def test_test_similarity(self):
""" check that test_similarity works correctly
"""
# select two probands with relatively rare terms. Ignore that we select
# the same person twice, the example will still work. Since the
# reference population only has three individuals, the chance of
# selecting two individuals with HPO similarity as rare as those two
# individuals is 1 in three. We check that the probability estimate for
# the two "rare" individuals is relatively close to 0.33.
probands = ["person_03", "person_03"]
p = test_similarity(self.hpo_graph,
self.hpo_terms,
probands,
n_sims=1000,
score_type="resnik")
self.assertLess(abs(p - 0.33), 0.04)
# now chose two individuals who do not share terms, and so the chance
# that two random probands share their terms to the same extent is
# effectively 1. The test is currently set up so that the maximum P-value
# is actually n/(n + 1), where n is the number of iterations. We use
# n + 1, since the observed similarity should be in the simulated
# distribution under the null hypothesis.
probands = ["person_01", "person_03"]
p = test_similarity(self.hpo_graph,
self.hpo_terms,
probands,
n_sims=1000,
score_type="resnik")
self.assertLess(abs(p - 0.999), 0.03)
def analyse_genes(hpo_graph, hpo_by_proband, probands_by_gene, output_path, iterations, score_type):
""" tests genes to see if their probands share HPO terms more than by chance.
Args:
hpo_graph: ICSimilarity object for the HPO term graph, with
information on how many times each term has been used across all
probands.
hpo_by_proband: dictionary of HPO terms per proband
probands_by_gene: dictionary of genes, to the probands who have variants
in those genes.
output_path: path to file to write the results to, or sys.stdout object.
iterations: number of iterations to run.
"""
# Sometimes output_path is actually sys.stdout, other times it is a path.
try:
output = open(output_path, "w")
except TypeError:
output = output_path
output.write("hgnc\thpo_similarity_p_value\n")
for gene in sorted(probands_by_gene):
probands = probands_by_gene[gene]
p_value = None
if len(probands) > 1:
p_value = test_similarity(hpo_graph, hpo_by_proband, probands, iterations, score_type)
if p_value is None:
continue
output.write("{0}\t{1}\n".format(gene, p_value))
output.close()
def test_test_similarity(self):
""" check that test_similarity works correctly
"""
# select two probands with relatively rare terms. Ignore that we select
# the same person twice, the example will still work. Since the
# reference population only has three individuals, the chance of
# selecting two individuals with HPO similarity as rare as those two
# individuals is 1 in three. We check that the probability estimate for
# the two "rare" individuals is relatively close to 0.33.
probands = ["person_03", "person_03"]
p = test_similarity(self.hpo_graph, self.hpo_terms, probands, n_sims=1000, score_type="resnik")
self.assertLess(abs(p - 0.33), 0.04)
# now chose two individuals who do not share terms, and so the chance
# that two random probands share their terms to the same extent is
# effectively 1. The test is currently set up so that the maximum P-value
# is actually n/(n + 1), where n is the number of iterations. We use
# n + 1, since the observed similarity should be in the simulated
# distribution under the null hypothesis.
probands = ["person_01", "person_03"]
p = test_similarity(self.hpo_graph, self.hpo_terms, probands, n_sims=1000, score_type="resnik")
self.assertLess(abs(p - 0.999), 0.03)
def analyse_genes(hpo_graph, hpo_by_proband, probands_by_gene, output_path,
iterations, score_type):
""" tests genes to see if their probands share HPO terms more than by chance.
Args:
hpo_graph: ICSimilarity object for the HPO term graph, with
information on how many times each term has been used across all
probands.
hpo_by_proband: dictionary of HPO terms per proband
probands_by_gene: dictionary of genes, to the probands who have variants
in those genes.
output_path: path to file to write the results to, or sys.stdout object.
iterations: number of iterations to run.
"""
check_terms_in_graph(hpo_graph, hpo_by_proband)
# Sometimes output_path is actually sys.stdout, other times it is a path.
try:
output = open(output_path, "w")
except TypeError:
output = output_path
output.write("hgnc\thpo_similarity_p_value\n")
for gene in sorted(probands_by_gene):
probands = probands_by_gene[gene]
p_value = None
if len(probands) > 1:
p_value = test_similarity(hpo_graph, hpo_by_proband, probands,
iterations, score_type)
if p_value is None:
continue
output.write("{0}\t{1}\n".format(gene, p_value))
output.close()
