scvi.external.POISSONVI#

class scvi.external.POISSONVI(adata, n_hidden=None, n_latent=None, n_layers=2, dropout_rate=0.1, latent_distribution='normal', **model_kwargs)[source]#

Peak Variational Inference using a Poisson distribution [Martens et al., 2023].

Parameters:
  • adata (AnnData) – AnnData object that has been registered via setup_anndata().

  • n_hidden (int | None (default: None)) – Number of nodes per hidden layer. If None, defaults to square root of number of regions.

  • n_latent (int | None (default: None)) – Dimensionality of the latent space. If None, defaults to square root of n_hidden.

  • n_layers (int (default: 2)) – Number of hidden layers used for encoder and decoder NNs.

  • dropout_rate (float (default: 0.1)) – Dropout rate for neural networks

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

    One of

    • 'normal' - Normal distribution (Default)

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

  • **model_kwargs – Keyword args for VAE

Examples

>>> adata = anndata.read_h5ad(path_to_anndata)
>>> scvi.external.POISSINVI.setup_anndata(adata, batch_key="batch")
>>> vae = scvi.external.POISSONVI(adata)
>>> vae.train()

Notes

See further usage examples in the following tutorials:

  1. PoissonVI: Analyzing quantitative scATAC-seq fragment 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.

history

Returns computed metrics during training.

is_trained

Whether the model has been trained.

summary_string

Summary string of the model.

test_indices

Observations that are in test set.

train_indices

Observations that are in train set.

validation_indices

Observations that are in validation set.

Methods table#

convert_legacy_save(dir_path, output_dir_path)

Converts a legacy saved model (<v0.15.0) to the updated save format.

deregister_manager([adata])

Deregisters the AnnDataManager instance associated with adata.

differential_accessibility([adata, groupby, ...])

.

differential_expression()

A unified method for differential expression analysis.

get_accessibility_estimates([adata, ...])

Returns the normalized accessibility matrix.

get_anndata_manager(adata[, required])

Retrieves the AnnDataManager for a given AnnData object.

get_elbo([adata, indices, batch_size])

Return the ELBO for 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_latent_library_size([adata, indices, ...])

Returns the latent library size for each cell.

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

Return the latent representation for each cell.

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

Return library size factors.

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

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

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

Return the marginal LL for the data.

get_normalized_expression()

Returns the normalized (decoded) gene expression.

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

Return the reconstruction error for the data.

get_region_factors()

Return region-specific factors.

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 predictive samples from the posterior predictive distribution.

prepare_query_anndata(adata, reference_model)

Prepare data 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 model to device.

train([max_epochs, lr, accelerator, ...])

Trains the model using amortized variational inference.

view_anndata_setup([adata, ...])

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

view_setup_args(dir_path[, prefix])

Print args used to setup a saved model.

Attributes#

POISSONVI.adata[source]#

Data attached to model instance.

POISSONVI.adata_manager[source]#

Manager instance associated with self.adata.

POISSONVI.device[source]#

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

POISSONVI.history[source]#

Returns computed metrics during training.

POISSONVI.is_trained[source]#

Whether the model has been trained.

POISSONVI.summary_string[source]#

Summary string of the model.

POISSONVI.test_indices[source]#

Observations that are in test set.

POISSONVI.train_indices[source]#

Observations that are in train set.

POISSONVI.validation_indices[source]#

Observations that are in validation set.

Methods#

classmethod POISSONVI.convert_legacy_save(dir_path, output_dir_path, overwrite=False, prefix=None, **save_kwargs)[source]#

Converts a legacy saved model (<v0.15.0) to the updated save format.

Parameters:
  • dir_path (str) – Path to directory where legacy model is saved.

  • output_dir_path (str) – Path to save converted save files.

  • overwrite (bool (default: False)) – Overwrite existing data or not. If False and directory already exists at output_dir_path, error will be raised.

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

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

Return type:

None

POISSONVI.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.

POISSONVI.differential_accessibility(adata=None, groupby=None, group1=None, group2=None, idx1=None, idx2=None, mode='change', delta=0.05, batch_size=None, all_stats=True, batch_correction=False, batchid1=None, batchid2=None, fdr_target=0.05, silent=False, two_sided=True, weights='uniform', filter_outlier_cells=False, importance_weighting_kwargs=None, **kwargs)[source]#

.

A unified method for differential accessibility 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 (Iterable[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 (Sequence[int] | Sequence[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 (Sequence[int] | Sequence[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: 'change')) – Method for differential expression. See user guide for full explanation.

  • delta (float (default: 0.05)) – 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 (Iterable[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 (Iterable[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.

  • two_sided (bool (default: True)) – Whether to perform a two-sided test, or a one-sided test.

  • 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 accessibility DataFrame with the following columns: prob_da

the probability of the region being differentially accessible

is_da_fdr

whether the region passes a multiple hypothesis correction procedure with the target_fdr threshold

bayes_factor

Bayes Factor indicating the level of significance of the analysis

effect_size

the effect size, computed as (accessibility in population 2) - (accessibility in population 1)

emp_effect

the empirical effect, based on observed detection rates instead of the estimated accessibility scores from the PeakVI model

est_prob1

the estimated probability of accessibility in population 1

est_prob2

the estimated probability of accessibility in population 2

emp_prob1

the empirical (observed) probability of accessibility in population 1

emp_prob2

the empirical (observed) probability of accessibility in population 2

POISSONVI.differential_expression()[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 object with equivalent structure to initial AnnData. If None, defaults to the AnnData object used to initialize the model.

  • groupby – The key of the observations grouping to consider.

  • group1 – Subset of groups, e.g. [‘g1’, ‘g2’, ‘g3’], to which comparison shall be restricted, or all groups in groupby (default).

  • group2 – 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.

  • idx1idx1 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 – Custom identifier for group2 that has the same properties as idx1. By default, includes all cells not specified in idx1.

  • mode – Method for differential expression. See user guide for full explanation.

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

  • all_stats – Concatenate count statistics (e.g., mean expression group 1) to DE results.

  • batch_correction – Whether to correct for batch effects in DE inference.

  • batchid1 – 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 – 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 – Tag features as DE based on posterior expected false discovery rate.

  • silent – If True, disables the progress bar. Default: False.

  • weights – Weights to use for sampling. If None, defaults to “uniform”.

  • filter_outlier_cells – Whether to filter outlier cells with filter_outlier_cells().

  • importance_weighting_kwargs – Keyword arguments passed into _get_importance_weights().

  • **kwargs – Keyword args for scvi.model.base.DifferentialComputation.get_bayes_factors()

Returns:

Differential expression DataFrame.

POISSONVI.get_accessibility_estimates(adata=None, indices=None, transform_batch=None, region_list=None, library_size=1, normalize_regions=False, n_samples=1, n_samples_overall=None, weights=None, batch_size=128, return_mean=True, return_numpy=False, **importance_weighting_kwargs)[source]#

Returns the normalized accessibility matrix.

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] (default: None)) – Indices of cells in adata to use. If None, all cells are used.

  • transform_batch (str | int | 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.

  • region_list (Sequence[str] | None (default: None)) – Return frequencies of accessibility for a subset of regions. This can save memory when working with large datasets and few regions are of interest.

  • library_size (Union[float, Literal['latent']] (default: 1)) – Scale the accessibility frequencies to a common library size. This allows accessibility counts to be interpreted on a common scale of relevant magnitude. If set to “latent”, use the latent library size.

  • normalize_regions (bool (default: False)) – Whether to reintroduce region factors to scale the normalized accessibility. This makes the estimates closer to the input, but removes the region-level bias correction. False by default.

  • 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 (default: 128)) – 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 (default: False)) – Return a ndarray instead of a DataFrame. DataFrame includes region names as columns. If either n_samples=1 or return_mean=True, defaults to False. Otherwise, it defaults to True.

  • importance_weighting_kwargs – Keyword arguments passed into _get_importance_weights().

Return type:

DataFrame | ndarray

Returns:

If n_samples is provided and return_mean is False, this method returns a 3d tensor of shape (n_samples, n_cells, n_regions). If n_samples is provided and return_mean is True, it returns a 2d tensor of shape (n_cells, n_regions). 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_regions).

POISSONVI.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 (Union[AnnData, MuData]) – AnnData object to find manager instance for.

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

Return type:

AnnDataManager | None

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

Return the ELBO for the data.

The ELBO is a lower bound on the log likelihood of the data used for optimization of VAEs. Note, this is not the negative ELBO, higher is better.

Parameters:
  • adata (Optional[AnnData] (default: None)) – AnnData object with equivalent structure to initial AnnData. If None, defaults to the AnnData object used to initialize the model.

  • indices (Optional[Sequence[int]] (default: None)) – Indices of cells in adata to use. If None, all cells are used.

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

Return type:

float

POISSONVI.get_feature_correlation_matrix(adata=None, indices=None, n_samples=10, batch_size=64, rna_size_factor=1000, transform_batch=None, correlation_type='spearman')[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[Union[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”.

Return type:

DataFrame

Returns:

Gene-gene correlation matrix

POISSONVI.get_from_registry(adata, registry_key)[source]#

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

AnnData object should be registered with the model prior to calling this function via the self._validate_anndata method.

Parameters:
  • registry_key (str) – key of object to get from data registry.

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

Return type:

ndarray

Returns:

The requested data as a NumPy array.

POISSONVI.get_latent_library_size(adata=None, indices=None, give_mean=True, batch_size=None)[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.

Return type:

ndarray

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

Return the latent representation for each cell.

This is typically denoted as \(z_n\).

Parameters:
  • adata (Optional[AnnData] (default: None)) – AnnData object with equivalent structure to initial AnnData. If None, defaults to the AnnData object used to initialize the model.

  • indices (Optional[Sequence[int]] (default: None)) – Indices of cells in adata to use. If None, all cells are used.

  • give_mean (bool (default: True)) – Give mean of distribution or sample from it.

  • mc_samples (int (default: 5000)) – For distributions with no closed-form mean (e.g., logistic normal), how many Monte Carlo samples to take for computing mean.

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

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

Return type:

Union[ndarray, tuple[ndarray, ndarray]]

Returns:

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

POISSONVI.get_library_size_factors(adata=None, indices=None, batch_size=128)[source]#

Return library size factors.

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] (default: None)) – Indices of cells in adata to use. If None, all cells are used.

  • batch_size (int (default: 128)) – Minibatch size for data loading into model. Defaults to scvi.settings.batch_size.

Return type:

dict[str, ndarray]

Returns:

Library size factor for expression and accessibility

POISSONVI.get_likelihood_parameters(adata=None, indices=None, n_samples=1, give_mean=False, batch_size=None)[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.

Return type:

dict[str, ndarray]

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

Return the marginal LL for the data.

The computation here is a biased estimator of the marginal log likelihood of the data. Note, this is not the negative log likelihood, higher is better.

Parameters:
  • adata (Optional[AnnData] (default: None)) – AnnData object with equivalent structure to initial AnnData. If None, defaults to the AnnData object used to initialize the model.

  • indices (Optional[Sequence[int]] (default: None)) – Indices of cells in adata to use. If None, all cells are used.

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

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

  • return_mean (Optional[bool] (default: True)) – If False, return the marginal log likelihood for each observation. Otherwise, return the mmean arginal log likelihood.

Return type:

Union[Tensor, float]

POISSONVI.get_normalized_expression()[source]#

Returns the normalized (decoded) gene expression.

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

Parameters:
  • adata – AnnData object with equivalent structure to initial AnnData. If None, defaults to the AnnData object used to initialize the model.

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

  • transform_batch

    Batch to condition on. If transform_batch is:

    • None, then real observed batch is used.

    • int, then batch transform_batch is used.

  • gene_list – 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 – 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 – Number of posterior samples to use for estimation.

  • n_samples_overall – Number of posterior samples to use for estimation. Overrides n_samples.

  • weights – Weights to use for sampling. If None, defaults to “uniform”.

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

  • return_mean – Whether to return the mean of the samples.

  • return_numpy – 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.

  • importance_weighting_kwargs – Keyword arguments passed into _get_importance_weights().

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).

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

Return the reconstruction error for the data.

This is typically written as \(p(x \mid z)\), the likelihood term given one posterior sample. Note, this is not the negative likelihood, higher is better.

Parameters:
  • adata (Optional[AnnData] (default: None)) – AnnData object with equivalent structure to initial AnnData. If None, defaults to the AnnData object used to initialize the model.

  • indices (Optional[Sequence[int]] (default: None)) – Indices of cells in adata to use. If None, all cells are used.

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

Return type:

float

POISSONVI.get_region_factors()[source]#

Return region-specific factors.

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

Instantiate a model from the saved output.

Parameters:
  • dir_path (str) – Path to saved outputs.

  • adata (Union[AnnData, MuData, None] (default: None)) – AnnData organized in the same way as data used to train model. It is not necessary to run setup_anndata, as AnnData is validated against the saved scvi setup dictionary. If None, will check for and load anndata saved with the model.

  • accelerator (str (default: 'auto')) – Supports passing different accelerator types (“cpu”, “gpu”, “tpu”, “ipu”, “hpu”, “mps, “auto”) as well as custom accelerator instances.

  • device (int | str (default: 'auto')) – The device to use. Can be set to a non-negative index (int or str) or “auto” for automatic selection based on the chosen accelerator. If set to “auto” and accelerator is not determined to be “cpu”, then device will be set to the first available device.

  • prefix (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.

Returns:

Model with loaded state dictionaries.

Examples

>>> model = ModelClass.load(save_path, adata)
>>> model.get_....
classmethod POISSONVI.load_query_data(adata, reference_model, 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)[source]#

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

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 (Union[str, BaseModelClass]) – Either an already instantiated model of the same class, or a path to saved outputs for reference model.

  • inplace_subset_query_vars (bool (default: False)) – Whether to subset and rearrange query vars inplace based on vars used to train 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 (Union[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 corersponding to expression in first layer

  • freeze_decoder_first_layer (bool (default: True)) – Freeze neurons corersponding 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.

static POISSONVI.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

POISSONVI.posterior_predictive_sample(adata=None, indices=None, n_samples=1, gene_list=None, batch_size=None)[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.

  • 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 _make_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).

static POISSONVI.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 (Union[str, BaseModelClass]) – Either an already instantiated model of the same class, or a path to saved outputs for 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:

Union[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.

classmethod POISSONVI.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.

POISSONVI.save(dir_path, prefix=None, overwrite=False, save_anndata=False, save_kwargs=None, **anndata_write_kwargs)[source]#

Save the state of the model.

Neither the trainer optimizer state nor the trainer history are saved. Model files are not expected to be reproducibly saved and loaded across versions until we reach version 1.0.

Parameters:
  • dir_path (str) – Path to a directory.

  • prefix (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, 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().

  • anndata_write_kwargs – Kwargs for write()

classmethod POISSONVI.setup_anndata(adata, layer=None, batch_key=None, labels_key=None, size_factor_key=None, categorical_covariate_keys=None, continuous_covariate_keys=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 raw count data.

  • batch_key (str | None (default: None)) – key in adata.obs for batch information. Categories will automatically be converted into integer categories and saved to adata.obs[‘_scvi_batch’]. If None, assigns the same batch to all the data.

  • 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.

  • size_factor_key (str | None (default: None)) – key in adata.obs for size factor information. Instead of using library size as a size factor, the provided size factor column will be used as offset in the mean of the likelihood. Assumed to be on linear scale.

  • categorical_covariate_keys (list[str] | None (default: None)) – keys in adata.obs that correspond to categorical data. These covariates can be added in addition to the batch covariate and are also treated as nuisance factors (i.e., the model tries to minimize their effects on the latent space). Thus, these should not be used for biologically-relevant factors that you do _not_ want to correct for.

  • continuous_covariate_keys (list[str] | None (default: None)) – keys in adata.obs that correspond to continuous data. These covariates can be added in addition to the batch covariate and are also treated as nuisance factors (i.e., the model tries to minimize their effects on the latent space). Thus, these should not be used for biologically-relevant factors that you do _not_ want to correct for.

POISSONVI.to_device(device)[source]#

Move model to device.

Parameters:

device (str | int) – Device to move model to. Options: ‘cpu’ for CPU, integer GPU index (eg. 0), or ‘cuda:X’ where X is the GPU index (eg. ‘cuda:0’). See torch.device for more info.

Examples

>>> adata = scvi.data.synthetic_iid()
>>> model = scvi.model.SCVI(adata)
>>> model.to_device('cpu')      # moves model to CPU
>>> model.to_device('cuda:0')   # moves model to GPU 0
>>> model.to_device(0)          # also moves model to GPU 0
POISSONVI.train(max_epochs=500, lr=0.0001, accelerator='auto', devices='auto', train_size=0.9, validation_size=None, shuffle_set_split=True, batch_size=128, weight_decay=0.001, eps=1e-08, early_stopping=True, early_stopping_patience=50, save_best=True, check_val_every_n_epoch=None, n_steps_kl_warmup=None, n_epochs_kl_warmup=50, datasplitter_kwargs=None, plan_kwargs=None, **kwargs)[source]#

Trains the model using amortized variational inference.

Parameters:
  • max_epochs (int (default: 500)) – Number of passes through the dataset.

  • lr (float (default: 0.0001)) – Learning rate for optimization.

  • accelerator (str (default: 'auto')) – Supports passing different accelerator types (“cpu”, “gpu”, “tpu”, “ipu”, “hpu”, “mps, “auto”) as well as custom accelerator instances.

  • devices (int | list[int] | str (default: 'auto')) – The devices to use. Can be set to a non-negative index (int or str), a sequence of device indices (list or comma-separated str), the value -1 to indicate all available devices, or “auto” for automatic selection based on the chosen accelerator. If set to “auto” and accelerator is not determined to be “cpu”, then devices will be set to the first available device.

  • train_size (float (default: 0.9)) – Size of training set in the range [0.0, 1.0].

  • validation_size (float | None (default: None)) – Size of the test set. If None, defaults to 1 - train_size. If train_size + validation_size < 1, the remaining cells belong to a test set.

  • shuffle_set_split (bool (default: True)) – Whether to shuffle indices before splitting. If False, the val, train, and test set are split in the sequential order of the data according to validation_size and train_size percentages.

  • batch_size (int (default: 128)) – Minibatch size to use during training.

  • weight_decay (float (default: 0.001)) – weight decay regularization term for optimization

  • eps (float (default: 1e-08)) – Optimizer eps

  • early_stopping (bool (default: True)) – Whether to perform early stopping with respect to the validation set.

  • early_stopping_patience (int (default: 50)) – How many epochs to wait for improvement before early stopping

  • save_best (bool (default: True)) – Save the best model state with respect to the validation loss (default), or use the final state in the training procedure

  • check_val_every_n_epoch (int | None (default: None)) – Check val every n train epochs. By default, val is not checked, unless early_stopping is True. If so, val 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: 50)) – Number of epochs to scale weight on KL divergences from 0 to 1. Overrides n_steps_kl_warmup when both are not None.

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

  • plan_kwargs (dict | None (default: None)) – Keyword args for TrainingPlan. Keyword arguments passed to train() will overwrite values present in plan_kwargs, when appropriate.

  • **kwargs – Other keyword args for Trainer.

POISSONVI.view_anndata_setup(adata=None, hide_state_registries=False)[source]#

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

Parameters:
  • adata (Union[AnnData, MuData, None] (default: None)) – AnnData object setup with setup_anndata or transfer_fields().

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

Return type:

None

static POISSONVI.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