def simulate_relationship(self,
sample_size=100000,
sparsity_factor=0.001,
gen_exp=1.4,
init_keep_rate=0.8,
**kwargs):
"""
Create a simulated example of a pedigree and compute its relationship.
:param sample_size: Size of the cohort.
:param sparsity_factor: Number of nonzero entries in the IBD matrix.
:param gen_exp: Gen size = gen_exp X prev gen size.
:param init_keep_rate: 1 - number of edges to remove before iteration begins.
:return: A class Pedigree object, and an entry list of the simulation.
"""
assert sample_size > 0, "Sample size should be a positive number"
assert (sparsity_factor > 0) and (sparsity_factor < 1), \
"Sparsity factor must be within the range (0, 1)"
assert gen_exp > 0, "gen_exp should be a positive number"
assert (init_keep_rate > 0) and (init_keep_rate < 1), \
"init_keep_rate must be within the range (0, 1)"
rel, sex, _ = simulate_tree(sample_size, sparsity_factor, gen_exp,
init_keep_rate)
write_fam(os.path.join(self.output_folder, "rel.fam"), rel, sex, None)
return self.compute_relationships(
os.path.join(self.output_folder, "rel.fam"))
def SciLMM(
simulate=False,
sample_size=100000,
sparsity_factor=0.001,
gen_exp=1.4,
init_keep_rate=0.8,
fam=None,
ibd=False,
epis=False,
dom=False,
ibd_path=False,
epis_path=False,
dom_path=False,
gen_y=False,
y=None,
cov=None,
he=False,
lmm=False,
reml=False,
sim_num=100,
intercept=False,
verbose=False,
output_folder=".",
remove_cycles=False,
check_num_parents=False,
):
if ibd or epis or dom:
if not os.path.exists(output_folder):
raise Exception("The output folder does not exists")
if he or lmm:
if y is None and gen_y is False:
raise Exception("Can't estimate without a target value (--y)")
rel, interest_in_relevant = None, None
if fam:
rel_org, sex, interest, entries_dict = read_fam(fam_file_path=fam)
rel, interest_in_relevant = organize_rel(
rel_org,
interest,
remove_cycles=remove_cycles,
check_num_parents=check_num_parents,
)
# TODO: have to do sex as well in this version
entries_list = np.array(list(entries_dict.values()))[
interest_in_relevant
]
np.save(os.path.join(output_folder, "entries_ids.npy"), entries_list)
elif simulate:
if sample_size <= 0:
raise Exception("Sample size should be a positive number")
if (sparsity_factor <= 0) or (sparsity_factor >= 1):
raise Exception("Sparsity factor is within the range (0, 1)")
if gen_exp <= 0:
raise Exception("gen_exp is a positive number")
if (init_keep_rate <= 0) or (init_keep_rate > 1):
raise Exception("init_keep_rate is within the range (0, 1)")
rel, sex, _ = simulate_tree(
sample_size, sparsity_factor, gen_exp, init_keep_rate
)
write_fam(os.path.join(output_folder, "rel.fam"), rel, sex, None)
# if no subset of interest has been specified, keep all indices
if interest_in_relevant is None:
interest_in_relevant = np.ones((rel.shape[0])).astype(np.bool)
if ibd_path:
ibd = load_sparse_csr(os.path.join(output_folder, "IBD.npz"))
elif ibd:
if rel is None:
raise Exception("No relationship matrix given")
ibd, L, D = simple_numerator(rel)
# keep the original L and D because they are useless otherwise
save_sparse_csr(os.path.join(output_folder, "IBD.npz"), ibd)
save_sparse_csr(os.path.join(output_folder, "L.npz"), L)
save_sparse_csr(os.path.join(output_folder, "D.npz"), D)
else:
ibd = None
if epis_path:
epis = load_sparse_csr(os.path.join(output_folder, "Epistasis.npz"))
elif epis:
if ibd is None:
raise Exception("Pairwise-epistasis requires an ibd matrix")
epis = pairwise_epistasis(ibd)
save_sparse_csr(os.path.join(output_folder, "Epistasis.npz"), epis)
else:
epis = None
if dom_path:
dom = load_sparse_csr(os.path.join(output_folder, "Dominance.npz"))
elif dom:
if ibd is None or rel is None:
raise Exception(
"Dominance requires both an ibd matrix and a relationship matrix"
)
dom = dominance(rel, ibd)
save_sparse_csr(os.path.join(output_folder, "Dominance.npz"), dom)
else:
dom = None
covariance_matrices = []
for mat in [ibd, epis, dom]:
if mat is not None:
covariance_matrices.append(mat)
if cov is not None:
cov = np.hstack((cov, np.load(cov)))
else:
cov = sex[:, np.newaxis]
y = None
if gen_y:
sigs = np.random.rand(len(covariance_matrices) + 1)
sigs /= sigs.sum()
fe = np.random.rand(cov.shape[1] + intercept) / 100
print(
"Generating y with fixed effects: {} and sigmas : {}".format(
fe, sigs
)
)
y = simulate_phenotype(covariance_matrices, cov, sigs, fe, intercept)
np.save(os.path.join(output_folder, "y.npy"), y)
if y is not None:
y = np.load(y)
if he:
print(compute_HE(y, cov, covariance_matrices, intercept))
if lmm:
print(
LMM(
SparseCholesky(),
covariance_matrices,
cov,
y,
with_intercept=intercept,
reml=reml,
sim_num=sim_num,
)
)