def main():
option_parser, opts, args =\
parse_command_line_parameters(**script_info)
if opts.limit_to_function:
limit_to_functions = opts.limit_to_function.split(',')
if opts.verbose:
print "Limiting output to only functions:",limit_to_functions
else:
limit_to_functions = []
if opts.verbose:
print "Loading otu table: ",opts.input_otu_table
otu_table = parse_biom_table(open(opts.input_otu_table,'U'))
ext=path.splitext(opts.input_count_table)[1]
if opts.verbose:
print "Loading count table: ", opts.input_count_table
if (ext == '.gz'):
genome_table = parse_biom_table(gzip.open(opts.input_count_table,'rb'))
else:
genome_table = parse_biom_table(open(opts.input_count_table,'U'))
if opts.verbose:
print "Predicting the metagenome..."
partitioned_metagenomes = partition_metagenome_contributions(otu_table,genome_table,limit_to_functions=limit_to_functions)
output_text = "\n".join(["\t".join(map(str,i)) for i in partitioned_metagenomes])
if opts.verbose:
print "Writing results to output file: ",opts.output_metagenome_table
make_output_dir_for_file(opts.output_metagenome_table)
open(opts.output_metagenome_table,'w').write(output_text)
def test_partition_metagenome_contributions_with_taxonomy(self):
obs = partition_metagenome_contributions(self.otu_table_with_taxonomy,self.genome_table1)
obs_text = "\n".join(["\t".join(map(str,i)) for i in obs])
exp_text_list = [map(str,r.split()) for r in self.predicted_gene_partition_table_with_taxonomy.split('\n')]
#BIOM adds spaces to metadata fields (not sure why), so add them here just for the taxonomy fields
for row in exp_text_list[1:]:
row[9]=' '+row[9]
row[10]=' '+row[10]
row[11]=' '+row[11]
row[12]=' '+row[12]
row[13]=' '+row[13]
row[14]=' '+row[14]
exp_text="\n".join(["\t".join(i) for i in exp_text_list])
self.assertEqual(obs_text,exp_text)
def test_partition_metagenome_contributions_with_taxonomy(self):
obs = partition_metagenome_contributions(self.otu_table_with_taxonomy,self.genome_table1)
obs_text = "\n".join(["\t".join(map(str,i)) for i in obs])
exp_text_list = [map(str,r.split()) for r in self.predicted_gene_partition_table_with_taxonomy.split('\n')]
#BIOM adds spaces to metadata fields (not sure why), so add them here just for the taxonomy fields
for row in exp_text_list[1:]:
row[9]=' '+row[9]
row[10]=' '+row[10]
row[11]=' '+row[11]
row[12]=' '+row[12]
row[13]=' '+row[13]
row[14]=' '+row[14]
exp_text="\n".join(["\t".join(i) for i in exp_text_list])
self.assertEqual(obs_text,exp_text)
def test_partition_metagenome_contributions(self):
"""partition_metagenome_contributions functions with valid input"""
#For reference, the OTU table should look like this:
##OTU ID Sample1 Sample2 Sample3 Sample4
#GG_OTU_1 1.0 2.0 3.0 5.0
#GG_OTU_2 5.0 1.0 0.0 2.0
#GG_OTU_3 0.0 0.0 1.0 4.0
#...and the genome table will look like this:
##OTU ID GG_OTU_1 GG_OTU_3 GG_OTU_2
#f1 1.0 2.0 3.0
#f2 0.0 1.0 0.0
#f3 0.0 0.0 1.0
#For which predict metagenomes should produce a table like this:
##OTU ID Sample1 Sample2 Sample3 Sample4
#f1 16.0 5.0 5.0 19.0
#f2 0.0 0.0 1.0 4.0
#f3 5.0 1.0 0.0 2.0
#First, sanity checks
#We expect to see the contributions broken down by OTU
metagenome_table = predict_metagenomes(self.otu_table1,self.genome_table1)
obs = partition_metagenome_contributions(self.otu_table1,self.genome_table1)
obs_text = "\n".join(["\t".join(map(str,i)) for i in obs])
exp_text = "\n".join(["\t".join(map(str,r.split())) for r in \
self.predicted_gene_partition_table.split('\n')])
#Test that the percent of all samples is always smaller than
#the percent of the current sample
for l in obs[1:]:
self.assertTrue(l[-1]<=l[-2])
#Test that the summed contributions equal the metagenome table value
sum_f1_sample1 = sum([i[5] for i in obs[1:] if (i[0]=="f1" and i[1]=="Sample1")])
self.assertFloatEqual(sum_f1_sample1,16.0)
sum_f2_sample1 = sum(\
[i[5] for i in obs[1:] if (i[0]=="f2" and i[1]=="Sample1")])
self.assertFloatEqual(sum_f2_sample1,0.0)
sum_f3_sample1 = sum(\
[i[5] for i in obs[1:] if (i[0]=="f3" and i[1]=="Sample1")])
self.assertFloatEqual(sum_f3_sample1,5.0)
for l in obs[1:]:
gene,sample,OTU,gene_count_per_genome,otu_abundance_in_sample,count,percent,percent_all = l
#Test that genomes without genes don't contribute
#Only GG_OTU_3 has f2, so for all others the gene
#contribution should be 0,0
if gene == "f2" and OTU != "GG_OTU_3":
self.assertFloatEqual(count,0.0)
self.assertFloatEqual(percent,0.0)
self.assertFloatEqual(percent_all,0.0)
#Ditto for GG_OTU_2 and f3
if gene == "f3" and OTU != "GG_OTU_2":
self.assertFloatEqual(count,0.0)
self.assertFloatEqual(percent,0.0)
self.assertFloatEqual(percent_all,0.0)
#Test that OTUs absent from a sample don't contribute
if sample == "Sample1" and OTU == "GG_OTU_3":
self.assertFloatEqual(count,0.0)
self.assertFloatEqual(percent,0.0)
self.assertFloatEqual(percent_all,0.0)
#Having validated that this looks OK, just compare to
#hand-checked result
self.assertEqual(obs_text,exp_text)
#Check if "limit to functions" works and retrieves the correct information
obs_limited = partition_metagenome_contributions(self.otu_table1,self.genome_table1,limit_to_functions=["f2"])
for l in obs_limited[1:]:
gene,sample,OTU,gene_count_per_genome,otu_abundance_in_sample,count,percent,percent_all = l
self.assertEqual(gene,"f2")
def test_partition_metagenome_contributions(self):
"""partition_metagenome_contributions functions with valid input"""
#For reference, the OTU table should look like this:
##OTU ID Sample1 Sample2 Sample3 Sample4
#GG_OTU_1 1.0 2.0 3.0 5.0
#GG_OTU_2 5.0 1.0 0.0 2.0
#GG_OTU_3 0.0 0.0 1.0 4.0
#...and the genome table will look like this:
##OTU ID GG_OTU_1 GG_OTU_3 GG_OTU_2
#f1 1.0 2.0 3.0
#f2 0.0 1.0 0.0
#f3 0.0 0.0 1.0
#For which predict metagenomes should produce a table like this:
##OTU ID Sample1 Sample2 Sample3 Sample4
#f1 16.0 5.0 5.0 19.0
#f2 0.0 0.0 1.0 4.0
#f3 5.0 1.0 0.0 2.0
#First, sanity checks
#We expect to see the contributions broken down by OTU
metagenome_table = predict_metagenomes(self.otu_table1,self.genome_table1)
obs = partition_metagenome_contributions(self.otu_table1,self.genome_table1)
obs_text = "\n".join(["\t".join(map(str,i)) for i in obs])
exp_text = "\n".join(["\t".join(map(str,r.split())) for r in \
self.predicted_gene_partition_table.split('\n')])
#Test that the percent of all samples is always smaller than
#the percent of the current sample
for l in obs[1:]:
self.assertTrue(l[-1]<=l[-2])
#Test that the summed contributions equal the metagenome table value
sum_f1_sample1 = sum([i[5] for i in obs[1:] if (i[0]=="f1" and i[1]=="Sample1")])
self.assertFloatEqual(sum_f1_sample1,16.0)
sum_f2_sample1 = sum(\
[i[5] for i in obs[1:] if (i[0]=="f2" and i[1]=="Sample1")])
self.assertFloatEqual(sum_f2_sample1,0.0)
sum_f3_sample1 = sum(\
[i[5] for i in obs[1:] if (i[0]=="f3" and i[1]=="Sample1")])
self.assertFloatEqual(sum_f3_sample1,5.0)
for l in obs[1:]:
gene,sample,OTU,gene_count_per_genome,otu_abundance_in_sample,count,percent,percent_all = l
#Test that genomes without genes don't contribute
#Only GG_OTU_3 has f2, so for all others the gene
#contribution should be 0,0
if gene == "f2" and OTU != "GG_OTU_3":
self.assertFloatEqual(count,0.0)
self.assertFloatEqual(percent,0.0)
self.assertFloatEqual(percent_all,0.0)
#Ditto for GG_OTU_2 and f3
if gene == "f3" and OTU != "GG_OTU_2":
self.assertFloatEqual(count,0.0)
self.assertFloatEqual(percent,0.0)
self.assertFloatEqual(percent_all,0.0)
#Test that OTUs absent from a sample don't contribute
if sample == "Sample1" and OTU == "GG_OTU_3":
self.assertFloatEqual(count,0.0)
self.assertFloatEqual(percent,0.0)
self.assertFloatEqual(percent_all,0.0)
#Having validated that this looks OK, just compare to
#hand-checked result
self.assertEqual(obs_text,exp_text)
#Check if "limit to functions" works and retrieves the correct information
obs_limited = partition_metagenome_contributions(self.otu_table1,self.genome_table1,limit_to_functions=["f2"])
for l in obs_limited[1:]:
gene,sample,OTU,gene_count_per_genome,otu_abundance_in_sample,count,percent,percent_all = l
self.assertEqual(gene,"f2")
def main():
option_parser, opts, args =\
parse_command_line_parameters(**script_info)
if opts.limit_to_function:
limit_to_functions = opts.limit_to_function.split(',')
if opts.verbose:
print "Limiting output to only functions:",limit_to_functions
else:
limit_to_functions = []
if opts.verbose:
print "Loading otu table: ",opts.input_otu_table
otu_table = load_table(opts.input_otu_table)
ids_to_load = otu_table.ids(axis='observation')
if(opts.input_count_table is None):
#precalc file has specific name (e.g. ko_13_5_precalculated.tab.gz)
precalc_file_name='_'.join([opts.type_of_prediction,opts.gg_version,'precalculated.tab.gz'])
input_count_table=join(get_picrust_project_dir(),'picrust','data',precalc_file_name)
else:
input_count_table=opts.input_count_table
if opts.verbose:
print "Loading trait table: ", input_count_table
ext=path.splitext(input_count_table)[1]
if opts.verbose:
print "Loading count table: ", input_count_table
if (ext == '.gz'):
genome_table_fh = gzip.open(input_count_table,'rb')
else:
genome_table_fh = open(input_count_table,'U')
#In the genome/trait table genomes are the samples and
#genes are the observations
if opts.load_precalc_file_in_biom:
if not opts.suppress_subset_loading:
#Now we want to use the OTU table information
#to load only rows in the count table corresponding
#to relevant OTUs
if opts.verbose:
print "Loading traits for %i organisms from the trait table" %len(ids_to_load)
genome_table = load_subset_from_biom_str(genome_table_fh.read(),ids_to_load,axis='samples')
else:
if opts.verbose:
print "Loading *full* count table because --suppress_subset_loading was passed. This may result in high memory usage"
genome_table = load_table(genome_table_fh)
else:
genome_table = convert_precalc_to_biom(genome_table_fh,ids_to_load)
ok_functional_categories = None
metadata_type = None
if opts.limit_to_functional_categories:
ok_functional_categories = opts.limit_to_functional_categories.split("|")
if opts.verbose:
print "Limiting to functional categories: %s" %(str(ok_functional_categories))
# Either KEGG_Pathways or COG_Category needs
# to be assigned to metadata_key to limit to
# functional categories (not needed for
# individual functions)
if opts.type_of_prediction == "ko":
metadata_type = "KEGG_Pathways"
elif opts.type_of_prediction == "cog":
metadata_type = "COG_Category"
elif opts.type_of_prediction == "rfam":
exit("Stopping program: when type of prediction is set to rfam you can only limit to individual functions (-l) rather than to functional categories (-f)")
partitioned_metagenomes = partition_metagenome_contributions(otu_table,genome_table,limit_to_functions=limit_to_functions,\
limit_to_functional_categories = ok_functional_categories , metadata_key = metadata_type )
output_text = "\n".join(["\t".join(map(str,i)) for i in partitioned_metagenomes])
if opts.verbose:
print "Writing results to output file: ",opts.output_fp
make_output_dir_for_file(opts.output_fp)
open(opts.output_fp,'w').write(output_text)
def main():
option_parser, opts, args =\
parse_command_line_parameters(**script_info)
if opts.limit_to_function:
limit_to_functions = opts.limit_to_function.split(',')
if opts.verbose:
print "Limiting output to only functions:",limit_to_functions
else:
limit_to_functions = []
if opts.verbose:
print "Loading otu table: ",opts.input_otu_table
otu_table = parse_biom_table(open(opts.input_otu_table,'U'))
ids_to_load = otu_table.ObservationIds
if(opts.input_count_table is None):
#precalc file has specific name (e.g. ko_13_5_precalculated.tab.gz)
precalc_file_name='_'.join([opts.type_of_prediction,opts.gg_version,'precalculated.tab.gz'])
input_count_table=join(get_picrust_project_dir(),'picrust','data',precalc_file_name)
else:
input_count_table=opts.input_count_table
if opts.verbose:
print "Loading trait table: ", input_count_table
ext=path.splitext(input_count_table)[1]
if opts.verbose:
print "Loading count table: ", input_count_table
if (ext == '.gz'):
genome_table_fh = gzip.open(input_count_table,'rb')
else:
genome_table_fh = open(input_count_table,'U')
#In the genome/trait table genomes are the samples and
#genes are the observations
if opts.load_precalc_file_in_biom:
if not opts.suppress_subset_loading:
#Now we want to use the OTU table information
#to load only rows in the count table corresponding
#to relevant OTUs
if opts.verbose:
print "Loading traits for %i organisms from the trait table" %len(ids_to_load)
genome_table = load_subset_from_biom_str(genome_table_fh.read(),ids_to_load,axis='samples')
else:
if opts.verbose:
print "Loading *full* count table because --suppress_subset_loading was passed. This may result in high memory usage"
genome_table = parse_biom_table(genome_table_fh.read())
else:
genome_table = convert_precalc_to_biom(genome_table_fh,ids_to_load)
partitioned_metagenomes = partition_metagenome_contributions(otu_table,genome_table,limit_to_functions=limit_to_functions)
output_text = "\n".join(["\t".join(map(str,i)) for i in partitioned_metagenomes])
if opts.verbose:
print "Writing results to output file: ",opts.output_fp
make_output_dir_for_file(opts.output_fp)
open(opts.output_fp,'w').write(output_text)
def main():
option_parser, opts, args =\
parse_command_line_parameters(**script_info)
if opts.limit_to_function:
limit_to_functions = opts.limit_to_function.split(',')
if opts.verbose:
print "Limiting output to only functions:",limit_to_functions
else:
limit_to_functions = []
if opts.verbose:
print "Loading otu table: ",opts.input_otu_table
otu_table = load_table(opts.input_otu_table)
ids_to_load = otu_table.ids(axis='observation')
if(opts.input_count_table is None):
#precalc file has specific name (e.g. ko_13_5_precalculated.tab.gz)
precalc_file_name='_'.join([opts.type_of_prediction,opts.gg_version,'precalculated.tab.gz'])
input_count_table=join(get_picrust_project_dir(),'picrust','data',precalc_file_name)
else:
input_count_table=opts.input_count_table
if opts.verbose:
print "Loading trait table: ", input_count_table
ext=path.splitext(input_count_table)[1]
if opts.verbose:
print "Loading count table: ", input_count_table
if (ext == '.gz'):
genome_table_fh = gzip.open(input_count_table,'rb')
else:
genome_table_fh = open(input_count_table,'U')
#In the genome/trait table genomes are the samples and
#genes are the observations
if opts.load_precalc_file_in_biom:
if not opts.suppress_subset_loading:
#Now we want to use the OTU table information
#to load only rows in the count table corresponding
#to relevant OTUs
if opts.verbose:
print "Loading traits for %i organisms from the trait table" %len(ids_to_load)
genome_table = load_subset_from_biom_str(genome_table_fh.read(),ids_to_load,axis='samples')
else:
if opts.verbose:
print "Loading *full* count table because --suppress_subset_loading was passed. This may result in high memory usage"
genome_table = load_table(genome_table_fh)
else:
genome_table = convert_precalc_to_biom(genome_table_fh,ids_to_load)
ok_functional_categories = None
metadata_type = None
if opts.limit_to_functional_categories:
ok_functional_categories = opts.limit_to_functional_categories.split("|")
if opts.verbose:
print "Limiting to functional categories: %s" %(str(ok_functional_categories))
# Either KEGG_Pathways or COG_Category needs
# to be assigned to metadata_key to limit to
# functional categories (not needed for
# individual functions)
if opts.type_of_prediction == "ko":
metadata_type = "KEGG_Pathways"
elif opts.type_of_prediction == "cog":
metadata_type = "COG_Category"
elif opts.type_of_prediction == "rfam":
exit("Stopping program: when type of prediction is set to rfam you can only limit to individual functions (-l) rather than to functional categories (-f)")
partitioned_metagenomes = partition_metagenome_contributions(otu_table,genome_table,limit_to_functions=limit_to_functions,\
limit_to_functional_categories = ok_functional_categories , metadata_key = metadata_type )
output_text = "\n".join(["\t".join(map(str,i)) for i in partitioned_metagenomes])
if opts.verbose:
print "Writing results to output file: ",opts.output_fp
make_output_dir_for_file(opts.output_fp)
open(opts.output_fp,'w').write(output_text)
