scvi.model.DestVI

class scvi.model.DestVI(st_adata, cell_type_mapping, decoder_state_dict, px_decoder_state_dict, px_r, n_hidden, n_latent, n_layers, dropout_decoder, l1_reg, **module_kwargs)

Multi-resolution deconvolution of Spatial Transcriptomics data (DestVI) [Lopez et al., 2022].

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 (ndarray) – 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. Multi-resolution deconvolution of spatial transcriptomics

2. Multi-resolution deconvolution of spatial transcriptomics in R

Attributes table

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.
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.

Methods table

convert_legacy_save(dir_path, output_dir_path)	Converts a legacy saved model (<v0.15.0) to the updated save format.
deregister_manager([adata])	Deregisters the AnnDataManager instance associated with adata.
from_rna_model(st_adata, sc_model[, ...])	Alternate constructor for exploiting a pre-trained model on a RNA-seq dataset.
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_gamma([indices, batch_size, return_numpy])	Returns the estimated cell-type specific latent space for the spatial data.
get_proportions([keep_noise, indices, ...])	Returns the estimated cell type proportion for the spatial data.
get_scale_for_ct(label[, indices, batch_size])	Return the scaled parameter of the NB for every spot in queried cell types.
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.
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.
to_device(device)	Move model to device.
train([max_epochs, lr, accelerator, ...])	Trains the model using MAP inference.
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.

Attributes

DestVI.adata

Data attached to model instance.

DestVI.adata_manager

Manager instance associated with self.adata.

DestVI.device

The current device that the module’s params are on.

DestVI.history

Returns computed metrics during training.

DestVI.is_trained

Whether the model has been trained.

DestVI.summary_string

Summary string of the model.

DestVI.test_indices

Observations that are in test set.

DestVI.train_indices

Observations that are in train set.

DestVI.validation_indices

Observations that are in validation set.

Methods

classmethod DestVI.convert_legacy_save(dir_path, output_dir_path, overwrite=False, prefix=None, **save_kwargs)

Converts a legacy saved model (<v0.15.0) to the updated save format.

Parameters:

• dir_path (str) – Path to directory where legacy model is saved.

• output_dir_path (str) – Path to save converted save files.

• overwrite (bool (default: False)) – Overwrite existing data or not. If False and directory already exists at output_dir_path, error will be raised.

• prefix (str | None (default: None)) – Prefix of saved file names.

• **save_kwargs – Keyword arguments passed into save().

Return type:

None

DestVI.deregister_manager(adata=None)

Deregisters the AnnDataManager instance associated with adata.

If adata is None, deregisters all AnnDataManager instances in both the class and instance-specific manager stores, except for the one associated with this model instance.

classmethod DestVI.from_rna_model(st_adata, sc_model, vamp_prior_p=15, l1_reg=0.0, **module_kwargs)

Alternate constructor for exploiting a pre-trained model on a RNA-seq dataset.

Parameters:

• st_adata (AnnData) – registered anndata object

• sc_model (CondSCVI) – trained CondSCVI model

• vamp_prior_p (int (default: 15)) – number of mixture parameter for VampPrior calculations

• l1_reg (float (default: 0.0)) – Scalar parameter indicating the strength of L1 regularization on cell type proportions. A value of 50 leads to sparser results.

• **model_kwargs – Keyword args for DestVI

DestVI.get_anndata_manager(adata, required=False)

Retrieves the AnnDataManager for a given AnnData object.

Requires self.id has been set. Checks for an AnnDataManager specific to this model instance.

Parameters:

• adata (AnnData | MuData) – AnnData object to find manager instance for.

• required (bool (default: False)) – If True, errors on missing manager. Otherwise, returns None when manager is missing.

Return type:

AnnDataManager | None

DestVI.get_from_registry(adata, registry_key)

Returns the object in AnnData associated with the key in the data registry.

AnnData object should be registered with the model prior to calling this function via the self._validate_anndata method.

Parameters:

• registry_key (str) – key of object to get from data registry.

• adata (AnnData | MuData) – AnnData to pull data from.

Return type:

ndarray

Returns:

The requested data as a NumPy array.

DestVI.get_gamma(indices=None, batch_size=None, return_numpy=False)

Returns the estimated cell-type specific latent space for the spatial data.

Parameters:

• indices (Sequence[int] | None (default: None)) – Indices of cells in adata to use. Only used if amortization. If None, all cells are used.

• batch_size (int | None (default: None)) – Minibatch size for data loading into model. Only used if amortization. Defaults to scvi.settings.batch_size.

• return_numpy (bool (default: False)) – if activated, will return a numpy array of shape is n_spots x n_latent x n_labels.

Return type:

ndarray | dict[str, DataFrame]

DestVI.get_proportions(keep_noise=False, indices=None, batch_size=None)

Returns the estimated cell type proportion for the spatial data.

Shape is n_cells x n_labels OR n_cells x (n_labels + 1) if keep_noise.

Parameters:

• keep_noise (bool (default: False)) – whether to account for the noise term as a standalone cell type in the proportion estimate.

• indices (Sequence[int] | None (default: None)) – Indices of cells in adata to use. Only used if amortization. If None, all cells are used.

• batch_size (int | None (default: None)) – Minibatch size for data loading into model. Only used if amortization. Defaults to scvi.settings.batch_size.

Return type:

DataFrame

DestVI.get_scale_for_ct(label, indices=None, batch_size=None)

Return the scaled parameter of the NB for every spot in queried cell types.

Parameters:

• label (str) – cell type of interest

• indices (Sequence[int] | None (default: None)) – Indices of cells in self.adata to use. If None, all cells are used.

• batch_size (int | None (default: None)) – Minibatch size for data loading into model. Defaults to scvi.settings.batch_size.

Return type:

DataFrame

Returns:

Pandas dataframe of gene_expression

classmethod DestVI.load(dir_path, adata=None, accelerator='auto', device='auto', prefix=None, backup_url=None)

Instantiate a model from the saved output.

Parameters:

• dir_path (str) – Path to saved outputs.

• adata (AnnData | MuData | None (default: None)) – AnnData organized in the same way as data used to train model. It is not necessary to run setup_anndata, as AnnData is validated against the saved scvi setup dictionary. If None, will check for and load anndata saved with the model.

• accelerator (str (default: 'auto')) – Supports passing different accelerator types (“cpu”, “gpu”, “tpu”, “ipu”, “hpu”, “mps, “auto”) as well as custom accelerator instances.

• device (int | str (default: 'auto')) – The device to use. Can be set to a non-negative index (int or str) or “auto” for automatic selection based on the chosen accelerator. If set to “auto” and accelerator is not determined to be “cpu”, then device will be set to the first available device.

• prefix (str | None (default: None)) – Prefix of saved file names.

• backup_url (str | None (default: None)) – URL to retrieve saved outputs from if not present on disk.

Returns:

Model with loaded state dictionaries.

Examples

>>> model = ModelClass.load(save_path, adata)
>>> model.get_....

static DestVI.load_registry(dir_path, prefix=None)

Return the full registry saved with the model.

Parameters:

• dir_path (str) – Path to saved outputs.

• prefix (str | None (default: None)) – Prefix of saved file names.

Return type:

dict

Returns:

The full registry saved with the model

classmethod DestVI.register_manager(adata_manager)

Registers an AnnDataManager instance with this model class.

Stores the AnnDataManager reference in a class-specific manager store. Intended for use in the setup_anndata() class method followed up by retrieval of the AnnDataManager via the _get_most_recent_anndata_manager() method in the model init method.

Notes

Subsequent calls to this method with an AnnDataManager instance referring to the same underlying AnnData object will overwrite the reference to previous AnnDataManager.

DestVI.save(dir_path, prefix=None, overwrite=False, save_anndata=False, save_kwargs=None, legacy_mudata_format=False, **anndata_write_kwargs)

Save the state of the model.

Neither the trainer optimizer state nor the trainer history are saved. Model files are not expected to be reproducibly saved and loaded across versions until we reach version 1.0.

Parameters:

• dir_path (str) – Path to a directory.

• prefix (str | None (default: None)) – Prefix to prepend to saved file names.

• overwrite (bool (default: False)) – Overwrite existing data or not. If False and directory already exists at dir_path, error will be raised.

• save_anndata (bool (default: False)) – If True, also saves the anndata

• save_kwargs (dict | None (default: None)) – Keyword arguments passed into save().

• legacy_mudata_format (bool (default: False)) – If True, saves the model var_names in the legacy format if the model was trained with a MuData object. The legacy format is a flat array with variable names across all modalities concatenated, while the new format is a dictionary with keys corresponding to the modality names and values corresponding to the variable names for each modality.

• anndata_write_kwargs – Kwargs for write()

classmethod DestVI.setup_anndata(adata, layer=None, **kwargs)

Sets up the AnnData object for this model.

A mapping will be created between data fields used by this model to their respective locations in adata. None of the data in adata are modified. Only adds fields to adata.

Parameters:

• adata (AnnData) – AnnData object. Rows represent cells, columns represent features.

• layer (str | None (default: None)) – if not None, uses this as the key in adata.layers for raw count data.

DestVI.to_device(device)

Move model to device.

Parameters:

device (str | int) – Device to move model to. Options: ‘cpu’ for CPU, integer GPU index (eg. 0), or ‘cuda:X’ where X is the GPU index (eg. ‘cuda:0’). See torch.device for more info.

Examples

>>> adata = scvi.data.synthetic_iid()
>>> model = scvi.model.SCVI(adata)
>>> model.to_device("cpu")  # moves model to CPU
>>> model.to_device("cuda:0")  # moves model to GPU 0
>>> model.to_device(0)  # also moves model to GPU 0

DestVI.train(max_epochs=2000, lr=0.003, accelerator='auto', devices='auto', train_size=1.0, validation_size=None, shuffle_set_split=True, batch_size=128, n_epochs_kl_warmup=200, datasplitter_kwargs=None, plan_kwargs=None, **kwargs)

Trains the model using MAP inference.

Parameters:

• max_epochs (int (default: 2000)) – Number of epochs to train for

• lr (float (default: 0.003)) – Learning rate for optimization.

• accelerator (str (default: 'auto')) – Supports passing different accelerator types (“cpu”, “gpu”, “tpu”, “ipu”, “hpu”, “mps, “auto”) as well as custom accelerator instances.

• devices (int | list[int] | str (default: 'auto')) – The devices to use. Can be set to a non-negative index (int or str), a sequence of device indices (list or comma-separated str), the value -1 to indicate all available devices, or “auto” for automatic selection based on the chosen accelerator. If set to “auto” and accelerator is not determined to be “cpu”, then devices will be set to the first available device.

• train_size (float (default: 1.0)) – Size of training set in the range [0.0, 1.0].

• validation_size (float | None (default: None)) – Size of the test set. If None, defaults to 1 - train_size. If train_size + validation_size < 1, the remaining cells belong to a test set.

• shuffle_set_split (bool (default: True)) – Whether to shuffle indices before splitting. If False, the val, train, and test set are split in the sequential order of the data according to validation_size and train_size percentages.

• batch_size (int (default: 128)) – Minibatch size to use during training.

• n_epochs_kl_warmup (int (default: 200)) – number of epochs needed to reach unit kl weight in the elbo

• datasplitter_kwargs (dict | None (default: None)) – Additional keyword arguments passed into DataSplitter.

• plan_kwargs (dict | None (default: None)) – Keyword args for TrainingPlan. Keyword arguments passed to train() will overwrite values present in plan_kwargs, when appropriate.

• **kwargs – Other keyword args for Trainer.

DestVI.view_anndata_setup(adata=None, hide_state_registries=False)

Print summary of the setup for the initial AnnData or a given AnnData object.

Parameters:

• adata (AnnData | MuData | None (default: None)) – AnnData object setup with setup_anndata or transfer_fields().

• hide_state_registries (bool (default: False)) – If True, prints a shortened summary without details of each state registry.

Return type:

None

static DestVI.view_setup_args(dir_path, prefix=None)

Print args used to setup a saved model.

Parameters:

• dir_path (str) – Path to saved outputs.

• prefix (str | None (default: None)) – Prefix of saved file names.

Return type:

None