def load_data_table(
data_table_fp,
load_data_table_in_biom=False,
suppress_subset_loading=False,
ids_to_load=None,
transpose=False,
verbose=False,
):
"""Load a data table, detecting gziiped files and subset loading
data_table_fp -- path to the input data table
load_data_table_in_biom -- if True, load the data table as a BIOM table rather
than as tab-delimited
suppress_subset_loading -- if True, load the entire table, rather than just
ids_of_interest
ids_to_load -- a list of OTU ids for which data should be loaded
gzipped files are detected based on the '.gz' suffix.
"""
if not path.exists(data_table_fp):
raise IOError("File " + data_table_fp + " doesn't exist! Did you forget to download it?")
ext = path.splitext(data_table_fp)[1]
if ext == ".gz":
genome_table_fh = gzip.open(data_table_fp, "rb")
else:
genome_table_fh = open(data_table_fp, "U")
if load_data_table_in_biom:
if not 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 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 verbose:
print "Loading *full* count table because --suppress_subset_loading was passed. This may result in high memory usage"
genome_table = load_table(data_table_fp)
else:
genome_table = convert_precalc_to_biom(genome_table_fh, ids_to_load, transpose=transpose)
if verbose:
print "Done loading trait table containing %i functions for %i organisms." % (
len(genome_table.ids(axis="observation")),
len(genome_table.ids()),
)
return genome_table
def load_data_table(data_table_fp,\
load_data_table_in_biom=False,suppress_subset_loading=False,ids_to_load=None,\
transpose=False,verbose=False):
"""Load a data table, detecting gziiped files and subset loading
data_table_fp -- path to the input data table
load_data_table_in_biom -- if True, load the data table as a BIOM table rather
than as tab-delimited
suppress_subset_loading -- if True, load the entire table, rather than just
ids_of_interest
ids_to_load -- a list of OTU ids for which data should be loaded
gzipped files are detected based on the '.gz' suffix.
"""
if not path.exists(data_table_fp):
raise IOError("File " + data_table_fp +
" doesn't exist! Did you forget to download it?")
ext = path.splitext(data_table_fp)[1]
if (ext == '.gz'):
genome_table_fh = gzip.open(data_table_fp, 'rb')
else:
genome_table_fh = open(data_table_fp, 'U')
if load_data_table_in_biom:
if not 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 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 verbose:
print "Loading *full* count table because --suppress_subset_loading was passed. This may result in high memory usage"
genome_table = load_table(data_table_fp)
else:
genome_table = convert_precalc_to_biom(genome_table_fh,
ids_to_load,
transpose=transpose)
if verbose:
print "Done loading trait table containing %i functions for %i organisms." % (
len(genome_table.ids(axis='observation')), len(genome_table.ids()))
return genome_table
def load_data_table(data_table_fp,\
load_data_table_in_biom=False,suppress_subset_loading=False,ids_to_load=None,\
transpose=False,verbose=False):
"""Load a data table, detecting gziiped files and subset loading
data_table_fp -- path to the input data table
load_data_table_in_biom -- if True, load the data table as a BIOM table rather
than as tab-delimited
suppress_subset_loading -- if True, load the entire table, rather than just
ids_of_interest
ids_to_load -- a list of OTU ids for which data should be loaded
gzipped files are detected based on the '.gz' suffix.
"""
ext=path.splitext(data_table_fp)[1]
if (ext == '.gz'):
genome_table_fh = gzip.open(data_table_fp,'rb')
else:
genome_table_fh = open(data_table_fp,'U')
if load_data_table_in_biom:
if not 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 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 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,transpose=transpose)
if verbose:
print "Done loading trait table containing %i functions for %i organisms." %(len(genome_table.ObservationIds),len(genome_table.SampleIds))
return genome_table
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))
