scvi.model.JaxSCVI

class scvi.model.JaxSCVI(adata, n_hidden=128, n_latent=10, dropout_rate=0.1, gene_likelihood='nb', **model_kwargs)

EXPERIMENTAL single-cell Variational Inference [Lopez et al., 2018], but with a Jax backend.

This implementation is in a very experimental state. API is completely subject to change.

Parameters:

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

• n_hidden (int (default: 128)) – Number of nodes per hidden layer.

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

• dropout_rate (float (default: 0.1)) – Dropout rate for neural networks.

• gene_likelihood (Literal['nb', 'poisson'] (default: 'nb')) –

  One of:

  • 'nb' - Negative binomial distribution

  • 'poisson' - Poisson distribution

• **model_kwargs – Keyword args for JaxVAE

Examples

>>> adata = anndata.read_h5ad(path_to_anndata)
>>> scvi.model.JaxSCVI.setup_anndata(adata, batch_key="batch")
>>> vae = scvi.model.JaxSCVI(adata)
>>> vae.train()
>>> adata.obsm["X_scVI"] = vae.get_latent_representation()

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.
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.
get_anndata_manager(adata[, required])	Retrieves the AnnDataManager for a given AnnData object specific to this model instance.
get_from_registry(adata, registry_key)	Returns the object in AnnData associated with the key in the data registry.
get_latent_representation([adata, indices, ...])	Return the latent representation for each cell.
load(dir_path[, adata, use_gpu, ...])	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_anndata])	Save the state of the model.
setup_anndata(adata[, layer, batch_key])	Sets up the AnnData object for this model.
to_device(device)	Move model to device.
train([max_epochs, use_gpu, accelerator, ...])	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

JaxSCVI.adata

Data attached to model instance.

JaxSCVI.adata_manager

Manager instance associated with self.adata.

JaxSCVI.device

JaxSCVI.history

Returns computed metrics during training.

JaxSCVI.is_trained

Whether the model has been trained.

JaxSCVI.test_indices

Observations that are in test set.

JaxSCVI.train_indices

Observations that are in train set.

JaxSCVI.validation_indices

Observations that are in validation set.

Methods

classmethod JaxSCVI.convert_legacy_save(dir_path, output_dir_path, overwrite=False, prefix=None)

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 (Optional[str] (default: None)) – Prefix of saved file names.

Return type:

None

JaxSCVI.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.

JaxSCVI.get_anndata_manager(adata, required=False)

Retrieves the AnnDataManager for a given AnnData object specific to this model instance.

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

Parameters:

• adata (Union[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:

Optional[AnnDataManager]

JaxSCVI.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 (Union[AnnData, MuData]) – AnnData to pull data from.

Return type:

ndarray

Returns:

The requested data as a NumPy array.

JaxSCVI.get_latent_representation(adata=None, indices=None, give_mean=True, n_samples=1, batch_size=None)

Return the latent representation for each cell.

This is denoted as \(z_n\) in our manuscripts.

Parameters:

• adata (Optional[AnnData] (default: None)) – AnnData object with equivalent structure to initial AnnData. If None, defaults to the AnnData object used to initialize the model.

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

• give_mean (bool (default: True)) – Whether to return the mean of the posterior distribution or a sample.

• n_samples (int (default: 1)) – Number of samples to use for computing the latent representation.

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

Return type:

ndarray

Returns:

-latent_representation (ndarray) Low-dimensional representation for each cell

classmethod JaxSCVI.load(dir_path, adata=None, use_gpu=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 (Union[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.

• use_gpu (Union[str, int, bool, None] (default: None)) – Use default GPU if available (if True), or index of GPU to use (if int), or name of GPU (if str, e.g., ‘cuda:0’), or use CPU (if False). Passing in use_gpu!=None will override accelerator and devices arguments. This argument is deprecated in v1.0 and will be removed in v1.1. Please use accelerator and devices instead.

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

• device (Union[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 (Optional[str] (default: None)) – Prefix of saved file names.

• backup_url (Optional[str] (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) # use the name of the model class used to save
>>> model.get_....

static JaxSCVI.load_registry(dir_path, prefix=None)

Return the full registry saved with the model.

Parameters:

• dir_path (str) – Path to saved outputs.

• prefix (Optional[str] (default: None)) – Prefix of saved file names.

Return type:

dict

Returns:

The full registry saved with the model

classmethod JaxSCVI.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.

JaxSCVI.save(dir_path, prefix=None, overwrite=False, save_anndata=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 (Optional[str] (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

• anndata_write_kwargs – Kwargs for write()

classmethod JaxSCVI.setup_anndata(adata, layer=None, batch_key=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 (Optional[str] (default: None)) – if not None, uses this as the key in adata.layers for raw count data.

• batch_key (Optional[str] (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.

JaxSCVI.to_device(device)

Move model to device.

Parameters:

device – 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

JaxSCVI.train(max_epochs=None, use_gpu=None, accelerator='auto', devices='auto', train_size=0.9, validation_size=None, shuffle_set_split=True, batch_size=128, plan_kwargs=None, **trainer_kwargs)

Train the model.

Parameters:

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

• use_gpu (Union[str, int, bool, None] (default: None)) – Use default GPU if available (if True), or index of GPU to use (if int), or name of GPU (if str, e.g., ‘cuda:0’), or use CPU (if False). Passing in use_gpu!=None will override accelerator and devices arguments. This argument is deprecated in v1.0 and will be removed in v1.1. Please use accelerator and devices instead.

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

• devices (Union[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: 0.9)) – Size of training set in the range [0.0, 1.0].

• validation_size (Optional[float] (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.

• lr – Learning rate to use during training.

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

• **trainer_kwargs – Other keyword args for Trainer.

JaxSCVI.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 (Union[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 JaxSCVI.view_setup_args(dir_path, prefix=None)

Print args used to setup a saved model.

Parameters:

• dir_path (str) – Path to saved outputs.

• prefix (Optional[str] (default: None)) – Prefix of saved file names.

Return type:

None