spatialvi.model.ResolVI

class spatialvi.model.ResolVI(adata, n_hidden=32, n_hidden_encoder=128, n_latent=10, n_layers=2, dropout_rate=0.05, dispersion='gene', gene_likelihood='nb', background_ratio=None, median_distance=None, semisupervised=False, mixture_k=50, downsample_counts=True, **model_kwargs)

ResolVI addresses noise and bias in single-cell resolved spatial transcriptomics data.

Parameters:

• adata (AnnData) – AnnData object that has been registered via setup_anndata().

• 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']) –

  One of the following:

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

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

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

  One of:

  • 'nb' - Negative binomial distribution

  • 'poisson' - Poisson distribution

• **model_kwargs – Keyword args for RESOLVAE

Examples

>>> adata = anndata.read_h5ad(path_to_anndata)
>>> ResolVI.setup_anndata(adata, batch_key="batch")
>>> model = ResolVI(adata)
>>> model.train()
>>> adata.obsm["X_resolVI"] = model.get_latent_representation()
>>> adata.layers["X_normalized_resolVI"] = model.get_normalized_expression()

__init__(adata, n_hidden=32, n_hidden_encoder=128, n_latent=10, n_layers=2, dropout_rate=0.05, dispersion='gene', gene_likelihood='nb', background_ratio=None, median_distance=None, semisupervised=False, mixture_k=50, downsample_counts=True, **model_kwargs)

Methods

__init__(adata[, n_hidden, ...])
compute_dataset_dependent_priors([n_small_genes])	Compute dataset-dependent priors for background ratio and median distance.
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(*args, **kwargs)
differential_expression([adata, groupby, ...])	A unified method for differential expression analysis.
differential_niche_abundance([adata, ...])	A unified method for niche differential abundance analysis.
from_spatialdata(sdata[, table_key, region])	Convenience constructor from a SpatialData object.
get_anndata_manager(adata[, required])	Retrieves the AnnDataManager for a given AnnData object.
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 latent representation with optional RAPIDS acceleration.
get_neighbor_abundance([adata, indices, ...])	Returns the abundance of cell-types within spatial proximity of center cells.
get_normalized_expression([adata, indices, ...])	Returns the normalized (decoded) gene expression.
get_normalized_expression_importance([...])	Returns the normalized (decoded) importance-sampled gene expression.
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.
plot_spatial_embedding([adata, basis, color])	Plot latent embedding overlaid on tissue spatial coordinates.
predict([adata, indices, soft, batch_size, ...])	Return cell label predictions.
prepare_query_anndata(adata, reference_model)	Prepare data for query integration.
prepare_query_mudata(mdata, reference_model)	Prepare multimodal dataset for query integration.
register_manager(adata_manager)	Registers an AnnDataManager instance with this model class.
sample_posterior([adata, input_dl, indices, ...])	Summarise posterior distribution.
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, lr, lr_extra, ...])	Train the model using amortized variational inference.
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.
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.