def _test_(): singleton_snps = genotypes.simulate_k_tons(n=500, m=1000) doubleton_snps = genotypes.simulate_k_tons(k=2, n=500, m=1000) common_snps = genotypes.simulate_common_genotypes(500, 1000) snps = sp.vstack([common_snps, singleton_snps, doubleton_snps]) print snps snps = snps.T snps = (snps - sp.mean(snps, 0)) / sp.std(snps, 0) snps = snps.T print snps, snps.shape file_prefix = os.environ['HOME'] + '/tmp/test' phen_list = phenotypes.simulate_traits_w_snps_to_hdf5(snps, hdf5_file_prefix=file_prefix, num_traits=30, p=0.1) singletons_thres = [] doubletons_thres = [] common_thres = [] for i, y in enumerate(phen_list['phenotypes']): K = kinship.calc_ibd_kinship(snps) K = kinship.scale_k(K) lmm = lm.LinearMixedModel(y) lmm.add_random_effect(K) r1 = lmm.get_REML() print 'pseudo_heritability:', r1['pseudo_heritability'] ex_res = lm.emmax(snps, y, K) plt.figure() plt.hist(y, 50) plt.savefig('%s_%d_phen.png' % (file_prefix, i)) plt.clf() agr.plot_simple_qqplots_pvals('%s_%d' % (file_prefix, i), [ex_res['ps'][:1000], ex_res['ps'][1000:2000], ex_res['ps'][2000:]], result_labels=['Common SNPs', 'Singletons', 'Doubletons'], line_colors=['b', 'r', 'y'], num_dots=200, max_neg_log_val=3) # Cholesky permutations.. res = lm.emmax_perm_test(singleton_snps, y, K, num_perm=1000) print 1.0 / (20 * 1000.0), res['threshold_05'] singletons_thres.append(res['threshold_05'][0]) res = lm.emmax_perm_test(doubleton_snps, y, K, num_perm=1000) print 1.0 / (20 * 1000.0), res['threshold_05'] doubletons_thres.append(res['threshold_05'][0]) res = lm.emmax_perm_test(common_snps, y, K, num_perm=1000) print 1.0 / (20 * 1000.0), res['threshold_05'] common_thres.append(res['threshold_05'][0]) #ATT permutations (Implement) #PC permutations (Implement) print sp.mean(singletons_thres), sp.std(singletons_thres) print sp.mean(doubletons_thres), sp.std(doubletons_thres) print sp.mean(common_thres), sp.std(common_thres)
def _test_scz_(): # Load Schizophrenia data singleton_snps = genotypes.simulate_k_tons(n=500, m=1000) doubleton_snps = genotypes.simulate_k_tons(k=2, n=500, m=1000) common_snps = genotypes.simulate_common_genotypes(500, 1000) snps = sp.vstack([common_snps, singleton_snps, doubleton_snps]) test_snps = sp.vstack([singleton_snps, doubleton_snps]) print snps phen_list = phenotypes.simulate_traits( snps, hdf5_file_prefix='/home/bv25/tmp/test', num_traits=30, p=1.0) singletons_thres = [] doubletons_thres = [] common_thres = [] for i, y in enumerate(phen_list): K = kinship.calc_ibd_kinship(snps) K = kinship.scale_k(K) lmm = lm.LinearMixedModel(y) lmm.add_random_effect(K) r1 = lmm.get_REML() print 'pseudo_heritability:', r1['pseudo_heritability'] ex_res = lm.emmax(snps, y, K) plt.figure() plt.hist(y, 50) plt.savefig('/home/bv25/tmp/test_%d_phen.png' % i) plt.clf() agr.plot_simple_qqplots_pvals('/home/bv25/tmp/test_%d' % i, [ ex_res['ps'][:1000], ex_res['ps'][1000:2000], ex_res['ps'][2000:] ], result_labels=[ 'Common SNPs', 'Singletons', 'Doubletons' ], line_colors=['b', 'r', 'y'], num_dots=200, max_neg_log_val=3) # Now permutations.. res = lm.emmax_perm_test(singleton_snps, y, K, num_perm=1000) print 1.0 / (20 * 1000.0), res['threshold_05'] singletons_thres.append(res['threshold_05'][0]) res = lm.emmax_perm_test(doubleton_snps, y, K, num_perm=1000) print 1.0 / (20 * 1000.0), res['threshold_05'] doubletons_thres.append(res['threshold_05'][0]) res = lm.emmax_perm_test(common_snps, y, K, num_perm=1000) print 1.0 / (20 * 1000.0), res['threshold_05'] common_thres.append(res['threshold_05'][0]) print sp.mean(singletons_thres), sp.std(singletons_thres) print sp.mean(doubletons_thres), sp.std(doubletons_thres) print sp.mean(common_thres), sp.std(common_thres)
def _test_scz_(): # Load Schizophrenia data singleton_snps = genotypes.simulate_k_tons(n=500, m=1000) doubleton_snps = genotypes.simulate_k_tons(k=2, n=500, m=1000) common_snps = genotypes.simulate_common_genotypes(500, 1000) snps = sp.vstack([common_snps, singleton_snps, doubleton_snps]) test_snps = sp.vstack([singleton_snps, doubleton_snps]) print snps phen_list = phenotypes.simulate_traits(snps, hdf5_file_prefix='/home/bv25/tmp/test', num_traits=30, p=1.0) singletons_thres = [] doubletons_thres = [] common_thres = [] for i, y in enumerate(phen_list): K = kinship.calc_ibd_kinship(snps) K = kinship.scale_k(K) lmm = lm.LinearMixedModel(y) lmm.add_random_effect(K) r1 = lmm.get_REML() print 'pseudo_heritability:', r1['pseudo_heritability'] ex_res = lm.emmax(snps, y, K) plt.figure() plt.hist(y, 50) plt.savefig('/home/bv25/tmp/test_%d_phen.png' % i) plt.clf() agr.plot_simple_qqplots_pvals('/home/bv25/tmp/test_%d' % i, [ex_res['ps'][:1000], ex_res['ps'][1000:2000], ex_res['ps'][2000:]], result_labels=['Common SNPs', 'Singletons', 'Doubletons'], line_colors=['b', 'r', 'y'], num_dots=200, max_neg_log_val=3) # Now permutations.. res = lm.emmax_perm_test(singleton_snps, y, K, num_perm=1000) print 1.0 / (20 * 1000.0), res['threshold_05'] singletons_thres.append(res['threshold_05'][0]) res = lm.emmax_perm_test(doubleton_snps, y, K, num_perm=1000) print 1.0 / (20 * 1000.0), res['threshold_05'] doubletons_thres.append(res['threshold_05'][0]) res = lm.emmax_perm_test(common_snps, y, K, num_perm=1000) print 1.0 / (20 * 1000.0), res['threshold_05'] common_thres.append(res['threshold_05'][0]) print sp.mean(singletons_thres), sp.std(singletons_thres) print sp.mean(doubletons_thres), sp.std(doubletons_thres) print sp.mean(common_thres), sp.std(common_thres)
def _test_(): singleton_snps = genotypes.simulate_k_tons(n=500, m=1000) doubleton_snps = genotypes.simulate_k_tons(k=2, n=500, m=1000) common_snps = genotypes.simulate_common_genotypes(500, 1000) snps = sp.vstack([common_snps, singleton_snps, doubleton_snps]) print snps snps = snps.T snps = (snps - sp.mean(snps, 0)) / sp.std(snps, 0) snps = snps.T print snps, snps.shape file_prefix = os.environ['HOME'] + '/tmp/test' phen_list = phenotypes.simulate_traits_w_snps_to_hdf5( snps, hdf5_file_prefix=file_prefix, num_traits=30, p=0.1) singletons_thres = [] doubletons_thres = [] common_thres = [] for i, y in enumerate(phen_list['phenotypes']): K = kinship.calc_ibd_kinship(snps) K = kinship.scale_k(K) lmm = lm.LinearMixedModel(y) lmm.add_random_effect(K) r1 = lmm.get_REML() print 'pseudo_heritability:', r1['pseudo_heritability'] ex_res = lm.emmax(snps, y, K) plt.figure() plt.hist(y, 50) plt.savefig('%s_%d_phen.png' % (file_prefix, i)) plt.clf() agr.plot_simple_qqplots_pvals('%s_%d' % (file_prefix, i), [ ex_res['ps'][:1000], ex_res['ps'][1000:2000], ex_res['ps'][2000:] ], result_labels=[ 'Common SNPs', 'Singletons', 'Doubletons' ], line_colors=['b', 'r', 'y'], num_dots=200, max_neg_log_val=3) # Cholesky permutations.. res = lm.emmax_perm_test(singleton_snps, y, K, num_perm=1000) print 1.0 / (20 * 1000.0), res['threshold_05'] singletons_thres.append(res['threshold_05'][0]) res = lm.emmax_perm_test(doubleton_snps, y, K, num_perm=1000) print 1.0 / (20 * 1000.0), res['threshold_05'] doubletons_thres.append(res['threshold_05'][0]) res = lm.emmax_perm_test(common_snps, y, K, num_perm=1000) print 1.0 / (20 * 1000.0), res['threshold_05'] common_thres.append(res['threshold_05'][0]) #ATT permutations (Implement) #PC permutations (Implement) print sp.mean(singletons_thres), sp.std(singletons_thres) print sp.mean(doubletons_thres), sp.std(doubletons_thres) print sp.mean(common_thres), sp.std(common_thres)