scvi.external.MRVI

class scvi.external.MRVI(adata=None, registry=None)

Multi-resolution Variational Inference (MrVI).

This is a convenience wrapper that instantiates the Torch or JAX implementation based on backend and returns that instance.

Parameters:

• adata (AnnData | MuData | None (default: None)) – AnnData object that has been registered via the appropriate setup_anndata.

• backend – Which backend to use: “torch” or “jax”.

• registry (object | None (default: None)) – (Torch-only) Registry dict for loading from saved state.

• **model_kwargs – Extra keyword args forwarded to the selected implementation.

Notes

• When setup anndata with backend=”torch”, this returns an instance of TorchMRVI.

• When setup anndata with backend=”jax”, this returns an instance of JaxMRVI.

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.
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.

Methods table

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)	Not implemented for this model class.
differential_expression(*args, **kwargs)	Perform differential expression analysis.
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_normalized_expression(*args, **kwargs)	Not implemented for this model class.
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.
minify_adata([minified_data_type, ...])	Minify the model's adata.
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, sample_key, ...])	Sets up the AnnData object for this model.
to_device(device)	Move the model to the device.
train()	Trains 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

MRVI.adata

Data attached to model instance.

MRVI.adata_manager

Manager instance associated with self.adata.

MRVI.device

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

MRVI.get_normalized_function_name

What the get normalized functions name is

MRVI.history

Returns computed metrics during training.

MRVI.is_trained

Whether the model has been trained.

MRVI.minified_data_type

The type of minified data associated with this model, if applicable.

MRVI.registry

Data attached to model instance.

MRVI.run_id

Returns the run id of the model. Used in MLFlow

MRVI.run_name

Returns the run name of the model. Used in MLFlow

MRVI.summary_string

Summary string of the model.

MRVI.test_indices

Observations that are in test set.

MRVI.train_indices

Observations that are in train set.

MRVI.validation_indices

Observations that are in validation set.

Methods

classmethod MRVI.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 the directory where the 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, an error will be raised.

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

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

Return type:

None

MRVI.data_registry(registry_key)

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

Parameters:

registry_key (str) – key of an object to get from self.data_registry

Return type:

ndarray | DataFrame

Returns:

The requested data.

MRVI.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.

MRVI.differential_abundance(*args, **kwargs)

Not implemented for this model class.

Available in models that inherit from VAEMixin.

Raises:

NotImplementedError –

MRVI.differential_expression(*args, **kwargs)

Perform differential expression analysis.

Delegates to the underlying TorchMRVI or JaxMRVI instance returned by the constructor.

See also

differential_expression()

MRVI.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 a manager instance for.

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

Return type:

AnnDataManager | None

MRVI.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 the data registry.

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

Return type:

ndarray

Returns:

The requested data as a NumPy array.

MRVI.get_normalized_expression(*args, **kwargs)

Not implemented for this model class.

Available in RNA models that inherit from RNASeqMixin.

Raises:

NotImplementedError –

MRVI.get_setup_arg(setup_arg)

Returns the string provided to setup of a specific setup_arg.

Return type:

attrdict

MRVI.get_state_registry(registry_key)

Returns the state registry for the AnnDataField registered with this instance.

Return type:

attrdict

MRVI.get_var_names(legacy_mudata_format=False)

Variable names of input data.

Return type:

dict

classmethod MRVI.load(dir_path, adata=None, accelerator='auto', device='auto', prefix=None, backup_url=None, datamodule=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. If False, will load the model without AnnData.

• 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.

• datamodule (LightningDataModule | None (default: None)) – EXPERIMENTAL A LightningDataModule instance to use for training in place of the default DataSplitter. Can only be passed in if the model was not initialized with AnnData.

Returns:

Model with loaded state dictionaries.

Examples

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

static MRVI.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

MRVI.minify_adata(minified_data_type='latent_posterior_parameters', use_latent_qzm_key='X_latent_qzm', use_latent_qzv_key='X_latent_qzv')

Minify the model’s adata.

Minifies the AnnData object associated with the model according to the method specified by minified_data_type and registers the new fields with the model’s AnnDataManager. This also sets the minified_data_type attribute of the underlying BaseModuleClass instance.

Parameters:

• minified_data_type (Literal['latent_posterior_parameters'] (default: 'latent_posterior_parameters')) –

  Method for minifying the data. One of the following:

  • "latent_posterior_parameters": Store the latent posterior mean and variance in

    obsm using the keys use_latent_qzm_key and use_latent_qzv_key.

  • "latent_posterior_parameters_with_counts": Store the latent posterior mean and

    variance in obsm using the keys use_latent_qzm_key and use_latent_qzv_key, and the raw count data in X.

• use_latent_qzm_key (str (default: 'X_latent_qzm')) – Key to use for storing the latent posterior mean in obsm when minified_data_type is "latent_posterior".

• use_latent_qzv_key (str (default: 'X_latent_qzv')) – Key to use for storing the latent posterior variance in obsm when minified_data_type is "latent_posterior".

Return type:

None

Notes

The modification is not done inplace – instead the model is assigned a new (minified) version of the AnnData.

classmethod MRVI.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.

MRVI.save(dir_path, prefix=None, overwrite=False, save_anndata=False, save_kwargs=None, legacy_mudata_format=False, datamodule=None, **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, an 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.

• datamodule (LightningDataModule | None (default: None)) – EXPERIMENTAL A LightningDataModule instance to use for training in place of the default DataSplitter. Can only be passed in if the model was not initialized with AnnData.

• anndata_write_kwargs – Kwargs for write()

classmethod MRVI.setup_anndata(adata, layer=None, sample_key=None, batch_key=None, labels_key=None, backend='torch', **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.

• sample_key (str | None (default: None)) – key in adata.obs for sample information. Categories will automatically be converted into integer categories and saved to adata.obs[‘_scvi_sample’]. If None, assigns the same sample to all the data.

• batch_key (str | None (default: None)) – key in adata.obs for batch information. Categories will automatically be converted into integer categories and saved to adata.obs[‘_scvi_batch’]. If None, assigns the same batch to all the data.

• labels_key (str | None (default: None)) – key in adata.obs for label information. Categories will automatically be converted into integer categories and saved to adata.obs[‘_scvi_labels’]. If None, assigns the same label to all the data.

• backend (Literal['torch', 'jax', None] (default: 'torch')) – Which backend to use: “torch” or “jax”.

• **kwargs – Additional keyword arguments passed into register_fields().

MRVI.to_device(device)

Move the model to the device.

Parameters:

device (str | int | device) – Device to move model to. Options: ‘cpu’ for CPU, integer GPU index (e.g., 0), ‘cuda:X’ where X is the GPU index (e.g. ‘cuda:0’), or a torch.device object (including XLA devices for TPU). 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

abstractmethod MRVI.train()

Trains the model.

MRVI.transfer_fields(adata, **kwargs)

Transfer fields from a model to an AnnData object.

Return type:

AnnData

MRVI.update_setup_method_args(setup_method_args)

Update setup method args.

Parameters:

setup_method_args (dict) – This is a bit of a misnomer, this is a dict representing kwargs of the setup method that will be used to update the existing values in the registry of this instance.

MRVI.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

MRVI.view_registry(hide_state_registries=False)

Prints summary of the registry.

Parameters:

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

Return type:

None

static MRVI.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

MRVI.view_setup_method_args()

Prints setup kwargs used to produce a given registry.

Parameters:

registry – Registry produced by an AnnDataManager.

Return type:

None