def _prepare_data(self, n_cells: int, n_genes: int, noise_model: str):
"""
:param n_cells: Number of cells to simulate (number of observations per test).
:param n_genes: Number of genes to simulate (number of tests).
:param noise_model: Noise model to use for data fitting.
"""
if noise_model == "nb":
from batchglm.api.models.tf1.glm_nb import Simulator
rand_fn_loc = lambda shape: np.random.uniform(5, 10, shape)
rand_fn_scale = lambda shape: np.random.uniform(1, 2, shape)
elif noise_model == "norm":
from batchglm.api.models import Simulator
rand_fn_loc = lambda shape: np.random.uniform(500, 1000, shape)
rand_fn_scale = lambda shape: np.random.uniform(1, 2, shape)
else:
raise ValueError("noise model %s not recognized" % noise_model)
num_non_de = n_genes // 2
sim = Simulator(num_observations=n_cells, num_features=n_genes)
sim.generate_sample_description(num_batches=0, num_conditions=2)
sim.generate_params(rand_fn_loc=rand_fn_loc,
rand_fn_scale=rand_fn_scale)
sim.a_var[1, :num_non_de] = 0
sim.b_var[1, :num_non_de] = 0
self.isDE = np.arange(n_genes) >= num_non_de
sim.generate_data()
return sim
def _test_wald_de(
self,
constrained: bool,
spline_basis: str,
ngenes: int
):
if self.noise_model == "nb":
from batchglm.api.models.tf1.glm_nb import Simulator
rand_fn_loc = lambda shape: np.random.uniform(2, 5, shape)
rand_fn_scale = lambda shape: np.random.uniform(1, 2, shape)
elif self.noise_model == "norm":
from batchglm.api.models import Simulator
rand_fn_loc = lambda shape: np.random.uniform(500, 1000, shape)
rand_fn_scale = lambda shape: np.random.uniform(1, 2, shape)
else:
raise ValueError("noise model %s not recognized" % self.noise_model)
n_timepoints = 7
sim = Simulator(num_observations=n_timepoints*200, num_features=ngenes)
sim.generate_sample_description(
num_batches=0,
num_conditions=n_timepoints
)
sim.generate_params(
rand_fn_loc=rand_fn_loc,
rand_fn_scale=rand_fn_scale
)
num_non_de = round(ngenes / 2)
sim.a_var[1:, :num_non_de] = 0 # Set all condition effects of non DE genes to zero.
sim.b_var[1:, :] = 0 # Use constant dispersion across all conditions.
self.isDE = np.arange(ngenes) >= num_non_de
sim.generate_data()
random_sample_description = sim.sample_description
random_sample_description["continuous"] = [int(x) for x in random_sample_description["condition"]]
random_sample_description["batch"] = [
str(int(x)) + str(np.random.randint(0, 3))
for x in random_sample_description["continuous"]
]
test = de.test.continuous_1d(
data=sim.input_data,
sample_description=random_sample_description,
gene_names=["gene" + str(i) for i in range(sim.input_data.num_features)],
formula_loc="~ 1 + continuous + batch" if constrained else "~ 1 + continuous",
formula_scale="~ 1",
factor_loc_totest="continuous",
continuous="continuous",
constraints_loc={"batch": "continuous"} if constrained else None,
df=5,
spline_basis=spline_basis,
test="wald",
quick_scale=True,
noise_model=self.noise_model
)
self._eval(sim=sim, test=test)
def _test_interaction(self, ngenes: int, test: str, constrained: bool,
spline_basis: str):
n_timepoints = 5
sim = Simulator(num_observations=n_timepoints * 200,
num_features=ngenes)
sim.generate_sample_description(num_batches=0, num_conditions=0)
sim.generate_params()
sim.generate_data()
random_sample_description = pd.DataFrame({
"continuous":
np.asarray(np.random.randint(0, n_timepoints, size=sim.nobs),
dtype=float)
})
random_sample_description["condition"] = [
str(np.random.randint(0, 2))
for x in random_sample_description["continuous"]
]
random_sample_description["batch"] = [
x + str(np.random.randint(0, 3))
for x in random_sample_description["condition"]
]
random_sample_description["size_factors"] = np.random.uniform(
0.9, 1.1, sim.nobs) # TODO put into simulation.
det = self._fit_continuous_interaction(
sim=sim,
sample_description=random_sample_description,
test=test,
constrained=constrained,
spline_basis=spline_basis,
)
return det
def test(self):
"""
Check that factors that are numeric receive the correct number of coefficients.
:return:
"""
logging.getLogger("tensorflow").setLevel(logging.ERROR)
logging.getLogger("batchglm").setLevel(logging.WARNING)
logging.getLogger("diffxpy").setLevel(logging.WARNING)
sim = Simulator(num_observations=2000, num_features=2)
sim.generate_sample_description(num_batches=0, num_conditions=2)
sim.generate_params()
sim.generate_data()
sample_description = sim.sample_description
sample_description["numeric1"] = np.random.random(size=sim.nobs)
sample_description["numeric2"] = np.random.random(size=sim.nobs)
test = de.test.wald(
data=sim.input_data,
sample_description=sample_description,
formula_loc="~ 1 + condition + numeric1 + numeric2",
formula_scale="~ 1",
factor_loc_totest="condition",
as_numeric=["numeric1", "numeric2"],
training_strategy="DEFAULT")
# Check that number of coefficients is correct.
assert test.model_estim.a_var.shape[0] == 4
return True
def _test_single_full_rank(self):
"""
Test if de.wald() generates a uniform p-value distribution
if it is given data simulated based on the null model. Returns the p-value
of the two-side Kolmgorov-Smirnov test for equality of the observed
p-value distribution and a uniform distribution.
:param n_cells: Number of cells to simulate (number of observations per test).
:param n_genes: Number of genes to simulate (number of tests).
:param noise_model: Noise model to use for data fitting.
"""
if self.noise_model == "nb":
from batchglm.api.models.tf1.glm_nb import Simulator
rand_fn_scale = lambda shape: np.random.uniform(1, 2, shape)
elif self.noise_model == "norm":
from batchglm.api.models import Simulator
rand_fn_scale = lambda shape: np.random.uniform(1, 2, shape)
else:
raise ValueError("noise model %s not recognized" % self.noise_model)
sim = Simulator(num_observations=200, num_features=2)
sim.generate_sample_description(num_batches=0, num_conditions=0)
sim.generate_params(rand_fn_scale=rand_fn_scale)
sim.generate_data()
random_sample_description = pd.DataFrame({
"condition": [str(x) for x in np.random.randint(2, size=sim.nobs)]
})
try:
random_sample_description["batch"] = random_sample_description["condition"]
_ = de.test.wald(
data=sim.input_data,
sample_description=random_sample_description,
factor_loc_totest="condition",
formula_loc="~ 1 + condition + batch",
noise_model=self.noise_model
)
except ValueError as error:
logging.getLogger("diffxpy").info(error)
else:
raise ValueError("rank error was erroneously not thrown on under-determined unconstrained system")
try:
random_sample_description["batch"] = [
x + str(np.random.randint(0, 2)) for x in random_sample_description["condition"].values
]
_ = de.test.wald(
data=sim.input_data,
sample_description=random_sample_description,
factor_loc_totest="condition",
formula_loc="~ 1 + condition + batch",
constraints_loc={"batch": "condition"},
noise_model=self.noise_model
)
except ValueError as error:
raise ValueError("rank error was erroneously thrown on defined constrained system")
def _test_null_distribution_wald(self, n_cells: int, n_genes: int,
noise_model: str):
"""
Test if de.wald() generates a uniform p-value distribution
if it is given data simulated based on the null model. Returns the p-value
of the two-side Kolmgorov-Smirnov test for equality of the observed
p-value distribution and a uniform distribution.
:param n_cells: Number of cells to simulate (number of observations per test).
:param n_genes: Number of genes to simulate (number of tests).
:param noise_model: Noise model to use for data fitting.
"""
if noise_model == "nb":
from batchglm.api.models.tf1.glm_nb import Simulator
rand_fn_scale = lambda shape: np.random.uniform(1, 2, shape)
elif noise_model == "norm":
from batchglm.api.models import Simulator
rand_fn_scale = lambda shape: np.random.uniform(1, 2, shape)
else:
raise ValueError("noise model %s not recognized" % noise_model)
sim = Simulator(num_observations=n_cells, num_features=n_genes)
sim.generate_sample_description(num_batches=0, num_conditions=0)
sim.generate_params(rand_fn_scale=rand_fn_scale)
sim.generate_data()
random_sample_description = pd.DataFrame({
"condition":
np.random.randint(2, size=sim.nobs),
"batch":
np.random.randint(2, size=sim.nobs)
})
random_sf = np.random.uniform(0.5, 1.5, sim.nobs)
test = de.test.wald(data=sim.input_data,
sample_description=random_sample_description,
factor_loc_totest="condition",
formula_loc="~ 1 + condition + batch",
size_factors=random_sf,
batch_size=500,
noise_model=noise_model,
training_strategy="DEFAULT",
dtype="float64")
_ = test.summary()
# Compare p-value distribution under null model against uniform distribution.
pval_h0 = stats.kstest(test.pval, 'uniform').pvalue
logging.getLogger("diffxpy").info(
'KS-test pvalue for null model match of wald(): %f' % pval_h0)
assert pval_h0 > 0.05, ("KS-Test failed: pval_h0=%f is <= 0.05!" %
np.round(pval_h0, 5))
return True
def _prepare_data(self, n_cells: int = 2000, n_genes: int = 100):
"""
:param n_cells: Number of cells to simulate (number of observations per test).
:param n_genes: Number of genes to simulate (number of tests).
"""
sim = Simulator(num_observations=n_cells, num_features=n_genes)
sim.generate_sample_description(num_batches=0, num_conditions=2)
sim.generate_params()
sim.generate_data()
return sim
def _test_all_moments(self):
if self.noise_model is None:
raise ValueError("noise_model is None")
else:
if self.noise_model == "nb":
from batchglm.api.models.tf1.glm_nb import Simulator
elif self.noise_model == "norm":
from batchglm.api.models import Simulator
elif self.noise_model == "beta":
from batchglm.api.models.tf1.glm_beta import Simulator
else:
raise ValueError("noise_model not recognized")
self.sim = Simulator(num_observations=100000, num_features=10)
self.sim.generate_sample_description(num_batches=1, num_conditions=1)
self.sim.generate_params()
self.sim.generate_data()
success = self.eval_simulation_mean()
assert success, "mean of simulation was inaccurate"
return True
def test_rank_test_zero_variance(self):
"""
Test if rank test works if it is given genes with zero variance.
"""
logging.getLogger("tensorflow").setLevel(logging.ERROR)
logging.getLogger("batchglm").setLevel(logging.WARNING)
logging.getLogger("diffxpy").setLevel(logging.WARNING)
np.random.seed(1)
sim = Simulator(num_observations=1000, num_features=10)
sim.generate_sample_description(num_batches=0, num_conditions=0)
sim.generate()
sim.input_data.x[:, 0] = 0
sim.input_data.x[:, 1] = 5
random_sample_description = pd.DataFrame(
{"condition": np.random.randint(2, size=sim.nobs)})
test = de.test.rank_test(data=sim.input_data,
sample_description=random_sample_description,
grouping="condition",
is_sig_zerovar=True)
assert np.isnan(test.pval[0]) and test.pval[1] == 1, \
"rank test did not assign p-value of zero to groups with zero variance and same mean, %f, %f" % \
(test.pval[0], test.pval[1])
return True
def _test_null_distribution_rank(self, n_cells: int, n_genes: int):
"""
Test if de.test.rank_test() generates a uniform p-value distribution
if it is given data simulated based on the null model. Returns the p-value
of the two-side Kolmgorov-Smirnov test for equality of the observed
p-value distribution and a uniform distribution.
:param n_cells: Number of cells to simulate (number of observations per test).
:param n_genes: Number of genes to simulate (number of tests).
"""
from batchglm.api.models.tf1.glm_norm import Simulator
sim = Simulator(num_observations=n_cells, num_features=n_genes)
sim.generate_sample_description(num_batches=0, num_conditions=0)
sim.generate()
random_sample_description = pd.DataFrame(
{"condition": np.random.randint(2, size=sim.nobs)})
test = de.test.rank_test(data=sim.input_data,
sample_description=random_sample_description,
grouping="condition")
_ = test.summary()
# Compare p-value distribution under null model against uniform distribution.
pval_h0 = stats.kstest(test.pval, 'uniform').pvalue
logging.getLogger("diffxpy").info(
'KS-test pvalue for null model match of rank_test(): %f' % pval_h0)
assert pval_h0 > 0.05, ("KS-Test failed: pval_h0=%f is <= 0.05!" %
np.round(pval_h0, 5))
return True
def test_forfatal_from_dict(self):
"""
Test if dictionary-based constraint interface is working.
"""
logging.getLogger("tensorflow").setLevel(logging.ERROR)
logging.getLogger("batchglm").setLevel(logging.WARNING)
logging.getLogger("diffxpy").setLevel(logging.WARNING)
np.random.seed(1)
n_cells = 2000
n_genes = 2
sim = Simulator(num_observations=n_cells, num_features=n_genes)
sim.generate_sample_description(num_batches=0, num_conditions=0)
sim.generate()
# Build design matrix:
sample_description = pd.DataFrame({
"cond": ["cond" + str(i // 1000) for i in range(n_cells)],
"batch": ["batch" + str(i // 500) for i in range(n_cells)]
})
test = de.test.wald(data=sim.input_data,
sample_description=sample_description,
formula_loc="~1+cond+batch",
formula_scale="~1+cond+batch",
constraints_loc={"batch": "cond"},
constraints_scale={"batch": "cond"},
coef_to_test=["cond[T.cond1]"])
_ = test.summary()
def get_simulator(self):
if self.noise_model is None:
raise ValueError("noise_model is None")
else:
if self.noise_model == "nb":
from batchglm.api.models.tf1.glm_nb import Simulator
elif self.noise_model == "norm":
from batchglm.api.models import Simulator
elif self.noise_model == "beta":
from batchglm.api.models.tf1.glm_beta import Simulator
else:
raise ValueError("noise_model not recognized")
return Simulator(num_observations=10000, num_features=10)
class TestSimulationGlmAll:
sim: _SimulatorGLM
input_data: InputDataGLM
noise_model: str
def eval_simulation_mean(self):
if self.noise_model is None:
raise ValueError("noise_model is None")
else:
if self.noise_model == "nb":
threshold_dev = 1e-2
threshold_std = 1e-1
elif self.noise_model == "norm":
threshold_dev = 1e-2
threshold_std = 1e-1
elif self.noise_model == "beta":
threshold_dev = 1e-2
threshold_std = 1e-1
else:
raise ValueError("noise_model not recognized")
means_sim = self.sim.a_var[0, :]
means_obs = self.sim.link_loc(np.mean(self.sim.input_data.x, axis=0))
mean_dev = np.mean(means_sim - means_obs)
std_dev = np.std(means_sim - means_obs)
logging.getLogger("batchglm").info("mean_dev_a %f" % mean_dev)
logging.getLogger("batchglm").info("std_dev_a %f" % std_dev)
if np.abs(mean_dev) < threshold_dev and \
std_dev < threshold_std:
return True
else:
return False
def _test_all_moments(self):
if self.noise_model is None:
raise ValueError("noise_model is None")
else:
if self.noise_model == "nb":
from batchglm.api.models.tf1.glm_nb import Simulator
elif self.noise_model == "norm":
from batchglm.api.models import Simulator
elif self.noise_model == "beta":
from batchglm.api.models.tf1.glm_beta import Simulator
else:
raise ValueError("noise_model not recognized")
self.sim = Simulator(num_observations=100000, num_features=10)
self.sim.generate_sample_description(num_batches=1, num_conditions=1)
self.sim.generate_params()
self.sim.generate_data()
success = self.eval_simulation_mean()
assert success, "mean of simulation was inaccurate"
return True
def simulate(self, n_cells: int = 200, n_genes: int = 2):
sim = Simulator(num_observations=n_cells, num_features=n_genes)
sim.generate_sample_description(num_batches=0, num_conditions=0)
sim.generate()
random_sample_description = pd.DataFrame({
"condition": np.random.randint(2, size=sim.input_data.num_observations)
})
return sim.x, random_sample_description
def _test_model_fit_partition(
self,
n_cells: int,
n_genes: int,
noise_model: str
):
"""
Test if de.wald() generates a uniform p-value distribution
if it is given data simulated based on the null model. Returns the p-value
of the two-side Kolmgorov-Smirnov test for equality of the observed
p-value distribution and a uniform distribution.
:param n_cells: Number of cells to simulate (number of observations per test).
:param n_genes: Number of genes to simulate (number of tests).
:param noise_model: Noise model to use for data fitting.
"""
if noise_model == "nb":
from batchglm.api.models.tf1.glm_nb import Simulator
rand_fn_scale = lambda shape: np.random.uniform(1, 2, shape)
elif noise_model == "norm":
from batchglm.api.models.tf1.glm_norm import Simulator
rand_fn_scale = lambda shape: np.random.uniform(1, 2, shape)
else:
raise ValueError("noise model %s not recognized" % noise_model)
sim = Simulator(num_observations=n_cells, num_features=n_genes)
sim.generate_sample_description(num_batches=0, num_conditions=0)
sim.generate_params(rand_fn_scale=rand_fn_scale)
sim.generate_data()
random_sample_description = pd.DataFrame({
"condition": np.random.randint(2, size=sim.nobs),
"batch": np.random.randint(2, size=sim.nobs)
})
partition = de.fit.partition(
data=sim.input_data,
sample_description=random_sample_description,
parts="condition"
)
estim = partition.model(
formula_loc="~ 1 + batch",
noise_model=noise_model
)
return True
def test_forfatal_from_string(self):
"""
Test if _from_string interface is working.
n_cells is constant as the design matrix and constraints depend on it.
"""
logging.getLogger("tensorflow").setLevel(logging.ERROR)
logging.getLogger("batchglm").setLevel(logging.WARNING)
logging.getLogger("diffxpy").setLevel(logging.WARNING)
np.random.seed(1)
n_cells = 2000
n_genes = 2
sim = Simulator(num_observations=n_cells, num_features=n_genes)
sim.generate_sample_description(num_batches=0, num_conditions=0)
sim.generate()
# Build design matrix:
dmat = np.zeros([n_cells, 6])
dmat[:, 0] = 1
dmat[:500, 1] = 1 # bio rep 1
dmat[500:1000, 2] = 1 # bio rep 2
dmat[1000:1500, 3] = 1 # bio rep 3
dmat[1500:2000, 4] = 1 # bio rep 4
dmat[1000:2000, 5] = 1 # condition effect
coefficient_names = [
'intercept', 'bio1', 'bio2', 'bio3', 'bio4', 'treatment1'
]
dmat_est = pd.DataFrame(data=dmat, columns=coefficient_names)
dmat_est_loc, _ = de.utils.design_matrix(dmat=dmat_est,
return_type="dataframe")
dmat_est_scale, _ = de.utils.design_matrix(dmat=dmat_est,
return_type="dataframe")
# Build constraints:
constraints_loc = de.utils.constraint_matrix_from_string(
dmat=dmat_est_loc.values,
coef_names=dmat_est_loc.columns,
constraints=["bio1+bio2=0", "bio3+bio4=0"])
constraints_scale = de.utils.constraint_matrix_from_string(
dmat=dmat_est_scale.values,
coef_names=dmat_est_scale.columns,
constraints=["bio1+bio2=0", "bio3+bio4=0"])
test = de.test.wald(data=sim.input_data,
dmat_loc=dmat_est_loc,
dmat_scale=dmat_est_scale,
constraints_loc=constraints_loc,
constraints_scale=constraints_scale,
coef_to_test=["treatment1"])
_ = test.summary()
def test_null_distribution_lrt(self,
n_cells: int = 4000,
n_genes: int = 200):
"""
Test if de.lrt() generates a uniform p-value distribution
if it is given data simulated based on the null model. Returns the p-value
of the two-side Kolmgorov-Smirnov test for equality of the observed
p-value distribution and a uniform distribution.
:param n_cells: Number of cells to simulate (number of observations per test).
:param n_genes: Number of genes to simulate (number of tests).
"""
logging.getLogger("tensorflow").setLevel(logging.ERROR)
logging.getLogger("batchglm").setLevel(logging.WARNING)
logging.getLogger("diffxpy").setLevel(logging.WARNING)
sim = Simulator(num_observations=n_cells, num_features=n_genes)
sim.generate_sample_description(num_batches=0, num_conditions=2)
sim.generate()
sample_description = pd.DataFrame({
"covar1":
np.random.randint(2, size=sim.nobs),
"covar2":
np.random.randint(2, size=sim.nobs)
})
sample_description["cond"] = sim.sample_description["condition"].values
partition = de.test.partition(data=sim.x,
parts="cond",
sample_description=sample_description)
det = partition.lrt(full_formula_loc="~ 1 + covar1",
full_formula_scale="~ 1",
reduced_formula_loc="~ 1",
reduced_formula_scale="~ 1",
training_strategy="DEFAULT",
dtype="float64")
_ = det.summary()
# Compare p-value distribution under null model against uniform distribution.
pval_h0 = stats.kstest(det.pval.flatten(), 'uniform').pvalue
logging.getLogger("diffxpy").info(
'KS-test pvalue for null model match of lrt(): %f' % pval_h0)
assert pval_h0 > 0.05, "KS-Test failed: pval_h0=%f is <= 0.05!" % np.round(
pval_h0, 5)
return True
def _prepate_data(self, n_cells: int, n_genes: int, n_groups: int):
if self.noise_model == "nb":
from batchglm.api.models.tf1.glm_nb import Simulator
rand_fn_loc = lambda shape: np.random.uniform(0.1, 1, shape)
rand_fn_scale = lambda shape: np.random.uniform(0.5, 1, shape)
elif self.noise_model == "norm" or self.noise_model is None:
from batchglm.api.models import Simulator
rand_fn_loc = lambda shape: np.random.uniform(500, 1000, shape)
rand_fn_scale = lambda shape: np.random.uniform(1, 2, shape)
else:
raise ValueError("noise model %s not recognized" %
self.noise_model)
sim = Simulator(num_observations=n_cells, num_features=n_genes)
sim.generate_sample_description(num_batches=0, num_conditions=0)
sim.generate_params(rand_fn_loc=rand_fn_loc,
rand_fn_scale=rand_fn_scale)
sim.generate_data()
random_sample_description = pd.DataFrame({
"condition":
[str(x) for x in np.random.randint(n_groups, size=sim.nobs)]
})
return sim, random_sample_description
def test_null_distribution_wald(self,
n_cells: int = 2000,
n_genes: int = 100,
n_groups: int = 2):
"""
Test if de.test_wald_loc() generates a uniform p-value distribution
if it is given data simulated based on the null model. Returns the p-value
of the two-side Kolmgorov-Smirnov test for equality of the observed
p-value distriubution and a uniform distribution.
:param n_cells: Number of cells to simulate (number of observations per test).
:param n_genes: Number of genes to simulate (number of tests).
"""
logging.getLogger("tensorflow").setLevel(logging.ERROR)
logging.getLogger("batchglm").setLevel(logging.WARNING)
logging.getLogger("diffxpy").setLevel(logging.WARNING)
from batchglm.api.models.tf1.glm_nb import Simulator
sim = Simulator(num_observations=n_cells, num_features=n_genes)
sim.generate_sample_description(num_batches=0, num_conditions=0)
sim.generate()
random_sample_description = pd.DataFrame(
{"condition": np.random.randint(n_groups, size=sim.nobs)})
test = de.test.versus_rest(
data=sim.x,
grouping="condition",
test="wald",
noise_model="nb",
sample_description=random_sample_description,
batch_size=500,
training_strategy="DEFAULT",
dtype="float64")
summary = test.summary()
# Compare p-value distribution under null model against uniform distribution.
pval_h0 = stats.kstest(test.pval.flatten(), 'uniform').pvalue
logging.getLogger("diffxpy").info(
'KS-test pvalue for null model match of test_wald_loc(): %f' %
pval_h0)
assert pval_h0 > 0.05, "KS-Test failed: pval_h0=%f is <= 0.05!" % np.round(
pval_h0, 5)
return True
def _test_null_distribution_lrt(self, n_cells: int, n_genes: int,
noise_model: str):
"""
Test if de.lrt() generates a uniform p-value distribution
if it is given data simulated based on the null model. Returns the p-value
of the two-side Kolmgorov-Smirnov test for equality of the observed
p-value distribution and a uniform distribution.
:param n_cells: Number of cells to simulate (number of observations per test).
:param n_genes: Number of genes to simulate (number of tests).
:param noise_model: Noise model to use for data fitting.
"""
if noise_model == "nb":
from batchglm.api.models.tf1.glm_nb import Simulator
elif noise_model == "norm":
from batchglm.api.models.tf1.glm_norm import Simulator
else:
raise ValueError("noise model %s not recognized" % noise_model)
sim = Simulator(num_observations=n_cells, num_features=n_genes)
sim.generate_sample_description(num_batches=0, num_conditions=0)
sim.generate()
random_sample_description = pd.DataFrame(
{"condition": np.random.randint(2, size=sim.nobs)})
test = de.test.lrt(data=sim.input_data,
sample_description=random_sample_description,
full_formula_loc="~ 1 + condition",
full_formula_scale="~ 1",
reduced_formula_loc="~ 1",
reduced_formula_scale="~ 1",
noise_model=noise_model)
_ = test.summary()
# Compare p-value distribution under null model against uniform distribution.
pval_h0 = stats.kstest(test.pval, 'uniform').pvalue
logging.getLogger("diffxpy").info(
'KS-test pvalue for null model match of lrt(): %f' % pval_h0)
assert pval_h0 > 0.05, ("KS-Test failed: pval_h0=%f is <= 0.05!" %
np.round(pval_h0, 5))
return True
def test_null_distribution_wald_constrained(self, n_genes: int = 100):
"""
Test if de.wald() with constraints generates a uniform p-value distribution
if it is given data simulated based on the null model. Returns the p-value
of the two-side Kolmgorov-Smirnov test for equality of the observed
p-value distribution and a uniform distribution.
n_cells is constant as the design matrix and constraints depend on it.
:param n_genes: Number of genes to simulate (number of tests).
"""
logging.getLogger("tensorflow").setLevel(logging.ERROR)
logging.getLogger("batchglm").setLevel(logging.WARNING)
logging.getLogger("diffxpy").setLevel(logging.WARNING)
np.random.seed(1)
n_cells = 2000
sim = Simulator(num_observations=n_cells, num_features=n_genes)
sim.generate_sample_description(num_batches=0, num_conditions=0)
sim.generate()
# Build design matrix:
sample_description = pd.DataFrame({
"cond": ["cond" + str(i // 1000) for i in range(n_cells)],
"batch": ["batch" + str(i // 500) for i in range(n_cells)]
})
test = de.test.wald(data=sim.input_data,
sample_description=sample_description,
formula_loc="~1+cond+batch",
formula_scale="~1+cond+batch",
constraints_loc={"batch": "cond"},
constraints_scale={"batch": "cond"},
coef_to_test=["cond[T.cond1]"])
_ = test.summary()
# Compare p-value distribution under null model against uniform distribution.
pval_h0 = stats.kstest(test.pval, 'uniform').pvalue
logging.getLogger("diffxpy").info(
'KS-test pvalue for null model match of wald(): %f' % pval_h0)
assert pval_h0 > 0.05, "KS-Test failed: pval_h0 is <= 0.05!"
return True
def simulate(self):
if self.noise_model is None:
raise ValueError("noise_model is None")
else:
if self.noise_model == "nb":
from batchglm.api.models.tf1.glm_nb import Simulator
elif self.noise_model == "norm":
from batchglm.api.models import Simulator
elif self.noise_model == "beta":
from batchglm.api.models.tf1.glm_beta import Simulator
else:
raise ValueError("noise_model not recognized")
num_observations = 500
sim = Simulator(num_observations=num_observations, num_features=4)
sim.generate_sample_description(num_conditions=2, num_batches=2)
sim.generate()
self.sim = sim
def test_for_fatal(self):
"""
"""
logging.getLogger("tensorflow").setLevel(logging.ERROR)
logging.getLogger("batchglm").setLevel(logging.WARNING)
logging.getLogger("diffxpy").setLevel(logging.WARNING)
sim = Simulator(num_observations=50, num_features=10)
sim.generate_sample_description(num_batches=0, num_conditions=2)
sim.generate()
test = de.test.wald(data=sim.X,
factor_loc_totest="condition",
formula_loc="~ 1 + condition",
sample_description=sim.sample_description,
gene_names=[str(x) for x in range(sim.X.shape[1])],
training_strategy="DEFAULT",
dtype="float64")
# Set up reference gene sets.
rs = de.enrich.RefSets()
rs.add(id="set1", source="manual", gene_ids=["1", "3"])
rs.add(id="set2", source="manual", gene_ids=["5", "6"])
for i in [True, False]:
for j in [True, False]:
enrich_test_i = de.enrich.test(
ref=rs,
det=test,
threshold=0.05,
incl_all_zero=i,
clean_ref=j,
)
_ = enrich_test_i.summary()
_ = enrich_test_i.significant_set_ids()
_ = enrich_test_i.significant_sets()
_ = enrich_test_i.set_summary(id="set1")
return True
def _test_compute_hessians(self, sparse):
if self.noise_model is None:
raise ValueError("noise_model is None")
else:
if self.noise_model == "nb":
from batchglm.api.models.tf1.glm_nb import Simulator, InputDataGLM
elif self.noise_model == "norm":
from batchglm.api.models import Simulator, InputDataGLM
elif self.noise_model == "beta":
from batchglm.api.models.tf1.glm_beta import Simulator, InputDataGLM
else:
raise ValueError("noise_model not recognized")
num_observations = 500
num_conditions = 2
sim = Simulator(num_observations=num_observations, num_features=4)
sim.generate_sample_description(num_conditions=num_conditions,
num_batches=2)
sim.generate()
sample_description = data_utils.sample_description_from_xarray(
sim.data, dim="observations")
design_loc = data_utils.design_matrix(
sample_description, formula="~ 1 + condition + batch")
design_scale = data_utils.design_matrix(sample_description,
formula="~ 1 + condition")
if sparse:
input_data = InputDataGLM(data=scipy.sparse.csr_matrix(sim.X),
design_loc=design_loc,
design_scale=design_scale)
else:
input_data = InputDataGLM(data=sim.X,
design_loc=design_loc,
design_scale=design_scale)
# Compute hessian based on analytic solution.
pkg_constants.HESSIAN_MODE = "analytic"
t0_analytic = time.time()
h_analytic = self.get_hessians(input_data)
t1_analytic = time.time()
t_analytic = t1_analytic - t0_analytic
# Compute hessian based on tensorflow auto-differentiation.
pkg_constants.HESSIAN_MODE = "tf1"
t0_tf = time.time()
h_tf = self.get_hessians(input_data)
t1_tf = time.time()
t_tf = t1_tf - t0_tf
logging.getLogger("batchglm").info(
"run time observation batch-wise analytic solution: %f" %
t_analytic)
logging.getLogger("batchglm").info("run time tensorflow solution: %f" %
t_tf)
logging.getLogger("batchglm").info("MAD: %f" %
np.max(np.abs((h_tf - h_analytic))))
#i = 1
#print(h_tf[i, :, :])
#print(h_analytic[i, :, :])
#print(h_tf[i, :, :] - h_analytic[i, :, :])
# Make sure that hessians are not all zero which might make evaluation of equality difficult.
assert np.sum(np.abs(h_analytic)) > 1e-10, \
"hessians too small to perform test: %f" % np.sum(np.abs(h_analytic))
mad = np.max(np.abs(h_tf - h_analytic))
assert mad < 1e-15, mad
return True
def _test_null_distribution_wald_constrained_2layer(
self, n_genes: int = 100):
"""
Test if de.wald() with constraints generates a uniform p-value distribution
if it is given data simulated based on the null model. Returns the p-value
of the two-side Kolmgorov-Smirnov test for equality of the observed
p-value distribution and a uniform distribution.
n_cells is constant as the design matrix and constraints depend on it.
:param n_genes: Number of genes to simulate (number of tests).
"""
logging.getLogger("tensorflow").setLevel(logging.ERROR)
logging.getLogger("batchglm").setLevel(logging.WARNING)
logging.getLogger("diffxpy").setLevel(logging.WARNING)
np.random.seed(1)
n_cells = 12000
sim = Simulator(num_observations=n_cells, num_features=n_genes)
sim.generate_sample_description(num_batches=0, num_conditions=0)
sim.generate()
# Build design matrix:
dmat = np.zeros([n_cells, 14])
dmat[:, 0] = 1
dmat[6000:12000, 1] = 1 # condition effect
dmat[:1000, 2] = 1 # bio rep 1 - treated 1
dmat[1000:3000, 3] = 1 # bio rep 2 - treated 2
dmat[3000:5000, 4] = 1 # bio rep 3 - treated 3
dmat[5000:6000, 5] = 1 # bio rep 4 - treated 4
dmat[6000:7000, 6] = 1 # bio rep 5 - untreated 1
dmat[7000:9000, 7] = 1 # bio rep 6 - untreated 2
dmat[9000:11000, 8] = 1 # bio rep 7 - untreated 3
dmat[11000:12000, 9] = 1 # bio rep 8 - untreated 4
dmat[1000:2000, 10] = 1 # tech rep 1
dmat[7000:8000, 10] = 1 # tech rep 1
dmat[2000:3000, 11] = 1 # tech rep 2
dmat[8000:9000, 11] = 1 # tech rep 2
dmat[3000:4000, 12] = 1 # tech rep 3
dmat[9000:10000, 12] = 1 # tech rep 3
dmat[4000:5000, 13] = 1 # tech rep 4
dmat[10000:11000, 13] = 1 # tech rep 4
coefficient_names = [
'intercept', 'treatment1', 'bio1', 'bio2', 'bio3', 'bio4', 'bio5',
'bio6', 'bio7', 'bio8', 'tech1', 'tech2', 'tech3', 'tech4'
]
dmat_est = pd.DataFrame(data=dmat, columns=coefficient_names)
dmat_est_loc = de.utils.design_matrix(dmat=dmat_est,
return_type="dataframe")
dmat_est_scale = de.utils.design_matrix(dmat=dmat_est.iloc[:, [0]],
return_type="dataframe")
# Build constraints:
constraints_loc = de.utils.constraint_matrix_from_string(
dmat=dmat_est_loc.values,
coef_names=dmat_est_loc.columns,
constraints=[
"bio1+bio2=0", "bio3+bio4=0", "bio5+bio6=0", "bio7+bio8=0",
"tech1+tech2=0", "tech3+tech4=0"
])
constraints_scale = None
test = de.test.wald(data=sim.input_data,
dmat_loc=dmat_est_loc,
dmat_scale=dmat_est_scale,
constraints_loc=constraints_loc,
constraints_scale=constraints_scale,
coef_to_test=["treatment1"])
_ = test.summary()
# Compare p-value distribution under null model against uniform distribution.
pval_h0 = stats.kstest(test.pval, 'uniform').pvalue
logging.getLogger("diffxpy").info(
'KS-test pvalue for null model match of wald(): %f' % pval_h0)
assert pval_h0 > 0.05, "KS-Test failed: pval_h0 is <= 0.05!"
return True
