def __init__(self):
self._mapper = TermMapping()
def __init__(self):
self._mapper = TermMapping()
class HypergeometricTest:
_mapper = None
def __init__(self):
self._mapper = TermMapping()
def perform_test(self, ontology_type, genes_of_interest, cutoff):
if cutoff is None:
cutoff = p_val_threshold
total_num_genes = self._mapper.get_gene_count(ontology_type)
term2genes = self._mapper.get_term_mapping(ontology_type)
gene2terms = self._mapper.get_gene_mapping(ontology_type)
filtered = self.__filter_input(genes_of_interest, gene2terms)
# print(filtered)
# print(len(filtered))
sample_map = self.__calculateTermFrequency(filtered, gene2terms)
n = len(filtered)
# Number of tests performed: will be used for correction.
num_tests = len(sample_map)
results = [{}] * num_tests
pvals = np.zeros(num_tests)
idx = 0
for term in sample_map:
# Calculate p-value
sampled = sample_map[term]
assigned_genes = term2genes[term]
k = len(sampled)
m = len(assigned_genes)
p = stats.hypergeom.pmf(k,total_num_genes,m,n)
p_corrected = p * num_tests
pvals[idx] = p
result = {}
result['id'] = term
result['p-value'] = p_corrected
result['background'] = m
result['genes'] = list(sampled)
results[idx] = result
idx = idx + 1
# print(term + " = " + str(p) + ", k = " + str(k) + ",
# m = " + str(m) + ", total = " + str(total_num_genes) + ", n= " + str(n))
# Correct border values (library does not accept 0 & 1)
i = 0
for p in pvals:
if p >= 1.0 or math.isnan(p):
pvals[i] = 0.9999999999999999999999
elif p <= 0:
pvals[i] = 0.0000000000000000000001
i += 1
qvals = qvalue.estimate(pvals)
filtered_results = []
idx = 0
for term in sample_map:
qv = qvals[idx]
res = results[idx]
pv = res['p-value']
k = len(res['genes'])
res['q-value'] = qv
if pv < cutoff and k >= gene_threshold:
filtered_results.append(res)
idx = idx + 1
return {
'results': filtered_results,
'total_genes': total_num_genes
}
# remove junks & duplicates
def __filter_input(self, input_list, gene2terms):
filtered = set([])
for gene in input_list:
if gene in gene2terms:
filtered.add(gene)
return list(filtered)
def __calculateTermFrequency(self, genes_of_interest, gene2terms):
sample_term_map = {}
for gene in genes_of_interest:
terms = []
if gene in gene2terms:
terms = gene2terms[gene]
else:
continue
for term in terms:
if term in sample_term_map:
assigned_genes = sample_term_map[term]
assigned_genes.add(gene)
sample_term_map[term] = assigned_genes
else:
assigned_genes = set([])
assigned_genes.add(gene)
sample_term_map[term] = assigned_genes
return sample_term_map
class HypergeometricTest:
_mapper = None
def __init__(self):
self._mapper = TermMapping()
def perform_test(self, ontology_type, genes_of_interest, cutoff):
if cutoff is None:
cutoff = p_val_threshold
total_num_genes = self._mapper.get_gene_count(ontology_type)
term2genes = self._mapper.get_term_mapping(ontology_type)
gene2terms = self._mapper.get_gene_mapping(ontology_type)
filtered = self.__filter_input(genes_of_interest, gene2terms)
# print(filtered)
# print(len(filtered))
sample_map = self.__calculateTermFrequency(filtered, gene2terms)
n = len(filtered)
# Number of tests performed: will be used for correction.
num_tests = len(sample_map)
results = [{}] * num_tests
pvals = np.zeros(num_tests)
idx = 0
for term in sample_map:
# Calculate p-value
sampled = sample_map[term]
assigned_genes = term2genes[term]
k = len(sampled)
m = len(assigned_genes)
p = stats.hypergeom.pmf(k, total_num_genes, m, n)
p_corrected = p * num_tests
pvals[idx] = p
result = {}
result['id'] = term
result['p-value'] = p_corrected
result['background'] = m
result['genes'] = list(sampled)
results[idx] = result
idx = idx + 1
# print(term + " = " + str(p) + ", k = " + str(k) + ",
# m = " + str(m) + ", total = " + str(total_num_genes) + ", n= " + str(n))
# Correct border values (library does not accept 0 & 1)
i = 0
for p in pvals:
if p >= 1.0 or math.isnan(p):
pvals[i] = 0.9999999999999999999999
elif p <= 0:
pvals[i] = 0.0000000000000000000001
i += 1
qvals = qvalue.estimate(pvals)
filtered_results = []
idx = 0
for term in sample_map:
qv = qvals[idx]
res = results[idx]
pv = res['p-value']
k = len(res['genes'])
res['q-value'] = qv
if pv < cutoff and k >= gene_threshold:
filtered_results.append(res)
idx = idx + 1
return {'results': filtered_results, 'total_genes': total_num_genes}
# remove junks & duplicates
def __filter_input(self, input_list, gene2terms):
filtered = set([])
for gene in input_list:
if gene in gene2terms:
filtered.add(gene)
return list(filtered)
def __calculateTermFrequency(self, genes_of_interest, gene2terms):
sample_term_map = {}
for gene in genes_of_interest:
terms = []
if gene in gene2terms:
terms = gene2terms[gene]
else:
continue
for term in terms:
if term in sample_term_map:
assigned_genes = sample_term_map[term]
assigned_genes.add(gene)
sample_term_map[term] = assigned_genes
else:
assigned_genes = set([])
assigned_genes.add(gene)
sample_term_map[term] = assigned_genes
return sample_term_map
