spatialvi.model.SCVIVA

class spatialvi.model.SCVIVA(adata=None, n_hidden=128, n_latent=10, n_layers=1, dropout_rate=0.1, dispersion='gene', gene_likelihood='poisson', latent_distribution='normal', **kwargs)

scVIVA: variational auto-encoder with niche decoders for ST:cite:p:Levy25.

Parameters:

• adata (AnnData | None) – AnnData object that has been registered via setup_anndata(). If None, then the underlying module will not be initialized until training, and a LightningDataModule must be passed in during training.

• n_hidden (int) – Number of nodes per hidden layer.

• n_latent (int) – Dimensionality of the latent space.

• n_layers (int) – Number of hidden layers used for encoder and decoder NNs.

• dropout_rate (float) – Dropout rate for neural networks.

• dispersion (Literal['gene', 'gene-batch', 'gene-label', 'gene-cell']) –

  One of the following:

  • 'gene' - dispersion parameter of NB is constant per gene across cells

  • 'gene-batch' - dispersion can differ between different batches

  • 'gene-label' - dispersion can differ between different labels

  • 'gene-cell' - dispersion can differ for every gene in every cell

• gene_likelihood (Literal['zinb', 'nb', 'poisson']) –

  One of:

  • 'nb' - Negative binomial distribution

  • 'zinb' - Zero-inflated negative binomial distribution

  • 'poisson' - Poisson distribution

• latent_distribution (Literal['normal', 'ln']) –

  One of:

  • 'normal' - Normal distribution

  • 'ln' - Logistic normal distribution (Normal(0, I) transformed by softmax)

• **kwargs – Additional keyword arguments for nicheVAE.

Examples

>>> adata = anndata.read_h5ad(path_to_anndata)
>>> spatialvi.model.SCVIVA.preprocessing_anndata(
    adata,
    k_nn = 20,
    sample_key = 'slide_ID',
    labels_key = "cell_type",
    cell_coordinates_key = "spatial",
    expression_embedding_key = "X_scVI",
    **kwargs
)
>>> spatialvi.model.SCVIVA.setup_anndata(adata, batch_key="batch")
>>> vae = spatialvi.model.SCVIVA(adata)
>>> vae.train()
>>> adata.obsm["X_scVIVA"] = vae.get_latent_representation()
>>> adata.obsm["X_normalized_scVIVA"] = vae.get_normalized_expression()

Notes

See further usage examples in the following tutorials:

1. /tutorials/notebooks/spatial/scVIVA_tutorial

See also

nicheVAE

__init__(adata=None, n_hidden=128, n_latent=10, n_layers=1, dropout_rate=0.1, dispersion='gene', gene_likelihood='poisson', latent_distribution='normal', **kwargs)

Methods

__init__([adata, n_hidden, n_latent, ...])
compute_neighbors(adata[, spatial_key, ...])	Compute spatial neighbor graph and store in adata.obsm.
convert_legacy_save(dir_path, output_dir_path)	Converts a legacy saved model (<v0.15.0) to the updated save format.
data_registry(registry_key)	Returns the object in AnnData associated with the key in the data registry.
deregister_manager([adata])	Deregisters the AnnDataManager instance associated with adata.
differential_abundance([adata, sample_key, ...])	Compute the differential abundance between samples.
differential_expression([adata, groupby, ...])	A unified method for differential expression analysis.
from_spatialdata(sdata[, table_key, region])	Convenience constructor from a SpatialData object.
get_aggregated_posterior([adata, indices, ...])	Compute the aggregated posterior over the u latent representations.
get_anndata_manager(adata[, required])	Retrieves the AnnDataManager for a given AnnData object.
get_batch_representation([adata, indices, ...])	Get the batch representation for a given set of indices.
get_composition_error([adata, indices, ...])	Compute the composition prediction error on the data.
get_elbo([adata, indices, batch_size, ...])	Compute the evidence lower bound (ELBO) on the data.
get_feature_correlation_matrix([adata, ...])	Generate gene-gene correlation matrix using scvi uncertainty and expression.
get_from_registry(adata, registry_key)	Returns the object in AnnData associated with the key in the data registry.
get_importance_weights(adata, indices, qz, ...)	Computes importance weights for the given samples.
get_latent_library_size([adata, indices, ...])	Returns the latent library size for each cell.
get_latent_representation([adata, indices, ...])	Return latent representation with optional RAPIDS backend.
get_likelihood_parameters([adata, indices, ...])	Estimates for the parameters of the likelihood \(p(x \mid z)\).
get_marginal_ll([adata, indices, ...])	Compute the marginal log-likehood of the data.
get_niche_error([adata, indices, ...])	Compute the niche state prediction error on the data.
get_normalized_expression([adata, indices, ...])	Returns the normalized (decoded) gene expression.
get_reconstruction_error([adata, indices, ...])	Compute the reconstruction error on the data.
get_setup_arg(setup_arg)	Returns the string provided to setup of a specific setup_arg.
get_state_registry(registry_key)	Returns the state registry for the AnnDataField registered with this instance.
get_var_names([legacy_mudata_format])	Variable names of input data.
load(dir_path[, adata, accelerator, device, ...])	Instantiate a model from the saved output.
load_query_data([adata, reference_model, ...])	Online update of a reference model with scArches algorithm [].
load_registry(dir_path[, prefix])	Return the full registry saved with the model.
minify_adata([minified_data_type, ...])	Minify the model's adata.
plot_spatial_embedding([adata, basis, color])	Plot latent embedding overlaid on tissue spatial coordinates.
posterior_predictive_sample([adata, ...])	Generate predictive samples from the posterior predictive distribution.
predict_neighborhood([adata, indices, ...])	Predict the cell type composition of each cell niche in the dataset.
predict_niche_activation([adata, indices, ...])	Predict the activation of each cell niche in the dataset.
prepare_query_anndata(adata, reference_model)	Prepare data for query integration.
prepare_query_mudata(mdata, reference_model)	Prepare multimodal dataset for query integration.
preprocessing_anndata(adata[, k_nn, ...])	Preprocess an AnnData object for scVIVA analysis.
preprocessing_query_anndata(adata, ...[, ...])	Prepare data for query integration.
register_manager(adata_manager)	Registers an AnnDataManager instance with this model class.
save(dir_path[, prefix, overwrite, ...])	Save the state of the model.
setup_anndata(adata[, layer, batch_key, ...])	Sets up the AnnData object for this model.
setup_spatialdata(sdata[, table_key, region])	Register fields from a SpatialData object.
to_device(device)	Move the model to the device.
train([max_epochs, accelerator, devices, ...])	Train the model.
transfer_fields(adata, **kwargs)	Transfer fields from a model to an AnnData object.
update_setup_method_args(setup_method_args)	Update setup method args.
view_anndata_setup([adata, ...])	Print summary of the setup for the initial AnnData or a given AnnData object.
view_registry([hide_state_registries])	Prints summary of the registry.
view_setup_args(dir_path[, prefix])	Print args used to setup a saved model.
view_setup_method_args()	Prints setup kwargs used to produce a given registry.

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.
get_normalized_function_name	What the get normalized functions name is
history	Returns computed metrics during training.
is_trained	Whether the model has been trained.
minified_data_type	The type of minified data associated with this model, if applicable.
registry	Data attached to model instance.
run_id	Returns the run id of the model.
run_name	Returns the run name of the model.
summary_string	Summary string of the model.
test_indices	Observations that are in test set.
train_indices	Observations that are in train set.
validation_indices	Observations that are in validation set.