def copyinputs(self, copier):
if not self.num_pcs == 0:
for i in xrange(self.chrom_count):
pc_fn = PrecomputeLocoPcs.create_out_fn(self.pc_prefix, i)
copier.input(pc_fn)
copier.input(self.pheno_fn)
copier.input(self.snp_reader)
def copyinputs(self, copier):
if not self.num_pcs == 0:
for i in xrange(self.chrom_count):
pc_fn = PrecomputeLocoPcs.create_out_fn(self.pc_prefix, i)
copier.input(pc_fn)
copier.input(self.pheno_fn)
copier.input(self.snp_reader)
def dowork(self, i, train_snp_idx, test_snp_idx, result, G, y):
logging.info("{0}, {1}".format(len(train_snp_idx), len(test_snp_idx)))
# intersect selected SNPs with train snps
if not self.selected_snps is None:
# intersect snp names
logging.info("intersecting train snps with selected snps for LOCO")
int_snp_idx = argintersect_left(self.snp_reader.rs[train_snp_idx],
self.selected_snps)
sim_keeper_idx = np.array(train_snp_idx)[int_snp_idx]
else:
sim_keeper_idx = train_snp_idx
# subset data
# fast indexing (needs to be C-order)
assert np.isfortran(G) == False
#G_train = G.take(train_snp_idx, axis=1)
G_sim = G.take(sim_keeper_idx, axis=1)
G_test = G.take(test_snp_idx, axis=1)
t0 = time.time()
if self.num_pcs == 0:
pcs = None
else:
if not self.pc_prefix is None:
out_fn = PrecomputeLocoPcs.create_out_fn(self.pc_prefix, i)
logging.info("loading pc from file: %s" % out_fn)
pcs = load(out_fn)[:, 0:self.num_pcs]
logging.info("..done")
else:
assert False, "please precompute PCs"
logging.info("done after %.4f seconds" % (time.time() - t0))
# only use PCs
if self.pcs_only:
G_sim = None
logging.info("Using PCs only in LocoGWAS")
gwas = FastGwas(G_sim,
G_test,
y,
self.delta,
train_pcs=pcs,
mixing=self.mixing)
gwas.run_gwas()
assert len(gwas.p_values) == len(test_snp_idx)
# wrap up results
return test_snp_idx, gwas.p_values, result
def dowork(self, i, train_snp_idx, test_snp_idx, result, G, y):
logging.info("{0}, {1}".format(len(train_snp_idx), len(test_snp_idx)))
# intersect selected SNPs with train snps
if not self.selected_snps is None:
# intersect snp names
logging.info("intersecting train snps with selected snps for LOCO")
int_snp_idx = argintersect_left(self.snp_reader.rs[train_snp_idx], self.selected_snps)
sim_keeper_idx = np.array(train_snp_idx)[int_snp_idx]
else:
sim_keeper_idx = train_snp_idx
# subset data
# fast indexing (needs to be C-order)
assert np.isfortran(G) == False
#G_train = G.take(train_snp_idx, axis=1)
G_sim = G.take(sim_keeper_idx, axis=1)
G_test = G.take(test_snp_idx, axis=1)
t0 = time.time()
if self.num_pcs == 0:
pcs = None
else:
if not self.pc_prefix is None:
out_fn = PrecomputeLocoPcs.create_out_fn(self.pc_prefix, i)
logging.info("loading pc from file: %s" % out_fn)
pcs = load(out_fn)[:,0:self.num_pcs]
logging.info("..done")
else:
assert False, "please precompute PCs"
logging.info("done after %.4f seconds" % (time.time() - t0))
# only use PCs
if self.pcs_only:
G_sim = None
logging.info("Using PCs only in LocoGWAS")
gwas = FastGwas(G_sim, G_test, y, self.delta, train_pcs=pcs, mixing=self.mixing)
gwas.run_gwas()
assert len(gwas.p_values) == len(test_snp_idx)
# wrap up results
return test_snp_idx, gwas.p_values, result