def test_destvi(save_path):
# Step1 learn CondSCVI
n_latent = 2
n_labels = 5
n_layers = 2
dataset = synthetic_iid(n_labels=n_labels)
sc_model = CondSCVI(dataset, n_latent=n_latent, n_layers=n_layers)
sc_model.train(1, train_size=1)
# step 2 learn destVI with multiple amortization scheme
for amor_scheme in ["both", "none", "proportion", "latent"]:
spatial_model = DestVI.from_rna_model(
dataset,
sc_model,
amortization=amor_scheme,
)
spatial_model.train(max_epochs=1)
assert not np.isnan(spatial_model.history["elbo_train"].values[0][0])
assert spatial_model.get_proportions().shape == (dataset.n_obs, n_labels)
assert spatial_model.get_gamma(return_numpy=True).shape == (
dataset.n_obs,
n_latent,
n_labels,
)
assert spatial_model.get_scale_for_ct("label_0", np.arange(50)).shape == (
50,
dataset.n_vars,
)
def from_rna_model(
cls,
st_adata: AnnData,
sc_model: CondSCVI,
vamp_prior_p: int = 50,
layer: Optional[str] = None,
**module_kwargs,
):
"""
Alternate constructor for exploiting a pre-trained model on a RNA-seq dataset.
Parameters
----------
st_adata
registed anndata object
sc_model
trained CondSCVI model
vamp_prior_p
number of mixture parameter for VampPrior calculations
**model_kwargs
Keyword args for :class:`~scvi.model.DestVI`
"""
decoder_state_dict = sc_model.module.decoder.state_dict()
px_decoder_state_dict = sc_model.module.px_decoder.state_dict()
px_r = sc_model.module.px_r.detach().cpu().numpy()
mapping = sc_model.adata_manager.get_state_registry(
REGISTRY_KEYS.LABELS_KEY
).categorical_mapping
if vamp_prior_p is None:
mean_vprior = None
var_vprior = None
else:
mean_vprior, var_vprior = sc_model.get_vamp_prior(
sc_model.adata, p=vamp_prior_p
)
cls.setup_anndata(st_adata, layer=layer)
return cls(
st_adata,
mapping,
decoder_state_dict,
px_decoder_state_dict,
px_r,
sc_model.module.n_hidden,
sc_model.module.n_latent,
sc_model.module.n_layers,
mean_vprior=mean_vprior,
var_vprior=var_vprior,
**module_kwargs,
)
def destvi_raw(adata, test=False):
from scvi.model import CondSCVI
from scvi.model import DestVI
adata_sc = adata.uns["sc_reference"].copy()
CondSCVI.setup_anndata(adata_sc, labels_key="label", layer=None)
sc_model = CondSCVI(adata_sc, weight_obs=False)
sc_model.train()
DestVI.setup_anndata(adata, layer=None)
st_model = DestVI.from_rna_model(adata, sc_model)
st_model.train(max_epochs=2500)
adata.obsm["proportions_pred"] = st_model.get_proportions()
return adata
def test_condscvi(save_path):
dataset = synthetic_iid(n_labels=5)
model = CondSCVI(dataset)
model.train(1, train_size=1)
model.get_latent_representation()
model.get_vamp_prior(dataset)
model = CondSCVI(dataset, weight_obs=True)
model.train(1, train_size=1)
model.get_latent_representation()
model.get_vamp_prior(dataset)