def gen_pylmm_results(self, tempdata):
print("USING PYLMM")
self.dataset.group.get_markers()
pheno_vector = np.array([val == "x" and np.nan or float(val) for val in self.vals])
if self.dataset.group.species == "human":
p_values, t_stats = self.gen_human_results(pheno_vector, tempdata)
else:
genotype_data = [marker['genotypes'] for marker in self.dataset.group.markers.markers]
no_val_samples = self.identify_empty_samples()
trimmed_genotype_data = self.trim_genotypes(genotype_data, no_val_samples)
genotype_matrix = np.array(trimmed_genotype_data).T
t_stats, p_values = lmm.run(
pheno_vector,
genotype_matrix,
restricted_max_likelihood=True,
refit=False,
temp_data=tempdata
)
print("p_values:", p_values)
self.dataset.group.markers.add_pvalues(p_values)
self.qtl_results = self.dataset.group.markers.markers
def gen_qtl_results_2(self, tempdata):
"""Generates qtl results for plotting interval map"""
self.dataset.group.get_markers()
self.dataset.read_genotype_file()
pheno_vector = np.array([val == "x" and np.nan or float(val) for val in self.vals])
#if self.dataset.group.species == "human":
# p_values, t_stats = self.gen_human_results(pheno_vector, tempdata)
#else:
genotype_data = [marker['genotypes'] for marker in self.dataset.group.markers.markers]
no_val_samples = self.identify_empty_samples()
trimmed_genotype_data = self.trim_genotypes(genotype_data, no_val_samples)
genotype_matrix = np.array(trimmed_genotype_data).T
t_stats, p_values = lmm.run(
pheno_vector,
genotype_matrix,
restricted_max_likelihood=True,
refit=False,
temp_data=tempdata
)
self.dataset.group.markers.add_pvalues(p_values)
self.qtl_results = self.dataset.group.markers.markers
def gen_data(self, tempdata):
"""Generates p-values for each marker"""
self.dataset.group.get_markers()
pheno_vector = np.array([val == "x" and np.nan or float(val) for val in self.vals])
if self.dataset.group.species == "human":
p_values, t_stats = self.gen_human_results(pheno_vector, tempdata)
else:
genotype_data = [marker['genotypes'] for marker in self.dataset.group.markers.markers]
no_val_samples = self.identify_empty_samples()
trimmed_genotype_data = self.trim_genotypes(genotype_data, no_val_samples)
genotype_matrix = np.array(trimmed_genotype_data).T
print("pheno_vector: ", pf(pheno_vector))
print("genotype_matrix: ", pf(genotype_matrix))
print("genotype_matrix.shape: ", pf(genotype_matrix.shape))
t_stats, p_values = lmm.run(
pheno_vector,
genotype_matrix,
restricted_max_likelihood=True,
refit=False,
temp_data=tempdata
)
self.dataset.group.markers.add_pvalues(p_values)
self.qtl_results = self.dataset.group.markers.markers