scvi.external.GIMVI

class scvi.external.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 [Lopez et al., 2019].

Parameters:

• adata_seq (AnnData) – AnnData object that has been registered via setup_anndata() and contains RNA-seq data.

• adata_spatial (AnnData) – AnnData object that has been registered via setup_anndata() and contains spatial data.

• n_hidden – Number of nodes per hidden layer.

• generative_distributions (list[str] | None (default: None)) – List of generative distribution for adata_seq data and adata_spatial data. Defaults to [‘zinb’, ‘nb’].

• model_library_size (list[bool] | None (default: None)) – List of bool of whether to model library size for adata_seq and adata_spatial. Defaults to [True, False].

• n_latent (int (default: 10)) – Dimensionality of the latent space.

• **model_kwargs – Keyword args for JVAE

Examples

>>> adata_seq = anndata.read_h5ad(path_to_anndata_seq)
>>> adata_spatial = anndata.read_h5ad(path_to_anndata_spatial)
>>> scvi.external.GIMVI.setup_anndata(adata_seq)
>>> scvi.external.GIMVI.setup_anndata(adata_spatial)
>>> vae = scvi.model.GIMVI(adata_seq, adata_spatial)
>>> vae.train(n_epochs=400)

Notes

See further usage examples in the following tutorials:

1. Introduction to gimVI

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 GIMVI model (<v0.15.0) to the updated save format.
deregister_manager([adata])	Deregisters the AnnDataManager instance associated with adata.
get_anndata_manager(adata[, required])	Retrieves the AnnDataManager for a given AnnData object.
get_elbo([adata, indices, batch_size, ...])	Compute the evidence lower bound (ELBO) on the data.
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_marginal_ll([adata, indices, ...])	Compute the marginal log-likehood of the data.
get_reconstruction_error([adata, indices, ...])	Compute the reconstruction error on the 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.
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.
to_device(device)	Move model to device.
train([max_epochs, accelerator, devices, ...])	Train the model.
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

GIMVI.adata

Data attached to model instance.

GIMVI.adata_manager

Manager instance associated with self.adata.

GIMVI.device

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

GIMVI.history

Returns computed metrics during training.

GIMVI.is_trained

Whether the model has been trained.

GIMVI.summary_string

Summary string of the model.

GIMVI.test_indices

Observations that are in test set.

GIMVI.train_indices

Observations that are in train set.

GIMVI.validation_indices

Observations that are in validation set.

Methods

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

Converts a legacy saved GIMVI 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.

Return type:

None

**save_kwargs

Keyword arguments passed into save().

GIMVI.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.

GIMVI.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

GIMVI.get_elbo(adata=None, indices=None, batch_size=None, dataloader=None, return_mean=True, **kwargs)

Compute the evidence lower bound (ELBO) on the data.

The ELBO is the reconstruction error plus the Kullback-Leibler (KL) divergences between the variational distributions and the priors. It is different from the marginal log-likelihood; specifically, it is a lower bound on the marginal log-likelihood plus a term that is constant with respect to the variational distribution. It still gives good insights on the modeling of the data and is fast to compute.

Parameters:

• adata (AnnData | None (default: None)) – AnnData object with var_names in the same order as the ones used to train the model. If None and dataloader is also None, it defaults to the object used to initialize the model.

• indices (Sequence[int] | None (default: None)) – Indices of observations in adata to use. If None, defaults to all observations. Ignored if dataloader is not None.

• batch_size (int | None (default: None)) – Minibatch size for the forward pass. If None, defaults to scvi.settings.batch_size. Ignored if dataloader is not None.

• dataloader (Iterator[dict[str, Tensor | None]] (default: None)) – An iterator over minibatches of data on which to compute the metric. The minibatches should be formatted as a dictionary of Tensor with keys as expected by the model. If None, a dataloader is created from adata.

• return_mean (bool (default: True)) – Whether to return the mean of the ELBO or the ELBO for each observation.

• **kwargs – Additional keyword arguments to pass into the forward method of the module.

Return type:

float

Returns:

Evidence lower bound (ELBO) of the data.

Notes

This is not the negative ELBO, so higher is better.

GIMVI.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.

GIMVI.get_imputed_values(adatas=None, deterministic=True, normalized=True, decode_mode=None, batch_size=128)

Return imputed values for all genes for each dataset.

Parameters:

• adatas (list[AnnData] (default: None)) – List of adata seq and adata spatial

• deterministic (bool (default: True)) – If true, use the mean of the encoder instead of a Gaussian sample for the latent vector.

• normalized (bool (default: True)) – Return imputed normalized values or not.

• decode_mode (int | None (default: None)) – If a decode_mode is given, use the encoder specific to each dataset as usual but use the decoder of the dataset of id decode_mode to impute values.

• batch_size (int (default: 128)) – Minibatch size for data loading into model.

Return type:

list[ndarray]

GIMVI.get_latent_representation(adatas=None, deterministic=True, batch_size=128)

Return the latent space embedding for each dataset.

Parameters:

• adatas (list[AnnData] (default: None)) – List of adata seq and adata spatial.

• deterministic (bool (default: True)) – If true, use the mean of the encoder instead of a Gaussian sample.

• batch_size (int (default: 128)) – Minibatch size for data loading into model.

Return type:

list[ndarray]

GIMVI.get_marginal_ll(adata=None, indices=None, n_mc_samples=1000, batch_size=None, return_mean=True, dataloader=None, **kwargs)

Compute the marginal log-likehood of the data.

The computation here is a biased estimator of the marginal log-likelihood of the data.

Parameters:

• adata (AnnData | None (default: None)) – AnnData object with var_names in the same order as the ones used to train the model. If None and dataloader is also None, it defaults to the object used to initialize the model.

• indices (Sequence[int] | None (default: None)) – Indices of observations in adata to use. If None, defaults to all observations. Ignored if dataloader is not None.

• n_mc_samples (int (default: 1000)) – Number of Monte Carlo samples to use for the estimator. Passed into the module’s marginal_ll method.

• batch_size (int | None (default: None)) – Minibatch size for the forward pass. If None, defaults to scvi.settings.batch_size. Ignored if dataloader is not None.

• return_mean (bool (default: True)) – Whether to return the mean of the marginal log-likelihood or the marginal-log likelihood for each observation.

• dataloader (Iterator[dict[str, Tensor | None]] (default: None)) – An iterator over minibatches of data on which to compute the metric. The minibatches should be formatted as a dictionary of Tensor with keys as expected by the model. If None, a dataloader is created from adata.

• **kwargs – Additional keyword arguments to pass into the module’s marginal_ll method.

Return type:

float | Tensor

Returns:

If True, returns the mean marginal log-likelihood. Otherwise returns a tensor of shape (n_obs,) with the marginal log-likelihood for each observation.

Notes

This is not the negative log-likelihood, so higher is better.

GIMVI.get_reconstruction_error(adata=None, indices=None, batch_size=None, dataloader=None, return_mean=True, **kwargs)

Compute the reconstruction error on the data.

The reconstruction error is the negative log likelihood of the data given the latent variables. It is different from the marginal log-likelihood, but still gives good insights on the modeling of the data and is fast to compute. This is typically written as \(p(x \mid z)\), the likelihood term given one posterior sample.

Parameters:

• adata (AnnData | None (default: None)) – AnnData object with var_names in the same order as the ones used to train the model. If None and dataloader is also None, it defaults to the object used to initialize the model.

• indices (Sequence[int] | None (default: None)) – Indices of observations in adata to use. If None, defaults to all observations. Ignored if dataloader is not None

• batch_size (int | None (default: None)) – Minibatch size for the forward pass. If None, defaults to scvi.settings.batch_size. Ignored if dataloader is not None

• dataloader (Iterator[dict[str, Tensor | None]] (default: None)) – An iterator over minibatches of data on which to compute the metric. The minibatches should be formatted as a dictionary of Tensor with keys as expected by the model. If None, a dataloader is created from adata.

• return_mean (bool (default: True)) – Whether to return the mean reconstruction loss or the reconstruction loss for each observation.

• **kwargs – Additional keyword arguments to pass into the forward method of the module.

Return type:

dict[str, float]

Returns:

Reconstruction error for the data.

Notes

This is not the negative reconstruction error, so higher is better.

classmethod GIMVI.load(dir_path, adata_seq=None, adata_spatial=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_seq (AnnData | 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. AnnData must be registered via setup_anndata().

• adata_spatial (AnnData | None (default: None)) – AnnData organized in the same way as data used to train model. 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

>>> vae = GIMVI.load(adata_seq, adata_spatial, save_path)
>>> vae.get_latent_representation()

static GIMVI.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 GIMVI.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.

GIMVI.save(dir_path, prefix=None, overwrite=False, save_anndata=False, save_kwargs=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, 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().

• anndata_write_kwargs – Kwargs for anndata write function

classmethod GIMVI.setup_anndata(adata, batch_key=None, labels_key=None, 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:

• 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.

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

GIMVI.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

GIMVI.train(max_epochs=200, accelerator='auto', devices='auto', kappa=5, train_size=0.9, validation_size=None, shuffle_set_split=True, batch_size=128, datasplitter_kwargs=None, plan_kwargs=None, **kwargs)

Train the model.

Parameters:

• max_epochs (int (default: 200)) – Number of passes through the dataset. If None, defaults to np.min([round((20000 / n_cells) * 400), 400])

• 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.

• kappa (int (default: 5)) – Scaling parameter for the discriminator loss.

• train_size (float (default: 0.9)) – 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.

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

• plan_kwargs (dict | None (default: None)) – Keyword args for model-specific Pytorch Lightning task. Keyword arguments passed to train() will overwrite values present in plan_kwargs, when appropriate.

• **kwargs – Other keyword args for Trainer.

GIMVI.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 GIMVI.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