spatialvi.model.GIMVI

class spatialvi.model.GIMVI(adata_seq, adata_spatial, generative_distributions=None, model_library_size=None, n_latent=10, **model_kwargs)

Joint VAE for imputing missing genes in spatial data [].

Learns a joint latent space for paired scRNA-seq and spatial transcriptomics data, enabling imputation of spatially unmeasured genes.

Parameters:

• adata_seq (AnnData) – AnnData object registered via setup_anndata() containing scRNA-seq data.

• adata_spatial (AnnData) – AnnData object registered via setup_anndata() containing spatial transcriptomics data.

• generative_distributions (list[str] | None) – List of generative distributions for seq and spatial data. Defaults to ['zinb', 'nb'].

• model_library_size (list[bool] | None) – Whether to model library size per dataset. Defaults to [True, False].

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

• **model_kwargs – Keyword args for JVAE.

Examples

>>> adata_seq = anndata.read_h5ad(path_to_seq)
>>> adata_spatial = anndata.read_h5ad(path_to_spatial)
>>> spatialvi.model.GIMVI.setup_anndata(adata_seq)
>>> spatialvi.model.GIMVI.setup_anndata(adata_spatial)
>>> model = spatialvi.model.GIMVI(adata_seq, adata_spatial)
>>> model.train(max_epochs=200)

Notes

See further usage examples in the following tutorial:

1. /tutorials/notebooks/spatial/gimvi_tutorial

__init__(adata_seq, adata_spatial, generative_distributions=None, model_library_size=None, n_latent=10, **model_kwargs)

Methods

__init__(adata_seq, adata_spatial[, ...])
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)
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_imputed_values([adatas, deterministic, ...])	Return imputed values for all genes for each dataset.
get_latent_representation([adatas, ...])	Return the latent space embedding for each dataset.
get_normalized_expression(*args, **kwargs)
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_seq, adata_spatial, ...])	Instantiate a model from the saved output.
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.
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[, batch_key, ...])	Sets up the AnnData object for this model.
setup_spatialdata(sdata[, table_key, region])	Register the spatial adata component 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.
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.