scvi.external.VIVS

scvi.external.VIVS#

class scvi.external.VIVS(adata, n_hidden=128, n_latent=10, x_likelihood='nb', xy_linear=False, xy_include_batch_in_input=False, x_model=None, **module_kwargs)[source]#

VIVS: calibrated identification of feature dependencies in multiomics [Boyeau et al., 2024].

Identifies which genes in X are conditionally dependent on an external response Y (e.g. protein expression, niche composition) using a conditional randomization test (CRT), with a deep generative model of X as the knockoff sampler.

Parameters:

  • adata (anndata.AnnData) – AnnData object registered via setup_anndata().

  • n_hidden (int (default: 128)) – Number of hidden units in the generative VAE (ignored if x_model is given).

  • n_latent (int (default: 10)) – Latent dimensionality of the generative VAE (ignored if x_model is given).

  • x_likelihood (Literal['nb', 'zinb', 'poisson'] (default: 'nb')) – Gene-expression likelihood for the generative VAE: "nb", "zinb", or "poisson" (ignored if x_model is given).

  • xy_linear (bool (default: False)) – If True, the importance-score net is a linear model instead of an MLP.

  • xy_include_batch_in_input (bool (default: False)) – Whether to concatenate one-hot batch to the importance-score net’s input.

  • x_model (BaseModelClass | None (default: None)) – An already-trained SCVI or SCVIVA instance (or any model whose .module is a VAE or subclass), registered on data compatible with adata. When given, its trained module is reused (frozen) as the knockoff sampler, and VIVS’s own generative training phase is skipped entirely. Models with a fundamentally different module API (e.g. DestVI, RESOLVI, GIMVI) are not supported here.

  • **module_kwargs – Additional keyword arguments passed to VIVSModule.

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.

get_normalized_function_name

What the get normalized functions name is

history

Returns computed metrics during training.

is_trained

Whether the model has been trained.

registry

Data attached to model instance.

run_id

Returns the run id of the model.

run_name

Returns the run name of the model.

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.

data_registry(registry_key)

Returns the object in AnnData associated with the key in the data registry.

deregister_manager([adata])

Deregisters the AnnDataManager instance associated with adata.

differential_abundance([adata, adata_sub, ...])

Compute the differential abundance between samples.

get_aggregated_posterior([adata, indices, ...])

Compute the aggregated posterior over the u latent representations.

get_anndata_manager(adata[, required])

Retrieves the AnnDataManager for a given AnnData object.

get_cell_scores(gene_ids[, response_ids, ...])

Per-cell (unsummed) importance scores for a specific set of genes.

get_elbo([adata, indices, batch_size, ...])

Compute the evidence lower bound (ELBO) on the data.

get_from_registry(adata, registry_key)

Returns the object in AnnData associated with the key in the data registry.

get_gene_correlations([adata, indices, ...])

Gene-by-gene correlation matrix of the decoder's normalized expression scale.

get_gene_groupings([adata, method, ...])

Hierarchically cluster genes by their decoder-scale correlation.

get_hier_importance(n_clusters_list[, ...])

Hierarchical CRT: gene importance at multiple gene-group resolutions.

get_importance([adata, indices, batch_size, ...])

Conditional-randomization-test importance of each gene for each response.

get_latent_representation([adata, indices, ...])

Compute the latent representation of the data.

get_marginal_ll([adata, indices, ...])

Compute the marginal log-likehood of the data.

get_normalized_expression(*args, **kwargs)

Not implemented for this model class.

get_reconstruction_error([adata, indices, ...])

Compute the reconstruction error on the data.

get_setup_arg(setup_arg)

Returns the string provided to setup of a specific setup_arg.

get_state_registry(registry_key)

Returns the state registry for the AnnDataField registered with this instance.

get_var_names([legacy_mudata_format])

Variable names of input 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.

predict_t([adata, indices, batch_size])

Raw per-cell importance-score-net predictions (no CRT knockoff perturbation).

register_manager(adata_manager)

Registers an AnnDataManager instance with this model class.

save(dir_path[, prefix, overwrite, ...])

Save the state of the model.

setup_annbatch(cls[, collection_path, ...])

Set up a model from on-disk AnnData files via the annbatch streaming loader.

setup_anndata(adata, y_obsm_key[, layer, ...])

Sets up the AnnData object for this model.

to_device(device)

Move the model to the device.

train([max_epochs, x_max_epochs, ...])

Train VIVS in two sequential phases.

transfer_fields(adata, **kwargs)

Transfer fields from a model to an AnnData object.

update_setup_method_args(setup_method_args)

Update setup method args.

view_anndata_setup([adata, ...])

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

view_registry([hide_state_registries])

Prints summary of the registry.

view_setup_args(dir_path[, prefix])

Print args used to setup a saved model.

view_setup_method_args()

Prints setup kwargs used to produce a given registry.

Attributes#

VIVS.adata[source]#

Data attached to model instance.

VIVS.adata_manager[source]#

Manager instance associated with self.adata.

VIVS.device[source]#

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

VIVS.get_normalized_function_name[source]#

What the get normalized functions name is

VIVS.history[source]#

Returns computed metrics during training.

VIVS.is_trained[source]#

Whether the model has been trained.

VIVS.registry[source]#

Data attached to model instance.

VIVS.run_id[source]#

Returns the run id of the model. Used in MLFlow

VIVS.run_name[source]#

Returns the run name of the model. Used in MLFlow

VIVS.summary_string[source]#

Summary string of the model.

VIVS.test_indices[source]#

Observations that are in test set.

VIVS.train_indices[source]#

Observations that are in train set.

VIVS.validation_indices[source]#

Observations that are in validation set.

Methods#

classmethod VIVS.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 the directory where the 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, an error will be raised.

  • prefix (str | None (default: None)) – Prefix of saved file names.

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

Return type:

None

VIVS.data_registry(registry_key)[source]#

Returns the object in AnnData associated with the key in the data registry.

Parameters:

registry_key (str) – key of an object to get from self.data_registry

Return type:

ndarray | DataFrame

Returns:

The requested data.

VIVS.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.

VIVS.differential_abundance(adata=None, adata_sub=None, sample_key=None, batch_size=128, num_cells_posterior=None, dof=None, dataloader=None)[source]#

Compute the differential abundance between samples.

Computes the log probabilities of each sample conditioned on the estimated aggregate posterior distribution of each cell.

Parameters:

  • adata (AnnData | MuData | None (default: None)) – The full data object used to compute each aggregated posterior. Defaults to the AnnData object used to initialize the model.

  • adata_sub (AnnData | MuData | None (default: None)) – The data object to compute the differential abundance for. For very large datasets, this should be used to pass in a subset of the full data object. The aggregated posteriors are still computed from the full data object. The resulting log_probs matrix is stored in adata_sub.obsm

  • sample_key (str | None (default: None)) – Key for the sample covariate.

  • batch_size (int (default: 128)) – Minibatch size for computing the differential abundance.

  • num_cells_posterior (int | None (default: None)) – Maximum number of cells used to compute aggregated posterior for each sample.

  • dof (float | None (default: None)) – Degrees of freedom for the Student’s t-distribution components for aggregated posterior. If None, components are Normal.

  • dataloader (Iterator[dict[str, torch.Tensor | None]] | None (default: None)) – Inference dataloader to materialize when the model was initialized without AnnData.

VIVS.get_aggregated_posterior(adata=None, indices=None, batch_size=None, dof=3.0)[source]#

Compute the aggregated posterior over the u latent representations.

Parameters:

  • adata (default: None) – AnnData object to use. Defaults to the AnnData object used to initialize the model.

  • indices (default: None) – Indices of cells to use.

  • batch_size (default: None) – Batch size to use for computing the latent representation.

  • dof (default: 3.0) – Degrees of freedom for the Student’s t-distribution components. If None, components are Normal.

Returns:

A mixture distribution of the aggregated posterior.

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

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 a manager instance for.

  • required (bool (default: False)) – If True, errors on missing manager. Otherwise, returns None when manager is missing.

Return type:

AnnDataManager | None

VIVS.get_cell_scores(gene_ids, response_ids=None, adata=None, indices=None, batch_size=None, n_mc_samples=None)[source]#

Per-cell (unsummed) importance scores for a specific set of genes.

Return type:

dict

VIVS.get_elbo(adata=None, indices=None, batch_size=None, dataloader=None, return_mean=True, data_loader_kwargs=None, **kwargs)[source]#

Compute the evidence lower bound (ELBO) on the data.

The ELBO is the reconstruction error plus the Kullback-Leibler (KL) divergences between the variational distributions and the priors. It is different from the marginal log-likelihood; specifically, it is a lower bound on the marginal log-likelihood plus a term that is constant with respect to the variational distribution. It still gives good insights on the modeling of the data and is fast to compute.

Parameters:

  • adata (anndata.AnnData | None (default: None)) – AnnData object with var_names in the same order as the ones used to train the model. If None and dataloader is also None, it defaults to the object used to initialize the model.

  • indices (Sequence[int] | None (default: None)) – Indices of observations in adata to use. If None, defaults to all observations. Ignored if dataloader is not None.

  • batch_size (int | None (default: None)) – Minibatch size for the forward pass. If None, defaults to scvi.settings.batch_size. Ignored if dataloader is not None.

  • dataloader (Iterator[dict[str, torch.Tensor | None]] | None (default: None)) – An iterator over minibatches of data on which to compute the metric. The minibatches should be formatted as a dictionary of Tensor with keys as expected by the model. If None, a dataloader is created from adata.

  • return_mean (bool (default: True)) – Whether to return the mean of the ELBO or the ELBO for each observation.

  • data_loader_kwargs (dict | None (default: None)) – Keyword args for data loader, in dict form.

  • **kwargs – Additional keyword arguments to pass into the forward method of the module.

Return type:

float

Returns:

Evidence lower bound (ELBO) of the data.

Notes

This is not the negative ELBO, so higher is better.

VIVS.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 the data registry.

  • adata (AnnData | MuData) – AnnData to pull data from.

Return type:

ndarray

Returns:

The requested data as a NumPy array.

VIVS.get_gene_correlations(adata=None, indices=None, batch_size=128)[source]#

Gene-by-gene correlation matrix of the decoder’s normalized expression scale.

Return type:

ndarray

VIVS.get_gene_groupings(adata=None, method='complete', return_z=False, n_clusters_list=None)[source]#

Hierarchically cluster genes by their decoder-scale correlation.

Parameters:

  • method (str (default: 'complete')) – Linkage method for hierarchical clustering.

  • return_z (default: False) – Whether to also return the linkage matrix and computed gene order.

  • n_clusters_list (list[int] | None (default: None)) – Cluster-count resolutions to compute a partition for.

VIVS.get_hier_importance(n_clusters_list, adata=None, indices=None, batch_size=128, gene_groupings=None, gene_order=None, clustering_method='complete', use_vmap='auto', n_mc_samples=500, silent=False)[source]#

Hierarchical CRT: gene importance at multiple gene-group resolutions.

First clusters genes by decoder-scale correlation at several resolutions (unless pre-computed groupings are given), then re-runs the CRT with group-level knockoff substitution at each resolution. See docs/user_guide/models/vivs.md for the full statistical description.

Parameters:

  • indices (default: None) – Cells to compute importance scores for. If None and adata is also None, defaults to self.validation_indices (the held-out split from training) so that, by default, statistical significance is assessed on data the importance-score net was not fit on, as required for calibrated p-values. Pass indices=np.arange(adata.n_obs) explicitly to use all cells instead.

  • silent (bool (default: False)) – If True, disables the progress bar tracking MC-sample/batch iterations.

Return type:

Dataset

VIVS.get_importance(adata=None, indices=None, batch_size=128, n_mc_samples=500, use_vmap='auto')[source]#

Conditional-randomization-test importance of each gene for each response.

Parameters:

  • indices (default: None) – Cells to compute importance scores for. If None and adata is also None, defaults to self.validation_indices (the held-out split from training) so that, by default, statistical significance is assessed on data the importance-score net was not fit on, as required for calibrated p-values. Pass indices=np.arange(adata.n_obs) explicitly to use all cells instead.

  • use_vmap (Literal['auto', True, False] (default: 'auto')) – Whether to vectorize the per-gene resampling loop with torch.vmap(). "auto" enables it when the number of genes is below 2000 (mirrors the original’s own recommended gene-filtering ceiling). Disable if you hit an out-of-memory error.

Return type:

dict

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

Compute the latent representation of the data.

This is typically denoted as \(z_n\).

Parameters:

  • adata (anndata.AnnData | None (default: None)) – AnnData object with var_names in the same order as the ones used to train the model. If None and dataloader is also None, it defaults to the object used to initialize the model.

  • indices (Sequence[int] | None (default: None)) – Indices of observations in adata to use. If None, defaults to all observations. Ignored if dataloader is not None

  • give_mean (bool (default: True)) – If True, returns the mean of the latent distribution. If False, returns an estimate of the mean using mc_samples Monte Carlo samples.

  • mc_samples (int (default: 5000)) – Number of Monte Carlo samples to use for the estimator for distributions with no closed-form mean (e.g., the logistic normal distribution). Not used if give_mean is True or if return_dist is True.

  • batch_size (int | None (default: None)) – Minibatch size for the forward pass. If None, defaults to scvi.settings.batch_size. Ignored if dataloader is not None

  • return_dist (bool (default: False)) – If True, returns the mean and variance of the latent distribution. Otherwise, returns the mean of the latent distribution.

  • dataloader (Iterator[dict[str, torch.Tensor | None]] (default: None)) – An iterator over minibatches of data on which to compute the metric. The minibatches should be formatted as a dictionary of Tensor with keys as expected by the model. If None, a dataloader is created from adata.

  • **data_loader_kwargs – Keyword args for data loader.

Return type:

TypeAliasType | tuple[TypeAliasType, TypeAliasType]

Returns:

An array of shape (n_obs, n_latent) if return_dist is False. Otherwise, returns a tuple of arrays (n_obs, n_latent) with the mean and variance of the latent distribution.

VIVS.get_marginal_ll(adata=None, indices=None, n_mc_samples=1000, batch_size=None, return_mean=True, dataloader=None, data_loader_kwargs=None, **kwargs)[source]#

Compute the marginal log-likehood of the data.

The computation here is a biased estimator of the marginal log-likelihood of the data.

Parameters:

  • adata (AnnData | None (default: None)) – AnnData object with var_names in the same order as the ones used to train the model. If None and dataloader is also None, it defaults to the object used to initialize the model.

  • indices (Sequence[int] | None (default: None)) – Indices of observations in adata to use. If None, defaults to all observations. Ignored if dataloader is not None.

  • n_mc_samples (int (default: 1000)) – Number of Monte Carlo samples to use for the estimator. Passed into the module’s marginal_ll method.

  • batch_size (int | None (default: None)) – Minibatch size for the forward pass. If None, defaults to scvi.settings.batch_size. Ignored if dataloader is not None.

  • return_mean (bool (default: True)) – Whether to return the mean of the marginal log-likelihood or the marginal-log likelihood for each observation.

  • dataloader (Iterator[dict[str, Tensor | None]] (default: None)) – An iterator over minibatches of data on which to compute the metric. The minibatches should be formatted as a dictionary of Tensor with keys as expected by the model. If None, a dataloader is created from adata.

  • data_loader_kwargs (dict | None (default: None)) – Keyword args for data loader, in dict form.

  • **kwargs – Additional keyword arguments to pass into the module’s marginal_ll method.

Return type:

float | Tensor

Returns:

If True, returns the mean marginal log-likelihood. Otherwise returns a tensor of shape (n_obs,) with the marginal log-likelihood for each observation.

Notes

This is not the negative log-likelihood, so higher is better.

VIVS.get_normalized_expression(*args, **kwargs)[source]#

Not implemented for this model class.

Available in RNA models that inherit from RNASeqMixin.

Raises:

NotImplementedError –

VIVS.get_reconstruction_error(adata=None, indices=None, batch_size=None, dataloader=None, return_mean=True, data_loader_kwargs=None, **kwargs)[source]#

Compute the reconstruction error on the data.

The reconstruction error is the negative log likelihood of the data given the latent variables. It is different from the marginal log-likelihood, but still gives good insights on the modeling of the data and is fast to compute. This is typically written as \(p(x \mid z)\), the likelihood term given one posterior sample.

Parameters:

  • adata (anndata.AnnData | None (default: None)) – AnnData object with var_names in the same order as the ones used to train the model. If None and dataloader is also None, it defaults to the object used to initialize the model.

  • indices (Sequence[int] | None (default: None)) – Indices of observations in adata to use. If None, defaults to all observations. Ignored if dataloader is not None

  • batch_size (int | None (default: None)) – Minibatch size for the forward pass. If None, defaults to scvi.settings.batch_size. Ignored if dataloader is not None

  • dataloader (Iterator[dict[str, torch.Tensor | None]] | None (default: None)) – An iterator over minibatches of data on which to compute the metric. The minibatches should be formatted as a dictionary of Tensor with keys as expected by the model. If None, a dataloader is created from adata.

  • return_mean (bool (default: True)) – Whether to return the mean reconstruction loss or the reconstruction loss for each observation.

  • data_loader_kwargs (dict | None (default: None)) – Keyword args for data loader, in dict form.

  • **kwargs – Additional keyword arguments to pass into the forward method of the module.

Return type:

dict[str, float]

Returns:

Reconstruction error for the data.

Notes

This is not the negative reconstruction error, so higher is better.

VIVS.get_setup_arg(setup_arg)[source]#

Returns the string provided to setup of a specific setup_arg.

Return type:

attrdict

VIVS.get_state_registry(registry_key)[source]#

Returns the state registry for the AnnDataField registered with this instance.

Return type:

attrdict

VIVS.get_var_names(legacy_mudata_format=False)[source]#

Variable names of input data.

Return type:

dict

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

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. If False, will load the model without AnnData.

  • 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.

  • datamodule (LightningDataModule | None (default: None)) – EXPERIMENTAL A LightningDataModule instance to use for training in place of the default DataSplitter. Can only be passed in if the model was not initialized with AnnData.

  • allowed_classes_names_list (list[str] | None (default: None)) – list of allowed classes names to be loaded (besides the original class name)

Returns:

Model with loaded state dictionaries.

Examples

>>> model = ModelClass.load(save_path, adata)
>>> model.get_....
static VIVS.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 | None (default: None)) – Prefix of saved file names.

Return type:

dict

Returns:

The full registry saved with the model

VIVS.predict_t(adata=None, indices=None, batch_size=128)[source]#

Raw per-cell importance-score-net predictions (no CRT knockoff perturbation).

Return type:

ndarray

classmethod VIVS.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.

VIVS.save(dir_path, prefix=None, overwrite=False, save_anndata=False, save_kwargs=None, legacy_mudata_format=False, datamodule=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 (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, an 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.

  • datamodule (LightningDataModule | None (default: None)) – EXPERIMENTAL A LightningDataModule instance to use for training in place of the default DataSplitter. Can only be passed in if the model was not initialized with AnnData.

  • anndata_write_kwargs – Kwargs for write()

classmethod VIVS.setup_annbatch(cls, collection_path=None, paths=None, batch_key=None, labels_key=None, sample_key=None, unlabeled_category='Unknown', layer=None, categorical_covariate_keys=None, continuous_covariate_keys=None, rebuild=True, batch_size=4096, chunk_size=256, preload_nchunks=32, preload_to_gpu=True, dataset_size='20GB', shuffle=False, var_subset=None, merge=None, adatas=None, use_class_sampler=False, class_sampler_key=None, class_weights=None)[source]#

Set up a model from on-disk AnnData files via the annbatch streaming loader.

Builds (or reuses) a zarr-backed DatasetCollection from the supplied h5ad file paths, then wraps it in a AnnbatchDataModule ready for training.

Parameters:

  • collection_path (str | None (default: None)) – Directory where the zarr collection is written (or already exists). If None (default), a path is auto-generated as "./{ModelName}_annbatch.zarr" in the current working directory.

  • paths (list[str] | None (default: None)) – Paths to h5ad files that make up the training dataset. If None, an existing collection at collection_path is opened without rebuilding — useful when the zarr store was created by a previous call. Must be provided when the store does not exist yet.

  • batch_key (str | None (default: None)) – Column in obs to use as the batch variable.

  • labels_key (str | None (default: None)) – Column in obs to use as the cell-type / label variable.

  • sample_key (str | None (default: None)) – Column in obs to use as the sample variable. Used by models like MrVI.

  • unlabeled_category (str (default: 'Unknown')) – Value used to mark unlabeled cells in labels_key. Required by semi-supervised models such as SCANVI. Defaults to "Unknown".

  • layer (str | None (default: None)) – Layer in the h5ad files to use as the count matrix. None uses adata.X (falling back to adata.raw.X when a raw slot exists).

  • categorical_covariate_keys (list[str] | None (default: None)) – Additional categorical covariate columns in obs.

  • continuous_covariate_keys (list[str] | None (default: None)) – Additional continuous covariate columns in obs.

  • rebuild (bool (default: True)) – If True, always rebuild the zarr collection even if it already exists on disk. If False (default), reuse an existing collection and skip the (potentially expensive) add_adatas step.

  • batch_size (int (default: 4096)) – Number of cells per batch yielded by the Loader.

  • chunk_size (int (default: 256)) – Number of cells loaded from disk contiguously per read.

  • preload_nchunks (int (default: 32)) – Number of chunks to preload and shuffle in memory.

  • preload_to_gpu (bool (default: True)) – Whether the loader should move data to GPU before yielding.

  • dataset_size (int | str (default: '20GB')) – Number of observations to load into memory for shuffling / pre-processing when building the collection, or annbatch’s human-readable size strings, e.g. "20GB".

  • shuffle (bool (default: False)) – Whether to pre-shuffle cells when building the collection.

  • var_subset (list[str] | None (default: None)) – Optional list of gene names to restrict the collection to (passed as var_subset to add_adatas).

  • merge (Literal['same', 'unique', 'first', 'only'] | None (default: None)) – How annbatch should merge var metadata across inputs. Passed through to annbatch.DatasetCollection.add_adatas().

Returns:

AnnbatchDataModule A configured datamodule whose registry can be passed directly to the model constructor, e.g. model = SCVI(registry=dm.registry).

Notes

After training, saving and reloading the model requires reconstructing the datamodule manually and passing it to load() via the datamodule argument — the same pattern used by all custom datamodules.

classmethod VIVS.setup_anndata(adata, y_obsm_key, 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:

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

  • y_obsm_key (str) – Key in adata.obsm for the response(s) Y whose conditional dependence on gene expression X is being tested (e.g. protein expression, niche composition).

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

  • batch_key (str | None (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.

VIVS.to_device(device)[source]#

Move the model to the device.

Parameters:

device (str | int | device) – Device to move model to. Options: ‘cpu’ for CPU, integer GPU index (e.g., 0), ‘cuda:X’ where X is the GPU index (e.g. ‘cuda:0’), or a torch.device object (including XLA devices for TPU). 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
VIVS.train(max_epochs=None, x_max_epochs=None, xy_max_epochs=None, train_size=0.9, validation_size=None, batch_size=128, early_stopping=False, **kwargs)[source]#

Train VIVS in two sequential phases.

Phase 1 fits the generative VAE over X (skipped entirely if a pretrained x_model was supplied at construction). Phase 2 freezes it and fits the importance-score net for Y|X. This order is required for CRT validity: the knockoff sampler must not be contaminated by information about Y.

VIVS.transfer_fields(adata, **kwargs)[source]#

Transfer fields from a model to an AnnData object.

Return type:

anndata.AnnData

VIVS.update_setup_method_args(setup_method_args)[source]#

Update setup method args.

Parameters:

setup_method_args (dict) – This is a bit of a misnomer, this is a dict representing kwargs of the setup method that will be used to update the existing values in the registry of this instance.

VIVS.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 | 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

VIVS.view_registry(hide_state_registries=False)[source]#

Prints summary of the registry.

Parameters:

hide_state_registries (bool (default: False)) – If True, prints a shortened summary without details of each state registry.

Return type:

None

static VIVS.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 | None (default: None)) – Prefix of saved file names.

Return type:

None

VIVS.view_setup_method_args()[source]#

Prints setup kwargs used to produce a given registry.

Parameters:

registry – Registry produced by an AnnDataManager.

Return type:

None