scvi.model.AUTOZI#

class scvi.model.AUTOZI(adata, n_hidden=128, n_latent=10, n_layers=1, dropout_rate=0.1, dispersion='gene', latent_distribution='normal', alpha_prior=0.5, beta_prior=0.5, minimal_dropout=0.01, zero_inflation='gene', use_observed_lib_size=True, **model_kwargs)[source]#

Automatic identification of ZI genes [Clivio19].

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

n_layers : int (default: 1)

Number of hidden layers used for encoder NN

dropout_rate : float (default: 0.1)

Dropout rate for neural networks

dispersion : {‘gene’, ‘gene-batch’, ‘gene-label’, ‘gene-cell’}Literal[‘gene’, ‘gene-batch’, ‘gene-label’, ‘gene-cell’] (default: 'gene')

One of the following

'gene' - dispersion parameter of NB is constant per gene across cells
'gene-batch' - dispersion can differ between different batches
'gene-label' - dispersion can differ between different labels
'gene-cell' - dispersion can differ for every gene in every cell

latent_distribution : {‘normal’, ‘ln’}Literal[‘normal’, ‘ln’] (default: 'normal')

One of

'normal' - Normal distribution
'ln' - Logistic normal distribution (Normal(0, I) transformed by softmax)

alpha_prior : float | NoneOptional[float] (default: 0.5)

Float denoting the alpha parameter of the prior Beta distribution of the zero-inflation Bernoulli parameter. Should be between 0 and 1, not included. When set to ``None’’, will be set to 1 - beta_prior if beta_prior is not ``None’’, otherwise the prior Beta distribution will be learned on an Empirical Bayes fashion.

beta_prior : float | NoneOptional[float] (default: 0.5)

Float denoting the beta parameter of the prior Beta distribution of the zero-inflation Bernoulli parameter. Should be between 0 and 1, not included. When set to ``None’’, will be set to 1 - alpha_prior if alpha_prior is not ``None’’, otherwise the prior Beta distribution will be learned on an Empirical Bayes fashion.

minimal_dropout : float (default: 0.01)

Float denoting the lower bound of the cell-gene ZI rate in the ZINB component. Must be non-negative. Can be set to 0 but not recommended as this may make the mixture problem ill-defined.

zero_inflation : One of the following

'gene' - zero-inflation Bernoulli parameter of AutoZI is constant per gene across cells
'gene-batch' - zero-inflation Bernoulli parameter can differ between different batches
'gene-label' - zero-inflation Bernoulli parameter can differ between different labels
'gene-cell' - zero-inflation Bernoulli parameter can differ for every gene in every cell

use_observed_lib_size : bool (default: True)

Use observed library size for RNA as scaling factor in mean of conditional distribution

**model_kwargs

Keyword args for AutoZIVAE

Examples

>>> adata = anndata.read_h5ad(path_to_anndata)
>>> scvi.model.AUTOZI.setup_anndata(adata, batch_key="batch")
>>> vae = scvi.model.AUTOZI(adata)
>>> vae.train(n_epochs=400)

Notes

See further usage examples in the following tutorials:

Identification of zero-inflated genes

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#

`get_alphas_betas`([as_numpy])	Return parameters of Bernoulli Beta distributions in a dictionary.
`get_anndata_manager`(adata[, required])	Retrieves the `AnnDataManager` for a given AnnData object specific to this model instance.
`get_elbo`([adata, indices, batch_size])	Return the ELBO for the data.
`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.
`get_marginal_ll`([adata, indices, ...])	Return the marginal LL for the data.
`get_reconstruction_error`([adata, indices, ...])	Return the reconstruction error for the data.
`load`(dir_path[, adata, use_gpu, prefix])	Instantiate a model from the saved output.
`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[, 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#

AUTOZI.adata#

Data attached to model instance.

Return type: AnnData

adata_manager#

AUTOZI.adata_manager#

Manager instance associated with self.adata.

Return type: AnnDataManager

device#

AUTOZI.device#

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

Return type: str

history#

AUTOZI.history#: Returns computed metrics during training.

is_trained#

AUTOZI.is_trained#

Whether the model has been trained.

Return type: bool

test_indices#

AUTOZI.test_indices#

Observations that are in test set.

Return type: ndarray

train_indices#

AUTOZI.train_indices#

Observations that are in train set.

Return type: ndarray

validation_indices#

AUTOZI.validation_indices#

Observations that are in validation set.

Return type: ndarray

Methods#

get_alphas_betas#

AUTOZI.get_alphas_betas(as_numpy=True)[source]#

Return parameters of Bernoulli Beta distributions in a dictionary.

Return type: {str: Tensor | ndarray}Dict[str, Union[Tensor, ndarray]]

get_anndata_manager#

AUTOZI.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 : AnnData: 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_elbo#

AUTOZI.get_elbo(adata=None, indices=None, batch_size=None)#

Return the ELBO for the data.

The ELBO is a lower bound on the log likelihood of the data used for optimization of VAEs. Note, this is not the negative ELBO, higher is better.

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

float

get_from_registry#

AUTOZI.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: AnnData to pull data from.

Return type

ndarray

Returns

The requested data as a NumPy array.

get_latent_representation#

AUTOZI.get_latent_representation(adata=None, indices=None, give_mean=True, mc_samples=5000, batch_size=None)#

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.
give_mean : bool (default: True): Give mean of distribution or sample from it.
mc_samples : int (default: 5000): For distributions with no closed-form mean (e.g., logistic normal), how many Monte Carlo samples to take for computing mean.
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

get_marginal_ll#

AUTOZI.get_marginal_ll(adata=None, indices=None, n_mc_samples=1000, batch_size=None)[source]#

Return the marginal LL for the data.

The computation here is a biased estimator of the marginal log likelihood of the data. Note, this is not the negative log likelihood, higher is better.

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.
n_mc_samples : int (default: 1000): Number of Monte Carlo samples to use for marginal LL estimation.
batch_size : int | NoneOptional[int] (default: None): Minibatch size for data loading into model. Defaults to scvi.settings.batch_size.

Return type

float

get_reconstruction_error#

AUTOZI.get_reconstruction_error(adata=None, indices=None, batch_size=None)#

Return the reconstruction error for the data.

This is typically written as \(p(x \mid z)\), the likelihood term given one posterior sample. Note, this is not the negative likelihood, higher is better.

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

float | {str: float}Union[float, Dict[str, float]]

load#

classmethod AUTOZI.load(dir_path, adata=None, use_gpu=None, prefix=None)#

Instantiate a model from the saved output.

Parameters

dir_path : str: Path to saved outputs.
adata : AnnData | NoneOptional[AnnData] (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.

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_....

register_manager#

classmethod AUTOZI.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.

save#

AUTOZI.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 : 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 AUTOZI.setup_anndata(adata, batch_key=None, labels_key=None, layer=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

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

to_device#

AUTOZI.to_device(device)#

Move model to device.

Parameters

device : str | intUnion[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

train#

AUTOZI.train(max_epochs=None, use_gpu=None, train_size=0.9, validation_size=None, batch_size=128, early_stopping=False, plan_kwargs=None, **trainer_kwargs)#

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 : str | int | bool | NoneUnion[str, int, bool, None] (default: None): Use default GPU if available (if None or True), or index of GPU to use (if int), or name of GPU (if str, e.g., ‘cuda:0’), or use CPU (if False).
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.
early_stopping : bool (default: False): Perform early stopping. Additional arguments can be passed in **kwargs. See Trainer for further options.
plan_kwargs : dict | NoneOptional[dict] (default: None): Keyword args for TrainingPlan. Keyword arguments passed to train() will overwrite values present in plan_kwargs, when appropriate.
**trainer_kwargs: Other keyword args for Trainer.

view_anndata_setup#

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

Return type

None