def __init__(
self,
adata: AnnData,
cell_type_markers: pd.DataFrame,
size_factor_key: str,
**model_kwargs,
):
try:
cell_type_markers = cell_type_markers.loc[adata.var_names]
except KeyError:
raise KeyError(
"Anndata and cell type markers do not contain the same genes."
)
super().__init__(adata)
register_tensor_from_anndata(adata, "_size_factor", "obs", size_factor_key)
self.n_genes = self.summary_stats["n_vars"]
self.cell_type_markers = cell_type_markers
rho = torch.Tensor(cell_type_markers.to_numpy())
n_cats_per_cov = (
self.scvi_setup_dict_["extra_categoricals"]["n_cats_per_key"]
if "extra_categoricals" in self.scvi_setup_dict_
else None
)
x = scvi.data.get_from_registry(adata, _CONSTANTS.X_KEY)
col_means = np.asarray(np.mean(x, 0)).ravel() # (g)
col_means_mu, col_means_std = np.mean(col_means), np.std(col_means)
col_means_normalized = torch.Tensor((col_means - col_means_mu) / col_means_std)
# compute basis means for phi - shape (B)
basis_means = np.linspace(np.min(x), np.max(x), B) # (B)
self.module = CellAssignModule(
n_genes=self.n_genes,
rho=rho,
basis_means=basis_means,
b_g_0=col_means_normalized,
n_batch=self.summary_stats["n_batch"],
n_cats_per_cov=n_cats_per_cov,
n_continuous_cov=self.summary_stats["n_continuous_covs"],
**model_kwargs,
)
self._model_summary_string = (
"CellAssign Model with params: \nn_genes: {}, n_labels: {}"
).format(
self.n_genes,
rho.shape[1],
)
self.init_params_ = self._get_init_params(locals())
def __init__(
self,
adata: AnnData,
cell_type_markers: pd.DataFrame,
**model_kwargs,
):
try:
cell_type_markers = cell_type_markers.loc[adata.var_names]
except KeyError:
raise KeyError(
"Anndata and cell type markers do not contain the same genes.")
super().__init__(adata)
self.n_genes = self.summary_stats.n_vars
self.cell_type_markers = cell_type_markers
rho = torch.Tensor(cell_type_markers.to_numpy())
n_cats_per_cov = (self.adata_manager.get_state_registry(
REGISTRY_KEYS.CAT_COVS_KEY).n_cats_per_key
if REGISTRY_KEYS.CAT_COVS_KEY
in self.adata_manager.data_registry else None)
adata = self._validate_anndata(adata)
x = self.get_from_registry(adata, REGISTRY_KEYS.X_KEY)
col_means = np.asarray(np.mean(x, 0)).ravel() # (g)
col_means_mu, col_means_std = np.mean(col_means), np.std(col_means)
col_means_normalized = torch.Tensor(
(col_means - col_means_mu) / col_means_std)
# compute basis means for phi - shape (B)
basis_means = np.linspace(np.min(x), np.max(x), B) # (B)
self.module = CellAssignModule(
n_genes=self.n_genes,
rho=rho,
basis_means=basis_means,
b_g_0=col_means_normalized,
n_batch=self.summary_stats.n_batch,
n_cats_per_cov=n_cats_per_cov,
n_continuous_cov=self.summary_stats.get("n_extra_continuous_covs",
0),
**model_kwargs,
)
self._model_summary_string = (
"CellAssign Model with params: \nn_genes: {}, n_labels: {}"
).format(
self.n_genes,
rho.shape[1],
)
self.init_params_ = self._get_init_params(locals())