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 zero-inflated genes [Clivio et al., 2019].

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 (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 (Literal['normal', 'ln'] (default: 'normal')) –

    One of the following:

    • 'normal' - Normal distribution

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

  • alpha_prior (Optional[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 (Optional[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 (str (default: 'gene')) –

    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:

  1. 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.

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 model (<v0.15.0) to the updated save format.

deregister_manager([adata])

Deregisters the AnnDataManager instance associated with adata.

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, 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[, 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#

AUTOZI.adata[source]#

Data attached to model instance.

AUTOZI.adata_manager[source]#

Manager instance associated with self.adata.

AUTOZI.device[source]#

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

AUTOZI.history[source]#

Returns computed metrics during training.

AUTOZI.is_trained[source]#

Whether the model has been trained.

AUTOZI.summary_string[source]#

Summary string of the model.

AUTOZI.test_indices[source]#

Observations that are in test set.

AUTOZI.train_indices[source]#

Observations that are in train set.

AUTOZI.validation_indices[source]#

Observations that are in validation set.

Methods#

classmethod AUTOZI.convert_legacy_save(dir_path, output_dir_path, overwrite=False, prefix=None, **save_kwargs)[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 (Optional[str] (default: None)) – Prefix of saved file names.

  • **save_kwargs – Keyword arguments passed into save().

Return type

None

AUTOZI.deregister_manager(adata=None)[source]#

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.

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

Return parameters of Bernoulli Beta distributions in a dictionary.

Return type

dict[str, Union[Tensor, ndarray]]

AUTOZI.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 (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]

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

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 (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.

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

Return type

float

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

Return type

ndarray

Returns

The requested data as a NumPy array.

AUTOZI.get_latent_representation(adata=None, indices=None, give_mean=True, mc_samples=5000, batch_size=None, return_dist=False)[source]#

Return the latent representation for each cell.

This is typically denoted as \(z_n\).

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

  • return_dist (bool (default: False)) – Return (mean, variance) of distributions instead of just the mean. If True, ignores give_mean and mc_samples. In the case of the latter, mc_samples is used to compute the mean of a transformed distribution. If return_dist is true the untransformed mean and variance are returned.

Return type

Union[ndarray, tuple[ndarray, ndarray]]

Returns

Low-dimensional representation for each cell or a tuple containing its mean and variance.

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 (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.

  • n_mc_samples (int (default: 1000)) – Number of Monte Carlo samples to use for marginal LL estimation.

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

Return type

float

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

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 (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.

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

Return type

float

classmethod AUTOZI.load(dir_path, adata=None, accelerator='auto', device='auto', prefix=None, backup_url=None)[source]#

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.

  • 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 (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 AUTOZI.load_registry(dir_path, prefix=None)[source]#

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 AUTOZI.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.

AUTOZI.save(dir_path, prefix=None, overwrite=False, save_anndata=False, save_kwargs=None, **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 (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

  • save_kwargs (Optional[dict] (default: None)) – Keyword arguments passed into save().

  • anndata_write_kwargs – Kwargs for write()

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
  • adata (AnnData) – AnnData object. Rows represent cells, columns represent features.

  • 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.

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

AUTOZI.to_device(device)[source]#

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
AUTOZI.train(max_epochs=None, accelerator='auto', devices='auto', train_size=0.9, validation_size=None, shuffle_set_split=True, load_sparse_tensor=False, batch_size=128, early_stopping=False, datasplitter_kwargs=None, plan_kwargs=None, **trainer_kwargs)[source]#

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])

  • 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.

  • 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.

  • load_sparse_tensor (bool (default: False)) – EXPERIMENTAL If True, loads data with sparse CSR or CSC layout as a Tensor with the same layout. Can lead to speedups in data transfers to GPUs, depending on the sparsity of the data.

  • batch_size (Tunable_[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.

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

  • plan_kwargs (Optional[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.

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

Return type

None