scgen.SCGEN

class scgen.SCGEN(adata, n_hidden=800, n_latent=100, n_layers=2, dropout_rate=0.2, **model_kwargs)

Implementation of scGen model for batch removal and perturbation prediction.

Parameters

adata : AnnData

AnnData object that has been registered via setup_anndata().

n_hidden : int (default: 800)

Number of nodes per hidden layer.

n_latent : int (default: 100)

Dimensionality of the latent space.

n_layers : int (default: 2)

Number of hidden layers used for encoder and decoder NNs.

dropout_rate : float (default: 0.2)

Dropout rate for neural networks.

**model_kwargs

Keyword args for SCGENVAE

Examples

>>> vae = scgen.SCGEN(adata)
>>> vae.train()
>>> adata.obsm["X_scgen"] = vae.get_latent_representation()

Attributes

adata	Data attached to model instance.
adata_manager	Manager instance associated with self.adata.
device	The current device that the module's params are on.
history	Returns computed metrics during training.
is_trained	Whether the model has been trained.
test_indices	Observations that are in test set.
train_indices	Observations that are in train set.
validation_indices	Observations that are in validation set.

Methods

batch_removal([adata])	Removes batch effects.
binary_classifier(adata, delta, ctrl_key, ...)	Latent space classifier.
convert_legacy_save(dir_path, output_dir_path)	Converts a legacy saved model (<v0.15.0) to the updated save format.
get_anndata_manager(adata[, required])	Retrieves the AnnDataManager for a given AnnData object specific to this model instance.
get_decoded_expression([adata, indices, ...])	Get decoded expression.
get_elbo([adata, indices, batch_size])	Return the ELBO for the data.
get_from_registry(adata, registry_key)	Returns the object in AnnData associated with the key in the data registry.
get_latent_representation([adata, indices, ...])	Return the latent representation for each cell.
get_marginal_ll([adata, indices, ...])	Return the marginal LL for the data.
get_reconstruction_error([adata, indices, ...])	Return the reconstruction error for the data.
load(dir_path[, adata, use_gpu, prefix, ...])	Instantiate a model from the saved output.
predict([ctrl_key, stim_key, ...])	Predicts the cell type provided by the user in stimulated condition.
reg_mean_plot(adata, axis_keys, labels[, ...])	Plots mean matching figure for a set of specific genes.
reg_var_plot(adata, axis_keys, labels[, ...])	Plots variance matching figure for a set of specific genes.
register_manager(adata_manager)	Registers an AnnDataManager instance with this model class.
save(dir_path[, prefix, overwrite, save_anndata])	Save the state of the model.
setup_anndata(adata[, batch_key, labels_key])	Sets up the AnnData object for this model.
to_device(device)	Move model to device.
train([max_epochs, use_gpu, train_size, ...])	Train the model.
view_anndata_setup([adata, ...])	Print summary of the setup for the initial AnnData or a given AnnData object.
view_setup_args(dir_path[, prefix])	Print args used to setup a saved model.