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