scvi.model.JaxSCVI#

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

EXPERIMENTAL single-cell Variational Inference [Lopez18], 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 : {‘nb’, ‘poisson’}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.SCVI(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.
`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, prefix, ...])	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, train_size, ...])	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#

adata#

JaxSCVI.adata[source]#

Data attached to model instance.

Return type:: AnnData | MuDataUnion[AnnData, MuData]

adata_manager#

JaxSCVI.adata_manager[source]#

Manager instance associated with self.adata.

Return type:: AnnDataManager

device#

JaxSCVI.device[source]#

history#

JaxSCVI.history[source]#: Returns computed metrics during training.

is_trained#

JaxSCVI.is_trained[source]#

Whether the model has been trained.

Return type:: bool

test_indices#

JaxSCVI.test_indices[source]#

Observations that are in test set.

Return type:: ndarray

train_indices#

JaxSCVI.train_indices[source]#

Observations that are in train set.

Return type:: ndarray

validation_indices#

JaxSCVI.validation_indices[source]#

Observations that are in validation set.

Return type:: ndarray

Methods#

convert_legacy_save#

classmethod JaxSCVI.convert_legacy_save(dir_path, output_dir_path, overwrite=False, prefix=None)[source]#

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 | NoneOptional[str] (default: None): Prefix of saved file names.

Return type:

None

get_anndata_manager#

JaxSCVI.get_anndata_manager(adata, required=False)[source]#

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 : AnnData | MuDataUnion[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 | NoneOptional[AnnDataManager]

get_from_registry#

JaxSCVI.get_from_registry(adata, registry_key)[source]#

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 | MuDataUnion[AnnData, MuData]: AnnData to pull data from.

Return type:

ndarray

Returns:

The requested data as a NumPy array.

get_latent_representation#

JaxSCVI.get_latent_representation(adata=None, indices=None, give_mean=True, mc_samples=1, batch_size=None)[source]#

Return the latent representation for each cell.

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

Parameters:

adata : AnnData | NoneOptional[AnnData] (default: None): AnnData object with equivalent structure to initial AnnData. If None, defaults to the AnnData object used to initialize the model.
indices : Sequence[int] | NoneOptional[Sequence[int]] (default: None): Indices of cells in adata to use. If None, all cells are used.
batch_size : int | NoneOptional[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

load#

classmethod JaxSCVI.load(dir_path, adata=None, use_gpu=None, prefix=None, backup_url=None)[source]#

Instantiate a model from the saved output.

Parameters:

dir_path : str: Path to saved outputs.
adata : AnnData | MuData | NoneUnion[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 : str | int | bool | NoneUnion[str, int, bool, None] (default: None): Load model on default GPU if available (if None or True), or index of GPU to use (if int), or name of GPU (if str), or use CPU (if False).
prefix : str | NoneOptional[str] (default: None): Prefix of saved file names.
backup_url : str | NoneOptional[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_....

load_registry#

static JaxSCVI.load_registry(dir_path, prefix=None)[source]#

Return the full registry saved with the model.

Parameters:

dir_path : str: Path to saved outputs.
prefix : str | NoneOptional[str] (default: None): Prefix of saved file names.

Return type:

dict

Returns:

The full registry saved with the model

register_manager#

classmethod JaxSCVI.register_manager(adata_manager)[source]#

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.

save#

JaxSCVI.save(dir_path, prefix=None, overwrite=False, save_anndata=False, **anndata_write_kwargs)[source]#

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 | NoneOptional[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()

setup_anndata#

classmethod JaxSCVI.setup_anndata(adata, layer=None, batch_key=None, **kwargs)[source]#

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:

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

to_device#

JaxSCVI.to_device(device)[source]#

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

train#

JaxSCVI.train(max_epochs=None, use_gpu=None, train_size=0.9, validation_size=None, batch_size=128, lr=0.001, **trainer_kwargs)[source]#

Train the model.

Parameters:

max_epochs : int | NoneOptional[int] (default: None): Number of passes through the dataset. If None, defaults to np.min([round((20000 / n_cells) * 400), 400])
use_gpu : bool | NoneOptional[bool] (default: None): Whether or not to use GPU resources. If None, will use GPU if available.
train_size : float (default: 0.9): Size of training set in the range [0.0, 1.0].
validation_size : float | NoneOptional[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.
batch_size : int (default: 128): Minibatch size to use during training.
lr : float (default: 0.001): Learning rate to use during training.
**trainer_kwargs: Other keyword args for Trainer.

view_anndata_setup#

JaxSCVI.view_anndata_setup(adata=None, hide_state_registries=False)[source]#

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

Parameters:

adata : AnnData | MuData | NoneUnion[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

view_setup_args#

static JaxSCVI.view_setup_args(dir_path, prefix=None)[source]#

Print args used to setup a saved model.

Parameters:

dir_path : str: Path to saved outputs.
prefix : str | NoneOptional[str] (default: None): Prefix of saved file names.

Return type:

None