scvi.model.SCANVI#

class scvi.model.SCANVI(adata=None, registry=None, n_hidden=128, n_latent=10, n_layers=1, dropout_rate=0.1, dispersion='gene', gene_likelihood='zinb', use_observed_lib_size=True, linear_classifier=False, datamodule=None, **model_kwargs)[source]#

Single-cell annotation using variational inference [Xu et al., 2021].

Inspired from M1 + M2 model, as described in (https://arxiv.org/pdf/1406.5298.pdf).

Parameters:

adata anndata.AnnData | None (default: None)

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 and decoder NNs.

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

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

One of:

'nb' - Negative binomial distribution
'zinb' - Zero-inflated negative binomial distribution
'poisson' - Poisson distribution

use_observed_lib_size bool (default: True)

If True, use the observed library size for RNA as the scaling factor in the mean of the conditional distribution.

linear_classifier bool (default: False)

If True, uses a single linear layer for classification instead of a multi-layer perceptron.

**model_kwargs

Keyword args for SCANVAE

Examples

>>> adata = anndata.read_h5ad(path_to_anndata)
>>> scvi.model.SCANVI.setup_anndata(adata, batch_key="batch", labels_key="labels")
>>> vae = scvi.model.SCANVI(adata, "Unknown")
>>> vae.train()
>>> adata.obsm["X_scVI"] = vae.get_latent_representation()
>>> adata.obs["pred_label"] = vae.predict()

Notes

See further usage examples in the following tutorials:

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.
`minified_data_type`	The type of minified data associated with this model, if applicable.
`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.
`differential_expression`([adata, groupby, ...])	A unified method for differential expression analysis.
`from_scvi_model`(scvi_model, unlabeled_category)	Initialize scanVI model with weights from pretrained `SCVI` model.
`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_elbo`([adata, indices, batch_size, ...])	Compute the evidence lower bound (ELBO) on the data.
`get_feature_correlation_matrix`([adata, ...])	Generate gene-gene correlation matrix using scvi uncertainty and expression.
`get_from_registry`(adata, registry_key)	Returns the object in AnnData associated with the key in the data registry.
`get_importance_weights`(adata, indices, qz, ...)	Computes importance weights for the given samples.
`get_latent_library_size`([adata, indices, ...])	Returns the latent library size for each cell.
`get_latent_representation`([adata, indices, ...])	Compute the latent representation of the data.
`get_likelihood_parameters`([adata, indices, ...])	Estimates for the parameters of the likelihood \(p(x \mid z)\).
`get_marginal_ll`([adata, indices, ...])	Compute the marginal log-likehood of the data.
`get_normalized_expression`([adata, indices, ...])	Returns the normalized (decoded) gene expression.
`get_ranked_features`([adata, attrs])	Get the ranked gene list based on highest attributions.
`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_query_data`([adata, reference_model, ...])	Online update of a reference model with scArches algorithm [Lotfollahi et al., 2021].
`load_registry`(dir_path[, prefix])	Return the full registry saved with the model.
`minify_adata`([minified_data_type, ...])	Minify the model's `adata`.
`posterior_predictive_sample`([adata, ...])	Generate predictive samples from the posterior predictive distribution.
`predict`([adata, indices, soft, batch_size, ...])	Return cell label predictions.
`prepare_query_anndata`(adata, reference_model)	Prepare data for query integration.
`prepare_query_mudata`(mdata, reference_model)	Prepare multimodal dataset for query integration.
`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`([collection_path, paths, ...])	Set up a model from on-disk AnnData files via the annbatch streaming loader.
`setup_anndata`(adata, labels_key, ...[, ...])	Sets up the `AnnData` object for this model.
`shap_adata_predict`(X)	SHAP Operator (gives soft predictions gives data X)
`shap_predict`([adata, indices, shap_args])	Run SHAP interpreter for a trained model and gives back shap values
`to_device`(device)	Move the model to the device.
`train`([max_epochs, n_samples_per_label, ...])	Train the model.
`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#

SCANVI.adata#: Data attached to model instance.

SCANVI.adata_manager#: Manager instance associated with self.adata.

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

SCANVI.get_normalized_function_name#: What the get normalized functions name is

SCANVI.history#: Returns computed metrics during training.

SCANVI.is_trained#: Whether the model has been trained.

SCANVI.minified_data_type#: The type of minified data associated with this model, if applicable.

SCANVI.registry#: Data attached to model instance.

SCANVI.run_id#: Returns the run id of the model. Used in MLFlow

SCANVI.run_name#: Returns the run name of the model. Used in MLFlow

SCANVI.summary_string#: Summary string of the model.

SCANVI.test_indices#: Observations that are in test set.

SCANVI.train_indices#: Observations that are in train set.

SCANVI.validation_indices#: Observations that are in validation set.

Methods#

classmethod SCANVI.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

SCANVI.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.

SCANVI.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.

SCANVI.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.

SCANVI.differential_expression(adata=None, groupby=None, group1=None, group2=None, idx1=None, idx2=None, mode='vanilla', delta=0.25, batch_size=None, all_stats=True, batch_correction=False, batchid1=None, batchid2=None, fdr_target=0.05, silent=False, weights='uniform', filter_outlier_cells=False, importance_weighting_kwargs=None, dataloader=None, **kwargs)[source]#

A unified method for differential expression analysis.

Implements 'vanilla' DE [Lopez et al., 2018] and 'change' mode DE [Boyeau et al., 2019].

Parameters:

adata anndata.AnnData | None (default: None): AnnData object with equivalent structure to initial AnnData. If None, defaults to the AnnData object used to initialize the model.
groupby str | None (default: None): The key of the observations grouping to consider.
group1 list[str] | None (default: None): Subset of groups, e.g. ['g1', 'g2', 'g3'], to which comparison shall be restricted, or all groups in groupby (default).
group2 str | None (default: None): If None, compare each group in group1 to the union of the rest of the groups in groupby. If a group identifier, compare with respect to this group.
idx1 list[int] | list[bool] | str | None (default: None): idx1 and idx2 can be used as an alternative to the AnnData keys. Custom identifier for group1 that can be of three sorts: (1) a boolean mask, (2) indices, or (3) a string. If it is a string, then it will query indices that verifies conditions on adata.obs, as described in pandas.DataFrame.query() If idx1 is not None, this option overrides group1 and group2.
idx2 list[int] | list[bool] | str | None (default: None): Custom identifier for group2 that has the same properties as idx1. By default, includes all cells not specified in idx1.
mode Literal['vanilla', 'change'] (default: 'vanilla'): Method for differential expression. See user guide for full explanation.
delta float (default: 0.25): specific case of region inducing differential expression. In this case, we suppose that \(R \setminus [-\delta, \delta]\) does not induce differential expression (change model default case).
batch_size int | None (default: None): Minibatch size for data loading into model. Defaults to scvi.settings.batch_size.
all_stats bool (default: True): Concatenate count statistics (e.g., mean expression group 1) to DE results.
batch_correction bool (default: False): Whether to correct for batch effects in DE inference.
batchid1 list[str] | None (default: None): Subset of categories from batch_key registered in setup_anndata, e.g. ['batch1', 'batch2', 'batch3'], for group1. Only used if batch_correction is True, and by default all categories are used.
batchid2 list[str] | None (default: None): Same as batchid1 for group2. batchid2 must either have null intersection with batchid1, or be exactly equal to batchid1. When the two sets are exactly equal, cells are compared by decoding on the same batch. When sets have null intersection, cells from group1 and group2 are decoded on each group in group1 and group2, respectively.
fdr_target float (default: 0.05): Tag features as DE based on posterior expected false discovery rate.
silent bool (default: False): If True, disables the progress bar. Default: False.
weights Literal['uniform', 'importance'] | None (default: 'uniform'): Weights to use for sampling. If None, defaults to "uniform".
filter_outlier_cells bool (default: False): Whether to filter outlier cells with filter_outlier_cells().
importance_weighting_kwargs dict | None (default: None): Keyword arguments passed into get_importance_weights().
dataloader Iterator[dict[str, torch.Tensor | None]] | None (default: None): Inference dataloader to materialize when the model was initialized without AnnData.
**kwargs: Keyword args for scvi.model.base.DifferentialComputation.get_bayes_factors()

Return type:

DataFrame

Returns:

Differential expression DataFrame.

classmethod SCANVI.from_scvi_model(scvi_model, unlabeled_category, labels_key=None, adata=None, registry=None, **scanvi_kwargs)[source]#

Initialize scanVI model with weights from pretrained SCVI model.

Parameters:

scvi_model SCVI: Pretrained scvi model
labels_key str | None (default: None): key in adata.obs for label information. Label categories can not be different if labels_key was used to setup the SCVI model. If None, uses the labels_key used to setup the SCVI model. If that was None, and error is raised.
unlabeled_category str: Value used for unlabeled cells in labels_key used to setup AnnData with scvi.
adata anndata.AnnData | None (default: None): AnnData object that has been registered via setup_anndata().
registry dict | None (default: None): Registry of the datamodule used to train scANVI model.
scanvi_kwargs: kwargs for scANVI model

SCANVI.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 AnnData | MuData | None (default: None): AnnData object to use. Defaults to the AnnData object used to initialize the model.
indices Sequence[int] | None (default: None): Indices of cells to use.
batch_size int | None (default: None): Batch size to use for computing the latent representation.
dof float | None (default: 3.0): Degrees of freedom for the Student’s t-distribution components. If None, components are Normal.

Return type:

Distribution

Returns:

A mixture distribution of the aggregated posterior.

SCANVI.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

SCANVI.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.

SCANVI.get_feature_correlation_matrix(adata=None, indices=None, n_samples=10, batch_size=64, rna_size_factor=1000, transform_batch=None, correlation_type='spearman', silent=True)[source]#

Generate gene-gene correlation matrix using scvi uncertainty and expression.

Parameters:

adata anndata.AnnData | None (default: None)

AnnData object with equivalent structure to initial AnnData. If None, defaults to the AnnData object used to initialize the model.

indices list[int] | None (default: None)

Indices of cells in adata to use. If None, all cells are used.

n_samples int (default: 10)

Number of posterior samples to use for estimation.

batch_size int (default: 64)

Minibatch size for data loading into model. Defaults to scvi.settings.batch_size.

rna_size_factor int (default: 1000)

size factor for RNA prior to sampling gamma distribution.

transform_batch list[int | float | str] | None (default: None)

Batches to condition on. If transform_batch is:

None, then real observed batch is used.
int, then batch transform_batch is used.
list of int, then values are averaged over provided batches.

correlation_type Literal['spearman', 'pearson'] (default: 'spearman')

One of “pearson”, “spearman”.

silent bool (default: True)

If True, disables the progress bar. Default: False.

Return type:

DataFrame

Returns:

Gene-gene correlation matrix

SCANVI.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.

SCANVI.get_importance_weights(adata, indices, qz, px, zs, max_cells=1024, truncation=False, n_mc_samples=500, n_mc_samples_per_pass=250, **data_loader_kwargs)[source]#

Computes importance weights for the given samples.

This method computes importance weights for every latent code in zs as a way to encourage latent codes providing high likelihoods across many cells in the considered subpopulation.

Parameters:

adata anndata.AnnData | None: Data to use for computing importance weights.
indices list[int] | None: Indices of cells in adata to use.
distributions: Dictionary of distributions associated with indices.
qz Distribution: Variational posterior distributions of the cells, aligned with indices.
px Distribution: Count distributions of the cells, aligned with indices.
zs Tensor: Samples associated with indices.
max_cells int (default: 1024): Maximum number of cells used to estimated the importance weights
truncation bool (default: False): Whether importance weights should be truncated. If True, the importance weights are truncated as described in [Ionides, 2008]. In particular, the provided value is used to threshold importance weights as a way to reduce the variance of the estimator.
n_mc_samples int (default: 500): Number of Monte Carlo samples to use for estimating the importance weights, by default 500
n_mc_samples_per_pass int (default: 250): Number of Monte Carlo samples to use for each pass, by default 250
**data_loader_kwargs: Keyword args for data loader.

Return type:

ndarray

Returns:

importance_weights Numpy array containing importance weights aligned with the provided indices.

Notes

This method assumes a normal prior on the latent space.

SCANVI.get_latent_library_size(adata=None, indices=None, give_mean=True, batch_size=None, dataloader=None, **data_loader_kwargs)[source]#

Returns the latent library size for each cell.

This is denoted as \(\ell_n\) in the scVI paper.

Parameters:

adata anndata.AnnData | None (default: None): AnnData object with equivalent structure to initial AnnData. If None, defaults to the AnnData object used to initialize the model.
indices list[int] | None (default: None): Indices of cells in adata to use. If None, all cells are used.
give_mean bool (default: True): Return the mean or a sample from the posterior distribution.
batch_size int | None (default: None): Minibatch size for data loading into model. Defaults to scvi.settings.batch_size.
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.
**data_loader_kwargs: Keyword args for data loader.

Return type:

ndarray

SCANVI.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:

NDArray | tuple[NDArray, NDArray]

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.

SCANVI.get_likelihood_parameters(adata=None, indices=None, n_samples=1, give_mean=False, batch_size=None, dataloader=None, **data_loader_kwargs)[source]#

Estimates for the parameters of the likelihood \(p(x \mid z)\).

Parameters:

adata anndata.AnnData | None (default: None): AnnData object with equivalent structure to initial AnnData. If None, defaults to the AnnData object used to initialize the model.
indices list[int] | None (default: None): Indices of cells in adata to use. If None, all cells are used.
n_samples int | None (default: 1): Number of posterior samples to use for estimation.
give_mean bool | None (default: False): Return expected value of parameters or a samples
batch_size int | None (default: None): Minibatch size for data loading into model. Defaults to scvi.settings.batch_size.
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.
**data_loader_kwargs: Keyword args for data loader.

Return type:

dict[str, ndarray]

SCANVI.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.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, 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 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 | torch.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.

SCANVI.get_normalized_expression(adata=None, indices=None, transform_batch=None, gene_list=None, library_size=1, n_samples=1, n_samples_overall=None, weights=None, batch_size=None, return_mean=True, return_numpy=None, silent=True, dataloader=None, data_loader_kwargs=None, **importance_weighting_kwargs)[source]#

Returns the normalized (decoded) gene expression.

This is denoted as \(\rho_n\) in the scVI paper.

Parameters:

adata anndata.AnnData | None (default: None): AnnData object with equivalent structure to initial AnnData. If None, defaults to the AnnData object used to initialize the model.
indices list[int] | None (default: None): Indices of cells in adata to use. If None, all cells are used.
transform_batch list[int | float | str] | None (default: None): Batch to condition on. If transform_batch is: - None, then real observed batch is used. - int, then batch transform_batch is used. - Otherwise based on string
gene_list list[str] | None (default: None): Return frequencies of expression for a subset of genes. This can save memory when working with large datasets and few genes are of interest.
library_size float | Literal['latent'] (default: 1): Scale the expression frequencies to a common library size. This allows gene expression levels to be interpreted on a common scale of relevant magnitude. If set to "latent", use the latent library size.
n_samples int (default: 1): Number of posterior samples to use for estimation.
n_samples_overall int (default: None): Number of posterior samples to use for estimation. Overrides n_samples.
weights Literal['uniform', 'importance'] | None (default: None): Weights to use for sampling. If None, defaults to "uniform".
batch_size int | None (default: None): Minibatch size for data loading into model. Defaults to scvi.settings.batch_size.
return_mean bool (default: True): Whether to return the mean of the samples.
return_numpy bool | None (default: None): Return a ndarray instead of a DataFrame. DataFrame includes gene names as columns. If either n_samples=1 or return_mean=True, defaults to False. Otherwise, it defaults to True.
silent bool (default: True): If True, disables the progress bar. Default: False.
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.
data_loader_kwargs dict | None (default: None): Keyword args for data loader, in dict form.
importance_weighting_kwargs: Keyword arguments passed into get_importance_weights().

Return type:

ndarray | DataFrame

Returns:

If n_samples is provided and return_mean is False, this method returns a 3d tensor of shape (n_samples, n_cells, n_genes). If n_samples is provided and return_mean is True, it returns a 2d tensor of shape (n_cells, n_genes). In this case, return type is DataFrame unless return_numpy is True. Otherwise, the method expects n_samples_overall to be provided and returns a 2d tensor of shape (n_samples_overall, n_genes).

SCANVI.get_ranked_features(adata=None, attrs=None)[source]#

Get the ranked gene list based on highest attributions.

Parameters:

adata AnnData | MuData | None (default: None): AnnData or MuData object that has been registered via the corresponding setup method in the model class.
attrs ndarray | None (default: None): Attributions matrix.

Return type:

DataFrame

Returns:

pandas.DataFrame A pandas dataframe containing the ranked attributions for each gene

Examples

>>> attrs_df = model.get_ranked_features(attrs)

SCANVI.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.

SCANVI.get_setup_arg(setup_arg)[source]#

Returns the string provided to setup of a specific setup_arg.

Return type:: attrdict

SCANVI.get_state_registry(registry_key)[source]#

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

Return type:: attrdict

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

Variable names of input data.

Return type:: dict

classmethod SCANVI.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_....

classmethod SCANVI.load_query_data(adata=None, reference_model=None, registry=None, inplace_subset_query_vars=False, accelerator='auto', device='auto', unfrozen=False, freeze_dropout=False, freeze_expression=True, freeze_decoder_first_layer=True, freeze_batchnorm_encoder=True, freeze_batchnorm_decoder=False, freeze_classifier=True, transfer_batch=True, datamodule=None)[source]#

Online update of a reference model with scArches algorithm [Lotfollahi et al., 2021].

Parameters:

adata AnnData | MuData (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 registry.
reference_model str | BaseModelClass (default: None): Either an already instantiated model of the same class or a path to saved outputs for the reference model.
inplace_subset_query_vars bool (default: False): Whether to subset and rearrange query vars inplace based on vars used to train the reference 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.
unfrozen bool (default: False): Override all other freeze options for a fully unfrozen model
freeze_dropout bool (default: False): Whether to freeze dropout during training
freeze_expression bool (default: True): Freeze neurons corresponding to expression in first layer
freeze_decoder_first_layer bool (default: True): Freeze neurons corresponding to first layer in decoder
freeze_batchnorm_encoder bool (default: True): Whether to freeze batchnorm weight and bias during training for encoder
freeze_batchnorm_decoder bool (default: False): Whether to freeze batchnorm weight and bias during training for decoder
freeze_classifier bool (default: True): Whether to freeze classifier completely. Only applies to SCANVI.
transfer_batch bool (default: True): Allow for surgery on the batch covariate. Only applies to SYSVI.
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.

static SCANVI.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

SCANVI.minify_adata(minified_data_type='latent_posterior_parameters', use_latent_qzm_key='X_latent_qzm', use_latent_qzv_key='X_latent_qzv')[source]#

Minify the model’s adata.

Minifies the AnnData object associated with the model according to the method specified by minified_data_type and registers the new fields with the model’s AnnDataManager. This also sets the minified_data_type attribute of the underlying BaseModuleClass instance.

Parameters:

minified_data_type Literal['latent_posterior_parameters'] (default: 'latent_posterior_parameters')

Method for minifying the data. One of the following:

"latent_posterior_parameters": Store the latent posterior mean and variance in
obsm using the keys use_latent_qzm_key and use_latent_qzv_key.
"latent_posterior_parameters_with_counts": Store the latent posterior mean and
variance in obsm using the keys use_latent_qzm_key and use_latent_qzv_key, and the raw count data in X.

use_latent_qzm_key str (default: 'X_latent_qzm')

Key to use for storing the latent posterior mean in obsm when minified_data_type is "latent_posterior".

use_latent_qzv_key str (default: 'X_latent_qzv')

Key to use for storing the latent posterior variance in obsm when minified_data_type is "latent_posterior".

Return type:

None

Notes

The modification is not done inplace – instead the model is assigned a new (minified) version of the AnnData.

SCANVI.posterior_predictive_sample(adata=None, indices=None, transform_batch=None, n_samples=1, gene_list=None, batch_size=None, dataloader=None, silent=True, **data_loader_kwargs)[source]#

Generate predictive samples from the posterior predictive distribution.

The posterior predictive distribution is denoted as \(p(\hat{x} \mid x)\), where \(x\) is the input data and \(\hat{x}\) is the sampled data.

We sample from this distribution by first sampling n_samples times from the posterior distribution \(q(z \mid x)\) for a given observation, and then sampling from the likelihood \(p(\hat{x} \mid z)\) for each of these.

Parameters:

adata anndata.AnnData | None (default: None): AnnData object with an equivalent structure to the model’s dataset. If None, defaults to the AnnData object used to initialize the model.
indices list[int] | None (default: None): Indices of the observations in adata to use. If None, defaults to all the observations.
transform_batch list[int | float | str] | None (default: None): Batch to condition on. If transform_batch is: - None, then real observed batch is used. - int, then batch transform_batch is used. - Otherwise based on string
n_samples int (default: 1): Number of Monte Carlo samples to draw from the posterior predictive distribution for each observation.
gene_list list[str] | None (default: None): Names of the genes to which to subset. If None, defaults to all genes.
batch_size int | None (default: None): Minibatch size to use for data loading and model inference. Defaults to scvi.settings.batch_size. Passed into BaseModelClass._make_data_loader.
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.
**data_loader_kwargs: Keyword args for data loader.

Return type:

GCXS

Returns:

Sparse multidimensional array of shape (n_obs, n_vars) if n_samples == 1, else (n_obs, n_vars, n_samples).

SCANVI.predict(adata=None, indices=None, soft=False, batch_size=None, use_posterior_mean=True, ig_interpretability=False, ig_args=None, dataloader=None)[source]#

Return cell label predictions.

Parameters:

adata AnnData | MuData | None (default: None): AnnData or MuData object that has been registered via the corresponding setup method in the model class.
indices Sequence[int] | None (default: None): Return probabilities for each class label.
soft bool (default: False): If True, returns per-class probabilities
batch_size int | None (default: None): Minibatch size for data loading into a model. Defaults to scvi.settings.batch_size.
use_posterior_mean bool (default: True): If True, uses the mean of the posterior distribution to predict celltype labels. Otherwise, uses a sample from the posterior distribution - this means that the predictions will be stochastic.
ig_interpretability bool (default: False): If True, run the integrated circuits interpretability per sample and returns a score matrix, in which for each sample we score each gene for its contribution to the sample prediction
ig_args dict | None (default: None): Keyword args for IntegratedGradients
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 type:

(ndarray | DataFrame, None | ndarray)

static SCANVI.prepare_query_anndata(adata, reference_model, return_reference_var_names=False, inplace=True)[source]#

Prepare data for query integration.

This function will return a new AnnData object with padded zeros for missing features, as well as correctly sorted features.

Parameters:

adata AnnData: 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 registry.
reference_model str | BaseModelClass: Either an already instantiated model of the same class or a path to saved outputs for the reference model.
return_reference_var_names bool (default: False): Only load and return reference var names if True.
inplace bool (default: True): Whether to subset and rearrange query vars inplace or return new AnnData.

Return type:

anndata.AnnData | Index | None

Returns:

Query adata ready to use in load_query_data unless return_reference_var_names in which case a pd.Index of reference var names is returned.

static SCANVI.prepare_query_mudata(mdata, reference_model, return_reference_var_names=False, inplace=True)[source]#

Prepare multimodal dataset for query integration.

This function will return a new MuData object such that the AnnData objects for individual modalities are given padded zeros for missing features, as well as correctly sorted features.

Parameters:

mdata MuData: MuData organized in the same way as data used to train the model. It is not necessary to run setup_mudata, as MuData is validated against the registry.
reference_model str | BaseModelClass: Either an already instantiated model of the same class or a path to saved outputs for the reference model.
return_reference_var_names bool (default: False): Only load and return reference var names if True.
inplace bool (default: True): Whether to subset and rearrange query vars inplace or return new MuData.

Return type:

MuData | dict[str, Index] | None

Returns:

Query mudata ready to use in load_query_data unless return_reference_var_names in which case a dictionary of pd.Index of reference var names is returned.

classmethod SCANVI.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.

SCANVI.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 SCANVI.setup_annbatch(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 SCANVI.setup_anndata(adata, labels_key, unlabeled_category, layer=None, batch_key=None, size_factor_key=None, categorical_covariate_keys=None, continuous_covariate_keys=None, use_minified=True, **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.
labels_key str: 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.
unlabeled_category str: value in adata.obs[labels_key] that indicates unlabeled observations.
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.
size_factor_key str | None (default: None): key in adata.obs for size factor information. Instead of using library size as a size factor, the provided size factor column will be used as offset in the mean of the likelihood. Assumed to be on linear scale.
categorical_covariate_keys list[str] | None (default: None): keys in adata.obs that correspond to categorical data. These covariates can be added in addition to the batch covariate and are also treated as nuisance factors (i.e., the model tries to minimize their effects on the latent space). Thus, these should not be used for biologically-relevant factors that you do _not_ want to correct for.
continuous_covariate_keys list[str] | None (default: None): keys in adata.obs that correspond to continuous data. These covariates can be added in addition to the batch covariate and are also treated as nuisance factors (i.e., the model tries to minimize their effects on the latent space). Thus, these should not be used for biologically-relevant factors that you do _not_ want to correct for.
use_minified bool (default: True): If True, will register the minified version of the adata if possible.

SCANVI.shap_adata_predict(X)[source]#

SHAP Operator (gives soft predictions gives data X)

Return type:: (ndarray | DataFrame, None | ndarray)

SCANVI.shap_predict(adata=None, indices=None, shap_args=None)[source]#

Run SHAP interpreter for a trained model and gives back shap values

Return type:: (ndarray | DataFrame, None | ndarray)

SCANVI.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

SCANVI.train(max_epochs=None, n_samples_per_label=None, check_val_every_n_epoch=None, train_size=0.9, validation_size=None, shuffle_set_split=True, batch_size=128, accelerator='auto', devices='auto', adversarial_classifier=None, datasplitter_kwargs=None, plan_config=None, plan_kwargs=None, datamodule=None, trainer_config=None, **trainer_kwargs)[source]#

Train the model.

Parameters:

max_epochs int | None (default: None): Number of passes through the dataset for semisupervised training.
n_samples_per_label float | None (default: None): Number of subsamples for each label class to sample per epoch. By default, there is no label subsampling.
check_val_every_n_epoch int | None (default: None): Frequency with which metrics are computed on the data for the validation set for both the unsupervised and semisupervised trainers. If you’d like a different frequency for the semisupervised trainer, set check_val_every_n_epoch in semisupervised_train_kwargs.
train_size float (default: 0.9): Size of the training set in the range [0.0, 1.0].
validation_size float | None (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.
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.
adversarial_classifier bool | None (default: None): Whether to use adversarial classifier in the latent space. This helps mixing when there are missing proteins in any of the batches. Defaults to True is missing proteins are detected.
datasplitter_kwargs dict | None (default: None): Additional keyword arguments passed into SemiSupervisedDataSplitter.
plan_kwargs Mapping[str, Any] | KwargsConfig | None (default: None): Keyword args for SemiSupervisedTrainingPlan. Keyword arguments passed to train() will overwrite values present in plan_kwargs, when appropriate.
plan_config Mapping[str, Any] | KwargsConfig | None (default: None): Configuration object or mapping used to build SemiSupervisedTrainingPlan. Values in plan_kwargs and explicit arguments take precedence.
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.
trainer_config Mapping[str, Any] | KwargsConfig | None (default: None): Configuration object or mapping used to build Trainer. Values in trainer_kwargs and explicit arguments take precedence.
**trainer_kwargs: Other keyword args for Trainer.

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

Transfer fields from a model to an AnnData object.

Return type:: AnnData

SCANVI.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.

SCANVI.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

SCANVI.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 SCANVI.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

SCANVI.view_setup_method_args()[source]#

Prints setup kwargs used to produce a given registry.

Parameters:

registry: Registry produced by an AnnDataManager.

Return type:

None

Core

Ecosystem

scvi.model.SCANVI#

Attributes table#

Methods table#

Attributes#

Methods#