scvi.external.SCAR

scvi.external.SCAR#

class scvi.external.SCAR(adata, ambient_profile=None, n_hidden=150, n_latent=15, n_layers=2, dropout_rate=0.0, gene_likelihood='b', latent_distribution='normal', scale_activation='softplus_sp', sparsity=0.9, **model_kwargs)[source]#

Ambient RNA removal in scRNA-seq data [Sheng et al., 2022].

Original implementation: Novartis/scar. The models are parameter matched in architecture, activations, dropout, sparsity, and batch normalization.

Parameters:

adata (AnnData) – AnnData object that has been registered via setup_anndata().
ambient_profile (str | np.ndarray | pd.DataFrame | torch.tensor | None (default: None)) – The probability of occurrence of each ambient transcript. If None, averaging cells to estimate the ambient profile, by default None.
n_hidden (int (default: 150)) – Number of nodes per hidden layer.
n_latent (int (default: 15)) – Dimensionality of the latent space.
n_layers (int (default: 2)) – Number of hidden layers used for encoder and decoder NNs.
dropout_rate (float (default: 0.0)) – Dropout rate for neural networks.
gene_likelihood (Literal[‘zinb’, ‘nb’, ‘b’, ‘poisson’] (default: 'b')) – One of: * 'b' - Binomial distribution * 'nb' - Negative binomial distribution * 'zinb' - Zero-inflated negative binomial distribution * 'poisson' - Poisson distribution
latent_distribution (Literal[‘normal’, ‘ln’] (default: 'normal')) – One of: * 'normal' - Normal distribution * 'ln' - Logistic normal distribution (Normal(0, I) transformed by softmax)
scale_activation (Literal[‘softmax’, ‘softplus’, ‘softplus_sp’] (default: 'softplus_sp')) – Activation layer to use for px_scale_decoder
sparsity (float (default: 0.9)) – The sparsity of expected native signals. It varies between datasets, e.g., if one prefilters genes – using only highly variable genes – the sparsity should be low; on the other hand, it should be set high in the case of unflitered genes.
**model_kwargs – Keyword args for SCAR

Examples

>>> adata = anndata.read_h5ad(path_to_anndata)
>>> raw_adata = anndata.read_h5ad(path_to_raw_anndata)
>>> scvi.external.SCAR.setup_anndata(adata, batch_key="batch")
>>> scvi.external.SCAR.get_ambient_profile(adata=adata, raw_adata=raw_adata, prob=0.995)
>>> vae = scvi.external.SCAR(adata)
>>> vae.train()
>>> adata.obsm["X_scAR"] = vae.get_latent_representation()
>>> adata.layers['denoised'] = vae.get_denoised_counts()

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, sample_key, ...])	Compute the differential abundance between samples.
`differential_expression`([adata, groupby, ...])	A unified method for differential expression analysis.
`get_aggregated_posterior`([adata, indices, ...])	Compute the aggregated posterior over the `u` latent representations.
`get_ambient_profile`(adata, raw_adata[, ...])	Calculate ambient profile for relevant features.
`get_anndata_manager`(adata[, required])	Retrieves the `AnnDataManager` for a given AnnData object.
`get_denoised_counts`([adata, n_samples, ...])	Generate observation samples from the posterior predictive distribution.
`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_reconstruction_error`([adata, indices, ...])	Compute the reconstruction error on the data.
`get_setup_arg`(setup_arg)	Returns the string provided to setup of a specific setup_arg.
`get_state_registry`(registry_key)	Returns the state registry for the AnnDataField registered with this instance.
`get_var_names`([legacy_mudata_format])	Variable names of input data.
`load`(dir_path[, adata, accelerator, device, ...])	Instantiate a model from the saved output.
`load_registry`(dir_path[, prefix])	Return the full registry saved with the model.
`posterior_predictive_sample`([adata, ...])	Generate predictive samples from the posterior predictive distribution.
`register_manager`(adata_manager)	Registers an `AnnDataManager` instance with this model class.
`save`(dir_path[, prefix, overwrite, ...])	Save the state of the model.
`setup_anndata`(adata[, batch_key, ...])	Sets up the `AnnData` object for this model.
`to_device`(device)	Move the model to the device.
`train`([max_epochs, accelerator, devices, ...])	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#

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

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

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

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

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

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

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

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

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

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

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

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

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

Methods#

classmethod SCAR.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

SCAR.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.

SCAR.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.

SCAR.differential_abundance(adata=None, sample_key=None, batch_size=128, num_cells_posterior=None, dof=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 data object to compute the differential abundance for. For very large datasets, this should be a subset of the original data object.
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.

SCAR.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, **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)) – 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 (Optional[Literal['uniform', 'importance']] (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().
**kwargs – Keyword args for scvi.model.base.DifferentialComputation.get_bayes_factors()

Return type:

DataFrame

Returns:

Differential expression DataFrame.

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

Compute the aggregated posterior over the u latent representations.

Parameters:

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

Returns:

A mixture distribution of the aggregated posterior.

static SCAR.get_ambient_profile(adata, raw_adata, prob=0.995, min_raw_counts=2, iterations=3, n_batch=1, sample=50000)[source]#

Calculate ambient profile for relevant features.

Identify the cell-free droplets through a multinomial distribution. See EmptyDrops [Lun et al., 2019] for details.

Parameters:

adata (AnnData) – A filtered adata object, loaded from filtered_feature_bc_matrix using scanpy.read, gene filtering is recommended to save memory.
raw_adata (AnnData) – A raw adata object, loaded from raw_feature_bc_matrix using read().
prob (float (default: 0.995)) – The probability of each gene, considered as containing ambient RNA if greater than prob (joint prob equals to the product of all genes for a droplet), by default 0.995.
min_raw_counts (int (default: 2)) – Total counts filter for raw_adata, filtering out low counts to save memory, by default 2.
iterations (int (default: 3)) – Total iterations, by default 3.
n_batch (int (default: 1)) – Total number of batches, set it to a bigger number when the out-of-memory issue occurs, by default 1.
sample (int (default: 50000)) – Randomly sample droplets to test, if greater than total droplets, use all droplets, by default 50000.

Returns:

The relevant ambient profile is added in adata.varm

SCAR.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

SCAR.get_denoised_counts(adata=None, n_samples=1, 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.
n_samples (int (default: 1)) – Number of samples for each cell.
batch_size (int | None (default: None)) – Minibatch size for data loading into the model. Defaults to scvi.settings.batch_size.

Return type:

ndarray

Returns:

tensor with shape (n_cells, n_genes)

SCAR.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.

SCAR.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

SCAR.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.

SCAR.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.

SCAR.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

SCAR.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.

SCAR.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]

SCAR.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.

SCAR.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 | 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 (Union[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 (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.
%(de_silent)s
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 (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).

SCAR.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.

SCAR.get_setup_arg(setup_arg)[source]#

Returns the string provided to setup of a specific setup_arg.

Return type:: attrdict

SCAR.get_state_registry(registry_key)[source]#

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

Return type:: attrdict

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

Variable names of input data.

Return type:: dict

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

Instantiate a model from the saved output.

Parameters:

dir_path (str) – Path to saved outputs.
adata (AnnData | MuData | None (default: None)) – AnnData organized in the same way as data used to train model. It is not necessary to run setup_anndata, as AnnData is validated against the saved scvi setup dictionary. If None, will check for and load anndata saved with the model. If False, will load the model without AnnData.
accelerator (str (default: 'auto')) – Supports passing different accelerator types (“cpu”, “gpu”, “tpu”, “ipu”, “hpu”, “mps, “auto”) as well as custom accelerator instances.
device (int | str (default: 'auto')) – The device to use. Can be set to a non-negative index (int or str) or “auto” for automatic selection based on the chosen accelerator. If set to “auto” and accelerator is not determined to be “cpu”, then device will be set to the first available device.
prefix (str | None (default: None)) – Prefix of saved file names.
backup_url (str | None (default: None)) – URL to retrieve saved outputs from if not present on disk.
datamodule (LightningDataModule | None (default: None)) – EXPERIMENTAL A LightningDataModule instance to use for training in place of the default DataSplitter. Can only be passed in if the model was not initialized with AnnData.
allowed_classes_names_list (list[str] | None (default: None)) – list of allowed classes names to be loaded (besides the original class name)

Returns:

Model with loaded state dictionaries.

Examples

>>> model = ModelClass.load(save_path, adata)
>>> model.get_....

static SCAR.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

SCAR.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 | 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, 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).

classmethod SCAR.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.

SCAR.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 SCAR.setup_anndata(adata, batch_key=None, labels_key=None, layer=None, size_factor_key=None, **kwargs)[source]#

Sets up the AnnData object for this model.

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

Parameters:

adata (AnnData) – AnnData object. Rows represent cells, columns represent features.
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.
layer (str | None (default: None)) – if not None, uses this as the key in adata.layers for raw count 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.

SCAR.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

SCAR.train(max_epochs=None, accelerator='auto', devices='auto', train_size=None, validation_size=None, shuffle_set_split=True, load_sparse_tensor=False, batch_size=128, early_stopping=False, 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)) – The maximum number of epochs to train the model. The actual number of epochs may be less if early stopping is enabled. If None, defaults to a heuristic based on get_max_epochs_heuristic(). Must be passed in if datamodule is passed in, and it does not have an n_obs attribute.
accelerator (str (default: 'auto')) – Supports passing different accelerator types (“cpu”, “gpu”, “tpu”, “ipu”, “hpu”, “mps, “auto”) as well as custom accelerator instances.
devices (int | list[int] | str (default: 'auto')) – The devices to use. Can be set to a non-negative index (int or str), a sequence of device indices (list or comma-separated str), the value -1 to indicate all available devices, or “auto” for automatic selection based on the chosen accelerator. If set to “auto” and accelerator is not determined to be “cpu”, then devices will be set to the first available device.
train_size (float | None (default: None)) – Float, or None. Size of training set in the range [0.0, 1.0]. The default is None, which is practically 0.9 and potentially adding a small last batch to validation cells. Passed into DataSplitter. Not used if datamodule is passed in.
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. Passed into DataSplitter. Not used if datamodule is passed in.
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. Passed into DataSplitter. Not used if datamodule is passed in.
load_sparse_tensor (bool (default: False)) – EXPERIMENTAL If True, loads data with sparse CSR or CSC layout as a Tensor with the same layout. Can lead to speedups in data transfers to GPUs, depending on the sparsity of the data. Passed into DataSplitter. Not used if datamodule is passed in.
batch_size (int (default: 128)) – Minibatch size to use during training. Passed into DataSplitter. Not used if datamodule is passed in.
early_stopping (bool (default: False)) – Perform early stopping. Additional arguments can be passed in through **kwargs. See Trainer for further options.
datasplitter_kwargs (dict | None (default: None)) – Additional keyword arguments passed into DataSplitter. Values in this argument can be overwritten by arguments directly passed into this method, when appropriate. Not used if datamodule is passed in.
plan_config (Mapping[str, Any] | KwargsConfig | None (default: None)) – Configuration object or mapping used to build TrainingPlan. Values in plan_kwargs and explicit arguments take precedence.
plan_kwargs (Mapping[str, Any] | KwargsConfig | None (default: None)) – Additional keyword arguments passed into TrainingPlan. Values in this argument can be overwritten by arguments directly passed into this method, when appropriate.
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.
**kwargs – Additional keyword arguments passed into Trainer.

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

Transfer fields from a model to an AnnData object.

Return type:: AnnData

SCAR.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.

SCAR.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

SCAR.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 SCAR.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

SCAR.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.SCAR

Contents

scvi.external.SCAR#

Attributes table#

Methods table#

Attributes#

Methods#