def main():
option_parser, opts, args =\
parse_command_line_parameters(**script_info)
if opts.verbose:
print "Loading sequencing depth table: ",opts.input_seq_depth_file
scaling_factors = {}
for sample_id,depth in parse_seq_count_file(open(opts.input_seq_depth_file,'U')):
scaling_factors[sample_id]=depth
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 "Scaling the metagenome..."
scaled_metagenomes = scale_metagenomes(genome_table,scaling_factors)
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(format_biom_table(scaled_metagenomes))
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))
def write_metagenome_to_file(predicted_metagenome,output_fp,\
tab_delimited=False,verbose_filetype_message="metagenome prediction",\
verbose=False):
"""Write a BIOM Table object to a file, creating the directory if needed
predicted_metagenome -- a BIOM table object
output_fp -- the filepath to write the output
tab_delimited -- if False, write in BIOm format, otherwise write as a tab-delimited file
verbose -- if True output verbose info to StdOut
"""
if verbose:
print "Writing %s results to output file: %s"\
%(verbose_filetype_message,output_fp)
make_output_dir_for_file(output_fp)
if tab_delimited:
#peek at first observation to decide on what observeration metadata
#to output in tab-delimited format
(obs_val,obs_id,obs_metadata)=\
predicted_metagenome.iterObservations().next()
#see if there is a metadata field that contains the "Description"
#(e.g. KEGG_Description or COG_Description)
h = re.compile('.*Description')
metadata_names=filter(h.search,obs_metadata.keys())
if metadata_names:
#use the "Description" field we found
metadata_name=metadata_names[0]
elif(obs_metadata.keys()):
#if no "Description" metadata then just output the first
#observation metadata
metadata_name=(obs_metadata.keys())[0]
else:
#if no observation metadata then don't output any
metadata_name=None
open(output_fp,'w').write(predicted_metagenome.delimitedSelf(\
header_key=metadata_name,header_value=metadata_name,metadata_formatter=biom_meta_to_string))
else:
#output in BIOM format
open(output_fp,'w').write(format_biom_table(predicted_metagenome))
def write_metagenome_to_file(predicted_metagenome,output_fp,\
tab_delimited=False,verbose_filetype_message="metagenome prediction",\
verbose=False):
"""Write a BIOM Table object to a file, creating the directory if needed
predicted_metagenome -- a BIOM table object
output_fp -- the filepath to write the output
tab_delimited -- if False, write in BIOm format, otherwise write as a tab-delimited file
verbose -- if True output verbose info to StdOut
"""
if verbose:
print "Writing %s results to output file: %s"\
%(verbose_filetype_message,output_fp)
make_output_dir_for_file(output_fp)
if tab_delimited:
#peek at first observation to decide on what observeration metadata
#to output in tab-delimited format
(obs_val,obs_id,obs_metadata)=\
predicted_metagenome.iterObservations().next()
#see if there is a metadata field that contains the "Description"
#(e.g. KEGG_Description or COG_Description)
h = re.compile('.*Description')
metadata_names = filter(h.search, obs_metadata.keys())
if metadata_names:
#use the "Description" field we found
metadata_name = metadata_names[0]
elif (obs_metadata.keys()):
#if no "Description" metadata then just output the first
#observation metadata
metadata_name = (obs_metadata.keys())[0]
else:
#if no observation metadata then don't output any
metadata_name = None
open(output_fp,'w').write(predicted_metagenome.delimitedSelf(\
header_key=metadata_name,header_value=metadata_name))
else:
#output in BIOM format
open(output_fp, 'w').write(format_biom_table(predicted_metagenome))
def main():
"""Generate test trees given parameters"""
option_parser, opts, args =\
parse_command_line_parameters(**script_info)
if opts.verbose:
print "Loading trait table..."
input_trait_table = open(opts.input_trait_table,"U")
if opts.verbose:
print "Loading tree..."
#PicrustNode seems to run into very slow/memory intentsive perfromance...
#tree = DndParser(open(opts.input_tree),constructor=PicrustNode)
tree = DndParser(open(opts.input_tree))
if opts.verbose:
print "Parsing trait table..."
#Find which taxa are to be used in tests
#(by default trait table taxa)
trait_table_header,trait_table_fields = \
parse_trait_table(input_trait_table)
if opts.verbose:
print "Ensuring tree and trait table labels are formatted consistently..."
label_conversion_fns = set_label_conversion_fns(verbose=opts.verbose)
fix_tree_labels(tree,label_conversion_fns)
trait_table_fields = convert_trait_table_entries(trait_table_fields,\
value_conversion_fns = [],\
label_conversion_fns = label_conversion_fns)
trait_table_fields = [t for t in trait_table_fields]
print "Number of trait table fields with single quotes:",\
len([t for t in trait_table_fields if "'" in t[0]])
if opts.verbose:
print "Making output directory..."
make_output_dir(opts.output_dir)
if opts.limit_to_tips:
included_tips = opts.limit_to_tips.split(",")
if opts.verbose:
print "Limiting test datasets to %i tips: %s" %(len(included_tips),included_tips)
else:
included_tips = False
method_fns =\
{"exclude_tips_by_distance":\
make_distance_based_exclusion_fn,\
"randomize_tip_labels_by_distance":\
make_distance_based_tip_label_randomizer
}
test_fn_factory = method_fns[opts.method]
if opts.verbose:
print "Setting tree modification method to:", opts.method
print "(%s)" % test_fn_factory.__doc__
modify_tree = True
if opts.suppress_tree_modification:
if opts.verbose:
print "Suppressing modification of tree when making test datasets"
modify_tree = False
if opts.verbose:
print "Starting generation of test datsets"
test_datasets = \
yield_genome_test_data_by_distance(tree,trait_table_fields,\
test_fn_factory,min_dist = opts.min_dist,\
max_dist=opts.max_dist,increment=opts.dist_increment,\
modify_tree=modify_tree,limit_to_tips= included_tips,verbose = opts.verbose)
if opts.verbose:
print "Writing files for test datasets"
for curr_dist,test_tree,tip_to_predict,\
expected_traits,test_trait_table_fields in test_datasets:
if included_tips is not False:
if tip_to_predict not in included_tips:
if opts.verbose:
print "Skipping tip %s: limiting to tip(s): %s" %(tip_to_predict,included_tips)
continue
#Make a safe version of tip to predict
# So odd characters like | don't mess up OS
safe_tip_to_predict = "'%s'"%tip_to_predict
#Write tree
base_name = "--".join(map(str,["test_tree",opts.method,curr_dist]))
curr_filepath = write_tree(opts.output_dir,base_name,test_tree,safe_tip_to_predict)
if opts.verbose:
print "Wrote test tree to: %s" % curr_filepath
#Write expected trait table
base_name = "--".join(map(str,["exp_traits",opts.method,curr_dist,safe_tip_to_predict]))
exp_trait_table_lines = [trait_table_header]
exp_trait_table_lines.append("\t".join(expected_traits)+"\n")
#print "Expected_trait_table_lines:",exp_trait_table_lines
filename=os.path.join(opts.output_dir,base_name)
if opts.verbose:
print "Writing expected trait table to:", filename
f=open(filename,"w")
f.write("".join(exp_trait_table_lines))
f.close()
#Output a transposed, BIOM format expectation table for comparison with predict_traits output
#NOTE: this is a clumsy way of getting the translated trait table
# but more elegant, direct methods (directly feeding data to biom's table_factory)
# weren't working for me readily. In the future, we should streamline this process
# Leaving as is for now since this code is mostly for developers so speed/elegence
# are probably not essential here.
#Let the hackishness begin
#Reload the tab-delimited trait table
header, fields = parse_trait_table(open(filename,"U"))
fields = [f for f in fields] #converts generator to list
#Transpose table for .BIOM format so that Observation ids are KOs
transposed_header, transposed_trait_table_lines =\
transpose_trait_table_fields(fields,header,\
id_row_idx=0, input_header_delimiter="\t",output_delimiter="\t")
#Eliminate newline in header
trans_trait_table_lines = [transposed_header.strip()]
trans_trait_table_lines.extend(["\t".join(r) for r in transposed_trait_table_lines])
trans_trait_table = '\n'.join(trans_trait_table_lines)
#Write BIOM format expected trait table
base_name = "--".join(map(str,["exp_biom_traits",opts.method,curr_dist,safe_tip_to_predict]))
expected_biom_table = parse_table_to_biom(trans_trait_table.split('\n'),\
table_format = "tab-delimited")
#print "Expected_trait_table_lines:",exp_trait_table_lines
filename=os.path.join(opts.output_dir,base_name)
if opts.verbose:
print "Writing BIOM-format expected trait table to:", filename
f=open(filename,"w")
f.write(format_biom_table(expected_biom_table))
f.close()
#Write test trait table
test_trait_table_fields = test_trait_table_fields
if expected_traits in test_trait_table_fields:
test_trait_table_fields.remove(expected_traits)
test_trait_table_lines = [trait_table_header]
test_trait_table_lines.extend(["\t".join(r)+"\n" for r in test_trait_table_fields])
#print "Test_trait_table_lines:",test_trait_table_lines
base_name = "--".join(map(str,["test_trait_table",opts.method,curr_dist,safe_tip_to_predict]))
filename=os.path.join(opts.output_dir,base_name)
if opts.verbose:
print "Writing test trait table to:", filename
f=open(filename,"w")
f.write("".join(test_trait_table_lines))
f.close()
if opts.verbose:
print "Done generating test datasets"
def main():
option_parser, opts, args =\
parse_command_line_parameters(**script_info)
#if we specify we want NSTI only then we have to calculate it first
if opts.output_accuracy_metrics_only:
opts.calculate_accuracy_metrics=True
if opts.verbose:
print "Loading tree from file:", opts.tree
# Load Tree
#tree = LoadTree(opts.tree)
tree = load_picrust_tree(opts.tree, opts.verbose)
table_headers =[]
traits={}
#load the asr trait table using the previous list of functions to order the arrays
if opts.reconstructed_trait_table:
table_headers,traits =\
update_trait_dict_from_file(opts.reconstructed_trait_table)
#Only load confidence intervals on the reconstruction
#If we actually have ASR values in the analysis
if opts.reconstruction_confidence:
if opts.verbose:
print "Loading ASR confidence data from file:",\
opts.reconstruction_confidence
print "Assuming confidence data is of type:",opts.confidence_format
asr_confidence_output = open(opts.reconstruction_confidence)
asr_min_vals,asr_max_vals, params,column_mapping =\
parse_asr_confidence_output(asr_confidence_output,format=opts.confidence_format)
if 'sigma' in params:
brownian_motion_parameter = params['sigma'][0]
brownian_motion_error = params['sigma'][1]
else:
brownian_motion_parameter = None
if opts.verbose:
print "Done. Loaded %i confidence interval values." %(len(asr_max_vals))
print "Brownian motion parameter:",brownian_motion_parameter
else:
brownian_motion_parameter = None
#load the trait table into a dict with organism names as keys and arrays as functions
table_headers,genome_traits =\
update_trait_dict_from_file(opts.observed_trait_table,table_headers)
#Combine the trait tables overwriting the asr ones if they exist in the genome trait table.
traits.update(genome_traits)
# Specify the attribute where we'll store the reconstructions
trait_label = "Reconstruction"
if opts.verbose:
print "Assigning traits to tree..."
# Decorate tree using the traits
tree = assign_traits_to_tree(traits,tree, trait_label=trait_label)
if opts.reconstruction_confidence:
if opts.verbose:
print "Assigning trait confidence intervals to tree..."
tree = assign_traits_to_tree(asr_min_vals,tree,\
trait_label="lower_bound")
tree = assign_traits_to_tree(asr_max_vals,tree,\
trait_label="upper_bound")
if brownian_motion_parameter is None:
if opts.verbose:
print "No Brownian motion parameters loaded. Inferring these from 95% confidence intervals..."
brownian_motion_parameter = get_brownian_motion_param_from_confidence_intervals(tree,\
upper_bound_trait_label="upper_bound",\
lower_bound_trait_label="lower_bound",\
trait_label=trait_label,\
confidence=0.95)
if opts.verbose:
print "Inferred the following rate parameters:",brownian_motion_parameter
if opts.verbose:
print "Collecting list of nodes to predict..."
#Start by predict all tip nodes.
nodes_to_predict = [tip.Name for tip in tree.tips()]
if opts.verbose:
print "Found %i nodes to predict." % len(nodes_to_predict)
if opts.limit_predictions_to_organisms:
organism_id_str = opts.limit_predictions_to_organisms
ok_organism_ids = organism_id_str.split(',')
ok_organism_ids = [n.strip() for n in ok_organism_ids]
for f in set_label_conversion_fns(True,True):
ok_organism_ids = [f(i) for i in ok_organism_ids]
if opts.verbose:
print "Limiting predictions to user-specified ids:",\
",".join(ok_organism_ids)
if not ok_organism_ids:
raise RuntimeError(\
"Found no valid ids in input: %s. Were comma-separated ids specified on the command line?"\
% opts.limit_predictions_to_organisms)
nodes_to_predict =\
[n for n in nodes_to_predict if n in ok_organism_ids]
if not nodes_to_predict:
raise RuntimeError(\
"Filtering by user-specified ids resulted in an empty set of nodes to predict. Are the ids on the commmand-line and tree ids in the same format? Example tree tip name: %s, example OTU id name: %s" %([tip.Name for tip in tree.tips()][0],ok_organism_ids[0]))
if opts.verbose:
print "After filtering organisms to predict by the ids specified on the commandline, %i nodes remain to be predicted" %(len(nodes_to_predict))
if opts.limit_predictions_by_otu_table:
if opts.verbose:
print "Limiting predictions to ids in user-specified OTU table:",\
opts.limit_predictions_by_otu_table
otu_table = open(opts.limit_predictions_by_otu_table,"U")
#Parse OTU table for ids
otu_ids =\
extract_ids_from_table(otu_table.readlines(),delimiter="\t")
if not otu_ids:
raise RuntimeError(\
"Found no valid ids in input OTU table: %s. Is the path correct?"\
% opts.limit_predictions_by_otu_table)
nodes_to_predict =\
[n for n in nodes_to_predict if n in otu_ids]
if not nodes_to_predict:
raise RuntimeError(\
"Filtering by OTU table resulted in an empty set of nodes to predict. Are the OTU ids and tree ids in the same format? Example tree tip name: %s, example OTU id name: %s" %([tip.Name for tip in tree.tips()][0],otu_ids[0]))
if opts.verbose:
print "After filtering by OTU table, %i nodes remain to be predicted" %(len(nodes_to_predict))
# Calculate accuracy of PICRUST for the given tree, sequenced genomes
# and set of ndoes to predict
accuracy_metrics = ['NSTI']
accuracy_metric_results = None
if opts.calculate_accuracy_metrics:
if opts.verbose:
print "Calculating accuracy metrics: %s" %([",".join(accuracy_metrics)])
accuracy_metric_results = {}
if 'NSTI' in accuracy_metrics:
nsti_result,min_distances =\
calc_nearest_sequenced_taxon_index(tree,\
limit_to_tips = nodes_to_predict,\
trait_label = trait_label, verbose=opts.verbose)
#accuracy_metric_results['NSTI'] = nsti_result
for organism in min_distances.keys():
accuracy_metric_results[organism] = {'NSTI': min_distances[organism]}
if opts.verbose:
print "NSTI:", nsti_result
if opts.output_accuracy_metrics_only:
#Write accuracy metrics to file
if opts.verbose:
print "Writing accuracy metrics to file:",opts.output_accuracy_metrics
f = open(opts.output_accuracy_metrics_only,'w+')
f.write("metric\torganism\tvalue\n")
lines =[]
for organism in accuracy_metric_results.keys():
for metric in accuracy_metric_results[organism].keys():
lines.append('\t'.join([metric,organism,\
str(accuracy_metric_results[organism][metric])])+'\n')
f.writelines(sorted(lines))
f.close()
exit()
if opts.verbose:
print "Generating predictions using method:",opts.prediction_method
if opts.weighting_method == 'exponential':
#For now, use exponential weighting
weight_fn = make_neg_exponential_weight_fn(e)
elif opts.weighting_method == 'linear':
#Linear weight function
weight_fn = linear_weight
elif opts.weighting_method == 'equal_weight':
weight_fn = equal_weight
variances=None #Overwritten by methods that calc variance
confidence_intervals=None #Overwritten by methods that calc variance
if opts.prediction_method == 'asr_and_weighting':
# Perform predictions using reconstructed ancestral states
if opts.reconstruction_confidence:
predictions,variances,confidence_intervals =\
predict_traits_from_ancestors(tree,nodes_to_predict,\
trait_label=trait_label,\
lower_bound_trait_label="lower_bound",\
upper_bound_trait_label="upper_bound",\
calc_confidence_intervals = True,\
brownian_motion_parameter=brownian_motion_parameter,\
weight_fn =weight_fn,verbose=opts.verbose)
else:
predictions =\
predict_traits_from_ancestors(tree,nodes_to_predict,\
trait_label=trait_label,\
weight_fn =weight_fn,verbose=opts.verbose)
elif opts.prediction_method == 'weighting_only':
#Ignore ancestral information
predictions =\
weighted_average_tip_prediction(tree,nodes_to_predict,\
trait_label=trait_label,\
weight_fn =weight_fn,verbose=opts.verbose)
elif opts.prediction_method == 'nearest_neighbor':
predictions = predict_nearest_neighbor(tree,nodes_to_predict,\
trait_label=trait_label,tips_only = True)
elif opts.prediction_method == 'random_neighbor':
predictions = predict_random_neighbor(tree,\
nodes_to_predict,trait_label=trait_label)
else:
error_template =\
"Prediction method '%s' is not supported. Valid methods are: %s'"
error_text = error_template %(opts.prediction_method,\
", ".join(METHOD_CHOICES))
if opts.verbose:
print "Done making predictions."
make_output_dir_for_file(opts.output_trait_table)
out_fh=open(opts.output_trait_table,'w')
#Generate the table of biom predictions
if opts.verbose:
print "Converting results to .biom format for output..."
biom_predictions=biom_table_from_predictions(predictions,table_headers,\
observation_metadata=None,\
sample_metadata=accuracy_metric_results,convert_to_int=False)
if opts.verbose:
print "Writing prediction results to file: ",opts.output_trait_table
if opts.output_precalc_file_in_biom:
#write biom table to file
out_fh.write(format_biom_table(biom_predictions))
else:
#convert to precalc (tab-delimited) format
out_fh.write(convert_biom_to_precalc(format_biom_table(biom_predictions)))
#Write out variance information to file
if variances:
if opts.verbose:
print "Converting variances to BIOM format"
if opts.output_precalc_file_in_biom:
suffix='.biom'
else:
suffix='.tab'
biom_prediction_variances=biom_table_from_predictions({k:v['variance'] for k,v in variances.iteritems()},table_headers,\
observation_metadata=None,\
sample_metadata=None,convert_to_int=False)
outfile_base,extension = splitext(opts.output_trait_table)
variance_outfile = outfile_base+"_variances"+suffix
make_output_dir_for_file(variance_outfile)
if opts.verbose:
print "Writing variance information to file:",variance_outfile
if opts.output_precalc_file_in_biom:
open(variance_outfile,'w').write(\
format_biom_table(biom_prediction_variances))
else:
open(variance_outfile,'w').write(\
convert_biom_to_precalc(format_biom_table(biom_prediction_variances)))
if confidence_intervals:
if opts.verbose:
print "Converting upper confidence interval values to BIOM format"
biom_prediction_upper_CI=biom_table_from_predictions({k:v['upper_CI'] for k,v in confidence_intervals.iteritems()},table_headers,\
observation_metadata=None,\
sample_metadata=None,convert_to_int=False)
outfile_base,extension = splitext(opts.output_trait_table)
upper_CI_outfile = outfile_base+"_upper_CI"+suffix
make_output_dir_for_file(upper_CI_outfile)
if opts.verbose:
print "Writing upper confidence limit information to file:",upper_CI_outfile
if opts.output_precalc_file_in_biom:
open(upper_CI_outfile,'w').write(\
format_biom_table(biom_prediction_upper_CI))
else:
open(upper_CI_outfile,'w').write(\
convert_biom_to_precalc(format_biom_table(biom_prediction_upper_CI)))
biom_prediction_lower_CI=biom_table_from_predictions({k:v['lower_CI'] for k,v in confidence_intervals.iteritems()},table_headers,\
observation_metadata=None,\
sample_metadata=None,convert_to_int=False)
outfile_base,extension = splitext(opts.output_trait_table)
lower_CI_outfile = outfile_base+"_lower_CI"+suffix
make_output_dir_for_file(lower_CI_outfile)
if opts.verbose:
print "Writing lower confidence limit information to file",lower_CI_outfile
if opts.output_precalc_file_in_biom:
open(lower_CI_outfile,'w').write(\
format_biom_table(biom_prediction_lower_CI))
else:
open(lower_CI_outfile,'w').write(\
convert_biom_to_precalc(format_biom_table(biom_prediction_lower_CI)))
def main():
option_parser, opts, args =\
parse_command_line_parameters(**script_info)
if opts.verbose:
print "Loading tree from file:", opts.tree
# Load Tree
#tree = LoadTree(opts.tree)
tree = load_picrust_tree(opts.tree, opts.verbose)
table_headers =[]
traits={}
#load the asr trait table using the previous list of functions to order the arrays
if opts.reconstructed_trait_table:
table_headers,traits =\
update_trait_dict_from_file(opts.reconstructed_trait_table)
#Only load confidence intervals on the reconstruction
#If we actually have ASR values in the analysis
if opts.reconstruction_confidence:
if opts.verbose:
print "Loading ASR confidence data from file:",\
opts.reconstruction_confidence
asr_confidence_output = open(opts.reconstruction_confidence)
asr_min_vals,asr_max_vals, params,column_mapping =\
parse_asr_confidence_output(asr_confidence_output)
brownian_motion_parameter = params['sigma'][0]
brownian_motion_error = params['sigma'][1]
if opts.verbose:
print "Done. Loaded %i confidence interval values." %(len(asr_max_vals))
print "Brownian motion parameter:",brownian_motion_parameter
else:
brownian_motion_parameter = None
#load the trait table into a dict with organism names as keys and arrays as functions
table_headers,genome_traits =\
update_trait_dict_from_file(opts.observed_trait_table,table_headers)
#Combine the trait tables overwriting the asr ones if they exist in the genome trait table.
traits.update(genome_traits)
# Specify the attribute where we'll store the reconstructions
trait_label = "Reconstruction"
if opts.verbose:
print "Assigning traits to tree..."
# Decorate tree using the traits
tree = assign_traits_to_tree(traits,tree, trait_label=trait_label)
if opts.reconstruction_confidence:
if opts.verbose:
print "Assigning trait confidence intervals to tree..."
tree = assign_traits_to_tree(asr_min_vals,tree,\
trait_label="lower_bound")
tree = assign_traits_to_tree(asr_max_vals,tree,\
trait_label="upper_bound")
if opts.verbose:
print "Collecting list of nodes to predict..."
#Start by predict all tip nodes.
nodes_to_predict = [tip.Name for tip in tree.tips()]
if opts.verbose:
print "Found %i nodes to predict." % len(nodes_to_predict)
if opts.limit_predictions_to_organisms:
organism_id_str = opts.limit_predictions_to_organisms
ok_organism_ids = organism_id_str.split(',')
ok_organism_ids = [n.strip() for n in ok_organism_ids]
for f in set_label_conversion_fns(True,True):
ok_organism_ids = [f(i) for i in ok_organism_ids]
if opts.verbose:
print "Limiting predictions to user-specified ids:",\
",".join(ok_organism_ids)
if not ok_organism_ids:
raise RuntimeError(\
"Found no valid ids in input: %s. Were comma-separated ids specified on the command line?"\
% opts.limit_predictions_to_organisms)
nodes_to_predict =\
[n for n in nodes_to_predict if n in ok_organism_ids]
if not nodes_to_predict:
raise RuntimeError(\
"Filtering by user-specified ids resulted in an empty set of nodes to predict. Are the ids on the commmand-line and tree ids in the same format? Example tree tip name: %s, example OTU id name: %s" %([tip.Name for tip in tree.tips()][0],ok_organism_ids[0]))
if opts.verbose:
print "After filtering organisms to predict by the ids specified on the commandline, %i nodes remain to be predicted" %(len(nodes_to_predict))
if opts.limit_predictions_by_otu_table:
if opts.verbose:
print "Limiting predictions to ids in user-specified OTU table:",\
opts.limit_predictions_by_otu_table
otu_table = open(opts.limit_predictions_by_otu_table,"U")
#Parse OTU table for ids
otu_ids =\
extract_ids_from_table(otu_table.readlines(),delimiter="\t")
if not otu_ids:
raise RuntimeError(\
"Found no valid ids in input OTU table: %s. Is the path correct?"\
% opts.limit_predictions_by_otu_table)
nodes_to_predict =\
[n for n in nodes_to_predict if n in otu_ids]
if not nodes_to_predict:
raise RuntimeError(\
"Filtering by OTU table resulted in an empty set of nodes to predict. Are the OTU ids and tree ids in the same format? Example tree tip name: %s, example OTU id name: %s" %([tip.Name for tip in tree.tips()][0],otu_ids[0]))
if opts.verbose:
print "After filtering by OTU table, %i nodes remain to be predicted" %(len(nodes_to_predict))
# Calculate accuracy of PICRUST for the given tree, sequenced genomes
# and set of ndoes to predict
accuracy_metrics = ['NSTI']
accuracy_metric_results = None
if opts.output_accuracy_metrics:
if opts.verbose:
print "Calculating accuracy metrics: %s" %([",".join(accuracy_metrics)])
accuracy_metric_results = {}
if 'NSTI' in accuracy_metrics:
nsti_result,min_distances =\
calc_nearest_sequenced_taxon_index(tree,\
limit_to_tips = nodes_to_predict,\
trait_label = trait_label, verbose=opts.verbose)
#accuracy_metric_results['NSTI'] = nsti_result
for organism in min_distances.keys():
accuracy_metric_results[organism] = {'NSTI': min_distances[organism]}
if opts.verbose:
print "NSTI:", nsti_result
#Write accuracy metrics to file
if opts.verbose:
print "Writing accuracy metrics to file:",opts.output_accuracy_metrics
f = open(opts.output_accuracy_metrics,'w+')
lines = ["metric\torganism\tvalue\n"]
for organism in accuracy_metric_results.keys():
for metric in accuracy_metric_results[organism].keys():
lines.append('\t'.join([metric,organism,\
str(accuracy_metric_results[organism][metric])])+'\n')
f.writelines(sorted(lines))
f.close()
if opts.verbose:
print "Generating predictions using method:",opts.prediction_method
if opts.weighting_method == 'exponential':
#For now, use exponential weighting
weight_fn = make_neg_exponential_weight_fn(e)
elif opts.weighting_method == 'linear':
#Linear weight function
weight_fn = linear_weight
elif opts.weighting_method == 'equal_weight':
weight_fn = equal_weight
variances=None #Overwritten by methods that calc variance
if opts.prediction_method == 'asr_and_weighting':
if opts.reconstruction_confidence:
# Perform predictions using reconstructed ancestral states
predictions,variances =\
predict_traits_from_ancestors(tree,nodes_to_predict,\
trait_label=trait_label,\
lower_bound_trait_label="lower_bound",\
upper_bound_trait_label="upper_bound",\
calc_confidence_intervals = True,\
brownian_motion_parameter=brownian_motion_parameter,\
use_self_in_prediction = True,\
weight_fn =weight_fn,verbose=opts.verbose)
else:
predictions =\
predict_traits_from_ancestors(tree,nodes_to_predict,\
trait_label=trait_label,\
use_self_in_prediction = True,\
weight_fn =weight_fn,verbose=opts.verbose)
elif opts.prediction_method == 'weighting_only':
#Ignore ancestral information
predictions =\
weighted_average_tip_prediction(tree,nodes_to_predict,\
trait_label=trait_label,\
use_self_in_prediction = True,\
weight_fn =weight_fn,verbose=opts.verbose)
elif opts.prediction_method == 'nearest_neighbor':
predictions = predict_nearest_neighbor(tree,nodes_to_predict,\
trait_label=trait_label,\
use_self_in_prediction = True, tips_only = True)
elif opts.prediction_method == 'random_neighbor':
predictions = predict_random_neighbor(tree,\
nodes_to_predict,trait_label=trait_label,\
use_self_in_prediction = True)
else:
error_template =\
"Prediction method '%s' is not supported. Valid methods are: %s'"
error_text = error_template %(opts.prediction_method,\
", ".join(METHOD_CHOICES))
if opts.verbose:
print "Converting results to .biom format for output..."
#convert to biom format (and transpose)
biom_predictions=biom_table_from_predictions(predictions,table_headers)
#In the .biom table, organisms are 'samples' and traits are 'observations
#(by analogy with a metagenomic sample)
#Therefore, we associate the trait variances with the per-observation metadata
#print "variances:",variances
#print "BIOM observations:", [o for o in biom_predictions.iterObservations()]
#print "BIOM samples:", [s for s in biom_predictions.iterSamples()]
if variances is not None:
if opts.verbose:
print "Adding variance information to output .biom table, as per-observation metadata with key 'variance'..."
biom_predictions.addSampleMetadata(variances)
if accuracy_metric_results is not None:
if opts.verbose:
print "Adding accuracy metrics (%s) to biom table as per-observation metadata..." %(",".join(accuracy_metrics))
biom_predictions.addSampleMetadata(accuracy_metric_results)
#Add variance information as per observation metadata
if opts.verbose:
print "Writing biom format prediction results to file: ",opts.output_trait_table
#write biom table to file
make_output_dir_for_file(opts.output_trait_table)
open(opts.output_trait_table,'w').write(\
format_biom_table(biom_predictions))
def main():
"""Generate test trees given parameters"""
option_parser, opts, args =\
parse_command_line_parameters(**script_info)
if opts.verbose:
print "Loading trait table..."
input_trait_table = open(opts.input_trait_table, "U")
if opts.verbose:
print "Loading tree..."
#PicrustNode seems to run into very slow/memory intentsive perfromance...
#tree = DndParser(open(opts.input_tree),constructor=PicrustNode)
tree = DndParser(open(opts.input_tree))
if opts.verbose:
print "Parsing trait table..."
#Find which taxa are to be used in tests
#(by default trait table taxa)
trait_table_header,trait_table_fields = \
parse_trait_table(input_trait_table)
if opts.verbose:
print "Ensuring tree and trait table labels are formatted consistently..."
label_conversion_fns = set_label_conversion_fns(verbose=opts.verbose)
fix_tree_labels(tree, label_conversion_fns)
trait_table_fields = convert_trait_table_entries(trait_table_fields,\
value_conversion_fns = [],\
label_conversion_fns = label_conversion_fns)
trait_table_fields = [t for t in trait_table_fields]
print "Number of trait table fields with single quotes:",\
len([t for t in trait_table_fields if "'" in t[0]])
if opts.verbose:
print "Making output directory..."
make_output_dir(opts.output_dir)
if opts.limit_to_tips:
included_tips = opts.limit_to_tips.split(",")
if opts.verbose:
print "Limiting test datasets to %i tips: %s" % (
len(included_tips), included_tips)
else:
included_tips = False
method_fns =\
{"exclude_tips_by_distance":\
make_distance_based_exclusion_fn,\
"randomize_tip_labels_by_distance":\
make_distance_based_tip_label_randomizer
}
test_fn_factory = method_fns[opts.method]
if opts.verbose:
print "Setting tree modification method to:", opts.method
print "(%s)" % test_fn_factory.__doc__
modify_tree = True
if opts.suppress_tree_modification:
if opts.verbose:
print "Suppressing modification of tree when making test datasets"
modify_tree = False
if opts.verbose:
print "Starting generation of test datsets"
test_datasets = \
yield_genome_test_data_by_distance(tree,trait_table_fields,\
test_fn_factory,min_dist = opts.min_dist,\
max_dist=opts.max_dist,increment=opts.dist_increment,\
modify_tree=modify_tree,limit_to_tips= included_tips,verbose = opts.verbose)
if opts.verbose:
print "Writing files for test datasets"
for curr_dist,test_tree,tip_to_predict,\
expected_traits,test_trait_table_fields in test_datasets:
if included_tips is not False:
if tip_to_predict not in included_tips:
if opts.verbose:
print "Skipping tip %s: limiting to tip(s): %s" % (
tip_to_predict, included_tips)
continue
#Make a safe version of tip to predict
# So odd characters like | don't mess up OS
safe_tip_to_predict = "'%s'" % tip_to_predict
#Write tree
base_name = "--".join(map(str, ["test_tree", opts.method, curr_dist]))
curr_filepath = write_tree(opts.output_dir, base_name, test_tree,
safe_tip_to_predict)
if opts.verbose:
print "Wrote test tree to: %s" % curr_filepath
#Write expected trait table
base_name = "--".join(
map(str,
["exp_traits", opts.method, curr_dist, safe_tip_to_predict]))
exp_trait_table_lines = [trait_table_header]
exp_trait_table_lines.append("\t".join(expected_traits) + "\n")
#print "Expected_trait_table_lines:",exp_trait_table_lines
filename = os.path.join(opts.output_dir, base_name)
if opts.verbose:
print "Writing expected trait table to:", filename
f = open(filename, "w")
f.write("".join(exp_trait_table_lines))
f.close()
#Output a transposed, BIOM format expectation table for comparison with predict_traits output
#NOTE: this is a clumsy way of getting the translated trait table
# but more elegant, direct methods (directly feeding data to biom's table_factory)
# weren't working for me readily. In the future, we should streamline this process
# Leaving as is for now since this code is mostly for developers so speed/elegence
# are probably not essential here.
#Let the hackishness begin
#Reload the tab-delimited trait table
header, fields = parse_trait_table(open(filename, "U"))
fields = [f for f in fields] #converts generator to list
#Transpose table for .BIOM format so that Observation ids are KOs
transposed_header, transposed_trait_table_lines =\
transpose_trait_table_fields(fields,header,\
id_row_idx=0, input_header_delimiter="\t",output_delimiter="\t")
#Eliminate newline in header
trans_trait_table_lines = [transposed_header.strip()]
trans_trait_table_lines.extend(
["\t".join(r) for r in transposed_trait_table_lines])
trans_trait_table = '\n'.join(trans_trait_table_lines)
#Write BIOM format expected trait table
base_name = "--".join(
map(str, [
"exp_biom_traits", opts.method, curr_dist, safe_tip_to_predict
]))
expected_biom_table = parse_table_to_biom(trans_trait_table.split('\n'),\
table_format = "tab-delimited")
#print "Expected_trait_table_lines:",exp_trait_table_lines
filename = os.path.join(opts.output_dir, base_name)
if opts.verbose:
print "Writing BIOM-format expected trait table to:", filename
f = open(filename, "w")
f.write(format_biom_table(expected_biom_table))
f.close()
#Write test trait table
test_trait_table_fields = test_trait_table_fields
if expected_traits in test_trait_table_fields:
test_trait_table_fields.remove(expected_traits)
test_trait_table_lines = [trait_table_header]
test_trait_table_lines.extend(
["\t".join(r) + "\n" for r in test_trait_table_fields])
#print "Test_trait_table_lines:",test_trait_table_lines
base_name = "--".join(
map(str, [
"test_trait_table", opts.method, curr_dist, safe_tip_to_predict
]))
filename = os.path.join(opts.output_dir, base_name)
if opts.verbose:
print "Writing test trait table to:", filename
f = open(filename, "w")
f.write("".join(test_trait_table_lines))
f.close()
if opts.verbose:
print "Done generating test datasets"
def main():
option_parser, opts, args =\
parse_command_line_parameters(**script_info)
input_ext = path.splitext(opts.input_otu_fp)[1]
if opts.input_format_classic:
otu_table = parse_classic_table_to_rich_table(
open(opts.input_otu_fp, 'U'), None, None, None, DenseOTUTable)
else:
try:
otu_table = parse_biom_table(open(opts.input_otu_fp, 'U'))
except ValueError:
raise ValueError(
"Error loading OTU table! If not in BIOM format use '-f' option.\n"
)
ids_to_load = otu_table.ObservationIds
if (opts.input_count_fp is None):
#precalc file has specific name (e.g. 16S_13_5_precalculated.tab.gz)
precalc_file_name = '_'.join(
['16S', 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_fp
if opts.verbose:
print "Loading trait table: ", input_count_table
ext = path.splitext(input_count_table)[1]
if (ext == '.gz'):
count_table_fh = gzip.open(input_count_table, 'rb')
else:
count_table_fh = open(input_count_table, 'U')
if opts.load_precalc_file_in_biom:
count_table = parse_biom_table(count_table_fh.read())
else:
count_table = convert_precalc_to_biom(count_table_fh, ids_to_load)
#Need to only keep data relevant to our otu list
ids = []
for x in otu_table.iterObservations():
ids.append(str(x[1]))
ob_id = count_table.ObservationIds[0]
filtered_otus = []
filtered_values = []
for x in ids:
if count_table.sampleExists(x):
filtered_otus.append(x)
filtered_values.append(otu_table.observationData(x))
#filtered_values = map(list,zip(*filtered_values))
filtered_otu_table = table_factory(filtered_values,
otu_table.SampleIds,
filtered_otus,
constructor=DenseOTUTable)
copy_numbers_filtered = {}
for x in filtered_otus:
value = count_table.getValueByIds(ob_id, x)
try:
#data can be floats so round them and make them integers
value = int(round(float(value)))
except ValueError:
raise ValueError,\
"Invalid type passed as copy number for OTU ID %s. Must be int-able." % (value)
if value < 1:
raise ValueError, "Copy numbers must be greater than or equal to 1."
copy_numbers_filtered[x] = {opts.metadata_identifer: value}
filtered_otu_table.addObservationMetadata(copy_numbers_filtered)
normalized_table = filtered_otu_table.normObservationByMetadata(
opts.metadata_identifer)
#move Observation Metadata from original to filtered OTU table
normalized_table = transfer_observation_metadata(otu_table,
normalized_table,
'ObservationMetadata')
normalized_otu_table = transfer_sample_metadata(otu_table,
normalized_table,
'SampleMetadata')
make_output_dir_for_file(opts.output_otu_fp)
open(opts.output_otu_fp, 'w').write(format_biom_table(normalized_table))
def main():
option_parser, opts, args =\
parse_command_line_parameters(**script_info)
#if we specify we want NSTI only then we have to calculate it first
if opts.output_accuracy_metrics_only:
opts.calculate_accuracy_metrics = True
if opts.verbose:
print "Loading tree from file:", opts.tree
# Load Tree
#tree = LoadTree(opts.tree)
tree = load_picrust_tree(opts.tree, opts.verbose)
table_headers = []
traits = {}
#load the asr trait table using the previous list of functions to order the arrays
if opts.reconstructed_trait_table:
table_headers,traits =\
update_trait_dict_from_file(opts.reconstructed_trait_table)
#Only load confidence intervals on the reconstruction
#If we actually have ASR values in the analysis
if opts.reconstruction_confidence:
if opts.verbose:
print "Loading ASR confidence data from file:",\
opts.reconstruction_confidence
print "Assuming confidence data is of type:", opts.confidence_format
asr_confidence_output = open(opts.reconstruction_confidence)
asr_min_vals,asr_max_vals, params,column_mapping =\
parse_asr_confidence_output(asr_confidence_output,format=opts.confidence_format)
if 'sigma' in params:
brownian_motion_parameter = params['sigma'][0]
brownian_motion_error = params['sigma'][1]
else:
brownian_motion_parameter = None
if opts.verbose:
print "Done. Loaded %i confidence interval values." % (
len(asr_max_vals))
print "Brownian motion parameter:", brownian_motion_parameter
else:
brownian_motion_parameter = None
#load the trait table into a dict with organism names as keys and arrays as functions
table_headers,genome_traits =\
update_trait_dict_from_file(opts.observed_trait_table,table_headers)
#Combine the trait tables overwriting the asr ones if they exist in the genome trait table.
traits.update(genome_traits)
# Specify the attribute where we'll store the reconstructions
trait_label = "Reconstruction"
if opts.verbose:
print "Assigning traits to tree..."
# Decorate tree using the traits
tree = assign_traits_to_tree(traits, tree, trait_label=trait_label)
if opts.reconstruction_confidence:
if opts.verbose:
print "Assigning trait confidence intervals to tree..."
tree = assign_traits_to_tree(asr_min_vals,tree,\
trait_label="lower_bound")
tree = assign_traits_to_tree(asr_max_vals,tree,\
trait_label="upper_bound")
if brownian_motion_parameter is None:
if opts.verbose:
print "No Brownian motion parameters loaded. Inferring these from 95% confidence intervals..."
brownian_motion_parameter = get_brownian_motion_param_from_confidence_intervals(tree,\
upper_bound_trait_label="upper_bound",\
lower_bound_trait_label="lower_bound",\
trait_label=trait_label,\
confidence=0.95)
if opts.verbose:
print "Inferred the following rate parameters:", brownian_motion_parameter
if opts.verbose:
print "Collecting list of nodes to predict..."
#Start by predict all tip nodes.
nodes_to_predict = [tip.Name for tip in tree.tips()]
if opts.verbose:
print "Found %i nodes to predict." % len(nodes_to_predict)
if opts.limit_predictions_to_organisms:
organism_id_str = opts.limit_predictions_to_organisms
ok_organism_ids = organism_id_str.split(',')
ok_organism_ids = [n.strip() for n in ok_organism_ids]
for f in set_label_conversion_fns(True, True):
ok_organism_ids = [f(i) for i in ok_organism_ids]
if opts.verbose:
print "Limiting predictions to user-specified ids:",\
",".join(ok_organism_ids)
if not ok_organism_ids:
raise RuntimeError(\
"Found no valid ids in input: %s. Were comma-separated ids specified on the command line?"\
% opts.limit_predictions_to_organisms)
nodes_to_predict =\
[n for n in nodes_to_predict if n in ok_organism_ids]
if not nodes_to_predict:
raise RuntimeError(\
"Filtering by user-specified ids resulted in an empty set of nodes to predict. Are the ids on the commmand-line and tree ids in the same format? Example tree tip name: %s, example OTU id name: %s" %([tip.Name for tip in tree.tips()][0],ok_organism_ids[0]))
if opts.verbose:
print "After filtering organisms to predict by the ids specified on the commandline, %i nodes remain to be predicted" % (
len(nodes_to_predict))
if opts.limit_predictions_by_otu_table:
if opts.verbose:
print "Limiting predictions to ids in user-specified OTU table:",\
opts.limit_predictions_by_otu_table
otu_table = open(opts.limit_predictions_by_otu_table, "U")
#Parse OTU table for ids
otu_ids =\
extract_ids_from_table(otu_table.readlines(),delimiter="\t")
if not otu_ids:
raise RuntimeError(\
"Found no valid ids in input OTU table: %s. Is the path correct?"\
% opts.limit_predictions_by_otu_table)
nodes_to_predict =\
[n for n in nodes_to_predict if n in otu_ids]
if not nodes_to_predict:
raise RuntimeError(\
"Filtering by OTU table resulted in an empty set of nodes to predict. Are the OTU ids and tree ids in the same format? Example tree tip name: %s, example OTU id name: %s" %([tip.Name for tip in tree.tips()][0],otu_ids[0]))
if opts.verbose:
print "After filtering by OTU table, %i nodes remain to be predicted" % (
len(nodes_to_predict))
# Calculate accuracy of PICRUST for the given tree, sequenced genomes
# and set of ndoes to predict
accuracy_metrics = ['NSTI']
accuracy_metric_results = None
if opts.calculate_accuracy_metrics:
if opts.verbose:
print "Calculating accuracy metrics: %s" % (
[",".join(accuracy_metrics)])
accuracy_metric_results = {}
if 'NSTI' in accuracy_metrics:
nsti_result,min_distances =\
calc_nearest_sequenced_taxon_index(tree,\
limit_to_tips = nodes_to_predict,\
trait_label = trait_label, verbose=opts.verbose)
#accuracy_metric_results['NSTI'] = nsti_result
for organism in min_distances.keys():
accuracy_metric_results[organism] = {
'NSTI': min_distances[organism]
}
if opts.verbose:
print "NSTI:", nsti_result
if opts.output_accuracy_metrics_only:
#Write accuracy metrics to file
if opts.verbose:
print "Writing accuracy metrics to file:", opts.output_accuracy_metrics
f = open(opts.output_accuracy_metrics_only, 'w+')
f.write("metric\torganism\tvalue\n")
lines = []
for organism in accuracy_metric_results.keys():
for metric in accuracy_metric_results[organism].keys():
lines.append('\t'.join([metric,organism,\
str(accuracy_metric_results[organism][metric])])+'\n')
f.writelines(sorted(lines))
f.close()
exit()
if opts.verbose:
print "Generating predictions using method:", opts.prediction_method
if opts.weighting_method == 'exponential':
#For now, use exponential weighting
weight_fn = make_neg_exponential_weight_fn(e)
elif opts.weighting_method == 'linear':
#Linear weight function
weight_fn = linear_weight
elif opts.weighting_method == 'equal_weight':
weight_fn = equal_weight
variances = None #Overwritten by methods that calc variance
confidence_intervals = None #Overwritten by methods that calc variance
if opts.prediction_method == 'asr_and_weighting':
# Perform predictions using reconstructed ancestral states
if opts.reconstruction_confidence:
predictions,variances,confidence_intervals =\
predict_traits_from_ancestors(tree,nodes_to_predict,\
trait_label=trait_label,\
lower_bound_trait_label="lower_bound",\
upper_bound_trait_label="upper_bound",\
calc_confidence_intervals = True,\
brownian_motion_parameter=brownian_motion_parameter,\
weight_fn =weight_fn,verbose=opts.verbose)
else:
predictions =\
predict_traits_from_ancestors(tree,nodes_to_predict,\
trait_label=trait_label,\
weight_fn =weight_fn,verbose=opts.verbose)
elif opts.prediction_method == 'weighting_only':
#Ignore ancestral information
predictions =\
weighted_average_tip_prediction(tree,nodes_to_predict,\
trait_label=trait_label,\
weight_fn =weight_fn,verbose=opts.verbose)
elif opts.prediction_method == 'nearest_neighbor':
predictions = predict_nearest_neighbor(tree,nodes_to_predict,\
trait_label=trait_label,tips_only = True)
elif opts.prediction_method == 'random_neighbor':
predictions = predict_random_neighbor(tree,\
nodes_to_predict,trait_label=trait_label)
else:
error_template =\
"Prediction method '%s' is not supported. Valid methods are: %s'"
error_text = error_template %(opts.prediction_method,\
", ".join(METHOD_CHOICES))
if opts.verbose:
print "Done making predictions."
make_output_dir_for_file(opts.output_trait_table)
out_fh = open(opts.output_trait_table, 'w')
#Generate the table of biom predictions
if opts.verbose:
print "Converting results to .biom format for output..."
biom_predictions=biom_table_from_predictions(predictions,table_headers,\
observation_metadata=None,\
sample_metadata=accuracy_metric_results,convert_to_int=False)
if opts.verbose:
print "Writing prediction results to file: ", opts.output_trait_table
if opts.output_precalc_file_in_biom:
#write biom table to file
out_fh.write(format_biom_table(biom_predictions))
else:
#convert to precalc (tab-delimited) format
out_fh.write(
convert_biom_to_precalc(format_biom_table(biom_predictions)))
#Write out variance information to file
if variances:
if opts.verbose:
print "Converting variances to BIOM format"
if opts.output_precalc_file_in_biom:
suffix = '.biom'
else:
suffix = '.tab'
biom_prediction_variances=biom_table_from_predictions({k:v['variance'] for k,v in variances.iteritems()},table_headers,\
observation_metadata=None,\
sample_metadata=None,convert_to_int=False)
outfile_base, extension = splitext(opts.output_trait_table)
variance_outfile = outfile_base + "_variances" + suffix
make_output_dir_for_file(variance_outfile)
if opts.verbose:
print "Writing variance information to file:", variance_outfile
if opts.output_precalc_file_in_biom:
open(variance_outfile,'w').write(\
format_biom_table(biom_prediction_variances))
else:
open(variance_outfile,'w').write(\
convert_biom_to_precalc(format_biom_table(biom_prediction_variances)))
if confidence_intervals:
if opts.verbose:
print "Converting upper confidence interval values to BIOM format"
biom_prediction_upper_CI=biom_table_from_predictions({k:v['upper_CI'] for k,v in confidence_intervals.iteritems()},table_headers,\
observation_metadata=None,\
sample_metadata=None,convert_to_int=False)
outfile_base, extension = splitext(opts.output_trait_table)
upper_CI_outfile = outfile_base + "_upper_CI" + suffix
make_output_dir_for_file(upper_CI_outfile)
if opts.verbose:
print "Writing upper confidence limit information to file:", upper_CI_outfile
if opts.output_precalc_file_in_biom:
open(upper_CI_outfile,'w').write(\
format_biom_table(biom_prediction_upper_CI))
else:
open(upper_CI_outfile,'w').write(\
convert_biom_to_precalc(format_biom_table(biom_prediction_upper_CI)))
biom_prediction_lower_CI=biom_table_from_predictions({k:v['lower_CI'] for k,v in confidence_intervals.iteritems()},table_headers,\
observation_metadata=None,\
sample_metadata=None,convert_to_int=False)
outfile_base, extension = splitext(opts.output_trait_table)
lower_CI_outfile = outfile_base + "_lower_CI" + suffix
make_output_dir_for_file(lower_CI_outfile)
if opts.verbose:
print "Writing lower confidence limit information to file", lower_CI_outfile
if opts.output_precalc_file_in_biom:
open(lower_CI_outfile,'w').write(\
format_biom_table(biom_prediction_lower_CI))
else:
open(lower_CI_outfile,'w').write(\
convert_biom_to_precalc(format_biom_table(biom_prediction_lower_CI)))
def main():
option_parser, opts, args =\
parse_command_line_parameters(**script_info)
input_ext=path.splitext(opts.input_otu_fp)[1]
if opts.input_format_classic:
otu_table=parse_classic_table_to_rich_table(open(opts.input_otu_fp,'U'),None,None,None,DenseOTUTable)
else:
try:
otu_table = parse_biom_table(open(opts.input_otu_fp,'U'))
except ValueError:
raise ValueError("Error loading OTU table! If not in BIOM format use '-f' option.\n")
ids_to_load = otu_table.ObservationIds
if(opts.input_count_fp is None):
#precalc file has specific name (e.g. 16S_13_5_precalculated.tab.gz)
precalc_file_name='_'.join(['16S',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_fp
if opts.verbose:
print "Loading trait table: ", input_count_table
ext=path.splitext(input_count_table)[1]
if (ext == '.gz'):
count_table_fh = gzip.open(input_count_table,'rb')
else:
count_table_fh = open(input_count_table,'U')
if opts.load_precalc_file_in_biom:
count_table = parse_biom_table(count_table_fh.read())
else:
count_table = convert_precalc_to_biom(count_table_fh,ids_to_load)
#Need to only keep data relevant to our otu list
ids=[]
for x in otu_table.iterObservations():
ids.append(str(x[1]))
ob_id=count_table.ObservationIds[0]
filtered_otus=[]
filtered_values=[]
for x in ids:
if count_table.sampleExists(x):
filtered_otus.append(x)
filtered_values.append(otu_table.observationData(x))
#filtered_values = map(list,zip(*filtered_values))
filtered_otu_table=table_factory(filtered_values,otu_table.SampleIds,filtered_otus, constructor=DenseOTUTable)
copy_numbers_filtered={}
for x in filtered_otus:
value = count_table.getValueByIds(ob_id,x)
try:
#data can be floats so round them and make them integers
value = int(round(float(value)))
except ValueError:
raise ValueError,\
"Invalid type passed as copy number for OTU ID %s. Must be int-able." % (value)
if value < 1:
raise ValueError, "Copy numbers must be greater than or equal to 1."
copy_numbers_filtered[x]={opts.metadata_identifer:value}
filtered_otu_table.addObservationMetadata(copy_numbers_filtered)
normalized_table = filtered_otu_table.normObservationByMetadata(opts.metadata_identifer)
#move Observation Metadata from original to filtered OTU table
normalized_table = transfer_observation_metadata(otu_table,normalized_table,'ObservationMetadata')
normalized_otu_table = transfer_sample_metadata(otu_table,normalized_table,'SampleMetadata')
make_output_dir_for_file(opts.output_otu_fp)
open(opts.output_otu_fp,'w').write(format_biom_table(normalized_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))
