def warped_stepwise(Y,
X=None,
K=None,
covariates=None,
num_restarts=1,
max_covariates=10,
qv_cutoff=None,
pv_cutoff=5e-8):
# Xt = X.copy()
# Xt -= Xt.mean(axis=0)
# Xt /= Xt.std(axis=0)
if covariates is None:
num_covariates = 0
else:
num_covariates = covariates.shape[1]
included = []
converged = False
iterations = 1
estimated_h2s = []
likelihoods = []
while not converged:
m = WarpedLMM(Y, X, K=K, X_selected=covariates, warping_terms=3)
m.optimize_restarts(num_restarts, messages=1)
y_pheno = m.Y.copy()
y_pheno -= y_pheno.mean()
y_pheno /= y_pheno.std()
if covariates is not None:
covariates -= covariates.mean(0)
covariates /= covariates.std(0)
#import panama.core.testing as testing
# pv_lmm_panama = testing.interface(X.copy(), y_pheno.copy(), K.copy(), covariates)[0].flatten()
pv_lmm, h2 = fastlmm.assoc_scan(y_pheno.copy(),
X.copy(),
K=K.copy(),
covariates=covariates)
if qv_cutoff is not None:
qv_lmm = qvalue.estimate(pv_lmm)
sorted_qv_ind = np.argsort(qv_lmm)
candidate_index = sorted_qv_ind[0]
candidate_sign = qv_lmm[candidate_index]
significant = (qv_lmm <= qv_cutoff)
cutoff = qv_cutoff
else:
significant = (pv_lmm <= pv_cutoff)
sorted_pv_ind = np.argsort(pv_lmm)
candidate_index = sorted_pv_ind[0]
candidate_sign = pv_lmm[candidate_index]
cutoff = pv_cutoff
likelihoods.append(m.log_likelihood())
# h2 = m.params['sigma_g']/m.params['sigma_e']
estimated_h2s.append(h2)
status = "Iteration: {0}, significant SNPs: {1}, included SNPs: {2}, heritability: {3:.4f}, f: {4}".format(
iterations, significant.sum(), len(included), estimated_h2s[-1],
likelihoods[-1])
print status
if candidate_sign > cutoff or len(included) >= max_covariates:
converged = True
continue
X_sign = X[:, candidate_index:candidate_index + 1]
if covariates is not None:
covariates = np.append(covariates, X_sign, axis=1)
else:
covariates = X_sign.copy()
covariates.flags.writeable = True
iterations += 1
included.append(candidate_index)
return y_pheno, m, included, estimated_h2s[-1]
options.covariates)
if options.run_test:
Y = np.exp(Y)
y_pheno, m, _, estimated_h2 = stepwise.warped_stepwise(
Y,
X,
K,
covariates=covar,
max_covariates=options.max_covariates,
num_restarts=options.random_restarts,
qv_cutoff=options.qv_cutoff,
pv_cutoff=options.pv_cutoff)
pv, h2 = fastlmm.assoc_scan(y_pheno.copy(), X, covariates=covar, K=K)
if options.out_dir is None:
results_file_name = options.phenotype_file.replace('.txt', '')
results_file_name += "_warpedlmm_results.txt"
else:
results_file_name = options.out_dir + "/warpedlmm_results.txt"
util.write_results_to_file(snp_data, pv, results_file_name)
if options.normal:
pv_base, h2_base = fastlmm.assoc_scan(Y.copy(),
X,
covariates=covar,
K=K)
util.write_results_to_file(
