def main():
option_parser, opts, args =\
parse_command_line_parameters(**script_info)
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'))
make_output_dir_for_file(opts.output_metagenome_table)
if opts.accuracy_metrics:
# Calculate accuracy metrics
#unweighted_nsti = calc_nsti(otu_table,genome_table,weighted=False)
#print "Unweighted NSTI:", unweighted_nsti
weighted_nsti = calc_nsti(otu_table,genome_table,weighted=True)
samples= weighted_nsti[0]
nstis = list(weighted_nsti[1])
#print "Samples:",samples
#print "NSTIs:",nstis
samples_and_nstis = zip(samples,nstis)
#print "Samples and NSTIs:",samples_and_nstis
lines = ["#Sample\tMetric\tValue\n"]
#print weighted_nsti
for sample,nsti in samples_and_nstis:
line = "%s\tWeighted NSTI\t%s\n" %(sample,str(nsti))
lines.append(line)
if opts.verbose:
for l in sorted(lines):
print l
if opts.verbose:
print "Writing accuracy information to file:", opts.accuracy_metrics
open(opts.accuracy_metrics,'w').writelines(sorted(lines))
if opts.verbose:
print "Predicting the metagenome..."
predicted_metagenomes = predict_metagenomes(otu_table,genome_table)
if opts.verbose:
print "Writing results to output file: ",opts.output_metagenome_table
make_output_dir_for_file(opts.output_metagenome_table)
if(opts.format_tab_delimited):
open(opts.output_metagenome_table,'w').write(predicted_metagenomes.delimitedSelf())
else:
open(opts.output_metagenome_table,'w').write(format_biom_table(predicted_metagenomes))
if var_id not in genome_table.ids():
print "Variance table SampleId %s not in genome_table SampleIds" % var_id
raise AssertionError(
"Variance table and genome table contain different OTU ids")
#sort the ObservationIds and SampleIds to be in the same order
variance_table = variance_table.sort_order(
genome_table.ids(axis='observation'), axis='observation')
variance_table = variance_table.sort_order(genome_table.ids(),
axis='sample')
make_output_dir_for_file(opts.output_metagenome_table)
if opts.accuracy_metrics:
# Calculate accuracy metrics
weighted_nsti = calc_nsti(otu_table, genome_table, weighted=True)
samples = weighted_nsti[0]
nstis = list(weighted_nsti[1])
samples_and_nstis = zip(samples, nstis)
if opts.verbose:
print "Writing NSTI information to file:", opts.accuracy_metrics
accuracy_output_fh = open(opts.accuracy_metrics, 'w')
accuracy_output_fh.write("#Sample\tMetric\tValue\n")
for sample, nsti in samples_and_nstis:
line = "%s\tWeighted NSTI\t%s\n" % (sample, str(nsti))
accuracy_output_fh.write(line)
if opts.with_confidence:
#If we are calculating variance, we get the prediction as part
#of the process
assert set(variance_table.SampleIds) == set(genome_table.SampleIds)
except AssertionError,e:
for var_id in variance_table.SampleIds:
if var_id not in genome_table.SampleIds:
print "Variance table SampleId %s not in genome_table SampleIds" %var_id
raise AssertionError("Variance table and genome table contain different OTU ids")
#sort the ObservationIds and SampleIds to be in the same order
variance_table=variance_table.sortObservationOrder(genome_table.ObservationIds)
variance_table=variance_table.sortSampleOrder(genome_table.SampleIds)
make_output_dir_for_file(opts.output_metagenome_table)
if opts.accuracy_metrics:
# Calculate accuracy metrics
weighted_nsti = calc_nsti(otu_table,genome_table,weighted=True)
samples= weighted_nsti[0]
nstis = list(weighted_nsti[1])
samples_and_nstis = zip(samples,nstis)
if opts.verbose:
print "Writing NSTI information to file:", opts.accuracy_metrics
accuracy_output_fh = open(opts.accuracy_metrics,'w')
accuracy_output_fh.write("#Sample\tMetric\tValue\n")
for sample,nsti in samples_and_nstis:
line = "%s\tWeighted NSTI\t%s\n" %(sample,str(nsti))
accuracy_output_fh.write(line)
if opts.with_confidence:
#If we are calculating variance, we get the prediction as part
#of the process
def main():
option_parser, opts, args =\
parse_command_line_parameters(**script_info)
if opts.verbose:
print "Loading OTU table: ",opts.input_otu_table
otu_table = parse_biom_table(open(opts.input_otu_table,'U'))
if opts.verbose:
print "Done loading OTU table containing %i samples and %i OTUs." %(len(otu_table.SampleIds),len(otu_table.ObservationIds))
if(opts.input_count_table is None):
if(opts.type_of_prediction == 'KO'):
input_count_table=join(get_picrust_project_dir(),'picrust','data','ko_precalculated.biom.gz')
elif(opts.type_of_prediction == 'COG'):
input_count_table=join(get_picrust_project_dir(),'picrust','data','cog_precalculated.biom.gz')
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 (ext == '.gz'):
genome_table_str = gzip.open(input_count_table,'rb').read()
else:
genome_table_str = open(input_count_table,'U').read()
#In the genome/trait table genomes are the samples and
#genes are the observations
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
ids_to_load = otu_table.ObservationIds
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_str,ids_to_load,axis='samples')
else:
if opts.verbose:
print "Loading *full* trait table because --suppress_subset_loading was passed. This may result in high memory usage."
genome_table = parse_biom_table(genome_table_str)
if opts.verbose:
print "Done loading trait table containing %i functions for %i organisms." %(len(genome_table.ObservationIds),len(genome_table.SampleIds))
make_output_dir_for_file(opts.output_metagenome_table)
if opts.accuracy_metrics:
# Calculate accuracy metrics
#unweighted_nsti = calc_nsti(otu_table,genome_table,weighted=False)
#print "Unweighted NSTI:", unweighted_nsti
weighted_nsti = calc_nsti(otu_table,genome_table,weighted=True)
samples= weighted_nsti[0]
nstis = list(weighted_nsti[1])
#print "Samples:",samples
#print "NSTIs:",nstis
samples_and_nstis = zip(samples,nstis)
#print "Samples and NSTIs:",samples_and_nstis
lines = ["#Sample\tMetric\tValue\n"]
#print weighted_nsti
for sample,nsti in samples_and_nstis:
line = "%s\tWeighted NSTI\t%s\n" %(sample,str(nsti))
lines.append(line)
if opts.verbose:
for l in sorted(lines):
print l
if opts.verbose:
print "Writing accuracy information to file:", opts.accuracy_metrics
open(opts.accuracy_metrics,'w').writelines(sorted(lines))
if opts.verbose:
print "Predicting the metagenome..."
predicted_metagenomes = predict_metagenomes(otu_table,genome_table)
if opts.verbose:
print "Writing results to output file: ",opts.output_metagenome_table
make_output_dir_for_file(opts.output_metagenome_table)
if(opts.format_tab_delimited):
open(opts.output_metagenome_table,'w').write(predicted_metagenomes.delimitedSelf(header_key="KEGG Pathways",header_value="KEGG Pathways",metadata_formatter=lambda s: '|'.join(['; '.join(l) for l in s])))
else:
open(opts.output_metagenome_table,'w').write(format_biom_table(predicted_metagenomes))
