def __init__(
self,
adata: AnnData,
n_latent: int = 20,
gene_dispersion: Literal["gene", "gene-batch", "gene-label",
"gene-cell"] = "gene",
protein_dispersion: Literal["protein", "protein-batch",
"protein-label"] = "protein",
gene_likelihood: Literal["zinb", "nb"] = "nb",
latent_distribution: Literal["normal", "ln"] = "normal",
empirical_protein_background_prior: Optional[bool] = None,
**model_kwargs,
):
super(TOTALVI, self).__init__(adata)
if "totalvi_batch_mask" in self.scvi_setup_dict_.keys():
batch_mask = self.scvi_setup_dict_["totalvi_batch_mask"]
else:
batch_mask = None
emp_prior = (empirical_protein_background_prior
if empirical_protein_background_prior is not None else
(self.summary_stats["n_proteins"] > 10))
if emp_prior:
prior_mean, prior_scale = _get_totalvi_protein_priors(adata)
else:
prior_mean, prior_scale = None, None
n_cats_per_cov = (
self.scvi_setup_dict_["extra_categoricals"]["n_cats_per_key"]
if "extra_categoricals" in self.scvi_setup_dict_ else None)
self.module = TOTALVAE(
n_input_genes=self.summary_stats["n_vars"],
n_input_proteins=self.summary_stats["n_proteins"],
n_batch=self.summary_stats["n_batch"],
n_latent=n_latent,
n_continuous_cov=self.summary_stats["n_continuous_covs"],
n_cats_per_cov=n_cats_per_cov,
gene_dispersion=gene_dispersion,
protein_dispersion=protein_dispersion,
gene_likelihood=gene_likelihood,
latent_distribution=latent_distribution,
protein_batch_mask=batch_mask,
protein_background_prior_mean=prior_mean,
protein_background_prior_scale=prior_scale,
**model_kwargs,
)
self._model_summary_string = (
"TotalVI Model with the following params: \nn_latent: {}, "
"gene_dispersion: {}, protein_dispersion: {}, gene_likelihood: {}, latent_distribution: {}"
).format(
n_latent,
gene_dispersion,
protein_dispersion,
gene_likelihood,
latent_distribution,
)
self.init_params_ = self._get_init_params(locals())
def __init__(
self,
adata: AnnData,
n_latent: int = 20,
gene_dispersion: Literal["gene", "gene-batch", "gene-label",
"gene-cell"] = "gene",
protein_dispersion: Literal["protein", "protein-batch",
"protein-label"] = "protein",
gene_likelihood: Literal["zinb", "nb"] = "nb",
latent_distribution: Literal["normal", "ln"] = "normal",
empirical_protein_background_prior: Optional[bool] = None,
override_missing_proteins: bool = False,
**model_kwargs,
):
super(TOTALVI, self).__init__(adata)
self.protein_state_registry = self.adata_manager.get_state_registry(
REGISTRY_KEYS.PROTEIN_EXP_KEY)
if (ProteinObsmField.PROTEIN_BATCH_MASK in self.protein_state_registry
and not override_missing_proteins):
batch_mask = self.protein_state_registry.protein_batch_mask
msg = (
"Some proteins have all 0 counts in some batches. " +
"These proteins will be treated as missing measurements; however, "
+ "this can occur due to experimental design/biology. " +
"Reinitialize the model with `override_missing_proteins=True`,"
+ "to override this behavior.")
warnings.warn(msg, UserWarning)
self._use_adversarial_classifier = True
else:
batch_mask = None
self._use_adversarial_classifier = False
emp_prior = (empirical_protein_background_prior
if empirical_protein_background_prior is not None else
(self.summary_stats.n_proteins > 10))
if emp_prior:
prior_mean, prior_scale = self._get_totalvi_protein_priors(adata)
else:
prior_mean, prior_scale = None, None
n_cats_per_cov = (self.adata_manager.get_state_registry(
REGISTRY_KEYS.CAT_COVS_KEY)[
CategoricalJointObsField.N_CATS_PER_KEY]
if REGISTRY_KEYS.CAT_COVS_KEY
in self.adata_manager.data_registry else None)
n_batch = self.summary_stats.n_batch
use_size_factor_key = (REGISTRY_KEYS.SIZE_FACTOR_KEY
in self.adata_manager.data_registry)
library_log_means, library_log_vars = None, None
if not use_size_factor_key:
library_log_means, library_log_vars = _init_library_size(
self.adata_manager, n_batch)
self.module = TOTALVAE(
n_input_genes=self.summary_stats.n_vars,
n_input_proteins=self.summary_stats.n_proteins,
n_batch=n_batch,
n_latent=n_latent,
n_continuous_cov=self.summary_stats.get("n_extra_continuous_covs",
0),
n_cats_per_cov=n_cats_per_cov,
gene_dispersion=gene_dispersion,
protein_dispersion=protein_dispersion,
gene_likelihood=gene_likelihood,
latent_distribution=latent_distribution,
protein_batch_mask=batch_mask,
protein_background_prior_mean=prior_mean,
protein_background_prior_scale=prior_scale,
use_size_factor_key=use_size_factor_key,
library_log_means=library_log_means,
library_log_vars=library_log_vars,
**model_kwargs,
)
self._model_summary_string = (
"TotalVI Model with the following params: \nn_latent: {}, "
"gene_dispersion: {}, protein_dispersion: {}, gene_likelihood: {}, latent_distribution: {}"
).format(
n_latent,
gene_dispersion,
protein_dispersion,
gene_likelihood,
latent_distribution,
)
self.init_params_ = self._get_init_params(locals())
def __init__(
self,
adata: AnnData,
n_latent: int = 20,
gene_dispersion: Literal[
"gene", "gene-batch", "gene-label", "gene-cell"
] = "gene",
protein_dispersion: Literal[
"protein", "protein-batch", "protein-label"
] = "protein",
gene_likelihood: Literal["zinb", "nb"] = "nb",
latent_distribution: Literal["normal", "ln"] = "normal",
empirical_protein_background_prior: Optional[bool] = None,
override_missing_proteins: bool = False,
**model_kwargs,
):
super(TOTALVI, self).__init__(adata)
if (
"totalvi_batch_mask" in self.scvi_setup_dict_.keys()
and not override_missing_proteins
):
batch_mask = self.scvi_setup_dict_["totalvi_batch_mask"]
info_msg = (
"Some proteins have all 0 counts in some batches. "
+ "These proteins will be treated as missing; however, "
+ "this can occur due to experimental design/biology. "
+ "Reinitialize the model with `override_missing_proteins=True`,"
+ "to override this behavior."
)
logger.info(info_msg)
else:
batch_mask = None
emp_prior = (
empirical_protein_background_prior
if empirical_protein_background_prior is not None
else (self.summary_stats["n_proteins"] > 10)
)
if emp_prior:
prior_mean, prior_scale = _get_totalvi_protein_priors(adata)
else:
prior_mean, prior_scale = None, None
n_cats_per_cov = (
self.scvi_setup_dict_["extra_categoricals"]["n_cats_per_key"]
if "extra_categoricals" in self.scvi_setup_dict_
else None
)
n_batch = self.summary_stats["n_batch"]
library_log_means, library_log_vars = _init_library_size(adata, n_batch)
self.module = TOTALVAE(
n_input_genes=self.summary_stats["n_vars"],
n_input_proteins=self.summary_stats["n_proteins"],
n_batch=n_batch,
n_latent=n_latent,
n_continuous_cov=self.summary_stats["n_continuous_covs"],
n_cats_per_cov=n_cats_per_cov,
gene_dispersion=gene_dispersion,
protein_dispersion=protein_dispersion,
gene_likelihood=gene_likelihood,
latent_distribution=latent_distribution,
protein_batch_mask=batch_mask,
protein_background_prior_mean=prior_mean,
protein_background_prior_scale=prior_scale,
library_log_means=library_log_means,
library_log_vars=library_log_vars,
**model_kwargs,
)
self._model_summary_string = (
"TotalVI Model with the following params: \nn_latent: {}, "
"gene_dispersion: {}, protein_dispersion: {}, gene_likelihood: {}, latent_distribution: {}"
).format(
n_latent,
gene_dispersion,
protein_dispersion,
gene_likelihood,
latent_distribution,
)
self.init_params_ = self._get_init_params(locals())