scvi.external.Decipher

class scvi.external.Decipher(adata, **kwargs)

Decipher model for single-cell data analysis [Nazaret et al., 2023].

Parameters:

• adata (AnnData) – AnnData object that has been registered via setup_anndata().

• dim_v – Dimension of the interpretable latent space v.

• dim_z – Dimension of the intermediate latent space z.

• layers_v_to_z – Hidden layer sizes for the v to z decoder network.

• layers_z_to_x – Hidden layer sizes for the z to x decoder network.

• beta – Regularization parameter for the KL divergence.

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

compute_decipher_time(adata, ...[, n_neighbors])	Compute the decipher time for each cell, based on the inferred trajectories.
compute_gene_patterns(adata, trajectory[, ...])	Compute the gene patterns for a trajectory.
compute_imputed_gene_expression([adata, ...])	Impute gene expression from the decipher model.
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.
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_latent_representation([adata, indices, ...])	Get the latent representation of the 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.
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, accelerator, device, ...])	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

Decipher.adata

Data attached to model instance.

Decipher.adata_manager

Manager instance associated with self.adata.

Decipher.device

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

Decipher.history

Returns computed metrics during training.

Decipher.is_trained

Whether the model has been trained.

Decipher.summary_string

Summary string of the model.

Decipher.test_indices

Observations that are in test set.

Decipher.train_indices

Observations that are in train set.

Decipher.validation_indices

Observations that are in validation set.

Methods

static Decipher.compute_decipher_time(adata, cluster_obs_key, trajectory, n_neighbors=10)

Compute the decipher time for each cell, based on the inferred trajectories.

The decipher time is computed by KNN regression of the cells’ decipher v on the trajectories.

Parameters:

• adata (AnnData) – The annotated data matrix.

• cluster_obs_key (str) – The key in adata.obs containing cluster assignments.

• trajectory (Trajectory) – A Trajectory object containing the trajectory information.

• n_neighbors (int) – The number of neighbors to use for the KNN regression.

Return type:

ndarray

Returns:

The decipher time of each cell.

Decipher.compute_gene_patterns(adata, trajectory, l_scale=10000, n_samples=100)

Compute the gene patterns for a trajectory.

The trajectory’s points are sent through the decoders, thus defining distributions over the gene expression. The gene patterns are computed by sampling from these distribution.

Parameters:

• adata (AnnData) – The annotated data matrix.

• trajectory (Trajectory) – A Trajectory object containing the trajectory information.

• l_scale (float) – The library size scaling factor.

• n_samples (int) – The number of samples to draw from the decoder to compute the gene pattern statistics.

Return type:

dict[str, ndarray]

Returns:

The gene patterns for the trajectory. Dictionary keys:

• mean: the mean gene expression pattern

• q25: the 25% quantile of the gene expression pattern

• q75: the 75% quantile of the gene expression pattern

• times: the times of the trajectory

Decipher.compute_imputed_gene_expression(adata=None, indices=None, batch_size=None, compute_covariances=False, v_obsm_key=None, z_obsm_key=None)

Impute gene expression from the decipher model.

Parameters:

• adata (AnnData | None (default: None)) – The annotated data matrix.

• indices (Sequence[int] | None (default: None)) – Indices of the data to get the latent representation of.

• batch_size (int | None (default: None)) – Batch size to use for the data loader.

• compute_covariances (bool (default: False)) – Whether to compute the covariances between the Decipher v and each gene.

• v_obsm_key (str | None (default: None)) – Key in adata.obsm to use for the Decipher v. Required if compute_covariances is True.

• z_obsm_key (str | None (default: None)) – Key in adata.obsm to use for the Decipher z. Required if compute_covariances is True.

Return type:

ndarray | tuple[ndarray, ndarray, ndarray]

Returns:

The imputed gene expression, and the covariances between the Decipher v and each gene if compute_covariances is True.

classmethod Decipher.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

Decipher.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.

Decipher.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

Decipher.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.

Decipher.get_latent_representation(adata=None, indices=None, batch_size=None, give_z=False)

Get the latent representation of the data.

Parameters:

• adata (AnnData | None (default: None)) – AnnData object with the data to get the latent representation of.

• indices (Sequence[int] | None (default: None)) – Indices of the data to get the latent representation of.

• batch_size (int | None (default: None)) – Batch size to use for the data loader.

• give_z (bool (default: False)) – Whether to return the intermediate latent space z or the top-level latent space v.

Return type:

ndarray

classmethod Decipher.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 Decipher.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 Decipher.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.

Decipher.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 Decipher.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.

Return type:

AnnData | None

Decipher.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

Decipher.train(max_epochs=None, accelerator='auto', device='auto', train_size=0.9, validation_size=None, shuffle_set_split=True, batch_size=128, early_stopping=False, training_plan=None, datasplitter_kwargs=None, plan_kwargs=None, **trainer_kwargs)

Train the model.

Parameters:

• max_epochs (int | None (default: None)) – 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.

• 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.

• 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. If None, no minibatching occurs and all data is copied to device (e.g., GPU).

• early_stopping (bool (default: False)) – Perform early stopping. Additional arguments can be passed in **kwargs. See Trainer for further options.

• lr – Optimiser learning rate (default optimiser is ClippedAdam). Specifying optimiser via plan_kwargs overrides this choice of lr.

• training_plan (PyroTrainingPlan | None (default: None)) – Training plan PyroTrainingPlan.

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

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

• **trainer_kwargs – Other keyword args for Trainer.

Decipher.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 Decipher.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