spatialvi.model.DestVI

class spatialvi.model.DestVI(st_adata, cell_type_mapping, decoder_state_dict, px_decoder_state_dict, px_r, per_ct_bias, n_hidden, n_latent, n_layers, dropout_decoder, **module_kwargs)

Multi-resolution deconvolution of Spatial Transcriptomics data (DestVI) [].

Most users will use the alternate constructor (see example).

Parameters:

• st_adata (AnnData) – spatial transcriptomics AnnData object that has been registered via setup_anndata().

• cell_type_mapping (ndarray) – mapping between numerals and cell type labels

• decoder_state_dict (OrderedDict) – state_dict from the decoder of the CondSCVI model

• px_decoder_state_dict (OrderedDict) – state_dict from the px_decoder of the CondSCVI model

• px_r (tensor) – parameters for the px_r tensor in the CondSCVI model

• 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.

• **module_kwargs – Keyword args for MRDeconv

Examples

>>> sc_adata = anndata.read_h5ad(path_to_scRNA_anndata)
>>> scvi.model.CondSCVI.setup_anndata(sc_adata)
>>> sc_model = scvi.model.CondSCVI(sc_adata)
>>> st_adata = anndata.read_h5ad(path_to_ST_anndata)
>>> DestVI.setup_anndata(st_adata)
>>> spatial_model = DestVI.from_rna_model(st_adata, sc_model)
>>> spatial_model.train(max_epochs=2000)
>>> st_adata.obsm["proportions"] = spatial_model.get_proportions(st_adata)
>>> gamma = spatial_model.get_gamma(st_adata)

Notes

See further usage examples in the following tutorials:

1. /tutorials/notebooks/spatial/DestVI_tutorial

2. /tutorials/notebooks/r/DestVI_in_R

__init__(st_adata, cell_type_mapping, decoder_state_dict, px_decoder_state_dict, px_r, per_ct_bias, n_hidden, n_latent, n_layers, dropout_decoder, **module_kwargs)

Methods

__init__(st_adata, cell_type_mapping, ...)
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_rna_model(st_adata, sc_model[, ...])	Alternate constructor for exploiting a pre-trained model on a RNA-seq dataset.
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_expression_for_ct(label[, indices, ...])	Return the scaled parameter of the NB for every spot in queried cell types.
get_fine_celltypes(sc_model[, indices, ...])	Returns the estimated cell-type specific latent space for the spatial data.
get_from_registry(adata, registry_key)	Returns the object in AnnData associated with the key in the data registry.
get_gamma([indices, batch_size, return_numpy])	Returns the estimated cell-type specific latent space for the spatial data.
get_latent_representation([adata, indices, ...])	Return the latent representation for each cell.
get_normalized_expression(*args, **kwargs)
get_proportions([keep_additional, ...])	Returns the estimated cell type proportion for the spatial data.
get_proportions_df([adata])	Return cell type proportions as a tidy DataFrame.
get_scale_for_ct(label[, indices, batch_size])	Return the scaled parameter of the NB for every spot in queried cell types.
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_registry(dir_path[, prefix])	Return the full registry saved with the model.
plot_cell_type_map([adata, cell_type, basis, ax])	Plot spatial map of a single cell type's proportion.
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[, layer, ...])	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, accelerator, ...])	Trains the model using MAP 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.