scvi.external.CYTOVI

scvi.external.CYTOVI#

class scvi.external.CYTOVI(adata, n_hidden=128, n_latent=None, n_layers=1, dropout_rate=0.1, protein_likelihood='normal', latent_distribution='normal', encode_backbone_only=None, encoder_marker_list=None, prior_mixture=True, prior_mixture_k=None, **model_kwargs)[source]#

Variational inference for cytometry (CytoVI).

Parameters:

adata (AnnData) – AnnData object that has been registered via setup_anndata().
n_hidden (int (default: 128)) – Number of nodes per hidden layer.
n_latent (int | None (default: None)) – Dimensionality of the latent space. If None, will be set using a heuristic based on number of input features.
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.
protein_likelihood (Literal['normal', 'beta'] (default: 'normal')) –
Likelihood function used for modeling protein expression. One of:
- 'normal' - Normal distribution
- 'beta' - Beta distribution (requires expression in (0, 1))
latent_distribution (Literal['normal', 'ln'] (default: 'normal')) –
Distribution of the latent space. One of:
- 'normal' - Normal distribution
- 'ln' - Logistic normal distribution (Normal(0, I) transformed by softmax)
encode_backbone_only (bool | None (default: None)) – If True, only encode backbone markers (i.e., those present in all samples). This is required when analyzing overlapping panels with missing values.
encoder_marker_list (list | None (default: None)) – Optional list of markers to use for encoding. Must be a subset of backbone markers if encode_backbone_only is True.
prior_mixture (bool | None (default: True)) – If True, uses a mixture of Gaussians as a prior in the latent space (MoG prior).
prior_mixture_k (int | None (default: None)) – Number of mixture components in the MoG prior. Defaults to n_latent if None.
**model_kwargs – Keyword arguments passed to CytoVAE.

Examples

>>> adata = anndata.read_h5ad(path_to_anndata)
>>> scvi.external.CYTOVI.setup_anndata(adata, batch_key="batch")
>>> model = scvi.external.CYTOVI(adata)
>>> model.train()
>>> adata.obsm["X_CytoVI"] = model.get_latent_representation()
>>> adata.layers["imputed"] = model.get_normalized_expression()

Notes

When analyzing overlapping cytometry panels (i.e., samples with partially shared markers), CytoVI uses the shared “backbone” markers for encoding and reconstructs the full set. An adversarial classifier loss can be used to encourage batch-invariance in the latent space. If the data includes missing values, ensure that nan_layer is correctly registered using setup_anndata(). This is handled automatically when using scvi.external.cytovi.merge_batches().

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.
`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, groupby, ...])	Compute differential abundance (DA) scores across experimental conditions.
`differential_expression`([adata, groupby, ...])	A unified method for differential expression analysis.
`get_aggregated_posterior`([adata, sample, ...])	Compute the aggregated posterior over the `z` 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) protein expression.
`get_reconstruction_error`([adata, indices, ...])	Compute the reconstruction error on the data.
`get_sample_logprobs`([adata, batch_size, ...])	Compute the differential abundance log probabilities for each sample.
`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.
`impute_categories_from_reference`(...[, ...])	Impute missing categories from a reference dataset using a shared representation.
`impute_rna_from_reference`(reference_batch, ...)	Impute RNA expression in a query dataset using a reference and a shared representation.
`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.
`posterior_predictive_sample`([adata, ...])	Generate observation samples from the posterior predictive distribution.
`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_anndata`(adata[, layer, batch_key, ...])	Sets up the `AnnData` object for this model.
`to_device`(device)	Move the model to the device.
`train`([max_epochs, lr, accelerator, ...])	Trains the model using amortized variational inference.
`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#

CYTOVI.adata[source]#: Data attached to model instance.

CYTOVI.adata_manager[source]#: Manager instance associated with self.adata.

CYTOVI.device[source]#: The current device that the module’s params are on.

CYTOVI.get_normalized_function_name[source]#: What the get normalized functions name is

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

CYTOVI.is_trained[source]#: Whether the model has been trained.

CYTOVI.registry[source]#: Data attached to model instance.

CYTOVI.run_id[source]#: Returns the run id of the model. Used in MLFlow

CYTOVI.run_name[source]#: Returns the run name of the model. Used in MLFlow

CYTOVI.summary_string[source]#: Summary string of the model.

CYTOVI.test_indices[source]#: Observations that are in test set.

CYTOVI.train_indices[source]#: Observations that are in train set.

CYTOVI.validation_indices[source]#: Observations that are in validation set.

Methods#

classmethod CYTOVI.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

CYTOVI.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.

CYTOVI.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.

CYTOVI.differential_abundance(adata=None, groupby=None, batch_size=128, downsample_cells=None, dof=None, aggregation_fn=<function log_median>, return_log_probs=False)[source]#

Compute differential abundance (DA) scores across experimental conditions.

This function estimates differential abundance by comparing aggregated log-sample-probabilities between groups defined by groupby. The aggregation is performed using the specified aggregation_fn, typically log_median or logsumexp, across posterior sample log-probabilities.

Parameters:

adata (AnnData | None (default: None)) – AnnData object to use. If None, defaults to the model’s internal AnnData.
groupby (str | None (default: None)) – Key in adata.obs that contains condition or group labels. If not provided, returns log-probabilities per sample without aggregation.
batch_size (int (default: 128)) – Mini-batch size for computing log-probabilities. Default: 128.
downsample_cells (int | None (default: None)) – If provided, randomly subsample this many cells before computing log-probabilities.
dof (float | None (default: None)) – Degrees of freedom to use in the sampling distribution, if applicable.
aggregation_fn (Callable[[ndarray], float] (default: <function log_median at 0x7dc8d18a31a0>)) – Function used to aggregate log-probabilities across samples. Common choices include log_median or logsumexp. Default: log_median.
return_log_probs (bool (default: False)) – If True, skip aggregation and return per-sample log-probabilities directly. Default: False.

Return type:

DataFrame

Returns:

If return_log_probs=True or groupby=None, returns a DataFrame of shape (n_cells, n_samples) containing log-sample-probabilities.

Otherwise, returns a DataFrame of shape (n_cells, n_conditions) where each column corresponds to the differential abundance log-ratio for one condition compared to all others.

Examples

>>> da_scores = model.differential_abundance(adata, groupby="condition")
>>> da_scores.head()

>>> log_probs = model.differential_abundance(adata, return_log_probs=True)

CYTOVI.differential_expression(adata=None, groupby=None, group1=None, group2=None, idx1=None, idx2=None, mode='change', test_mode='two', delta=0.25, batch_size=None, all_stats=False, batch_correction=False, batchid1=None, batchid2=None, fdr_target=0.05, silent=False, weights='uniform', filter_outlier_cells=False, lfc_clipping=True, clipping_range=(0, 1), balance_samples=None, importance_weighting_kwargs=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 | None (default: None)) – Annotated data matrix, by default None
groupby (str | None (default: None)) – Key in adata.obs containing the groups, by default None
group1 (list[str] | None (default: None)) – List of group names for group 1, by default None
group2 (list[str] | None (default: None)) – List of group names for group 2, by default None
idx1 (list[int] | list[bool] | str | None (default: None)) – Indices or boolean mask for group 1, by default None
idx2 (list[int] | list[bool] | str | None (default: None)) – Indices or boolean mask for group 2, by default None
mode (Literal['vanilla', 'change'] (default: 'change')) – Differential expression mode, by default “change”
test_mode (Literal['two', 'three'] (default: 'two')) – Test mode for differential expression, by default “two”
delta (float (default: 0.25)) – Minimum fold change for differential expression, by default 0.25
batch_size (int | None (default: None)) – Batch size for computation, by default None
all_stats (bool (default: False)) – Whether to compute all statistics, by default False
batch_correction (bool (default: False)) – Whether to perform batch correction, by default False
batchid1 (list[str] | None (default: None)) – List of batch IDs for group 1, by default None
batchid2 (list[str] | None (default: None)) – List of batch IDs for group 2, by default None
fdr_target (float (default: 0.05)) – Target false discovery rate, by default 0.05
silent (bool (default: False)) – Whether to suppress progress bar and messages, by default False
weights (Optional[Literal['uniform', 'importance']] (default: 'uniform')) – Weights to use for sampling, by default “uniform”
filter_outlier_cells (bool (default: False)) – Whether to filter outlier cells, by default False
lfc_clipping (bool (default: True)) – Whether to clip log fold change values, by default True
clipping_range (tuple (default: (0, 1))) – Range for clipping log fold change values, by default (0, 1)
balance_samples (bool | None (default: None)) – Whether to subsample equal amount of cells per sample based on sample_key. If None, defaults to True if more than one sample is present in the data.
importance_weighting_kwargs (dict | None (default: None)) – Keyword arguments for importance weighting, by default None
**kwargs – Additional keyword arguments for differential expression computation

Return type:

DataFrame

Returns:

Differential expression DataFrame.

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

Compute the aggregated posterior over the z latent representations.

Parameters:

adata (AnnData (default: None)) – AnnData object to use. Defaults to the AnnData object used to initialize the model.
sample (int | str (default: None)) – Name or index of the sample to filter on. If None, uses all cells.
indices (Sequence[int] (default: None)) – Indices of cells to use.
batch_size (int (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.

CYTOVI.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

CYTOVI.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 | 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, 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.

CYTOVI.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 | 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”.
%(de_silent)s

Return type:

DataFrame

Returns:

Gene-gene correlation matrix

CYTOVI.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.

CYTOVI.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 | 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.

CYTOVI.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 | 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, 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

CYTOVI.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 | 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, 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[Any, ...], dtype[TypeVar(_ScalarT, bound= generic)]] | tuple[ndarray[tuple[Any, ...], dtype[TypeVar(_ScalarT, bound= generic)]], ndarray[tuple[Any, ...], dtype[TypeVar(_ScalarT, bound= generic)]]]

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.

CYTOVI.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 | 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, 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]

CYTOVI.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.

CYTOVI.get_normalized_expression(adata=None, indices=None, transform_batch='all', protein_list=None, n_samples=1, n_samples_overall=None, weights=None, batch_size=None, return_mean=True, return_numpy=None, nan_warning=True, **importance_weighting_kwargs)[source]#

Returns the normalized (decoded) protein expression.

The model’s reconstructed (normalized) expression is written as :math:hat{x} = p(x mid z).

Parameters:

adata (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 (Sequence[int] | None (default: None)) – Indices of cells in adata to use. If None, all cells are used.
transform_batch (Sequence[int | float | str] | None (default: 'all')) – Batch to condition on. If transform_batch is: - ‘all’, then the mean across batches is used - None, then real observed batch is used. - int, then batch transform_batch is used. This behaviour affects only proteins that are detected across multiple batches. Unobserved proteins are decoded in the batch(es), in which they were measured.
protein_list (Sequence[str] | None (default: None)) – Return frequencies of expression for a subset of protein. This can save memory when working with large datasets and few proteins are of interest.
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 (Optional[Literal['uniform', 'importance']] (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.
nan_warning (bool | None (default: True)) – Whether to show a warning if missing proteins are detected between batches.
**importance_weighting_kwargs – Additional keyword arguments for importance weighting.

Return type:

ndarray | DataFrame

Returns:

If n_samples > 1 and return_mean is False, then the shape is (samples, cells, genes). Otherwise, shape is (cells, genes). In this case, return type is DataFrame unless return_numpy is True.

CYTOVI.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 | 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, 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.

CYTOVI.get_sample_logprobs(adata=None, batch_size=128, downsample_cells=None, dof=None)[source]#

Compute the differential abundance log probabilities for each sample.

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

Parameters:

adata (AnnData | None (default: None)) – The data object to compute the differential abundance for.
batch_size (int (default: 128)) – Minibatch size for computing the differential abundance.
downsample_cells (int | None (default: None)) – Number of cells to subset to before computing the differential abundance.
dof (float | None (default: None)) – Degrees of freedom for the Student’s t-distribution components for aggregated posterior. If None, components are Normal.

Return type:

DataFrame

Returns:

DataFrame of shape (n_cells, n_samples) containing the log probabilities for each cell across samples. The rows correspond to cell names from adata.obs_names, and the columns correspond to unique sample identifiers.

CYTOVI.get_setup_arg(setup_arg)[source]#

Returns the string provided to setup of a specific setup_arg.

Return type:: attrdict

CYTOVI.get_state_registry(registry_key)[source]#

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

Return type:: attrdict

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

Variable names of input data.

Return type:: dict

CYTOVI.impute_categories_from_reference(adata_reference, cat_key, use_rep=None, n_neighbors=20, return_uncertainty=False)[source]#

Impute missing categories from a reference dataset using a shared representation.

Parameters:

adata_reference (AnnData) – Annotated data matrix for the reference dataset. This dataset contains the categories to be imputed onto the query data.
cat_key (str) – The key in the .obs attribute of adata_reference that specifies the categorical variable (e.g., cell types or clusters) to impute.
use_rep (str | None (default: None)) – The key in the .obsm attribute to use as the representation space (e.g., latent space). If None, the function will attempt to use a default latent representation X_CytoVI.
n_neighbors (int (default: 20)) – The number of nearest neighbors to use for imputation. The imputation is based on similarity in the chosen representation space. Default: 20.
return_uncertainty (bool (default: False)) – If True, the function will also return the uncertainty of the imputation. Default: False.

Returns:

Array of imputed categories for the query dataset, corresponding to the categorical variable specified by cat_key.

Notes

This function assumes that both the query and reference datasets have a precomputed representation in .obsm (typically CytoVI latent space). If not, you must either provide a common representation manually or ensure that one is generated.

CYTOVI.impute_rna_from_reference(reference_batch, adata_rna, layer_key, use_rep=None, n_neighbors=20, compute_uncertainty=False, return_query_only=False)[source]#

Impute RNA expression in a query dataset using a reference and a shared representation.

Parameters:

reference_batch (str) – Identifier for the reference batch in adata.obs[batch_key].
adata_rna (AnnData) – Annotated data matrix containing the expression data to impute.
layer_key (str) – Key in adata_rna.layers for the reference expression data.
use_rep (str | None (default: None)) – Key in .obsm to use as the representation space (e.g., latent space). If None, defaults to X_CytoVI.
n_neighbors (int (default: 20)) – Number of nearest neighbors to use for imputation. Default: 20.
compute_uncertainty (bool (default: False)) – If True, also computes the uncertainty of the imputation. Default: False.
return_query_only (bool (default: False)) – If True, return only the imputed query dataset as an AnnData object. Default: False.

Returns:

Imputed AnnData object. If return_query_only is True, only the query dataset is returned. If compute_uncertainty is True, an uncertainty matrix is also returned.

classmethod CYTOVI.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 CYTOVI.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 CYTOVI.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

CYTOVI.posterior_predictive_sample(adata=None, indices=None, n_samples=1, protein_list=None, batch_size=None)[source]#

Generate observation samples from the posterior predictive distribution.

The posterior predictive distribution is written as \(p(\hat{x} \mid x)\).

Parameters:

adata (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 (Sequence[int] | None (default: None)) – Indices of cells in adata to use. If None, all cells are used.
n_samples (int (default: 1)) – Number of samples for each cell.
protein_list (Sequence[str] | None (default: None)) – Names of proteins of interest.
batch_size (int | None (default: None)) – Minibatch size for data loading into model. Defaults to scvi.settings.batch_size.

Returns:

x_new : torch.Tensor Tensor with shape (n_cells, n_proteins, n_samples)

static CYTOVI.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 | 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 CYTOVI.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 CYTOVI.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.

CYTOVI.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 CYTOVI.setup_anndata(adata, layer=None, batch_key=None, labels_key=None, sample_key=None, categorical_covariate_keys=None, continuous_covariate_keys=None, nan_layer=None, **kwargs)[source]#

Sets up the AnnData object for this model.

A mapping will be created between data fields used by this model to their respective locations in adata. None of the data in adata are modified. Only adds fields to adata.

Parameters:

adata (AnnData) – AnnData object. Rows represent cells, columns represent features.
layer (str | None (default: None)) – if not None, uses this as the key in adata.layers for the transformed protein expression 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.
labels_key (str | None (default: None)) – key in adata.obs for label information. Categories will automatically be converted into integer categories and saved to adata.obs[‘_scvi_labels’]. If None, assigns the same label to all the data.
sample_key (str | None (default: None)) – key in adata.obs for sample information. Categories will automatically be converted into integer categories and saved to adata.obs[‘_scvi_sample’]. If None, assigns the same sample to all the data.
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.
nan_layer (str | None (default: None)) – Optional layer key containing binary NaN feature mask to handle overlapping antibody panels.

CYTOVI.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

CYTOVI.train(max_epochs=1000, lr=0.001, accelerator='auto', devices='auto', train_size=0.9, validation_size=None, batch_size=128, early_stopping=True, check_val_every_n_epoch=None, n_steps_kl_warmup=None, n_epochs_kl_warmup=400, adversarial_classifier=None, plan_kwargs=None, early_stopping_patience=30, **kwargs)[source]#

Trains the model using amortized variational inference.

Parameters:

max_epochs (int | None (default: 1000)) – Number of passes through the dataset, by default 1000.
lr (float (default: 0.001)) – Learning rate for optimization, by default 1e-3.
accelerator (str (default: 'auto')) – Accelerator to use for training, by default “auto”.
devices (int | list[int] | str (default: 'auto')) – Devices to use for training, by default “auto”.
train_size (float (default: 0.9)) – Size of the training set in the range [0.0, 1.0], by default 0.9.
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.
batch_size (int (default: 128)) – Minibatch size to use during training, by default 128.
early_stopping (bool (default: True)) – Whether to perform early stopping with respect to the validation set, by default True.
check_val_every_n_epoch (int | None (default: None)) – Check validation set every n train epochs. By default, the validation set is not checked, unless early_stopping is True or reduce_lr_on_plateau is True. If either of the latter conditions are met, the validation set is checked every epoch.
n_steps_kl_warmup (int | None (default: None)) – Number of training steps (minibatches) to scale weight on KL divergences from 0 to 1. Only activated when n_epochs_kl_warmup is set to None. If None, defaults to floor(0.75 * adata.n_obs).
n_epochs_kl_warmup (int | None (default: 400)) – Number of epochs to scale weight on KL divergences from 0 to 1. Overrides n_steps_kl_warmup when both are not None, by default 400.
adversarial_classifier (bool | None (default: None)) – Whether to use an adversarial classifier in the latent space. This helps mixing when there are missing proteins in any of the batches. Defaults to True if missing proteins are detected.
plan_kwargs (dict | None (default: None)) – Keyword arguments for the AdversarialTrainingPlan class. Keyword arguments passed to train() will overwrite values present in plan_kwargs, when appropriate.
early_stopping_patience (int | None (default: 30)) – Number of epochs to wait before early stopping, by default 30.
**kwargs – Other keyword arguments for the Trainer class.

Returns:

-runner (object) The runner object used for training.

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

Transfer fields from a model to an AnnData object.

Return type:: AnnData

CYTOVI.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.

CYTOVI.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

CYTOVI.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 CYTOVI.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

CYTOVI.view_setup_method_args()[source]#

Prints setup kwargs used to produce a given registry.

Parameters:: registry – Registry produced by an AnnDataManager.
Return type:: None

scvi.external.CYTOVI

Contents

scvi.external.CYTOVI#

Attributes table#

Methods table#

Attributes#

Methods#