def association_scan(self):
print "Association scan... ",
K = self.kernel_testing(genetics=False, confounders=True)
pval = testing.interface(self.S_centered, self.Y, K, I = None,
model='LMM', parallel=False, # TODO parallelize
file_directory = None, jobs = 0)[0]
print "[DONE]"
# convert to qvalues
qval = qvalue.estimate(pval)
return qval, pval
def panama_step(self):
X = self.get_latent()
K = self.kernel_testing(genetics=False, confounders=False)
K = scaleK(K)
if len(self.candidate_associations) != 0:
covs = self.S_centered[:, self.candidate_associations].copy()
else:
covs = None
pv = testing.interface(self.S_centered, X[:, :self.Q], K, covs=covs, model = "LMM",
parallel = False, jobs = 0,
file_directory=None)[0] # TODO cleanup
# Number of tests conducted
num_tests = X.shape[1]*self.S.shape[1]*self.iteration
qv = qvalue.estimate(pv, m=num_tests)
# Set the qvalue of the current associations to 1
qv[self.candidate_associations,:] = 1
# Greedily construct addition set by adding the BEST (lowest qv) SNP for each factor
# (if significant)
new_candidates = []
for i in xrange(qv.shape[1]):
i_best = qv[:,i].argmin()
qv_best = qv[:,i].min()
# if significant, add it
if qv_best<=self.FDR_associations:
new_candidates.append(i_best)
# and set the corrisponding qvalue to 1
qv[i_best,:] = 1
# Add candidates
nc_old = len(self.candidate_associations)
self.candidate_associations.extend(new_candidates)
nc = len(self.candidate_associations)
dl = nc-nc_old
assert len(np.unique(self.candidate_associations)) == len(self.candidate_associations)
return dl
