def test_predict_metagenome_variances_propagates_variance(self):
""" predict_metagenomes correctly propagates differences in gene family variance as expected in a simple example"""
curr_otu_table = self.otu_table1
curr_genome_table = self.genome_table1
curr_variance_table = self.variance_table1_one_gene_one_otu
curr_exp_metagenome_table = self.predicted_metagenome_table1
curr_exp_metagenome_varaiance_table = self.predicted_metagenome_variance_table1_one_gene_one_otu
curr_exp_upper_CI_95 = self.predicted_metagenome_table1_one_gene_one_otu_upper_CI
curr_exp_lower_CI_95 = self.predicted_metagenome_table1_one_gene_one_otu_lower_CI
obs_prediction,obs_variances,obs_lower_CI_95,obs_upper_CI_95 =\
predict_metagenome_variances(curr_otu_table,curr_genome_table,gene_variances=curr_variance_table)
self.assertEqual(obs_prediction.delimitedSelf(),
curr_exp_metagenome_table.delimitedSelf())
#Expect no variance in f1 or f2 in any sample, and no variance in OTU 1 or 3.
#Otu 2 occurs in all samples except sample 3, so all samples except 3 should
#have variance. The exact values follow from variance of scaled random variables or
#The sum of random variables
self.assertEqual(
obs_variances,
self.predicted_metagenome_variance_table1_one_gene_one_otu)
#Check CIs against hand calculated CIs
self.assertEqual(obs_upper_CI_95.delimitedSelf(),
curr_exp_upper_CI_95.delimitedSelf())
self.assertEqual(obs_lower_CI_95.delimitedSelf(),
curr_exp_lower_CI_95.delimitedSelf())
def test_predict_metagenome_variances_returns_zero_variance_from_zero_variance(
self):
""" predict_metagenomes outputs correct results given zero variance input"""
curr_otu_table = self.otu_table1
curr_genome_table = self.genome_table1
curr_variance_table = self.zero_variance_table1
curr_exp_metagenome_table = self.predicted_metagenome_table1
curr_exp_metagenome_variance_table = self.predicted_metagenome_table1_zero_variance
obs_prediction,obs_variances,obs_lower_CI_95,obs_upper_CI_95 =\
predict_metagenome_variances(curr_otu_table,curr_genome_table,gene_variances=curr_variance_table)
#Test that the prediction itself is as expected
self.assertEqual(obs_prediction.delimitedSelf(),
curr_exp_metagenome_table.delimitedSelf())
#Test that the variance prediction is all zeros, as expected
self.assertEqual(obs_variances.delimitedSelf(),
curr_exp_metagenome_variance_table.delimitedSelf())
#Test that with zero variance, the upper and lower CIs are equal to the expected value (i.e. the prediction)
self.assertEqual(obs_lower_CI_95.delimitedSelf(),
curr_exp_metagenome_table.delimitedSelf())
self.assertEqual(obs_upper_CI_95.delimitedSelf(),
curr_exp_metagenome_table.delimitedSelf())
def test_predict_metagenome_variances_returns_zero_variance_from_zero_variance(self):
""" predict_metagenomes outputs correct results given zero variance input"""
curr_otu_table = self.otu_table1
curr_genome_table = self.genome_table1
curr_variance_table = self.zero_variance_table1
curr_exp_metagenome_table = self.predicted_metagenome_table1
curr_exp_metagenome_variance_table = self.predicted_metagenome_table1_zero_variance
obs_prediction,obs_variances,obs_lower_CI_95,obs_upper_CI_95 =\
predict_metagenome_variances(curr_otu_table,curr_genome_table,gene_variances=curr_variance_table)
#Test that the prediction itself is as expected
self.assertEqual(str(obs_prediction),
str(curr_exp_metagenome_table))
#Test that the variance prediction is all zeros, as expected
self.assertEqual(str(obs_variances),
str(curr_exp_metagenome_variance_table))
#Test that with zero variance, the upper and lower CIs are equal to the expected value (i.e. the prediction)
self.assertEqual(str(obs_lower_CI_95),
str(curr_exp_metagenome_table))
self.assertEqual(str(obs_upper_CI_95),
str(curr_exp_metagenome_table))
def test_predict_metagenome_variances_propagates_variance(self):
""" predict_metagenomes correctly propagates differences in gene family variance as expected in a simple example"""
curr_otu_table = self.otu_table1
curr_genome_table = self.genome_table1
curr_variance_table = self.variance_table1_one_gene_one_otu
curr_exp_metagenome_table = self.predicted_metagenome_table1
curr_exp_metagenome_varaiance_table = self.predicted_metagenome_variance_table1_one_gene_one_otu
curr_exp_upper_CI_95 = self.predicted_metagenome_table1_one_gene_one_otu_upper_CI
curr_exp_lower_CI_95 = self.predicted_metagenome_table1_one_gene_one_otu_lower_CI
obs_prediction,obs_variances,obs_lower_CI_95,obs_upper_CI_95 =\
predict_metagenome_variances(curr_otu_table,curr_genome_table,gene_variances=curr_variance_table)
self.assertEqual(str(obs_prediction),
str(curr_exp_metagenome_table))
#Expect no variance in f1 or f2 in any sample, and no variance in OTU 1 or 3.
#Otu 2 occurs in all samples except sample 3, so all samples except 3 should
#have variance. The exact values follow from variance of scaled random variables or
#The sum of random variables
self.assertEqual(obs_variances,self.predicted_metagenome_variance_table1_one_gene_one_otu)
#Check CIs against hand calculated CIs
self.assertEqual(str(obs_upper_CI_95),
str(curr_exp_upper_CI_95))
self.assertEqual(str(obs_lower_CI_95),
str(curr_exp_lower_CI_95))
def test_predict_metagenome_variances_propagates_variance_in_gene_categories(self):
""" predict_metagenomes correctly propagates the rank order of gene family variance"""
curr_otu_table = self.otu_table1
curr_genome_table = self.genome_table1
curr_variance_table = self.variance_table1_var_by_gene
curr_exp_metagenome_table = self.predicted_metagenome_table1
obs_prediction,obs_variances,obs_lower_CI_95,obs_upper_CI_95 =\
predict_metagenome_variances(curr_otu_table,curr_genome_table,gene_variances=curr_variance_table)
#Check that the metagenome prediction hasn't changed
self.assertEqual(obs_prediction.delimitedSelf(),curr_exp_metagenome_table.delimitedSelf())
def test_predict_metagenome_variances_propagates_variance_in_gene_categories(
self):
""" predict_metagenomes correctly propagates the rank order of gene family variance"""
curr_otu_table = self.otu_table1
curr_genome_table = self.genome_table1
curr_variance_table = self.variance_table1_var_by_gene
curr_exp_metagenome_table = self.predicted_metagenome_table1
obs_prediction,obs_variances,obs_lower_CI_95,obs_upper_CI_95 =\
predict_metagenome_variances(curr_otu_table,curr_genome_table,gene_variances=curr_variance_table)
#Check that the metagenome prediction hasn't changed
self.assertEqual(str(obs_prediction), str(curr_exp_metagenome_table))
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
if opts.verbose:
print "Predicting the metagenome, metagenome variance and confidence intervals for the metagenome..."
predicted_metagenomes,predicted_metagenome_variances,\
predicted_metagenomes_lower_CI_95,predicted_metagenomes_upper_CI_95=\
predict_metagenome_variances(otu_table,genome_table,variance_table,whole_round=round_flag)
else:
#If we don't need confidence intervals, we can do a faster pure numpy prediction
if opts.verbose:
print "Predicting the metagenome..."
predicted_metagenomes = predict_metagenomes(otu_table,
genome_table,
whole_round=round_flag)
if opts.normalize_by_otu:
#normalize (e.g. divide) the abundances by the sum of the OTUs per sample
if opts.verbose:
print "Normalizing functional abundances by sum of OTUs per sample"
inverse_otu_sums = [1 / x for x in otu_table.sum(axis='sample')]
scaling_factors = dict(zip(otu_table.ids(), inverse_otu_sums))
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
if opts.verbose:
print "Predicting the metagenome, metagenome variance and confidence intervals for the metagenome..."
predicted_metagenomes,predicted_metagenome_variances,\
predicted_metagenomes_lower_CI_95,predicted_metagenomes_upper_CI_95=\
predict_metagenome_variances(otu_table,genome_table,variance_table)
else:
#If we don't need confidence intervals, we can do a faster pure numpy prediction
if opts.verbose:
print "Predicting the metagenome..."
predicted_metagenomes = predict_metagenomes(otu_table,genome_table)
if opts.normalize_by_otu:
#normalize (e.g. divide) the abundances by the sum of the OTUs per sample
if opts.verbose:
print "Normalizing functional abundances by sum of OTUs per sample"
inverse_otu_sums = [1/x for x in otu_table.sum(axis='sample')]
scaling_factors = dict(zip(otu_table.SampleIds,inverse_otu_sums))
predicted_metagenomes = scale_metagenomes(predicted_metagenomes,scaling_factors)
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
if opts.verbose:
print "Predicting the metagenome, metagenome variance and confidence intervals for the metagenome..."
predicted_metagenomes,predicted_metagenome_variances,\
predicted_metagenomes_lower_CI_95,predicted_metagenomes_upper_CI_95=\
predict_metagenome_variances(otu_table,genome_table,variance_table)
else:
#If we don't need confidence intervals, we can do a faster pure numpy prediction
if opts.verbose:
print "Predicting the metagenome..."
predicted_metagenomes = predict_metagenomes(otu_table, genome_table)
write_metagenome_to_file(predicted_metagenomes,opts.output_metagenome_table,\
opts.format_tab_delimited,"metagenome prediction",verbose=opts.verbose)
if opts.with_confidence:
output_path, output_filename = split(opts.output_metagenome_table)
base_output_filename, ext = splitext(output_filename)
variance_output_fp =\
join(output_path,"%s_variances%s" %(base_output_filename,ext))
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
if opts.verbose:
print "Predicting the metagenome, metagenome variance and confidence intervals for the metagenome..."
predicted_metagenomes,predicted_metagenome_variances,\
predicted_metagenomes_lower_CI_95,predicted_metagenomes_upper_CI_95=\
predict_metagenome_variances(otu_table,genome_table,variance_table,whole_round=round_flag)
else:
#If we don't need confidence intervals, we can do a faster pure numpy prediction
if opts.verbose:
print "Predicting the metagenome..."
predicted_metagenomes = predict_metagenomes(otu_table,genome_table,whole_round=round_flag)
if opts.normalize_by_otu:
#normalize (e.g. divide) the abundances by the sum of the OTUs per sample
if opts.verbose:
print "Normalizing functional abundances by sum of OTUs per sample"
inverse_otu_sums = [1/x for x in otu_table.sum(axis='sample')]
scaling_factors = dict(zip(otu_table.ids(),inverse_otu_sums))
predicted_metagenomes = scale_metagenomes(predicted_metagenomes,scaling_factors)
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
if opts.verbose:
print "Predicting the metagenome, metagenome variance and confidence intervals for the metagenome..."
predicted_metagenomes, predicted_metagenome_variances, predicted_metagenomes_lower_CI_95, predicted_metagenomes_upper_CI_95 = predict_metagenome_variances(
otu_table, genome_table, variance_table
)
else:
# If we don't need confidence intervals, we can do a faster pure numpy prediction
if opts.verbose:
print "Predicting the metagenome..."
predicted_metagenomes = predict_metagenomes(otu_table, genome_table)
if opts.normalize_by_otu:
# normalize (e.g. divide) the abundances by the sum of the OTUs per sample
if opts.verbose:
print "Normalizing functional abundances by sum of OTUs per sample"
inverse_otu_sums = [1 / x for x in otu_table.sum(axis="sample")]
scaling_factors = dict(zip(otu_table.ids(), inverse_otu_sums))
predicted_metagenomes = scale_metagenomes(predicted_metagenomes, scaling_factors)
