otu_table,
sample_ids_to_keep,
min_count, max_count)
output_f.write(format_biom_table(filtered_otu_table))
output_f.close()
# filter mapping file if requested
if output_mapping_fp:
mapping_data, mapping_headers, _ = parse_mapping_file(
open(mapping_fp, 'U'))
mapping_headers, mapping_data = \
filter_mapping_file(
mapping_data,
mapping_headers,
filtered_otu_table.SampleIds)
open(output_mapping_fp,
'w').write(format_mapping_file(mapping_headers, mapping_data))
def test_split_otu_table_on_sample_metadata(self):
""" split_otu_table_on_sample_metadata functions as expected with valid input """
actual = list(split_otu_table_on_sample_metadata(self.otu_table_f1,
self.mapping_f1,
"Treatment"))
for id_, e in actual:
try:
parse_biom_table(e)
except:
print e
actual = [(id_,parse_biom_table(e)) for id_, e in actual]
exp = [(id_,parse_biom_table(e)) for id_, e in otu_table_exp1]
actual.sort()
exp.sort()
for a,e in zip(actual,exp):
self.assertEqual(a,e,"OTU tables are not equal:\n%s\n%s" % \
(format_biom_table(a[1]),format_biom_table(e[1])))
def _write_rarefaction(self, depth, rep, sub_otu_table):
""" depth and rep can be numbers or strings
"""
if sub_otu_table.isEmpty():
return
fname = 'rarefaction_' + str(depth) + '_' + str(rep) + '.biom'
f = open(os.path.join(self.output_dir, fname), 'w')
f.write(format_biom_table(sub_otu_table))
f.close()
def main():
option_parser, opts, args = parse_command_line_parameters(**script_info)
sample_mapping_fp = opts.sample_mapping_fp
output_fp = opts.output_fp
verbose = opts.verbose
sample_mapping_file = open(sample_mapping_fp, 'U')
result = sample_mapping_to_biom_table(sample_mapping_file)
open(output_fp, 'w').write(format_biom_table(result))
def main():
option_parser, opts, args = parse_command_line_parameters(**script_info)
sample_mapping_fp = opts.sample_mapping_fp
output_fp = opts.output_fp
verbose = opts.verbose
sample_mapping_file = open(sample_mapping_fp, 'U')
result = sample_mapping_to_biom_table(sample_mapping_file)
open(output_fp, 'w').write(format_biom_table(result))
def _write_rarefaction(self, depth, rep, sub_otu_table):
""" depth and rep can be numbers or strings
"""
if sub_otu_table.isEmpty():
return
fname = 'rarefaction_'+str(depth)+'_'+str(rep)+'.biom'
f = open(os.path.join(self.output_dir,fname), 'w')
f.write(format_biom_table(sub_otu_table))
f.close()
def test_split_otu_table_on_sample_metadata(self):
""" split_otu_table_on_sample_metadata functions as expected with valid input """
actual = list(split_otu_table_on_sample_metadata(self.otu_table_f1,
self.mapping_f1,
"Treatment"))
for id_, e in actual:
try:
parse_biom_table(e)
except:
print e
actual = [(id_, parse_biom_table(e)) for id_, e in actual]
exp = [(id_, parse_biom_table(e)) for id_, e in otu_table_exp1]
actual.sort()
exp.sort()
for a, e in zip(actual, exp):
self.assertEqual(a, e, "OTU tables are not equal:\n%s\n%s" %
(format_biom_table(a[1]), format_biom_table(e[1])))
def setUp(self):
self.qiime_config = load_qiime_config()
self.tmp_dir = self.qiime_config['temp_dir'] or '/tmp/'
self.otu_table_data = numpy.array([[2,1,0],
[0,5,0],
[0,3,0],
[1,2,0]])
self.sample_names = list('YXZ')
self.taxon_names = list('bacd')
self.otu_metadata = [{'domain':'Archaea'},
{'domain':'Bacteria'},
{'domain':'Bacteria'},
{'domain':'Bacteria'}]
self.otu_table = table_factory(self.otu_table_data,
self.sample_names,
self.taxon_names)
self.otu_table_meta = table_factory(self.otu_table_data,
self.sample_names, self.taxon_names,
observation_metadata=self.otu_metadata)
self.otu_table_str = format_biom_table(self.otu_table)
self.otu_table_meta_str = format_biom_table(self.otu_table_meta)
self.otu_table_fp = get_tmp_filename(tmp_dir=self.tmp_dir,
prefix='test_rarefaction',suffix='.biom')
self.otu_table_meta_fp = get_tmp_filename(tmp_dir=self.tmp_dir,
prefix='test_rarefaction',suffix='.biom')
self.rare_dir = get_tmp_filename(tmp_dir=self.tmp_dir,
prefix='test_rarefaction_dir',suffix='',result_constructor=str)
os.mkdir(self.rare_dir)
open(self.otu_table_fp,'w').write(self.otu_table_str)
open(self.otu_table_meta_fp,'w').write(self.otu_table_meta_str)
self._paths_to_clean_up=[self.otu_table_fp,self.otu_table_meta_fp]
self._dirs_to_clean_up=[self.rare_dir]
def main():
option_parser, opts, args =\
parse_command_line_parameters(**script_info)
input_fps = opts.input_fps
master = parse_biom_table(open(input_fps[0], 'U'))
for input_fp in input_fps[1:]:
master = master.merge(parse_biom_table(open(input_fp, 'U')))
out_f = open(opts.output_fp, 'w')
out_f.write(format_biom_table(master))
out_f.close()
def main():
option_parser, opts, args =\
parse_command_line_parameters(**script_info)
input_fp = opts.input_fp
output_fp = opts.output_fp
mapping_fp = opts.mapping_fp
output_mapping_fp = opts.output_mapping_fp
valid_states = opts.valid_states
min_count = opts.min_count
max_count = opts.max_count
sample_id_fp = opts.sample_id_fp
if not ((mapping_fp and valid_states) or
min_count != 0 or
not isinf(max_count) or
sample_id_fp != None):
option_parser.error("No filtering requested. Must provide either "
"mapping_fp and valid states, min counts, "
"max counts, or sample_id_fp (or some combination of those).")
if output_mapping_fp and not mapping_fp:
option_parser.error("Must provide input mapping file to generate"
" output mapping file.")
otu_table = parse_biom_table(open(opts.input_fp,'U'))
output_f = open(opts.output_fp,'w')
if (mapping_fp and valid_states):
sample_ids_to_keep = sample_ids_from_metadata_description(
open(mapping_fp,'U'),valid_states)
else:
sample_ids_to_keep = otu_table.SampleIds
if (sample_id_fp != None):
sample_id_f_ids = set([l.strip().split()[0] for l in open(sample_id_fp,'U') if not l.startswith('#')])
sample_ids_to_keep = set(sample_ids_to_keep) & sample_id_f_ids
filtered_otu_table = filter_samples_from_otu_table(otu_table,
sample_ids_to_keep,
min_count,
max_count)
output_f.write(format_biom_table(filtered_otu_table))
output_f.close()
# filter mapping file if requested
if output_mapping_fp:
mapping_data, mapping_headers, _ = parse_mapping_file(open(mapping_fp,'U'))
mapping_headers, mapping_data = \
filter_mapping_file(mapping_data, mapping_headers, filtered_otu_table.SampleIds)
open(output_mapping_fp,'w').write(format_mapping_file(mapping_headers,mapping_data))
def main():
option_parser, opts, args =\
parse_command_line_parameters(**script_info)
if not isfile(opts.input_path):
raise IOError, \
"Input path (%s) not valid. Does it exist?" % opts.input_path
samples, otus, data = parse_trflp(open(opts.input_path,'U'))
output_f = open(opts.output_path, 'w')
t = table_factory(data,samples,otus)
output_f.write(format_biom_table(t))
output_f.close()
def setUp(self):
self.qiime_config = load_qiime_config()
self.tmp_dir = self.qiime_config["temp_dir"] or "/tmp/"
self.otu_table_data = numpy.array([[2, 1, 0], [0, 5, 0], [0, 3, 0], [1, 2, 0]])
self.sample_names = list("YXZ")
self.taxon_names = list("bacd")
self.otu_metadata = [
{"domain": "Archaea"},
{"domain": "Bacteria"},
{"domain": "Bacteria"},
{"domain": "Bacteria"},
]
self.otu_table = table_factory(self.otu_table_data, self.sample_names, self.taxon_names)
self.otu_table_meta = table_factory(
self.otu_table_data, self.sample_names, self.taxon_names, observation_metadata=self.otu_metadata
)
self.otu_table_str = format_biom_table(self.otu_table)
self.otu_table_meta_str = format_biom_table(self.otu_table_meta)
self.otu_table_fp = get_tmp_filename(tmp_dir=self.tmp_dir, prefix="test_rarefaction", suffix=".biom")
self.otu_table_meta_fp = get_tmp_filename(tmp_dir=self.tmp_dir, prefix="test_rarefaction", suffix=".biom")
self.rare_dir = get_tmp_filename(
tmp_dir=self.tmp_dir, prefix="test_rarefaction_dir", suffix="", result_constructor=str
)
os.mkdir(self.rare_dir)
open(self.otu_table_fp, "w").write(self.otu_table_str)
open(self.otu_table_meta_fp, "w").write(self.otu_table_meta_str)
self._paths_to_clean_up = [self.otu_table_fp, self.otu_table_meta_fp]
self._dirs_to_clean_up = [self.rare_dir]
def simsam_range_to_files(table,
tree,
simulated_sample_sizes,
dissimilarities,
output_dir,
mapping_f=None,
output_table_basename="table",
output_map_basename="map"):
"""Applies sim_otu_table over a range of parameters, writing output to file
table: the input table to simulate samples from
tree: tree related OTUs in input table
simulated_sample_sizes: a list of ints defining how many
output samples should be create per input sample
dissimilarities: a list of floats containing the
dissimilarities to use in simulating tables
output_dir: the directory where all output tables and
mapping files should be written
mapping_f: file handle for metadata mapping file, if
a mapping file should be created with the samples from
each simulated table
output_table_basename: basename for output table files
(default: table)
output_map_basename: basename for output mapping files
(default: map)
"""
create_dir(output_dir)
for e in simsam_range(table, tree, simulated_sample_sizes, dissimilarities,
mapping_f):
output_table = e[0]
output_mapping_lines = e[1]
simulated_sample_size = e[2]
dissimilarity = e[3]
output_table_fp = join(
output_dir, '%s_n%d_d%r.biom' %
(output_table_basename, simulated_sample_size, dissimilarity))
output_table_f = open(output_table_fp, 'w')
output_table_f.write(format_biom_table(output_table))
output_table_f.close()
if output_mapping_lines != None:
output_map_fp = join(
output_dir, '%s_n%d_d%r.txt' %
(output_map_basename, simulated_sample_size, dissimilarity))
output_map_f = open(output_map_fp, 'w')
output_map_f.write(''.join(output_mapping_lines))
output_map_f.close()
def simsam_range_to_files(table,
tree,
simulated_sample_sizes,
dissimilarities,
output_dir,
mapping_f=None,
output_table_basename="table",
output_map_basename="map"):
"""Applies sim_otu_table over a range of parameters, writing output to file
table: the input table to simulate samples from
tree: tree related OTUs in input table
simulated_sample_sizes: a list of ints defining how many
output samples should be create per input sample
dissimilarities: a list of floats containing the
dissimilarities to use in simulating tables
output_dir: the directory where all output tables and
mapping files should be written
mapping_f: file handle for metadata mapping file, if
a mapping file should be created with the samples from
each simulated table
output_table_basename: basename for output table files
(default: table)
output_map_basename: basename for output mapping files
(default: map)
"""
create_dir(output_dir)
for e in simsam_range(table,tree,simulated_sample_sizes,dissimilarities,mapping_f):
output_table = e[0]
output_mapping_lines = e[1]
simulated_sample_size = e[2]
dissimilarity = e[3]
output_table_fp = join(output_dir,'%s_n%d_d%r.biom' %
(output_table_basename, simulated_sample_size, dissimilarity))
output_table_f = open(output_table_fp, 'w')
output_table_f.write(format_biom_table(output_table))
output_table_f.close()
if output_mapping_lines != None:
output_map_fp = join(output_dir,'%s_n%d_d%r.txt' %
(output_map_basename, simulated_sample_size, dissimilarity))
output_map_f = open(output_map_fp, 'w')
output_map_f.write(''.join(output_mapping_lines))
output_map_f.close()
def main():
option_parser, opts, args =\
parse_command_line_parameters(**script_info)
out_fh = open(opts.output_file,'w')
otu_table_fh = open(opts.otu_table,'U')
otu_table = parse_biom_table(otu_table_fh)
tree_fh = open(opts.tree_file,'U')
tree = DndParser(tree_fh)
res_sam_names, res_otus, res_otu_mtx, res_otu_metadata = \
sim_otu_table(otu_table.SampleIds, otu_table.ObservationIds, otu_table.iterSamples(),
otu_table.ObservationMetadata, tree, opts.num, opts.dissim)
rich_table = table_factory(res_otu_mtx,res_sam_names,res_otus,
observation_metadata=res_otu_metadata)
out_fh.write(format_biom_table(rich_table))
def split_otu_table_on_sample_metadata(otu_table_f,mapping_f,mapping_field):
""" split otu table into sub otu tables where each represent samples corresponding to only a certain value in mapping_field
"""
mapping_f = list(mapping_f)
mapping_values = get_mapping_values(mapping_f,mapping_field)
otu_table = parse_biom_table(otu_table_f)
for v in mapping_values:
v_fp_str = v.replace(' ','_')
sample_ids_to_keep = sample_ids_from_metadata_description(
mapping_f,valid_states_str="%s:%s" % (mapping_field,v))
try:
filtered_otu_table = otu_table.filterSamples(
lambda values,id_,metadata: id_ in sample_ids_to_keep)
except TableException:
# all samples are filtered out, so no otu table to write
continue
yield v_fp_str, format_biom_table(filtered_otu_table)
def main():
option_parser, opts, args =\
parse_command_line_parameters(**script_info)
out_fh = open(opts.output_file, 'w')
otu_table_fh = open(opts.otu_table, 'U')
otu_table = parse_biom_table(otu_table_fh)
tree_fh = open(opts.tree_file, 'U')
tree = DndParser(tree_fh)
res_sam_names, res_otus, res_otu_mtx, res_otu_metadata = \
sim_otu_table(otu_table.SampleIds, otu_table.ObservationIds, otu_table.iterSamples(),
otu_table.ObservationMetadata, tree, opts.num, opts.dissim)
rich_table = table_factory(res_otu_mtx,
res_sam_names,
res_otus,
observation_metadata=res_otu_metadata)
out_fh.write(format_biom_table(rich_table))
def main():
option_parser, opts, args = parse_command_line_parameters(**script_info)
mapping_fp = opts.mapping_fp
mapping_category = opts.mapping_category
otu_table_fp = opts.otu_table_fp
output_fp = opts.output_fp
normalize = opts.normalize
# define a function that returns the bin a sample shouldbe placed into
bin_function = lambda sample_metadata: sample_metadata[mapping_category]
# parse the sample metadata and add it to the OTU table (we assume that
# sample metadata is not already present in the table)
mapping, headers, comments = parse_mapping_file(open(mapping_fp, 'U'))
# added in ability to combine metadata columns and summarize based on the
# new combined category
if '&&' in mapping_category:
new_mapping = []
new_mapping.append(headers)
for i in range(len(mapping)):
new_mapping.append(mapping[i])
#Create an array using multiple columns from mapping file
combinecolorby = mapping_category.split('&&')
mapping = combine_map_label_cols(combinecolorby, new_mapping)
sample_metadata = mapping_file_to_dict(mapping, headers)
table = parse_biom_table(open(otu_table_fp, 'U'))
table.addSampleMetadata(sample_metadata)
# create a new OTU table where samples are binned based on their return
# value from bin_function
result = table.collapseSamplesByMetadata(bin_function,
norm=False,
min_group_size=1)
# normalize the result if requested by the user
if normalize:
result = result.normObservationBySample()
# write a new BIOM file
f = open(output_fp, 'w')
f.write(format_biom_table(result))
f.close()
def split_otu_table_on_sample_metadata(otu_table_f, mapping_f, mapping_field):
""" split otu table into sub otu tables where each represent samples corresponding to only a certain value in mapping_field
"""
mapping_f = list(mapping_f)
mapping_values = get_mapping_values(mapping_f, mapping_field)
otu_table = parse_biom_table(otu_table_f)
for v in mapping_values:
v_fp_str = v.replace(' ', '_')
sample_ids_to_keep = sample_ids_from_metadata_description(
mapping_f, valid_states_str="%s:%s" % (mapping_field, v))
try:
filtered_otu_table = otu_table.filterSamples(
lambda values, id_, metadata: id_ in sample_ids_to_keep)
except TableException:
# all samples are filtered out, so no otu table to write
continue
yield v_fp_str, format_biom_table(filtered_otu_table)
def main():
option_parser, opts, args = parse_command_line_parameters(**script_info)
mapping_fp = opts.mapping_fp
mapping_category = opts.mapping_category
otu_table_fp = opts.otu_table_fp
output_fp = opts.output_fp
normalize = opts.normalize
# define a function that returns the bin a sample shouldbe placed into
bin_function = lambda sample_metadata: sample_metadata[mapping_category]
# parse the sample metadata and add it to the OTU table (we assume that
# sample metadata is not already present in the table)
mapping,headers,comments = parse_mapping_file(open(mapping_fp,'U'))
# added in ability to combine metadata columns and summarize based on the
# new combined category
if '&&' in mapping_category:
new_mapping=[]
new_mapping.append(headers)
for i in range(len(mapping)):
new_mapping.append(mapping[i])
#Create an array using multiple columns from mapping file
combinecolorby=mapping_category.split('&&')
mapping=combine_map_label_cols(combinecolorby,new_mapping)
sample_metadata=mapping_file_to_dict(mapping,headers)
table = parse_biom_table(open(otu_table_fp,'U'))
table.addSampleMetadata(sample_metadata)
# create a new OTU table where samples are binned based on their return
# value from bin_function
result = table.collapseSamplesByMetadata(bin_function, norm=False,
min_group_size=1)
# normalize the result if requested by the user
if normalize:
result = result.normObservationBySample()
# write a new BIOM file
f = open(output_fp,'w')
f.write(format_biom_table(result))
f.close()
def pick_subsampled_open_reference_otus(
input_fp,
refseqs_fp,
output_dir,
percent_subsample,
new_ref_set_id,
command_handler,
params,
qiime_config,
prefilter_refseqs_fp=None,
run_assign_tax=True,
run_align_and_tree=True,
prefilter_percent_id=0.60,
min_otu_size=2,
step1_otu_map_fp=None,
step1_failures_fasta_fp=None,
parallel=False,
suppress_step4=False,
logger=None,
suppress_md5=False,
denovo_otu_picking_method='uclust',
reference_otu_picking_method='uclust_ref',
status_update_callback=print_to_stdout):
""" Run the data preparation steps of Qiime
The steps performed by this function are:
- Pick reference OTUs against refseqs_fp
- Subsample the failures to n sequences.
- Pick OTUs de novo on the n failures.
- Pick representative sequences for the resulting OTUs.
- Pick reference OTUs on all failures using the
representative set from step 4 as the reference set.
"""
# for now only allowing uclust for otu picking
allowed_denovo_otu_picking_methods = ['uclust', 'usearch61']
allowed_reference_otu_picking_methods = ['uclust_ref', 'usearch61_ref']
assert denovo_otu_picking_method in allowed_denovo_otu_picking_methods,\
"Unknown de novo OTU picking method: %s. Known methods are: %s"\
% (denovo_otu_picking_method,
','.join(allowed_denovo_otu_picking_methods))
assert reference_otu_picking_method in allowed_reference_otu_picking_methods,\
"Unknown reference OTU picking method: %s. Known methods are: %s"\
% (reference_otu_picking_method,
','.join(allowed_reference_otu_picking_methods))
# Prepare some variables for the later steps
input_dir, input_filename = split(input_fp)
input_basename, input_ext = splitext(input_filename)
create_dir(output_dir)
commands = []
if logger == None:
logger = WorkflowLogger(generate_log_fp(output_dir),
params=params,
qiime_config=qiime_config)
close_logger_on_success = True
else:
close_logger_on_success = False
if not suppress_md5:
log_input_md5s(
logger,
[input_fp, refseqs_fp, step1_otu_map_fp, step1_failures_fasta_fp])
# if the user has not passed a different reference collection for the pre-filter,
# used the main refseqs_fp. this is useful if the user wants to provide a smaller
# reference collection, or to use the input reference collection when running in
# iterative mode (rather than an iteration's new refseqs)
if prefilter_refseqs_fp == None:
prefilter_refseqs_fp = refseqs_fp
## Step 1: Closed-reference OTU picking on the input file (if not already complete)
if step1_otu_map_fp and step1_failures_fasta_fp:
step1_dir = '%s/step1_otus' % output_dir
create_dir(step1_dir)
logger.write("Using pre-existing reference otu map and failures.\n\n")
else:
if prefilter_percent_id != None:
prefilter_dir = '%s/prefilter_otus/' % output_dir
prefilter_failures_list_fp = '%s/%s_failures.txt' % \
(prefilter_dir,input_basename)
prefilter_pick_otu_cmd = pick_reference_otus(\
input_fp,prefilter_dir,reference_otu_picking_method,
prefilter_refseqs_fp,parallel,params,logger,prefilter_percent_id)
commands.append([('Pick Reference OTUs (prefilter)',
prefilter_pick_otu_cmd)])
prefiltered_input_fp = '%s/prefiltered_%s%s' %\
(prefilter_dir,input_basename,input_ext)
filter_fasta_cmd = 'filter_fasta.py -f %s -o %s -s %s -n' %\
(input_fp,prefiltered_input_fp,prefilter_failures_list_fp)
commands.append([('Filter prefilter failures from input',
filter_fasta_cmd)])
# Call the command handler on the list of commands
command_handler(commands,
status_update_callback,
logger=logger,
close_logger_on_success=False)
commands = []
input_fp = prefiltered_input_fp
input_dir, input_filename = split(input_fp)
input_basename, input_ext = splitext(input_filename)
if getsize(prefiltered_input_fp) == 0:
raise ValueError(
"All sequences were discarded by the prefilter. "
"Are the input sequences in the same orientation "
"in your input file and reference file (you can "
"add 'pick_otus:enable_rev_strand_match True' to "
"your parameters file if not)? Are you using the "
"correct reference file?")
## Build the OTU picking command
step1_dir = \
'%s/step1_otus' % output_dir
step1_otu_map_fp = \
'%s/%s_otus.txt' % (step1_dir,input_basename)
step1_pick_otu_cmd = pick_reference_otus(\
input_fp,step1_dir,reference_otu_picking_method,
refseqs_fp,parallel,params,logger)
commands.append([('Pick Reference OTUs', step1_pick_otu_cmd)])
## Build the failures fasta file
step1_failures_list_fp = '%s/%s_failures.txt' % \
(step1_dir,input_basename)
step1_failures_fasta_fp = \
'%s/failures.fasta' % step1_dir
step1_filter_fasta_cmd = 'filter_fasta.py -f %s -s %s -o %s' %\
(input_fp,step1_failures_list_fp,step1_failures_fasta_fp)
commands.append([('Generate full failures fasta file',
step1_filter_fasta_cmd)])
# Call the command handler on the list of commands
command_handler(commands,
status_update_callback,
logger=logger,
close_logger_on_success=False)
commands = []
step1_repset_fasta_fp = \
'%s/step1_rep_set.fna' % step1_dir
step1_pick_rep_set_cmd = 'pick_rep_set.py -i %s -o %s -f %s' %\
(step1_otu_map_fp, step1_repset_fasta_fp, input_fp)
commands.append([('Pick rep set', step1_pick_rep_set_cmd)])
# Call the command handler on the list of commands
command_handler(commands,
status_update_callback,
logger=logger,
close_logger_on_success=False)
commands = []
## Subsample the failures fasta file to retain (roughly) the
## percent_subsample
step2_input_fasta_fp = \
'%s/subsampled_failures.fasta' % step1_dir
subsample_fasta(step1_failures_fasta_fp, step2_input_fasta_fp,
percent_subsample)
logger.write('# Subsample the failures fasta file using API \n' +
'python -c "import qiime; qiime.util.subsample_fasta' +
'(\'%s\', \'%s\', \'%f\')\n\n"' %
(abspath(step1_failures_fasta_fp),
abspath(step2_input_fasta_fp), percent_subsample))
## Prep the OTU picking command for the subsampled failures
step2_dir = '%s/step2_otus/' % output_dir
step2_cmd = pick_denovo_otus(step2_input_fasta_fp, step2_dir,
new_ref_set_id, denovo_otu_picking_method,
params, logger)
step2_otu_map_fp = '%s/subsampled_failures_otus.txt' % step2_dir
commands.append([('Pick de novo OTUs for new clusters', step2_cmd)])
## Prep the rep set picking command for the subsampled failures
step2_repset_fasta_fp = '%s/step2_rep_set.fna' % step2_dir
step2_rep_set_cmd = 'pick_rep_set.py -i %s -o %s -f %s' %\
(step2_otu_map_fp,step2_repset_fasta_fp,step2_input_fasta_fp)
commands.append([('Pick representative set for subsampled failures',
step2_rep_set_cmd)])
step3_dir = '%s/step3_otus/' % output_dir
step3_otu_map_fp = '%s/failures_otus.txt' % step3_dir
step3_failures_list_fp = '%s/failures_failures.txt' % step3_dir
step3_cmd = pick_reference_otus(step1_failures_fasta_fp, step3_dir,
reference_otu_picking_method,
step2_repset_fasta_fp, parallel, params,
logger)
commands.append([('Pick reference OTUs using de novo rep set', step3_cmd)])
# name the final otu map
merged_otu_map_fp = '%s/final_otu_map.txt' % output_dir
if not suppress_step4:
step3_failures_fasta_fp = '%s/failures_failures.fasta' % step3_dir
step3_filter_fasta_cmd = 'filter_fasta.py -f %s -s %s -o %s' %\
(step1_failures_fasta_fp,step3_failures_list_fp,step3_failures_fasta_fp)
commands.append([('Create fasta file of step3 failures',
step3_filter_fasta_cmd)])
step4_dir = '%s/step4_otus/' % output_dir
step4_cmd = pick_denovo_otus(step3_failures_fasta_fp, step4_dir,
'.'.join([new_ref_set_id, 'CleanUp']),
denovo_otu_picking_method, params, logger)
step4_otu_map_fp = '%s/failures_failures_otus.txt' % step4_dir
commands.append([('Pick de novo OTUs on step3 failures', step4_cmd)])
# Merge the otu maps, note that we are explicitly using the '>' operator
# otherwise passing the --force flag on the script interface would
# append the newly created maps to the map that was previously created
cat_otu_tables_cmd = 'cat %s %s %s > %s' %\
(step1_otu_map_fp,step3_otu_map_fp,step4_otu_map_fp,merged_otu_map_fp)
commands.append([('Merge OTU maps', cat_otu_tables_cmd)])
step4_repset_fasta_fp = '%s/step4_rep_set.fna' % step4_dir
step4_rep_set_cmd = 'pick_rep_set.py -i %s -o %s -f %s' %\
(step4_otu_map_fp,step4_repset_fasta_fp,step3_failures_fasta_fp)
commands.append([('Pick representative set for subsampled failures',
step4_rep_set_cmd)])
else:
# Merge the otu maps, note that we are explicitly using the '>' operator
# otherwise passing the --force flag on the script interface would
# append the newly created maps to the map that was previously created
cat_otu_tables_cmd = 'cat %s %s > %s' %\
(step1_otu_map_fp,step3_otu_map_fp,merged_otu_map_fp)
commands.append([('Merge OTU maps', cat_otu_tables_cmd)])
# Move the step 3 failures file to the top-level directory
commands.append([('Move final failures file to top-level directory',
'mv %s %s/final_failures.txt' %
(step3_failures_list_fp, output_dir))])
command_handler(commands,
status_update_callback,
logger=logger,
close_logger_on_success=False)
commands = []
otu_fp = merged_otu_map_fp
# Filter singletons from the otu map
otu_no_singletons_fp = '%s/final_otu_map_mc%d.txt' % (output_dir,
min_otu_size)
otus_to_keep = filter_otus_from_otu_map(otu_fp, otu_no_singletons_fp,
min_otu_size)
logger.write(
'# Filter singletons from the otu map using API \n' +
'python -c "import qiime; qiime.filter.filter_otus_from_otu_map' +
'(\'%s\', \'%s\', \'%d\')"\n\n' %
(abspath(otu_fp), abspath(otu_no_singletons_fp), min_otu_size))
## make the final representative seqs file and a new refseqs file that
## could be used in subsequent otu picking runs.
## this is clunky. first, we need to do this without singletons to match
## the otu map without singletons. next, there is a difference in what
## we need the reference set to be and what we need the repseqs to be.
## the reference set needs to be a superset of the input reference set
## to this set. the repset needs to be only the sequences that were observed
## in this data set, and we want reps for the step1 reference otus to be
## reads from this run so we don't hit issues building a tree using
## sequences of very different lengths. so...
final_repset_fp = '%s/rep_set.fna' % output_dir
final_repset_f = open(final_repset_fp, 'w')
new_refseqs_fp = '%s/new_refseqs.fna' % output_dir
# write non-singleton otus representative sequences from step1 to the
# final rep set file
for otu_id, seq in MinimalFastaParser(open(step1_repset_fasta_fp, 'U')):
if otu_id.split()[0] in otus_to_keep:
final_repset_f.write('>%s\n%s\n' % (otu_id, seq))
logger.write('# Write non-singleton otus representative sequences ' +
'from step1 to the final rep set file: %s\n\n' %
final_repset_fp)
# copy the full input refseqs file to the new refseqs_fp
copy(refseqs_fp, new_refseqs_fp)
new_refseqs_f = open(new_refseqs_fp, 'a')
new_refseqs_f.write('\n')
logger.write(
'# Copy the full input refseqs file to the new refseq file\n' +
'cp %s %s\n\n' % (refseqs_fp, new_refseqs_fp))
# iterate over all representative sequences from step2 and step4 and write
# those corresponding to non-singleton otus to the final representative set
# file and the new reference sequences file.
for otu_id, seq in MinimalFastaParser(open(step2_repset_fasta_fp, 'U')):
if otu_id.split()[0] in otus_to_keep:
new_refseqs_f.write('>%s\n%s\n' % (otu_id, seq))
final_repset_f.write('>%s\n%s\n' % (otu_id, seq))
if not suppress_step4:
for otu_id, seq in MinimalFastaParser(open(step4_repset_fasta_fp,
'U')):
if otu_id.split()[0] in otus_to_keep:
new_refseqs_f.write('>%s\n%s\n' % (otu_id, seq))
final_repset_f.write('>%s\n%s\n' % (otu_id, seq))
new_refseqs_f.close()
final_repset_f.close()
logger.write(
'# Write non-singleton otus representative sequences from ' +
'step 2 and step 4 to the final representative set and the new reference'
+ ' set (%s and %s respectively)\n\n' %
(final_repset_fp, new_refseqs_fp))
# Prep the make_otu_table.py command
otu_table_fp = '%s/otu_table_mc%d.biom' % (output_dir, min_otu_size)
make_otu_table_cmd = 'make_otu_table.py -i %s -o %s' %\
(otu_no_singletons_fp,otu_table_fp)
commands.append([("Make the otu table", make_otu_table_cmd)])
command_handler(commands,
status_update_callback,
logger=logger,
close_logger_on_success=False)
commands = []
# initialize output file names - these differ based on what combination of
# taxonomy assignment and alignment/tree building is happening.
if run_assign_tax and run_align_and_tree:
tax_input_otu_table_fp = otu_table_fp
otu_table_w_tax_fp = \
'%s/otu_table_mc%d_w_tax.biom' % (output_dir,min_otu_size)
align_and_tree_input_otu_table = otu_table_w_tax_fp
pynast_failure_filtered_otu_table_fp = \
'%s/otu_table_mc%d_w_tax_no_pynast_failures.biom' % (output_dir,min_otu_size)
elif run_assign_tax:
tax_input_otu_table_fp = otu_table_fp
otu_table_w_tax_fp = \
'%s/otu_table_mc%d_w_tax.biom' % (output_dir,min_otu_size)
elif run_align_and_tree:
align_and_tree_input_otu_table = otu_table_fp
pynast_failure_filtered_otu_table_fp = \
'%s/otu_table_mc%d_no_pynast_failures.biom' % (output_dir,min_otu_size)
if run_assign_tax:
if exists(otu_table_w_tax_fp) and getsize(otu_table_w_tax_fp) > 0:
logger.write("Final output file exists (%s). Will not rebuild." %
otu_table_w_tax_fp)
else:
# remove files from partially completed runs
remove_files([otu_table_w_tax_fp], error_on_missing=False)
taxonomy_fp = assign_tax(
repset_fasta_fp=final_repset_fp,
output_dir=output_dir,
command_handler=command_handler,
params=params,
qiime_config=qiime_config,
parallel=parallel,
logger=logger,
status_update_callback=status_update_callback)
# Add taxa to otu table
add_metadata_cmd = 'biom add-metadata -i %s --observation-metadata-fp %s -o %s --sc-separated taxonomy --observation-header OTUID,taxonomy' %\
(tax_input_otu_table_fp,taxonomy_fp,otu_table_w_tax_fp)
commands.append([("Add taxa to OTU table", add_metadata_cmd)])
command_handler(commands,
status_update_callback,
logger=logger,
close_logger_on_success=False)
commands = []
if run_align_and_tree:
if exists(pynast_failure_filtered_otu_table_fp) and\
getsize(pynast_failure_filtered_otu_table_fp) > 0:
logger.write("Final output file exists (%s). Will not rebuild." %\
pynast_failure_filtered_otu_table_fp)
else:
# remove files from partially completed runs
remove_files([pynast_failure_filtered_otu_table_fp],
error_on_missing=False)
pynast_failures_fp = align_and_tree(
repset_fasta_fp=final_repset_fp,
output_dir=output_dir,
command_handler=command_handler,
params=params,
qiime_config=qiime_config,
parallel=parallel,
logger=logger,
status_update_callback=status_update_callback)
# Build OTU table without PyNAST failures
filtered_otu_table = filter_otus_from_otu_table(
parse_biom_table(open(align_and_tree_input_otu_table, 'U')),
get_seq_ids_from_fasta_file(open(pynast_failures_fp, 'U')),
0,
inf,
0,
inf,
negate_ids_to_keep=True)
otu_table_f = open(pynast_failure_filtered_otu_table_fp, 'w')
otu_table_f.write(format_biom_table(filtered_otu_table))
otu_table_f.close()
command_handler(commands,
status_update_callback,
logger=logger,
close_logger_on_success=False)
commands = []
if close_logger_on_success:
logger.close()
def pick_subsampled_open_reference_otus(input_fp,
refseqs_fp,
output_dir,
percent_subsample,
new_ref_set_id,
command_handler,
params,
qiime_config,
prefilter_refseqs_fp=None,
run_assign_tax=True,
run_align_and_tree=True,
prefilter_percent_id=0.60,
min_otu_size=2,
step1_otu_map_fp=None,
step1_failures_fasta_fp=None,
parallel=False,
suppress_step4=False,
logger=None,
suppress_md5=False,
denovo_otu_picking_method='uclust',
reference_otu_picking_method='uclust_ref',
status_update_callback=print_to_stdout):
""" Run the data preparation steps of Qiime
The steps performed by this function are:
- Pick reference OTUs against refseqs_fp
- Subsample the failures to n sequences.
- Pick OTUs de novo on the n failures.
- Pick representative sequences for the resulting OTUs.
- Pick reference OTUs on all failures using the
representative set from step 4 as the reference set.
"""
# for now only allowing uclust for otu picking
allowed_denovo_otu_picking_methods = ['uclust','usearch61']
allowed_reference_otu_picking_methods = ['uclust_ref','usearch61_ref']
assert denovo_otu_picking_method in allowed_denovo_otu_picking_methods,\
"Unknown de novo OTU picking method: %s. Known methods are: %s"\
% (denovo_otu_picking_method,
','.join(allowed_denovo_otu_picking_methods))
assert reference_otu_picking_method in allowed_reference_otu_picking_methods,\
"Unknown reference OTU picking method: %s. Known methods are: %s"\
% (reference_otu_picking_method,
','.join(allowed_reference_otu_picking_methods))
# Prepare some variables for the later steps
input_dir, input_filename = split(input_fp)
input_basename, input_ext = splitext(input_filename)
create_dir(output_dir)
commands = []
if logger == None:
logger = WorkflowLogger(generate_log_fp(output_dir),
params=params,
qiime_config=qiime_config)
close_logger_on_success = True
else:
close_logger_on_success = False
if not suppress_md5:
log_input_md5s(logger,[input_fp,
refseqs_fp,
step1_otu_map_fp,
step1_failures_fasta_fp])
# if the user has not passed a different reference collection for the pre-filter,
# used the main refseqs_fp. this is useful if the user wants to provide a smaller
# reference collection, or to use the input reference collection when running in
# iterative mode (rather than an iteration's new refseqs)
if prefilter_refseqs_fp == None:
prefilter_refseqs_fp = refseqs_fp
## Step 1: Closed-reference OTU picking on the input file (if not already complete)
if step1_otu_map_fp and step1_failures_fasta_fp:
step1_dir = '%s/step1_otus' % output_dir
create_dir(step1_dir)
logger.write("Using pre-existing reference otu map and failures.\n\n")
else:
if prefilter_percent_id != None:
prefilter_dir = '%s/prefilter_otus/' % output_dir
prefilter_failures_list_fp = '%s/%s_failures.txt' % \
(prefilter_dir,input_basename)
prefilter_pick_otu_cmd = pick_reference_otus(\
input_fp,prefilter_dir,reference_otu_picking_method,
prefilter_refseqs_fp,parallel,params,logger,prefilter_percent_id)
commands.append([('Pick Reference OTUs (prefilter)', prefilter_pick_otu_cmd)])
prefiltered_input_fp = '%s/prefiltered_%s%s' %\
(prefilter_dir,input_basename,input_ext)
filter_fasta_cmd = 'filter_fasta.py -f %s -o %s -s %s -n' %\
(input_fp,prefiltered_input_fp,prefilter_failures_list_fp)
commands.append([('Filter prefilter failures from input', filter_fasta_cmd)])
input_fp = prefiltered_input_fp
input_dir, input_filename = split(input_fp)
input_basename, input_ext = splitext(input_filename)
## Build the OTU picking command
step1_dir = \
'%s/step1_otus' % output_dir
step1_otu_map_fp = \
'%s/%s_otus.txt' % (step1_dir,input_basename)
step1_pick_otu_cmd = pick_reference_otus(\
input_fp,step1_dir,reference_otu_picking_method,
refseqs_fp,parallel,params,logger)
commands.append([('Pick Reference OTUs', step1_pick_otu_cmd)])
## Build the failures fasta file
step1_failures_list_fp = '%s/%s_failures.txt' % \
(step1_dir,input_basename)
step1_failures_fasta_fp = \
'%s/failures.fasta' % step1_dir
step1_filter_fasta_cmd = 'filter_fasta.py -f %s -s %s -o %s' %\
(input_fp,step1_failures_list_fp,step1_failures_fasta_fp)
commands.append([('Generate full failures fasta file',
step1_filter_fasta_cmd)])
# Call the command handler on the list of commands
command_handler(commands,
status_update_callback,
logger=logger,
close_logger_on_success=False)
commands = []
step1_repset_fasta_fp = \
'%s/step1_rep_set.fna' % step1_dir
step1_pick_rep_set_cmd = 'pick_rep_set.py -i %s -o %s -f %s' %\
(step1_otu_map_fp, step1_repset_fasta_fp, input_fp)
commands.append([('Pick rep set',step1_pick_rep_set_cmd)])
## Subsample the failures fasta file to retain (roughly) the
## percent_subsample
step2_input_fasta_fp = \
'%s/subsampled_failures.fasta' % step1_dir
subsample_fasta(step1_failures_fasta_fp,
step2_input_fasta_fp,
percent_subsample)
## Prep the OTU picking command for the subsampled failures
step2_dir = '%s/step2_otus/' % output_dir
step2_cmd = pick_denovo_otus(step2_input_fasta_fp,
step2_dir,
new_ref_set_id,
denovo_otu_picking_method,
params,
logger)
step2_otu_map_fp = '%s/subsampled_failures_otus.txt' % step2_dir
commands.append([('Pick de novo OTUs for new clusters', step2_cmd)])
## Prep the rep set picking command for the subsampled failures
step2_repset_fasta_fp = '%s/step2_rep_set.fna' % step2_dir
step2_rep_set_cmd = 'pick_rep_set.py -i %s -o %s -f %s' %\
(step2_otu_map_fp,step2_repset_fasta_fp,step2_input_fasta_fp)
commands.append([('Pick representative set for subsampled failures',step2_rep_set_cmd)])
step3_dir = '%s/step3_otus/' % output_dir
step3_otu_map_fp = '%s/failures_otus.txt' % step3_dir
step3_failures_list_fp = '%s/failures_failures.txt' % step3_dir
step3_cmd = pick_reference_otus(
step1_failures_fasta_fp,
step3_dir,
reference_otu_picking_method,
step2_repset_fasta_fp,
parallel,
params,
logger)
commands.append([
('Pick reference OTUs using de novo rep set',step3_cmd)])
# name the final otu map
merged_otu_map_fp = '%s/final_otu_map.txt' % output_dir
if not suppress_step4:
step3_failures_fasta_fp = '%s/failures_failures.fasta' % step3_dir
step3_filter_fasta_cmd = 'filter_fasta.py -f %s -s %s -o %s' %\
(step1_failures_fasta_fp,step3_failures_list_fp,step3_failures_fasta_fp)
commands.append([('Create fasta file of step3 failures',
step3_filter_fasta_cmd)])
step4_dir = '%s/step4_otus/' % output_dir
step4_cmd = pick_denovo_otus(step3_failures_fasta_fp,
step4_dir,
'.'.join([new_ref_set_id,'CleanUp']),
denovo_otu_picking_method,
params,
logger)
step4_otu_map_fp = '%s/failures_failures_otus.txt' % step4_dir
commands.append([('Pick de novo OTUs on step3 failures', step4_cmd)])
# Merge the otu maps
cat_otu_tables_cmd = 'cat %s %s %s >> %s' %\
(step1_otu_map_fp,step3_otu_map_fp,step4_otu_map_fp,merged_otu_map_fp)
commands.append([('Merge OTU maps',cat_otu_tables_cmd)])
step4_repset_fasta_fp = '%s/step4_rep_set.fna' % step4_dir
step4_rep_set_cmd = 'pick_rep_set.py -i %s -o %s -f %s' %\
(step4_otu_map_fp,step4_repset_fasta_fp,step3_failures_fasta_fp)
commands.append([('Pick representative set for subsampled failures',step4_rep_set_cmd)])
else:
# Merge the otu maps
cat_otu_tables_cmd = 'cat %s %s >> %s' %\
(step1_otu_map_fp,step3_otu_map_fp,merged_otu_map_fp)
commands.append([('Merge OTU maps',cat_otu_tables_cmd)])
# Move the step 3 failures file to the top-level directory
commands.append([('Move final failures file to top-level directory',
'mv %s %s/final_failures.txt' % (step3_failures_list_fp,output_dir))])
command_handler(commands,
status_update_callback,
logger=logger,
close_logger_on_success=False)
commands = []
otu_fp = merged_otu_map_fp
# Filter singletons from the otu map
otu_no_singletons_fp = '%s/final_otu_map_mc%d.txt' % (output_dir,min_otu_size)
otus_to_keep = filter_otus_from_otu_map(otu_fp,otu_no_singletons_fp,min_otu_size)
## make the final representative seqs file and a new refseqs file that
## could be used in subsequent otu picking runs.
## this is clunky. first, we need to do this without singletons to match
## the otu map without singletons. next, there is a difference in what
## we need the reference set to be and what we need the repseqs to be.
## the reference set needs to be a superset of the input reference set
## to this set. the repset needs to be only the sequences that were observed
## in this data set, and we want reps for the step1 reference otus to be
## reads from this run so we don't hit issues building a tree using
## sequences of very different lengths. so...
final_repset_fp = '%s/rep_set.fna' % output_dir
final_repset_f = open(final_repset_fp,'w')
new_refseqs_fp = '%s/new_refseqs.fna' % output_dir
# write non-singleton otus representative sequences from step1 to the
# final rep set file
for otu_id, seq in MinimalFastaParser(open(step1_repset_fasta_fp,'U')):
if otu_id.split()[0] in otus_to_keep:
final_repset_f.write('>%s\n%s\n' % (otu_id,seq))
# copy the full input refseqs file to the new refseqs_fp
copy(refseqs_fp,new_refseqs_fp)
new_refseqs_f = open(new_refseqs_fp,'a')
new_refseqs_f.write('\n')
# iterate over all representative sequences from step2 and step4 and write
# those corresponding to non-singleton otus to the final representative set
# file and the new reference sequences file.
for otu_id, seq in MinimalFastaParser(open(step2_repset_fasta_fp,'U')):
if otu_id.split()[0] in otus_to_keep:
new_refseqs_f.write('>%s\n%s\n' % (otu_id,seq))
final_repset_f.write('>%s\n%s\n' % (otu_id,seq))
if not suppress_step4:
for otu_id, seq in MinimalFastaParser(open(step4_repset_fasta_fp,'U')):
if otu_id.split()[0] in otus_to_keep:
new_refseqs_f.write('>%s\n%s\n' % (otu_id,seq))
final_repset_f.write('>%s\n%s\n' % (otu_id,seq))
new_refseqs_f.close()
final_repset_f.close()
# Prep the make_otu_table.py command
otu_table_fp = '%s/otu_table_mc%d.biom' % (output_dir,min_otu_size)
make_otu_table_cmd = 'make_otu_table.py -i %s -o %s' %\
(otu_no_singletons_fp,otu_table_fp)
commands.append([("Make the otu table",make_otu_table_cmd)])
command_handler(commands,
status_update_callback,
logger=logger,
close_logger_on_success=False)
commands = []
# initialize output file names - these differ based on what combination of
# taxonomy assignment and alignment/tree building is happening.
if run_assign_tax and run_align_and_tree:
tax_input_otu_table_fp = otu_table_fp
otu_table_w_tax_fp = \
'%s/otu_table_mc%d_w_tax.biom' % (output_dir,min_otu_size)
align_and_tree_input_otu_table = otu_table_w_tax_fp
pynast_failure_filtered_otu_table_fp = \
'%s/otu_table_mc%d_w_tax_no_pynast_failures.biom' % (output_dir,min_otu_size)
elif run_assign_tax:
tax_input_otu_table_fp = otu_table_fp
otu_table_w_tax_fp = \
'%s/otu_table_mc%d_w_tax.biom' % (output_dir,min_otu_size)
elif run_align_and_tree:
align_and_tree_input_otu_table = otu_table_fp
pynast_failure_filtered_otu_table_fp = \
'%s/otu_table_mc%d_no_pynast_failures.biom' % (output_dir,min_otu_size)
if run_assign_tax:
if exists(otu_table_w_tax_fp) and getsize(otu_table_w_tax_fp) > 0:
logger.write("Final output file exists (%s). Will not rebuild." % otu_table_w_tax_fp)
else:
# remove files from partially completed runs
remove_files([otu_table_w_tax_fp],error_on_missing=False)
taxonomy_fp = assign_tax(
repset_fasta_fp=final_repset_fp,
output_dir=output_dir,
command_handler=command_handler,
params=params,
qiime_config=qiime_config,
parallel=parallel,
logger=logger,
status_update_callback=status_update_callback)
# Add taxa to otu table
add_metadata_cmd = 'biom add-metadata -i %s --observation-metadata-fp %s -o %s --sc-separated taxonomy --observation-header OTUID,taxonomy' %\
(tax_input_otu_table_fp,taxonomy_fp,otu_table_w_tax_fp)
commands.append([("Add taxa to OTU table",add_metadata_cmd)])
command_handler(commands,
status_update_callback,
logger=logger,
close_logger_on_success=False)
commands = []
if run_align_and_tree:
if exists(pynast_failure_filtered_otu_table_fp) and\
getsize(pynast_failure_filtered_otu_table_fp) > 0:
logger.write("Final output file exists (%s). Will not rebuild." %\
pynast_failure_filtered_otu_table_fp)
else:
# remove files from partially completed runs
remove_files([pynast_failure_filtered_otu_table_fp],
error_on_missing=False)
pynast_failures_fp = align_and_tree(
repset_fasta_fp=final_repset_fp,
output_dir=output_dir,
command_handler=command_handler,
params=params,
qiime_config=qiime_config,
parallel=parallel,
logger=logger,
status_update_callback=status_update_callback)
# Build OTU table without PyNAST failures
filtered_otu_table = filter_otus_from_otu_table(
parse_biom_table(open(align_and_tree_input_otu_table,'U')),
get_seq_ids_from_fasta_file(open(pynast_failures_fp,'U')),
0,inf,0,inf,negate_ids_to_keep=True)
otu_table_f = open(pynast_failure_filtered_otu_table_fp,'w')
otu_table_f.write(format_biom_table(filtered_otu_table))
otu_table_f.close()
command_handler(commands,
status_update_callback,
logger=logger,
close_logger_on_success=False)
commands = []
if close_logger_on_success:
logger.close()
def iterative_pick_subsampled_open_reference_otus(
input_fps,
refseqs_fp,
output_dir,
percent_subsample,
new_ref_set_id,
command_handler,
params,
qiime_config,
prefilter_refseqs_fp=None,
prefilter_percent_id=0.60,
min_otu_size=2,
run_assign_tax=True,
run_align_and_tree=True,
step1_otu_map_fp=None,
step1_failures_fasta_fp=None,
parallel=False,
suppress_step4=False,
logger=None,
suppress_md5=False,
denovo_otu_picking_method='uclust',
reference_otu_picking_method='uclust_ref',
status_update_callback=print_to_stdout):
""" Call the pick_subsampled_open_reference_otus workflow on multiple inputs
and handle processing of the results.
"""
create_dir(output_dir)
commands = []
if logger == None:
logger = WorkflowLogger(generate_log_fp(output_dir),
params=params,
qiime_config=qiime_config)
close_logger_on_success = True
else:
close_logger_on_success = False
# if the user has not passed a different reference collection for the pre-filter,
# used the input refseqs_fp for all iterations. we want to pre-filter all data against
# the input data as lower percent identity searches with uclust can be slow, so we
# want the reference collection to stay at a reasonable size.
if prefilter_refseqs_fp == None:
prefilter_refseqs_fp = refseqs_fp
otu_table_fps = []
repset_fasta_fps = []
for i,input_fp in enumerate(input_fps):
iteration_output_dir = '%s/%d/' % (output_dir,i)
if iteration_output_exists(iteration_output_dir,min_otu_size):
# if the output from an iteration already exists, skip that
# iteration (useful for continuing failed runs)
log_input_md5s(logger,[input_fp,refseqs_fp])
logger.write('Iteration %d (input file: %s) output data already exists. '
'Skipping and moving to next.\n\n' % (i,input_fp))
else:
pick_subsampled_open_reference_otus(input_fp=input_fp,
refseqs_fp=refseqs_fp,
output_dir=iteration_output_dir,
percent_subsample=percent_subsample,
new_ref_set_id='.'.join([new_ref_set_id,str(i)]),
command_handler=command_handler,
params=params,
qiime_config=qiime_config,
run_assign_tax=False,
run_align_and_tree=False,
prefilter_refseqs_fp=prefilter_refseqs_fp,
prefilter_percent_id=prefilter_percent_id,
min_otu_size=min_otu_size,
step1_otu_map_fp=step1_otu_map_fp,
step1_failures_fasta_fp=step1_failures_fasta_fp,
parallel=parallel,
suppress_step4=suppress_step4,
logger=logger,
suppress_md5=suppress_md5,
denovo_otu_picking_method=denovo_otu_picking_method,
reference_otu_picking_method=reference_otu_picking_method,
status_update_callback=status_update_callback)
## perform post-iteration file shuffling whether the previous iteration's
## data previously existed or was just computed.
# step1 otu map and failures can only be used for the first iteration
# as subsequent iterations need to use updated refseqs files
step1_otu_map_fp = step1_failures_fasta_fp = None
new_refseqs_fp = '%s/new_refseqs.fna' % iteration_output_dir
refseqs_fp = new_refseqs_fp
otu_table_fps.append('%s/otu_table_mc%d.biom' % (iteration_output_dir,min_otu_size))
repset_fasta_fps.append('%s/rep_set.fna' % iteration_output_dir)
# Merge OTU tables - check for existence first as this step has historically
# been a frequent failure, so is sometimes run manually in failed runs.
otu_table_fp = '%s/otu_table_mc%d.biom' % (output_dir,min_otu_size)
if not (exists(otu_table_fp) and getsize(otu_table_fp) > 0):
merge_cmd = 'merge_otu_tables.py -i %s -o %s' %\
(','.join(otu_table_fps),otu_table_fp)
commands.append([("Merge OTU tables",merge_cmd)])
# Build master rep set
final_repset_fp = '%s/rep_set.fna' % output_dir
final_repset_from_iteration_repsets_fps(repset_fasta_fps,final_repset_fp)
command_handler(commands,
status_update_callback,
logger=logger,
close_logger_on_success=False)
commands = []
# initialize output file names - these differ based on what combination of
# taxonomy assignment and alignment/tree building is happening.
if run_assign_tax and run_align_and_tree:
tax_input_otu_table_fp = otu_table_fp
otu_table_w_tax_fp = \
'%s/otu_table_mc%d_w_tax.biom' % (output_dir,min_otu_size)
align_and_tree_input_otu_table = otu_table_w_tax_fp
pynast_failure_filtered_otu_table_fp = \
'%s/otu_table_mc%d_w_tax_no_pynast_failures.biom' % (output_dir,min_otu_size)
elif run_assign_tax:
tax_input_otu_table_fp = otu_table_fp
otu_table_w_tax_fp = \
'%s/otu_table_mc%d_w_tax.biom' % (output_dir,min_otu_size)
elif run_align_and_tree:
align_and_tree_input_otu_table = otu_table_fp
pynast_failure_filtered_otu_table_fp = \
'%s/otu_table_mc%d_no_pynast_failures.biom' % (output_dir,min_otu_size)
if run_assign_tax:
if exists(otu_table_w_tax_fp) and getsize(otu_table_w_tax_fp) > 0:
logger.write("Final output file exists (%s). Will not rebuild." % otu_table_w_tax_fp)
else:
# remove files from partially completed runs
remove_files([otu_table_w_tax_fp],error_on_missing=False)
taxonomy_fp = assign_tax(
repset_fasta_fp=final_repset_fp,
output_dir=output_dir,
command_handler=command_handler,
params=params,
qiime_config=qiime_config,
parallel=parallel,
logger=logger,
status_update_callback=status_update_callback)
# Add taxa to otu table
add_metadata_cmd = 'biom add-metadata -i %s --observation-metadata-fp %s -o %s --sc-separated taxonomy --observation-header OTUID,taxonomy' %\
(tax_input_otu_table_fp,taxonomy_fp,otu_table_w_tax_fp)
commands.append([("Add taxa to OTU table",add_metadata_cmd)])
command_handler(commands,
status_update_callback,
logger=logger,
close_logger_on_success=False)
commands = []
if run_align_and_tree:
if exists(pynast_failure_filtered_otu_table_fp) and\
getsize(pynast_failure_filtered_otu_table_fp) > 0:
logger.write("Final output file exists (%s). Will not rebuild." %\
pynast_failure_filtered_otu_table_fp)
else:
# remove files from partially completed runs
remove_files([pynast_failure_filtered_otu_table_fp],
error_on_missing=False)
pynast_failures_fp = align_and_tree(
repset_fasta_fp=final_repset_fp,
output_dir=output_dir,
command_handler=command_handler,
params=params,
qiime_config=qiime_config,
parallel=parallel,
logger=logger,
status_update_callback=status_update_callback)
# Build OTU table without PyNAST failures
filtered_otu_table = filter_otus_from_otu_table(
parse_biom_table(open(align_and_tree_input_otu_table,'U')),
get_seq_ids_from_fasta_file(open(pynast_failures_fp,'U')),
0,inf,0,inf,negate_ids_to_keep=True)
otu_table_f = open(pynast_failure_filtered_otu_table_fp,'w')
otu_table_f.write(format_biom_table(filtered_otu_table))
otu_table_f.close()
command_handler(commands,
status_update_callback,
logger=logger,
close_logger_on_success=False)
commands = []
logger.close()
def iterative_pick_subsampled_open_referenence_otus(
input_fps,
refseqs_fp,
output_dir,
percent_subsample,
new_ref_set_id,
command_handler,
params,
qiime_config,
prefilter_refseqs_fp=None,
prefilter_percent_id=0.60,
min_otu_size=2,
run_tax_align_tree=True,
step1_otu_map_fp=None,
step1_failures_fasta_fp=None,
parallel=False,
suppress_step4=False,
logger=None,
status_update_callback=print_to_stdout):
""" Call the pick_subsampled_open_referenence_otus workflow on multiple inputs
and handle processing of the results.
"""
create_dir(output_dir)
commands = []
if logger == None:
logger = WorkflowLogger(generate_log_fp(output_dir),
params=params,
qiime_config=qiime_config)
close_logger_on_success = True
else:
close_logger_on_success = False
# if the user has not passed a different reference collection for the pre-filter,
# used the input refseqs_fp for all iterations. we want to pre-filter all data against
# the input data as lower percent identity searches with uclust can be slow, so we
# want the reference collection to stay at a reasonable size.
if prefilter_refseqs_fp == None:
prefilter_refseqs_fp = refseqs_fp
otu_table_fps = []
repset_fasta_fps = []
for i, input_fp in enumerate(input_fps):
iteration_output_dir = '%s/%d/' % (output_dir, i)
if iteration_output_exists(iteration_output_dir, min_otu_size):
# if the output from an iteration already exists, skip that
# iteration (useful for continuing failed runs)
log_input_md5s(logger, [input_fp, refseqs_fp])
logger.write(
'Iteration %d (input file: %s) output data already exists. '
'Skipping and moving to next.\n\n' % (i, input_fp))
else:
pick_subsampled_open_referenence_otus(
input_fp=input_fp,
refseqs_fp=refseqs_fp,
output_dir=iteration_output_dir,
percent_subsample=percent_subsample,
new_ref_set_id='.'.join([new_ref_set_id,
str(i)]),
command_handler=command_handler,
params=params,
qiime_config=qiime_config,
run_tax_align_tree=False,
prefilter_refseqs_fp=prefilter_refseqs_fp,
prefilter_percent_id=prefilter_percent_id,
min_otu_size=min_otu_size,
step1_otu_map_fp=step1_otu_map_fp,
step1_failures_fasta_fp=step1_failures_fasta_fp,
parallel=parallel,
suppress_step4=suppress_step4,
logger=logger,
status_update_callback=status_update_callback)
## perform post-iteration file shuffling whether the previous iteration's
## data previously existed or was just computed.
# step1 otu map and failures can only be used for the first iteration
# as subsequent iterations need to use updated refseqs files
step1_otu_map_fp = step1_failures_fasta_fp = None
new_refseqs_fp = '%s/new_refseqs.fna' % iteration_output_dir
refseqs_fp = new_refseqs_fp
otu_table_fps.append('%s/otu_table_mc%d.biom' %
(iteration_output_dir, min_otu_size))
repset_fasta_fps.append('%s/rep_set.fna' % iteration_output_dir)
# Merge OTU tables - check for existence first as this step has historically
# been a frequent failure, so is sometimes run manually in failed runs.
otu_table_fp = '%s/otu_table_mc%d.biom' % (output_dir, min_otu_size)
if not (exists(otu_table_fp) and getsize(otu_table_fp) > 0):
merge_cmd = 'merge_otu_tables.py -i %s -o %s' %\
(','.join(otu_table_fps),otu_table_fp)
commands.append([("Merge OTU tables", merge_cmd)])
# Build master rep set
final_repset_fp = '%s/rep_set.fna' % output_dir
final_repset_from_iteration_repsets_fps(repset_fasta_fps, final_repset_fp)
command_handler(commands,
status_update_callback,
logger=logger,
close_logger_on_success=False)
commands = []
if run_tax_align_tree:
otu_table_w_tax_fp = \
'%s/otu_table_mc%d_w_tax.biom' % (output_dir,min_otu_size)
final_otu_table_fp = \
'%s/otu_table_mc%d_w_tax_no_pynast_failures.biom' % (output_dir,min_otu_size)
if exists(final_otu_table_fp) and getsize(final_otu_table_fp) > 0:
logger.write("Final output file exists (%s). Will not rebuild." %
otu_table_fp)
else:
# remove files from partially completed runs
remove_files([otu_table_w_tax_fp, final_otu_table_fp],
error_on_missing=False)
taxonomy_fp, pynast_failures_fp = tax_align_tree(
repset_fasta_fp=final_repset_fp,
output_dir=output_dir,
command_handler=command_handler,
params=params,
qiime_config=qiime_config,
parallel=parallel,
logger=logger,
status_update_callback=status_update_callback)
# Add taxa to otu table
add_taxa_cmd = 'add_taxa.py -i %s -t %s -o %s' %\
(otu_table_fp,taxonomy_fp,otu_table_w_tax_fp)
commands.append([("Add taxa to OTU table", add_taxa_cmd)])
command_handler(commands,
status_update_callback,
logger=logger,
close_logger_on_success=False)
commands = []
# Build OTU table without PyNAST failures
filtered_otu_table = filter_otus_from_otu_table(
parse_biom_table(open(otu_table_w_tax_fp, 'U')),
get_seq_ids_from_fasta_file(open(pynast_failures_fp, 'U')),
0,
inf,
0,
inf,
negate_ids_to_keep=True)
otu_table_f = open(final_otu_table_fp, 'w')
otu_table_f.write(format_biom_table(filtered_otu_table))
otu_table_f.close()
command_handler(commands,
status_update_callback,
logger=logger,
close_logger_on_success=False)
commands = []
logger.close()
def pick_subsampled_open_referenence_otus(
input_fp,
refseqs_fp,
output_dir,
percent_subsample,
new_ref_set_id,
command_handler,
params,
qiime_config,
prefilter_refseqs_fp=None,
run_tax_align_tree=True,
prefilter_percent_id=0.60,
min_otu_size=2,
step1_otu_map_fp=None,
step1_failures_fasta_fp=None,
parallel=False,
suppress_step4=False,
logger=None,
status_update_callback=print_to_stdout):
""" Run the data preparation steps of Qiime
The steps performed by this function are:
- Pick reference OTUs against refseqs_fp
- Subsample the failures to n sequences.
- Pick OTUs de novo on the n failures.
- Pick representative sequences for the resulting OTUs.
- Pick reference OTUs on all failures using the
representative set from step 4 as the reference set.
"""
# for now only allowing uclust for otu picking
denovo_otu_picking_method = 'uclust'
reference_otu_picking_method = 'uclust_ref'
# Prepare some variables for the later steps
input_dir, input_filename = split(input_fp)
input_basename, input_ext = splitext(input_filename)
create_dir(output_dir)
commands = []
python_exe_fp = qiime_config['python_exe_fp']
script_dir = get_qiime_scripts_dir()
if logger == None:
logger = WorkflowLogger(generate_log_fp(output_dir),
params=params,
qiime_config=qiime_config)
close_logger_on_success = True
else:
close_logger_on_success = False
log_input_md5s(
logger,
[input_fp, refseqs_fp, step1_otu_map_fp, step1_failures_fasta_fp])
# if the user has not passed a different reference collection for the pre-filter,
# used the main refseqs_fp. this is useful if the user wants to provide a smaller
# reference collection, or to use the input reference collection when running in
# iterative mode (rather than an iteration's new refseqs)
if prefilter_refseqs_fp == None:
prefilter_refseqs_fp = refseqs_fp
## Step 1: Closed-reference OTU picking on the input file (if not already complete)
if step1_otu_map_fp and step1_failures_fasta_fp:
step1_dir = '%s/step1_otus' % output_dir
create_dir(step1_dir)
logger.write("Using pre-existing reference otu map and failures.\n\n")
else:
if prefilter_percent_id != None:
prefilter_dir = '%s/prefilter_otus/' % output_dir
prefilter_otu_map_fp = \
'%s/%s_otus.txt' % (prefilter_dir,input_basename)
prefilter_failures_list_fp = '%s/%s_failures.txt' % \
(prefilter_dir,input_basename)
prefilter_pick_otu_cmd = pick_reference_otus(\
input_fp,prefilter_dir,reference_otu_picking_method,
prefilter_refseqs_fp,parallel,params,logger,prefilter_percent_id)
commands.append([('Pick Reference OTUs (prefilter)',
prefilter_pick_otu_cmd)])
prefiltered_input_fp = '%s/prefiltered_%s%s' %\
(prefilter_dir,input_basename,input_ext)
filter_fasta_cmd = 'filter_fasta.py -f %s -o %s -s %s -n' %\
(input_fp,prefiltered_input_fp,prefilter_failures_list_fp)
commands.append([('Filter prefilter failures from input',
filter_fasta_cmd)])
input_fp = prefiltered_input_fp
input_dir, input_filename = split(input_fp)
input_basename, input_ext = splitext(input_filename)
## Build the OTU picking command
step1_dir = \
'%s/step1_otus' % output_dir
step1_otu_map_fp = \
'%s/%s_otus.txt' % (step1_dir,input_basename)
step1_pick_otu_cmd = pick_reference_otus(\
input_fp,step1_dir,reference_otu_picking_method,
refseqs_fp,parallel,params,logger)
commands.append([('Pick Reference OTUs', step1_pick_otu_cmd)])
## Build the failures fasta file
step1_failures_list_fp = '%s/%s_failures.txt' % \
(step1_dir,input_basename)
step1_failures_fasta_fp = \
'%s/failures.fasta' % step1_dir
step1_filter_fasta_cmd = 'filter_fasta.py -f %s -s %s -o %s' %\
(input_fp,step1_failures_list_fp,step1_failures_fasta_fp)
commands.append([('Generate full failures fasta file',
step1_filter_fasta_cmd)])
# Call the command handler on the list of commands
command_handler(commands,
status_update_callback,
logger=logger,
close_logger_on_success=False)
commands = []
step1_repset_fasta_fp = \
'%s/step1_rep_set.fna' % step1_dir
step1_pick_rep_set_cmd = 'pick_rep_set.py -i %s -o %s -f %s' %\
(step1_otu_map_fp, step1_repset_fasta_fp, input_fp)
commands.append([('Pick rep set', step1_pick_rep_set_cmd)])
## Subsample the failures fasta file to retain (roughly) the
## percent_subsample
step2_input_fasta_fp = \
'%s/subsampled_failures.fasta' % step1_dir
subsample_fasta(step1_failures_fasta_fp, step2_input_fasta_fp,
percent_subsample)
## Prep the OTU picking command for the subsampled failures
step2_dir = '%s/step2_otus/' % output_dir
step2_cmd = pick_denovo_otus(step2_input_fasta_fp, step2_dir,
new_ref_set_id, denovo_otu_picking_method,
params, logger)
step2_otu_map_fp = '%s/subsampled_failures_otus.txt' % step2_dir
commands.append([('Pick de novo OTUs for new clusters', step2_cmd)])
## Prep the rep set picking command for the subsampled failures
step2_repset_fasta_fp = '%s/step2_rep_set.fna' % step2_dir
step2_rep_set_cmd = 'pick_rep_set.py -i %s -o %s -f %s' %\
(step2_otu_map_fp,step2_repset_fasta_fp,step2_input_fasta_fp)
commands.append([('Pick representative set for subsampled failures',
step2_rep_set_cmd)])
step3_dir = '%s/step3_otus/' % output_dir
step3_otu_map_fp = '%s/failures_otus.txt' % step3_dir
step3_failures_list_fp = '%s/failures_failures.txt' % step3_dir
step3_cmd = pick_reference_otus(step1_failures_fasta_fp, step3_dir,
reference_otu_picking_method,
step2_repset_fasta_fp, parallel, params,
logger)
commands.append([('Pick reference OTUs using de novo rep set', step3_cmd)])
# name the final otu map
merged_otu_map_fp = '%s/final_otu_map.txt' % output_dir
if not suppress_step4:
step3_failures_fasta_fp = '%s/failures_failures.fasta' % step3_dir
step3_filter_fasta_cmd = 'filter_fasta.py -f %s -s %s -o %s' %\
(step1_failures_fasta_fp,step3_failures_list_fp,step3_failures_fasta_fp)
commands.append([('Create fasta file of step3 failures',
step3_filter_fasta_cmd)])
step4_dir = '%s/step4_otus/' % output_dir
step4_cmd = pick_denovo_otus(step3_failures_fasta_fp, step4_dir,
'.'.join([new_ref_set_id, 'CleanUp']),
denovo_otu_picking_method, params, logger)
step4_otu_map_fp = '%s/failures_failures_otus.txt' % step4_dir
commands.append([('Pick de novo OTUs on step3 failures', step4_cmd)])
# Merge the otu maps
cat_otu_tables_cmd = 'cat %s %s %s >> %s' %\
(step1_otu_map_fp,step3_otu_map_fp,step4_otu_map_fp,merged_otu_map_fp)
commands.append([('Merge OTU maps', cat_otu_tables_cmd)])
step4_repset_fasta_fp = '%s/step4_rep_set.fna' % step4_dir
step4_rep_set_cmd = 'pick_rep_set.py -i %s -o %s -f %s' %\
(step4_otu_map_fp,step4_repset_fasta_fp,step3_failures_fasta_fp)
commands.append([('Pick representative set for subsampled failures',
step4_rep_set_cmd)])
else:
# Merge the otu maps
cat_otu_tables_cmd = 'cat %s %s >> %s' %\
(step1_otu_map_fp,step3_otu_map_fp,merged_otu_map_fp)
commands.append([('Merge OTU maps', cat_otu_tables_cmd)])
# Move the step 3 failures file to the top-level directory
commands.append([('Move final failures file to top-level directory',
'mv %s %s/final_failures.txt' %
(step3_failures_list_fp, output_dir))])
command_handler(commands,
status_update_callback,
logger=logger,
close_logger_on_success=False)
commands = []
otu_fp = merged_otu_map_fp
# Filter singletons from the otu map
otu_no_singletons_fp = '%s/final_otu_map_mc%d.txt' % (output_dir,
min_otu_size)
otus_to_keep = filter_otus_from_otu_map(otu_fp, otu_no_singletons_fp,
min_otu_size)
## make the final representative seqs file and a new refseqs file that
## could be used in subsequent otu picking runs.
## this is clunky. first, we need to do this without singletons to match
## the otu map without singletons. next, there is a difference in what
## we need the reference set to be and what we need the repseqs to be.
## the reference set needs to be a superset of the input reference set
## to this set. the repset needs to be only the sequences that were observed
## in this data set, and we want reps for the step1 reference otus to be
## reads from this run so we don't hit issues building a tree using
## sequences of very different lengths. so...
final_repset_fp = '%s/rep_set.fna' % output_dir
final_repset_f = open(final_repset_fp, 'w')
new_refseqs_fp = '%s/new_refseqs.fna' % output_dir
# write non-singleton otus representative sequences from step1 to the
# final rep set file
for otu_id, seq in MinimalFastaParser(open(step1_repset_fasta_fp, 'U')):
if otu_id.split()[0] in otus_to_keep:
final_repset_f.write('>%s\n%s\n' % (otu_id, seq))
# copy the full input refseqs file to the new refseqs_fp
copy(refseqs_fp, new_refseqs_fp)
new_refseqs_f = open(new_refseqs_fp, 'a')
new_refseqs_f.write('\n')
# iterate over all representative sequences from step2 and step4 and write
# those corresponding to non-singleton otus to the final representative set
# file and the new reference sequences file.
for otu_id, seq in MinimalFastaParser(open(step2_repset_fasta_fp, 'U')):
if otu_id.split()[0] in otus_to_keep:
new_refseqs_f.write('>%s\n%s\n' % (otu_id, seq))
final_repset_f.write('>%s\n%s\n' % (otu_id, seq))
if not suppress_step4:
for otu_id, seq in MinimalFastaParser(open(step4_repset_fasta_fp,
'U')):
if otu_id.split()[0] in otus_to_keep:
new_refseqs_f.write('>%s\n%s\n' % (otu_id, seq))
final_repset_f.write('>%s\n%s\n' % (otu_id, seq))
new_refseqs_f.close()
final_repset_f.close()
# Prep the make_otu_table.py command
otu_table_fp = '%s/otu_table_mc%d.biom' % (output_dir, min_otu_size)
make_otu_table_cmd = 'make_otu_table.py -i %s -o %s' %\
(otu_no_singletons_fp,otu_table_fp)
commands.append([("Make the otu table", make_otu_table_cmd)])
command_handler(commands,
status_update_callback,
logger=logger,
close_logger_on_success=False)
commands = []
if run_tax_align_tree:
taxonomy_fp, pynast_failures_fp = tax_align_tree(
repset_fasta_fp=final_repset_fp,
output_dir=output_dir,
command_handler=command_handler,
params=params,
qiime_config=qiime_config,
parallel=parallel,
logger=logger,
status_update_callback=status_update_callback)
# Add taxa to otu table
otu_table_w_tax_fp = \
'%s/otu_table_mc%d_w_tax.biom' % (output_dir,min_otu_size)
add_taxa_cmd = 'add_taxa.py -i %s -t %s -o %s' %\
(otu_table_fp,taxonomy_fp,otu_table_w_tax_fp)
commands.append([("Add taxa to OTU table", add_taxa_cmd)])
command_handler(commands,
status_update_callback,
logger=logger,
close_logger_on_success=False)
commands = []
# Build OTU table without PyNAST failures
otu_table_fp = \
'%s/otu_table_mc%d_w_tax_no_pynast_failures.biom' % (output_dir,min_otu_size)
filtered_otu_table = filter_otus_from_otu_table(
parse_biom_table(open(otu_table_w_tax_fp, 'U')),
get_seq_ids_from_fasta_file(open(pynast_failures_fp, 'U')),
0,
inf,
0,
inf,
negate_ids_to_keep=True)
otu_table_f = open(otu_table_fp, 'w')
otu_table_f.write(format_biom_table(filtered_otu_table))
otu_table_f.close()
command_handler(commands,
status_update_callback,
logger=logger,
close_logger_on_success=False)
commands = []
command_handler(commands,
status_update_callback,
logger=logger,
close_logger_on_success=close_logger_on_success)
def main():
option_parser, opts, args =\
parse_command_line_parameters(**script_info)
input_fp = opts.input_fp
output_dir = opts.output_dir
if opts.num_fraction_for_core_steps < 2:
option_parser.error("Must perform at least two steps. Increase --num_fraction_for_core_steps.")
fractions_for_core = linspace(opts.min_fraction_for_core,
opts.max_fraction_for_core,
opts.num_fraction_for_core_steps)
otu_md = opts.otu_md
valid_states = opts.valid_states
mapping_fp = opts.mapping_fp
create_dir(output_dir)
if valid_states and opts.mapping_fp:
sample_ids = sample_ids_from_metadata_description(open(mapping_fp,'U'),
valid_states)
if len(sample_ids) < 1:
option_parser.error(\
"--valid_states pattern didn't match any entries in mapping file: \"%s\"" %\
valid_states)
else:
# get core across all samples if user doesn't specify a subset of the
# samples to work with
sample_ids = None
input_table = parse_biom_table(open(input_fp,'U'))
otu_counts = []
summary_figure_fp = join(output_dir,'core_otu_size.pdf')
for fraction_for_core in fractions_for_core:
# build a string representation of the fraction as that gets used
# several times
fraction_for_core_str = "%1.0f" % (fraction_for_core * 100.)
# prep output files
output_fp = join(output_dir,'core_otus_%s.txt' % fraction_for_core_str)
output_table_fp = join(output_dir,'core_table_%s.biom' % fraction_for_core_str)
output_f = open(output_fp,'w')
try:
core_table = filter_table_to_core(input_table,
sample_ids,
fraction_for_core)
except TableException:
output_f.write("# No OTUs present in %s %% of samples." % fraction_for_core_str)
output_f.close()
otu_counts.append(0)
continue
# write some header information to file
if sample_ids == None:
output_f.write("# Core OTUs across %s %% of samples.\n" % fraction_for_core_str)
else:
output_f.write(\
"# Core OTUs across %s %% of samples matching the sample metadata pattern \"%s\":\n# %s\n" %\
(fraction_for_core_str, valid_states,' '.join(sample_ids)))
# write the otu id and corresponding metadata for all core otus
otu_count = 0
for value, id_, md in core_table.iterObservations():
output_f.write('%s\t%s\n' % (id_,md[otu_md]))
otu_count += 1
output_f.close()
# write the core biom table
output_table_f = open(output_table_fp,'w')
output_table_f.write(format_biom_table(core_table))
output_table_f.close()
# append the otu count to the list of counts
otu_counts.append(otu_count)
plot(fractions_for_core, otu_counts)
xlim(min(fractions_for_core),max(fractions_for_core))
ylim(0,max(otu_counts)+1)
xlabel("Fraction of samples that OTU must be observed in to be considered 'core'")
ylabel("Number of OTUs")
savefig(summary_figure_fp)
def setUp(self):
self.qiime_config = load_qiime_config()
self.tmp_dir = self.qiime_config['temp_dir'] or '/tmp/'
self.map_file = """#SampleID Day time Description
#This is some comment about the study
1 090809 1200 some description of sample1
2 090809 1800 some description of sample2
3 090909 1200 some description of sample3
4 090909 1800 some description of sample4
5 091009 1200 some description of sample5"""
self.cat_by_sample = {
"1": [("Day", "090809"), ("time", "1200")],
"2": [("Day", "090809"), ("time", "1800")],
"3": [("Day", "090909"), ("time", "1200")],
"4": [("Day", "090909"), ("time", "1800")],
"5": [("Day", "091009"), ("time", "1200")]
}
self.sample_by_cat = {
("Day", "090809"): ["1", "2"],
("Day", "090909"): ["3", "4"],
("Day", "091009"): ["5"],
("time", "1200"): ["1", "3", "5"],
("time", "1800"): ["2", "4"]
}
self.num_cats = 2
self.meta_dict = {
"1": ["090809 1200", 0],
"2": ["090809 1800", 0],
"3": ["090909 1200", 0],
"4": ["090909 1800", 0],
"5": ["091009 1200", 0]
}
self.labels = ["from", "to", "eweight", "consensus_lin", "Day", "time"]
self.node_labels = [
"node_name", "node_disp_name", "ntype", "degree",
"weighted_degree", "consensus_lin", "Day", "time"
]
self.label_list = [["090809", "090909", "091009"], ["1200", "1800"]]
self.otu_table_vals = array([[0, 1, 0, 0, 6], [2, 0, 0, 0, 0],
[0, 0, 3, 1, 0], [0, 0, 0, 0, 5],
[0, 4, 2, 0, 0], [3, 6, 0, 0, 0],
[0, 0, 4, 2, 0], [0, 0, 0, 0, 3],
[2, 0, 0, 5, 0], [0, 2, 0, 4, 0]])
otu_table_str = format_biom_table(
table_factory(self.otu_table_vals, ['1', '2', '3', '4', '5'], [
'otu_1', 'otu_2', 'otu_3', 'otu_4', 'otu_5', 'otu_6', 'otu_7',
'otu_8', 'otu_9', 'otu_10'
], [None, None, None, None, None], [{
"taxonomy": ["Bacteria", "Actinobacteria", "Coriobacteridae"]
}, {
"taxonomy": [
"Bacteria", "Bacteroidetes", "Bacteroidales",
"Bacteroidaceae"
]
}, {
"taxonomy":
["Bacteria", "Firmicutes", "Clostridia", "Clostridiales"]
}, {
"taxonomy": [
"Bacteria", "Spirochaetes", "Spirochaetales",
"Spirochaetaceae"
]
}, {
"taxonomy": [
"Bacteria", "Bacteroidetes", "Bacteroidales",
"Rikenellaceae"
]
}, {
"taxonomy": [
"Bacteria", "Bacteroidetes", "Bacteroidales",
"Dysgonomonaceae"
]
}, {
"taxonomy": [
"Bacteria", "Bacteroidetes", "Bacteroidales",
"Odoribacteriaceae"
]
}, {
"taxonomy": [
"Bacteria", "Bacteroidetes", "Bacteroidales",
"Dysgonomonaceae", "otu_425"
]
}, {
"taxonomy": [
"Bacteria", "Bacteroidetes", "Bacteroidales",
"Dysgonomonaceae", "otu_425"
]
}, {
"taxonomy": [
"Bacteria", "Firmicutes", "Mollicutes",
"Clostridium_aff_innocuum_CM970"
]
}]))
_, self.otu_table_fp = mkstemp(
dir=self.tmp_dir,
prefix='test_make_otu_network_otu_table',
suffix='.biom')
close(_)
open(self.otu_table_fp, 'w').write(otu_table_str)
self.otu_sample_file = """#Full OTU Counts
#OTU ID 1 2 3 4 5 Consensus Lineage
otu_1 0 1 0 0 6 Bacteria; Actinobacteria; Coriobacteridae
otu_2 2 0 0 0 0 Bacteria; Bacteroidetes; Bacteroidales; Bacteroidaceae
otu_3 0 0 3 1 0 Bacteria; Firmicutes; Clostridia; Clostridiales
otu_4 0 0 0 0 5 Bacteria; Spirochaetes; Spirochaetales; Spirochaetaceae
otu_5 0 4 2 0 0 Bacteria; Bacteroidetes; Bacteroidales; Rikenellaceae
otu_6 3 6 0 0 0 Bacteria; Bacteroidetes; Bacteroidales; Dysgonomonaceae
otu_7 0 0 4 2 0 Bacteria; Bacteroidetes; Bacteroidales; Odoribacteriaceae
otu_8 0 0 0 0 3 Bacteria; Bacteroidetes; Bacteroidales; Dysgonomonaceae; otu_425
otu_9 2 0 0 5 0 Bacteria; Bacteroidetes; Bacteroidales; Dysgonomonaceae; otu_425
otu_10 0 2 0 4 0 Bacteria; Firmicutes; Mollicutes; Clostridium_aff_innocuum_CM970"""
self.con_by_sample = {
'1': set(['2', '4']),
'2': set(['5', '3', '1', '4']),
'3': set(['4', '2']),
'4': set(['3', '1', '2']),
'5': set(['2'])
}
self.edge_file_str = [
"2 otu_1 1.0 Bacteria:Actinobacteria:Coriobacteridae 090809 1800",
"5 otu_1 6.0 Bacteria:Actinobacteria:Coriobacteridae 091009 1200",
"1 otu_2 2.0 Bacteria:Bacteroidetes:Bacteroidales:Bacteroidaceae 090809 1200",
"3 otu_3 3.0 Bacteria:Firmicutes:Clostridia:Clostridiales 090909 1200",
"4 otu_3 1.0 Bacteria:Firmicutes:Clostridia:Clostridiales 090909 1800",
"5 otu_4 5.0 Bacteria:Spirochaetes:Spirochaetales:Spirochaetaceae 091009 1200",
"2 otu_5 4.0 Bacteria:Bacteroidetes:Bacteroidales:Rikenellaceae 090809 1800",
"3 otu_5 2.0 Bacteria:Bacteroidetes:Bacteroidales:Rikenellaceae 090909 1200",
"1 otu_6 3.0 Bacteria:Bacteroidetes:Bacteroidales:Dysgonomonaceae 090809 1200",
"2 otu_6 6.0 Bacteria:Bacteroidetes:Bacteroidales:Dysgonomonaceae 090809 1800",
"3 otu_7 4.0 Bacteria:Bacteroidetes:Bacteroidales:Odoribacteriaceae 090909 1200",
"4 otu_7 2.0 Bacteria:Bacteroidetes:Bacteroidales:Odoribacteriaceae 090909 1800",
"5 otu_8 3.0 Bacteria:Bacteroidetes:Bacteroidales:Dysgonomonaceae:otu_425 091009 1200",
"1 otu_9 2.0 Bacteria:Bacteroidetes:Bacteroidales:Dysgonomonaceae:otu_425 090809 1200",
"4 otu_9 5.0 Bacteria:Bacteroidetes:Bacteroidales:Dysgonomonaceae:otu_425 090909 1800",
"2 otu_10 2.0 Bacteria:Firmicutes:Mollicutes:Clostridium_aff_innocuum_CM970 090809 1800",
"4 otu_10 4.0 Bacteria:Firmicutes:Mollicutes:Clostridium_aff_innocuum_CM970 090909 1800"
]
self.node_file_str = [
"1 1 user_node 3 7.0 other 090809 1200",
"2 2 user_node 4 13.0 other 090809 1800",
"3 3 user_node 3 9.0 other 090909 1200",
"4 4 user_node 4 12.0 other 090909 1800",
"5 5 user_node 3 14.0 other 091009 1200",
"otu_1 otu_node 2 7.0 Bacteria:Actinobacteria:Coriobacteridae otu otu",
"otu_2 otu_node 1 2.0 Bacteria:Bacteroidetes:Bacteroidales:Bacteroidaceae otu otu",
"otu_3 otu_node 2 4.0 Bacteria:Firmicutes:Clostridia:Clostridiales otu otu",
"otu_4 otu_node 1 5.0 Bacteria:Spirochaetes:Spirochaetales:Spirochaetaceae otu otu",
"otu_5 otu_node 2 6.0 Bacteria:Bacteroidetes:Bacteroidales:Rikenellaceae otu otu",
"otu_6 otu_node 2 9.0 Bacteria:Bacteroidetes:Bacteroidales:Dysgonomonaceae otu otu",
"otu_7 otu_node 2 6.0 Bacteria:Bacteroidetes:Bacteroidales:Odoribacteriaceae otu otu",
"otu_8 otu_node 1 3.0 Bacteria:Bacteroidetes:Bacteroidales:Dysgonomonaceae:otu_425 otu otu",
"otu_9 otu_node 2 7.0 Bacteria:Bacteroidetes:Bacteroidales:Dysgonomonaceae:otu_425 otu otu",
"otu_10 otu_node 2 6.0 Bacteria:Firmicutes:Mollicutes:Clostridium_aff_innocuum_CM970 otu otu"
]
self.red_edge_file_str = [
"2 otu_1 1.0 Bacteria:Actinobacteria:Coriobacteridae 090809 1800",
"5 otu_1 6.0 Bacteria:Actinobacteria:Coriobacteridae 091009 1200",
"1 @1 1.0 missed 090809 1200",
"3 otu_3 3.0 Bacteria:Firmicutes:Clostridia:Clostridiales 090909 1200",
"4 otu_3 1.0 Bacteria:Firmicutes:Clostridia:Clostridiales 090909 1800",
"5 @5 1.0 missed 091009 1200",
"2 otu_5 4.0 Bacteria:Bacteroidetes:Bacteroidales:Rikenellaceae 090809 1800",
"3 otu_5 2.0 Bacteria:Bacteroidetes:Bacteroidales:Rikenellaceae 090909 1200",
"1 otu_6 3.0 Bacteria:Bacteroidetes:Bacteroidales:Dysgonomonaceae 090809 1200",
"2 otu_6 6.0 Bacteria:Bacteroidetes:Bacteroidales:Dysgonomonaceae 090809 1800",
"3 otu_7 4.0 Bacteria:Bacteroidetes:Bacteroidales:Odoribacteriaceae 090909 1200",
"4 otu_7 2.0 Bacteria:Bacteroidetes:Bacteroidales:Odoribacteriaceae 090909 1800",
"1 otu_9 2.0 Bacteria:Bacteroidetes:Bacteroidales:Dysgonomonaceae:otu_425 090809 1200",
"4 otu_9 5.0 Bacteria:Bacteroidetes:Bacteroidales:Dysgonomonaceae:otu_425 090909 1800",
"2 otu_10 2.0 Bacteria:Firmicutes:Mollicutes:Clostridium_aff_innocuum_CM970 090809 1800",
"4 otu_10 4.0 Bacteria:Firmicutes:Mollicutes:Clostridium_aff_innocuum_CM970 090909 1800"
]
self.red_node_file_str = [
"1 1 user_node 3 7.0 other 090809 1200",
"2 2 user_node 4 13.0 other 090809 1800",
"3 3 user_node 3 9.0 other 090909 1200",
"4 4 user_node 4 12.0 other 090909 1800",
"5 5 user_node 3 14.0 other 091009 1200",
"otu_1 otu_node 2 7.0 Bacteria:Actinobacteria:Coriobacteridae otu otu",
"@1 otu_collapsed 1 1.0 other otu otu",
"otu_3 otu_node 2 4.0 Bacteria:Firmicutes:Clostridia:Clostridiales otu otu",
"@5 otu_collapsed 2 2.0 other otu otu",
"otu_5 otu_node 2 6.0 Bacteria:Bacteroidetes:Bacteroidales:Rikenellaceae otu otu",
"otu_6 otu_node 2 9.0 Bacteria:Bacteroidetes:Bacteroidales:Dysgonomonaceae otu otu",
"otu_7 otu_node 2 6.0 Bacteria:Bacteroidetes:Bacteroidales:Odoribacteriaceae otu otu",
"otu_9 otu_node 2 7.0 Bacteria:Bacteroidetes:Bacteroidales:Dysgonomonaceae:otu_425 otu otu",
"otu_10 otu_node 2 6.0 Bacteria:Firmicutes:Mollicutes:Clostridium_aff_innocuum_CM970 otu otu"
]
self.otu_dc = {1: 3, 2: 7}
self.sample_dc = {3: 3, 4: 2}
self.degree_counts = {1: 3, 2: 7, 3: 3, 4: 2}
self.num_con_cat = {"Day": 2, "time": 1}
self.num_con = 6
self.num_cat = {"Day": 2, "time": 4}
self.num_cat_less = {"Day": 1, "time": 3}
self._paths_to_clean_up = [self.otu_table_fp]
self._dir_to_clean_up = ''
sample_metadata=None,
constructor=SparseOTUTable):
data, sample_ids, otu_ids = parse_otu_map(otu_map_f,delim)
if otu_to_taxonomy != None:
otu_metadata = []
for o in otu_ids:
try:
otu_metadata.append({'taxonomy':otu_to_taxonomy[o].split(';')})
except KeyError:
otu_metadata.append({'taxonomy':["None"]})
else:
otu_metadata = None
if sample_metadata != None:
raise NotImplementedError,\
"Passing of sample metadata to make_otu_table is not currently supported."
try:
otu_table = table_factory(data, sample_ids, otu_ids,
sample_metadata=sample_metadata,
observation_metadata=otu_metadata,
table_id=table_id,
constructor=constructor,
dtype=int)
except ValueError,e:
raise ValueError,\
("Couldn't create OTU table. Is your OTU map empty?"
" Original error message: %s" % (str(e)))
return format_biom_table(otu_table)
def main():
option_parser, opts, args =\
parse_command_line_parameters(**script_info)
input_fp = opts.input_fp
output_fp = opts.output_fp
min_count = opts.min_count
max_count = opts.max_count
min_count_fraction = opts.min_count_fraction
if min_count_fraction < 0. or min_count_fraction > 1.:
option_parser.error("min_count_fraction must be between 0 and 1")
if min_count != 0 and min_count_fraction != 0:
option_parser.error(
"cannot specify both min_count and min_count_fraction")
min_samples = opts.min_samples
max_samples = opts.max_samples
otu_ids_to_exclude_fp = opts.otu_ids_to_exclude_fp
negate_ids_to_exclude = opts.negate_ids_to_exclude
if not (min_count != 0 or \
min_count_fraction != 0 or \
not isinf(max_count) or \
otu_ids_to_exclude_fp != None or \
min_samples !=0 or not isinf(max_samples)):
option_parser.error(
"No filtering requested. Must provide either "
"min counts, max counts, min samples, max samples, min_count_fraction, "
"or exclude_fp (or some combination of those).")
otu_table = parse_biom_table(open(opts.input_fp, 'U'))
if min_count_fraction > 0:
min_count = otu_table.sum() * min_count_fraction
print otu_table.sum(), min_count
output_f = open(opts.output_fp, 'w')
otu_ids_to_keep = set(otu_table.ObservationIds)
if otu_ids_to_exclude_fp:
if otu_ids_to_exclude_fp.endswith('.fasta') or \
otu_ids_to_exclude_fp.endswith('.fna'):
otu_ids_to_exclude = set([
id_.strip().split()[0] for id_, seq in MinimalFastaParser(
open(otu_ids_to_exclude_fp, 'U'))
])
else:
otu_ids_to_exclude = set([
l.strip().split('\t')[0]
for l in open(otu_ids_to_exclude_fp, 'U')
])
otu_ids_to_keep -= otu_ids_to_exclude
filtered_otu_table = filter_otus_from_otu_table(otu_table, otu_ids_to_keep,
min_count, max_count,
min_samples, max_samples,
negate_ids_to_exclude)
output_f.write(format_biom_table(filtered_otu_table))
output_f.close()
def main():
option_parser, opts, args = parse_command_line_parameters(**script_info)
verbose = opts.verbose
output_fp = opts.output_fp
category_mapping_fp = opts.category_mapping_fp
category_mapping = open(category_mapping_fp, 'U')
category_mapping = parse_mapping_file(category_mapping)
individual_column = opts.individual_column
reference_sample_column = opts.reference_sample_column
conv_output_fp = opts.converted_otu_table_output_fp
relative_abundance = opts.relative_abundance
filter = opts.filter
test = opts.test
category = opts.category
if not category:
if test != 'paired_T':
raise ValueError('a category in the category mapping file must be' +\
' specified with the -c option for this test')
threshold = opts.threshold
if threshold and threshold != 'None':
threshold = float(threshold)
otu_include_fp = opts.otu_include_fp
if otu_include_fp and otu_include_fp != 'None':
otu_include = open(otu_include_fp)
else:
otu_include = None
otu_table_fp = opts.otu_table_fp
if not isdir(opts.otu_table_fp):
# if single file, process normally
otu_table = open(otu_table_fp, 'U')
try:
otu_table = parse_biom_table(otu_table)
except AttributeError:
otu_table = parse_biom_table_str(otu_table)
#synchronize the mapping file with the otu table
category_mapping, removed_samples = sync_mapping_to_otu_table(otu_table, \
category_mapping)
if removed_samples:
print "Warning, the following samples were in the category mapping file " +\
"but not the OTU table and will be ignored: "
for i in removed_samples:
print i + '\n'
if test == 'longitudinal_correlation' or test == 'paired_T':
converted_otu_table = longitudinal_otu_table_conversion_wrapper(
otu_table, category_mapping, individual_column,
reference_sample_column)
if conv_output_fp:
of = open(conv_output_fp, 'w')
of.write(format_biom_table(converted_otu_table))
of.close()
if test == 'longitudinal_correlation':
#set the otu_include list to all of the OTUs, this effectively
#deactivates the filter for correlation, because the filtered OTU_list is
#rewritten with the otu_include list in the test_wrapper
if not otu_include:
otu_include = set(otu_table.ObservationIds)
output = test_wrapper('correlation', converted_otu_table, \
category_mapping, category, threshold, filter, otu_include, \
999999999.0, True)
elif test == 'paired_T':
output = test_wrapper('paired_T', converted_otu_table, \
category_mapping, category, threshold, \
filter, otu_include, 999999999.0, True, \
individual_column, reference_sample_column)
else:
output = test_wrapper(test, otu_table, category_mapping, \
category, threshold, filter, otu_include, \
otu_table_relative_abundance=relative_abundance)
else:
if test != 'longitudinal_correlation' and test != 'paired_T':
otu_table_paths = glob('%s/*biom' % otu_table_fp)
# if directory, get aggregated results
parsed_otu_tables = []
for path in otu_table_paths:
ot = open(path, 'U')
ot = parse_biom_table(ot)
parsed_otu_tables.append(ot)
#synchronize the mapping file with the otu table
#checks with just the first OTU table and assumes that all otu tables
#have the same collection of samples
category_mapping, removed_samples = sync_mapping_to_otu_table(parsed_otu_tables[0], \
category_mapping)
if removed_samples:
print "Warning, the following samples were in the category mapping file " +\
"but not the OTU table and will be ignored: "
for i in removed_samples:
print i + '\n'
output = test_wrapper_multiple(test, parsed_otu_tables, \
category_mapping, category, threshold, filter, otu_include,\
otu_table_relative_abundance=relative_abundance)
else:
raise ValueError(
"the longitudinal_correlation and paired_T options cannot be run on a directory"
)
of = open(output_fp, 'w')
of.write('\n'.join(output))
of.close()
def main():
option_parser, opts, args =\
parse_command_line_parameters(**script_info)
input_fp = opts.input_fp
output_fp = opts.output_fp
min_count = opts.min_count
max_count = opts.max_count
min_count_fraction = opts.min_count_fraction
if min_count_fraction < 0. or min_count_fraction > 1.:
option_parser.error("min_count_fraction must be between 0 and 1")
if min_count != 0 and min_count_fraction != 0:
option_parser.error(
"cannot specify both min_count and min_count_fraction")
min_samples = opts.min_samples
max_samples = opts.max_samples
otu_ids_to_exclude_fp = opts.otu_ids_to_exclude_fp
negate_ids_to_exclude = opts.negate_ids_to_exclude
if not (min_count != 0 or
min_count_fraction != 0 or
not isinf(max_count) or
otu_ids_to_exclude_fp is not None or
min_samples != 0 or not isinf(max_samples)):
option_parser.error("No filtering requested. Must provide either "
"min counts, max counts, min samples, max samples, min_count_fraction, "
"or exclude_fp (or some combination of those).")
otu_table = parse_biom_table(open(opts.input_fp, 'U'))
if min_count_fraction > 0:
min_count = otu_table.sum() * min_count_fraction
print otu_table.sum(), min_count
output_f = open(opts.output_fp, 'w')
otu_ids_to_keep = set(otu_table.ObservationIds)
if otu_ids_to_exclude_fp:
if otu_ids_to_exclude_fp.endswith('.fasta') or \
otu_ids_to_exclude_fp.endswith('.fna'):
otu_ids_to_exclude = set([id_.strip().split()[0]
for id_, seq in MinimalFastaParser(open(otu_ids_to_exclude_fp, 'U'))])
else:
otu_ids_to_exclude = set([l.strip().split('\t')[0]
for l in open(otu_ids_to_exclude_fp, 'U')])
otu_ids_to_keep -= otu_ids_to_exclude
filtered_otu_table = filter_otus_from_otu_table(otu_table,
otu_ids_to_keep,
min_count,
max_count,
min_samples,
max_samples,
negate_ids_to_exclude)
output_f.write(format_biom_table(filtered_otu_table))
output_f.close()
def setUp(self):
self.qiime_config = load_qiime_config()
self.tmp_dir = self.qiime_config['temp_dir'] or '/tmp/'
self.map_file = """#SampleID Day time Description
#This is some comment about the study
1 090809 1200 some description of sample1
2 090809 1800 some description of sample2
3 090909 1200 some description of sample3
4 090909 1800 some description of sample4
5 091009 1200 some description of sample5"""
self.cat_by_sample = {"1": [("Day", "090809"), ("time", "1200")],
"2": [("Day", "090809"), ("time", "1800")],
"3": [("Day", "090909"), ("time", "1200")],
"4": [("Day", "090909"), ("time", "1800")],
"5": [("Day", "091009"), ("time", "1200")]}
self.sample_by_cat = {("Day", "090809"): ["1", "2"],
("Day", "090909"): ["3", "4"],
("Day", "091009"): ["5"],
("time", "1200"): ["1", "3", "5"],
("time", "1800"): ["2", "4"]}
self.num_cats = 2
self.meta_dict = {"1": ["090809 1200", 0],
"2": ["090809 1800", 0],
"3": ["090909 1200", 0],
"4": ["090909 1800", 0],
"5": ["091009 1200", 0]}
self.labels = ["from", "to", "eweight", "consensus_lin", "Day", "time"]
self.node_labels = ["node_name", "node_disp_name", "ntype", "degree",
"weighted_degree", "consensus_lin", "Day", "time"]
self.label_list = [["090809", "090909", "091009"], ["1200", "1800"]]
self.otu_table_vals = array([[0, 1, 0, 0, 6],
[2, 0, 0, 0, 0],
[0, 0, 3, 1, 0],
[0, 0, 0, 0, 5],
[0, 4, 2, 0, 0],
[3, 6, 0, 0, 0],
[0, 0, 4, 2, 0],
[0, 0, 0, 0, 3],
[2, 0, 0, 5, 0],
[0, 2, 0, 4, 0]])
otu_table_str = format_biom_table(table_factory(self.otu_table_vals,
['1',
'2',
'3',
'4',
'5'],
['otu_1',
'otu_2',
'otu_3',
'otu_4',
'otu_5',
'otu_6',
'otu_7',
'otu_8',
'otu_9',
'otu_10'],
[None,
None,
None,
None,
None],
[{"taxonomy": ["Bacteria", "Actinobacteria", "Coriobacteridae"]},
{"taxonomy": ["Bacteria",
"Bacteroidetes",
"Bacteroidales",
"Bacteroidaceae"]},
{"taxonomy": ["Bacteria",
"Firmicutes",
"Clostridia",
"Clostridiales"]},
{"taxonomy": ["Bacteria",
"Spirochaetes",
"Spirochaetales",
"Spirochaetaceae"]},
{"taxonomy": ["Bacteria",
"Bacteroidetes",
"Bacteroidales",
"Rikenellaceae"]},
{"taxonomy": ["Bacteria",
"Bacteroidetes",
"Bacteroidales",
"Dysgonomonaceae"]},
{"taxonomy": ["Bacteria",
"Bacteroidetes",
"Bacteroidales",
"Odoribacteriaceae"]},
{"taxonomy": ["Bacteria",
"Bacteroidetes",
"Bacteroidales",
"Dysgonomonaceae",
"otu_425"]},
{"taxonomy": ["Bacteria",
"Bacteroidetes",
"Bacteroidales",
"Dysgonomonaceae",
"otu_425"]},
{"taxonomy": ["Bacteria", "Firmicutes", "Mollicutes", "Clostridium_aff_innocuum_CM970"]}]))
self.otu_table_fp = get_tmp_filename(tmp_dir=self.tmp_dir,
prefix='test_make_otu_network_otu_table', suffix='.biom')
open(self.otu_table_fp, 'w').write(otu_table_str)
self.otu_sample_file = """#Full OTU Counts
#OTU ID 1 2 3 4 5 Consensus Lineage
otu_1 0 1 0 0 6 Bacteria; Actinobacteria; Coriobacteridae
otu_2 2 0 0 0 0 Bacteria; Bacteroidetes; Bacteroidales; Bacteroidaceae
otu_3 0 0 3 1 0 Bacteria; Firmicutes; Clostridia; Clostridiales
otu_4 0 0 0 0 5 Bacteria; Spirochaetes; Spirochaetales; Spirochaetaceae
otu_5 0 4 2 0 0 Bacteria; Bacteroidetes; Bacteroidales; Rikenellaceae
otu_6 3 6 0 0 0 Bacteria; Bacteroidetes; Bacteroidales; Dysgonomonaceae
otu_7 0 0 4 2 0 Bacteria; Bacteroidetes; Bacteroidales; Odoribacteriaceae
otu_8 0 0 0 0 3 Bacteria; Bacteroidetes; Bacteroidales; Dysgonomonaceae; otu_425
otu_9 2 0 0 5 0 Bacteria; Bacteroidetes; Bacteroidales; Dysgonomonaceae; otu_425
otu_10 0 2 0 4 0 Bacteria; Firmicutes; Mollicutes; Clostridium_aff_innocuum_CM970"""
self.con_by_sample = {
'1': set(['2', '4']), '2': set(['5', '3', '1', '4']),
'3': set(['4', '2']), '4': set(['3', '1', '2']),
'5': set(['2'])}
self.edge_file_str = [
"2 otu_1 1.0 Bacteria:Actinobacteria:Coriobacteridae 090809 1800",
"5 otu_1 6.0 Bacteria:Actinobacteria:Coriobacteridae 091009 1200",
"1 otu_2 2.0 Bacteria:Bacteroidetes:Bacteroidales:Bacteroidaceae 090809 1200",
"3 otu_3 3.0 Bacteria:Firmicutes:Clostridia:Clostridiales 090909 1200",
"4 otu_3 1.0 Bacteria:Firmicutes:Clostridia:Clostridiales 090909 1800",
"5 otu_4 5.0 Bacteria:Spirochaetes:Spirochaetales:Spirochaetaceae 091009 1200",
"2 otu_5 4.0 Bacteria:Bacteroidetes:Bacteroidales:Rikenellaceae 090809 1800",
"3 otu_5 2.0 Bacteria:Bacteroidetes:Bacteroidales:Rikenellaceae 090909 1200",
"1 otu_6 3.0 Bacteria:Bacteroidetes:Bacteroidales:Dysgonomonaceae 090809 1200",
"2 otu_6 6.0 Bacteria:Bacteroidetes:Bacteroidales:Dysgonomonaceae 090809 1800",
"3 otu_7 4.0 Bacteria:Bacteroidetes:Bacteroidales:Odoribacteriaceae 090909 1200",
"4 otu_7 2.0 Bacteria:Bacteroidetes:Bacteroidales:Odoribacteriaceae 090909 1800",
"5 otu_8 3.0 Bacteria:Bacteroidetes:Bacteroidales:Dysgonomonaceae:otu_425 091009 1200",
"1 otu_9 2.0 Bacteria:Bacteroidetes:Bacteroidales:Dysgonomonaceae:otu_425 090809 1200",
"4 otu_9 5.0 Bacteria:Bacteroidetes:Bacteroidales:Dysgonomonaceae:otu_425 090909 1800",
"2 otu_10 2.0 Bacteria:Firmicutes:Mollicutes:Clostridium_aff_innocuum_CM970 090809 1800",
"4 otu_10 4.0 Bacteria:Firmicutes:Mollicutes:Clostridium_aff_innocuum_CM970 090909 1800"]
self.node_file_str = ["1 1 user_node 3 7.0 other 090809 1200",
"2 2 user_node 4 13.0 other 090809 1800",
"3 3 user_node 3 9.0 other 090909 1200",
"4 4 user_node 4 12.0 other 090909 1800",
"5 5 user_node 3 14.0 other 091009 1200",
"otu_1 otu_node 2 7.0 Bacteria:Actinobacteria:Coriobacteridae otu otu",
"otu_2 otu_node 1 2.0 Bacteria:Bacteroidetes:Bacteroidales:Bacteroidaceae otu otu",
"otu_3 otu_node 2 4.0 Bacteria:Firmicutes:Clostridia:Clostridiales otu otu",
"otu_4 otu_node 1 5.0 Bacteria:Spirochaetes:Spirochaetales:Spirochaetaceae otu otu",
"otu_5 otu_node 2 6.0 Bacteria:Bacteroidetes:Bacteroidales:Rikenellaceae otu otu",
"otu_6 otu_node 2 9.0 Bacteria:Bacteroidetes:Bacteroidales:Dysgonomonaceae otu otu",
"otu_7 otu_node 2 6.0 Bacteria:Bacteroidetes:Bacteroidales:Odoribacteriaceae otu otu",
"otu_8 otu_node 1 3.0 Bacteria:Bacteroidetes:Bacteroidales:Dysgonomonaceae:otu_425 otu otu",
"otu_9 otu_node 2 7.0 Bacteria:Bacteroidetes:Bacteroidales:Dysgonomonaceae:otu_425 otu otu",
"otu_10 otu_node 2 6.0 Bacteria:Firmicutes:Mollicutes:Clostridium_aff_innocuum_CM970 otu otu"]
self.red_edge_file_str = [
"2 otu_1 1.0 Bacteria:Actinobacteria:Coriobacteridae 090809 1800",
"5 otu_1 6.0 Bacteria:Actinobacteria:Coriobacteridae 091009 1200",
"1 @1 1.0 missed 090809 1200",
"3 otu_3 3.0 Bacteria:Firmicutes:Clostridia:Clostridiales 090909 1200",
"4 otu_3 1.0 Bacteria:Firmicutes:Clostridia:Clostridiales 090909 1800",
"5 @5 1.0 missed 091009 1200",
"2 otu_5 4.0 Bacteria:Bacteroidetes:Bacteroidales:Rikenellaceae 090809 1800",
"3 otu_5 2.0 Bacteria:Bacteroidetes:Bacteroidales:Rikenellaceae 090909 1200",
"1 otu_6 3.0 Bacteria:Bacteroidetes:Bacteroidales:Dysgonomonaceae 090809 1200",
"2 otu_6 6.0 Bacteria:Bacteroidetes:Bacteroidales:Dysgonomonaceae 090809 1800",
"3 otu_7 4.0 Bacteria:Bacteroidetes:Bacteroidales:Odoribacteriaceae 090909 1200",
"4 otu_7 2.0 Bacteria:Bacteroidetes:Bacteroidales:Odoribacteriaceae 090909 1800",
"1 otu_9 2.0 Bacteria:Bacteroidetes:Bacteroidales:Dysgonomonaceae:otu_425 090809 1200",
"4 otu_9 5.0 Bacteria:Bacteroidetes:Bacteroidales:Dysgonomonaceae:otu_425 090909 1800",
"2 otu_10 2.0 Bacteria:Firmicutes:Mollicutes:Clostridium_aff_innocuum_CM970 090809 1800",
"4 otu_10 4.0 Bacteria:Firmicutes:Mollicutes:Clostridium_aff_innocuum_CM970 090909 1800"]
self.red_node_file_str = ["1 1 user_node 3 7.0 other 090809 1200",
"2 2 user_node 4 13.0 other 090809 1800",
"3 3 user_node 3 9.0 other 090909 1200",
"4 4 user_node 4 12.0 other 090909 1800",
"5 5 user_node 3 14.0 other 091009 1200",
"otu_1 otu_node 2 7.0 Bacteria:Actinobacteria:Coriobacteridae otu otu",
"@1 otu_collapsed 1 1.0 other otu otu",
"otu_3 otu_node 2 4.0 Bacteria:Firmicutes:Clostridia:Clostridiales otu otu",
"@5 otu_collapsed 2 2.0 other otu otu",
"otu_5 otu_node 2 6.0 Bacteria:Bacteroidetes:Bacteroidales:Rikenellaceae otu otu",
"otu_6 otu_node 2 9.0 Bacteria:Bacteroidetes:Bacteroidales:Dysgonomonaceae otu otu",
"otu_7 otu_node 2 6.0 Bacteria:Bacteroidetes:Bacteroidales:Odoribacteriaceae otu otu",
"otu_9 otu_node 2 7.0 Bacteria:Bacteroidetes:Bacteroidales:Dysgonomonaceae:otu_425 otu otu",
"otu_10 otu_node 2 6.0 Bacteria:Firmicutes:Mollicutes:Clostridium_aff_innocuum_CM970 otu otu"]
self.otu_dc = {1: 3, 2: 7}
self.sample_dc = {3: 3, 4: 2}
self.degree_counts = {1: 3, 2: 7, 3: 3, 4: 2}
self.num_con_cat = {"Day": 2,
"time": 1}
self.num_con = 6
self.num_cat = {"Day": 2,
"time": 4}
self.num_cat_less = {"Day": 1,
"time": 3}
self._paths_to_clean_up = [self.otu_table_fp]
self._dir_to_clean_up = ''
def test_format_biom_table(self):
""" Formatting of BIOM table correctly includes "generated-by" information
"""
generated_by = "QIIME " + get_qiime_library_version()
self.assertTrue(generated_by in format_biom_table(self.biom1))
def main():
option_parser, opts, args = parse_command_line_parameters(**script_info)
otu_table_fp = opts.otu_table_fp
otu_include_fp = opts.otu_include_fp
output_fp = opts.output_fp
verbose = opts.verbose
category_mapping_fp = opts.category_mapping_fp
individual_column = opts.individual_column
reference_sample_column = opts.reference_sample_column
conv_output_fp = opts.converted_otu_table_output_fp
relative_abundance = opts.relative_abundance
filter = opts.filter
test = opts.test
category = opts.category
threshold = opts.threshold
collate_results = opts.collate_results
# check mapping file
category_mapping = open(category_mapping_fp, 'U')
category_mapping = parse_mapping_file(category_mapping)
if not category:
if test != 'paired_T':
option_parser.error(
'a category in the category mapping file must be'
' specified with the -c option for this test')
# set up threshold value for filtering, if any
if threshold and threshold != 'None':
threshold = float(threshold)
# if specifying a list of OTUs to look at specifically
if otu_include_fp and otu_include_fp != 'None':
otu_include = open(otu_include_fp)
else:
otu_include = None
# if only passing in a single OTU table
if isdir(otu_table_fp) is False:
# raise error if collate option is being passed to single table
if collate_results is True:
option_parser.error(
'Cannot collate the results of only one table.'
' Please rerun the command without passing the -w option')
else:
#open and parse the biom table fp
otu_table = parse_biom_table(open(otu_table_fp, 'U'))
# run the statistical test
output = test_wrapper(
test,
otu_table,
category_mapping,
category,
threshold,
filter,
otu_include,
otu_table_relative_abundance=relative_abundance)
# write output
output_file = open(output_fp, 'w')
output_file.write('\n'.join(output))
output_file.close()
# if the user has passed in a directory
if isdir(otu_table_fp) is True:
# negate_collate to return an results file on a per table basis
if collate_results is False:
# build list of otu tables
otu_table_paths = glob('%s/*biom' % otu_table_fp)
# if output dir doesn't exist, then make it
if exists(output_fp):
pass
else:
makedirs(output_fp)
for otu_table_fp in otu_table_paths:
#open and parse the biom table fp
otu_table = parse_biom_table(open(otu_table_fp, 'U'))
#synchronize the mapping file with the otu table
category_mapping, removed_samples = \
sync_mapping_to_otu_table(otu_table, category_mapping)
if removed_samples:
print "Warning, the following samples were in the category mapping file " +\
"but not the OTU table and will be ignored: "
for i in removed_samples:
print i + '\n'
# create naming convention for output file
# will look like: otu_table_ANOVA_Treatment.txt
output_basename = basename(otu_table_fp)
output_basename = output_basename.replace(".biom", "")
output_fp_sweep = "%s_%s_%s.txt" % \
(output_basename,test,category)
# if the convert_otu_table_fp is passed, save the converted table
if test == 'longitudinal_correlation' or test == 'paired_T':
converted_otu_table = longitudinal_otu_table_conversion_wrapper(
table, category_mapping, individual_column,
reference_sample_column)
if conv_output_fp:
of = open(conv_output_fp, 'w')
of.write(format_biom_table(converted_otu_table))
of.close()
if test == 'longitudinal_correlation':
#set the otu_include list to all of the OTUs, this effectively
#deactivates the filter for correlation, because the filtered OTU_list is
#rewritten with the otu_include list in the test_wrapper
if not otu_include:
otu_include = set(otu_table.ObservationIds)
output = test_wrapper('correlation',
converted_otu_table,
category_mapping, category,
threshold, filter, otu_include,
999999999.0, True)
elif test == 'paired_T':
output = test_wrapper('paired_T', converted_otu_table,
category_mapping, category,
threshold, filter, otu_include,
999999999.0, True,
individual_column,
reference_sample_column)
# run test single input table from the directory
else:
output = test_wrapper(
test,
otu_table,
category_mapping,
category,
threshold,
filter,
otu_include,
otu_table_relative_abundance=relative_abundance)
# write output file with new naming convention
output_file = open(join(output_fp, output_fp_sweep), 'w')
output_file.write('\n'.join(output))
output_file.close()
# Use when the input dir contains rarefied OTU tables, and you want
# to collate the p-values & results into one results file
if collate_results is True:
if test != 'longitudinal_correlation' and test != 'paired_T':
# get biom tables
otu_table_paths = glob('%s/*biom' % otu_table_fp)
#get aggregated tables
parsed_otu_tables = []
for otu_table_fp in otu_table_paths:
otu_table = open(otu_table_fp, 'U')
otu_table = parse_biom_table(otu_table)
parsed_otu_tables.append(otu_table)
#synchronize the mapping file with the otu table
#checks with just the first OTU table and assumes that all otu tables
#have the same collection of samples
category_mapping, removed_samples = \
sync_mapping_to_otu_table(parsed_otu_tables[0],category_mapping)
if removed_samples:
print "Warning, the following samples were in the category mapping file " +\
"but not the OTU table and will be ignored: "
for i in removed_samples:
print i + '\n'
# get output from statistical test
output = test_wrapper_multiple(
test,
parsed_otu_tables,
category_mapping,
category,
threshold,
filter,
otu_include,
otu_table_relative_abundance=relative_abundance)
#write out aggregated results
output_file = open(output_fp, 'w')
output_file.write('\n'.join(output))
output_file.close()
else:
option_parser.error(
"You cannot collate the results obtained from "
"using the longitudinal_correlation and paired_T options.")